Photopotentiation of Ganglion Cell Photoreceptors and Pupillary Light Responses

Dissertation

Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University

Phillip Thomas Yuhas, O.D. M.S.

Graduate Program in Vision Science

The Ohio State University

2019

Dissertation Committee:

Dr. Andrew Hartwick, Advisor

Dr. Angela Brown

Dr. Dean VanNasdale

Dr. Jordan Renna

Phillip Thomas Yuhas

2019

Abstract

A rare subset of intrinsically photosensitive retinal ganglion cells (ipRGCs) contain the photopigment melanopsin that enables them to capture light and signal downstream targets independently from rods and cones. These blue-light sensitive, sluggish neurons act as irradiance detectors, signaling environmental light levels to brain centers that control aspects of non-image-forming vision, including the pupillary light response. Under physiological conditions, these cells are not isolated from external modulators. The overall objective of this dissertation was to quantify how ipRGC function can be influenced by retinal neuromodulators and then explore conditions in vivo in which ipRGC modulation may occur or be altered.

First, multielectrode array recordings were obtained from rat retinas in vitro to determine whether dopamine D1 receptor agonists and antagonists affect light-evoked spiking in RGCs, including ipRGCs. The D1 receptor agonist, SKF 38393, significantly increased the spiking of synaptically intact ipRGCs and ON RGCs in response to a bright, flickering blue light, compared to baseline. A delayed SKF 38393-mediated enhancement was observed for ipRGCs that were pharmacologically isolated from glutamatergic input. Exposure to a D1 receptor antagonist, SCH 23390, did not significantly alter light-evoked spiking in pharmacologically isolated ipRGCs.

Second, I analyzed human pupillary light responses to different flickering light stimuli in order to determine whether prior light exposure influences the contribution of ipRGCs to the pupillary light reflex. I found that a bright stimulus that flickered between ii darkness and red and blue lights was able to progressively increase the amount of sustained pupil constriction that occurred after the offset of each light. This aspect of the pupillary light response is primarily driven by intrinsic melanopsin-related photoresponses of ipRGCs. More gradual photopotentiation was observed in the pupillary light responses to a bright, red flickering stimulus and to three dim flickering stimuli of various spectral compositions (red, blue, and red-blue alternating). Initial pulses of all these stimuli appeared to be below threshold necessary for melanopsin activation in dark adapted ipRGCs. However, these results support the premise that these relatively dim stimuli can activate melanopsin with repeated light exposures, indicating that ipRGCs have a larger dynamic range of light sensitivity than previously thought.

Third, I investigated the pupillary light responses to red and blue flickering lights in human subjects suffering from post-traumatic brain injury (TBI) photophobia and compared the results to matched controls. The mean pupil responses did not differ between these two groups, but there was significantly more variability in the TBI group.

This finding suggests that, although ipRGC light sensitivity does not uniformly change after a TBI, there may be heterogeneous effects of the injury on ipRGC function. In addition, I found that clinical observers cannot identify light-aversion behavior elicited by flickering red and blue lights in photophobic TBI subjects. The need remains for an objective test for detecting photophobia and monitoring its progression in individuals with

TBI.

iii

Dedication

To my wife, children, and parents

Acknowledgements

I am deeply appreciative to my PhD advisor, Dr. Andrew Hartwick, for his mentorship during the last eight years. His thoughtful guidance during my graduate school training and constructive feedback on all my various projects have made my time with him not only productive but also enjoyable. I aim to model my academic career after his example. In addition, I am thankful to Dr. Hartwick’s former and current doctoral students, including Puneet Sodhi, Patrick Shorter, and Elizabeth Galko, for their contributions to this dissertation and their continued companionship. Finally, I am grateful to Jacsen Luthy, a bright and industrious high school student. His immunohistochemistry experiments enhanced chapter two of this dissertation.

Vita

2006 ...... Saint Ignatius High School

2010 ...... B.A. Theology & Pre-medicine, University of Notre Dame

2014 ...... M.S. Vision Science, The Ohio State University

2014 ...... O.D. Optometry, The Ohio State University

2014 to present ...... Clinical Instructor, The Ohio State University

Publications

Yuhas, P. T., Shorter, P. D., McDaniel, C. E., Earley, M. J., & Hartwick, A. T. (2017). Blue and Red Light-Evoked Pupil Responses in Photophobic Subjects with TBI. Optom Vis Sci, 94(1), 108-117.

Yuhas, P. T., Shorter, P. D., McDaniel, C. E., Earley, M. J., & Hartwick, A. T. (2019). Observer-perceived light aversion behaviour in photophobic subjects with traumatic brain injury. Clin Exp Optom. Epub ahead of print.

Fields of Study

Major Field: Vision Science

Table of Contents

Abstract ...... ii

Dedication ...... iv

Acknowledgements ...... v

Vita ...... vi

List of Tables ...... viii

List of Figures ...... ix

Chapter 1: Introduction ...... 1

Chapter 2: Dopamine D1 receptor-mediated enhancement of ipRGC photoresponses ...... 13

Chapter 3: Photopotentiation of the human pupillary light response after stimulation with red and blue flickering lights ...... 71

Chapter 4: Observer-perceived light aversion behavior and light-evoked pupil responses in photophobic subjects with TBI ...... 139

Chapter 5: Conclusions ...... 201

References ...... 214

Appendix: Automated processing of pupil recordings ...... 253

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List of Tables

3.1 Retinal illumination caused by the red and blue light stimuli utilized on healthy human subjects ...... 95

4.1 Head injury history of TBI subjects ...... 160

4.2 Retinal illumination caused by the red and blue light stimuli utilized on the TBI, control, and student subjects ...... 161

4.3 Demographics of TBI and control subjects ...... 172

4.4 Medications prescribed to the TBI and control subjects during the study ...... 173

4.5 Demographics of student study group ...... 173

4.6 Standard video grading scale ...... 182

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List of Figures

2.1 Schematic representation of in vitro retinal recordings ...... 31

2.2 Comparison of ipRGC density in rat retina to density of electrodes on MEA array ...... 42

2.3 Group data showing effect of D1 dopamine receptor agonist on ON RGCs ..... 43

2.4 Representative spike rasters showing the effects of D1 dopamine receptor agonist on an ipRGC ...... 44

2.5 Group data showing effect of D1 dopamine receptor agonist on ipRGCs ...... 45

2.6 Pulse-by-pulse effect of a D1 dopamine receptor agonist on ipRGCs ...... 47

2.7 Effect of D1 dopamine receptor agonist on pharmacologically

isolated ipRGCs ...... 48

2.8 Effect of D1 dopamine receptor antagonism on ON RGCs ...... 50

2.9 Effect of D1 dopamine receptor antagonism on ipRGCs...... 51

2.10 Effects of D1 dopamine receptor antagonism on pharmacologically-isolated ipRGCs ...... 53

2.11 Representative spike rasters showing the effects of D1 dopamine receptor agonist on an OFF RGC ...... 55

2.12 Representative spike rasters showing the effects of D1 dopamine receptor antagonist on an OFF RGC ...... 56

3.1 Schematic representation of the pupil testing protocol during the first session ...... 92 ix

3.2 Schematic representation of the pupil testing protocol during the second session ...... 93

3.3 Schematic of the extended-Maxwellian view system utilized in this study ...... 96

3.4 Normalization of pupil size fluctuation evoked by flickering light stimuli ...... 97

3.5 Pupillary light response to bright flickering red and blue lights in healthy subjects ...... 112

3.6 Changes in pupil decay and constriction over the course of bright flickering light stimuli ...... 114

3.7 Comparison of pupil characteristics between the first and sixth periods of the bright light stimuli ...... 116

3.8 The effects of previous light exposure on the pupillary light response to bright flickering red and blue lights in healthy subjects...... 118

3.9 Pupillary light response to dim flickering red and blue lights in healthy subjects ...... 120

3.10 Changes in pupil decay and constriction over the course of dim flickering light stimuli ...... 122

3.11 Comparison of pupil characteristics between the first and sixth periods

Of the dim light stimuli ...... 124

3.12 The effects of previous light exposure on the pupillary light response to dim flickering red and blue lights in healthy subjects ...... 126

4.1 Effects of photophobia on daily activities of subjects...... 174

4.2 Self-reported discomfort grades of the TBI and control groups ...... 175

4.3 Macular pigment optical density (MPOD) measurements ...... 176

4.4 Pupil responses to flashes of red or blue light ...... 177

4.5 Inter-individual variability of pupil responses ...... 179 x

4.6 Light aversion grading of the student group ...... 181

4.7 Representative, still images from the standardized videos described in Table 4.6 ...... 183

4.8 Light aversion grades of the TBI and control groups ...... 184

4.9 Pupil responses to flashes of red or blue light ...... 185

Chapter 1: Introduction

Optometrists, ophthalmologists, and their patients often associate vision solely with image formation, especially high-contrast visual acuity. Image formation is not the lone purpose of vision, however. Assessment of environmental irradiance levels is essential for the non-image-forming aspects of vision, including photoentrainment of circadian rhythms and maintenance of pupil size. Rods and cones translate electromagnetic radiation into the electrophysiological signals that are the basis for image formation, and it was long suspected that they too were the solitary mediators of the non-image-forming aspects of vision. This two-photoreceptor paradigm began to crumble when researches noted that mice with degenerated rods and cones could shift their sleep-wake cycles to match shifted day-night cycles (Foster et al., 1991; Provencio,

Wong, Lederman, Argamaso, & Foster, 1994), albeit with an altered spectral profile from wild-type animals (Yoshimura & Ebihara, 1996). At the same time, human subjects blind from outer-retinal diseases, such as Leber’s congenital amaurosis, were observed to maintain photoentrained sleep cycles (Czeisler et al., 1995). Then in 1998 Provencio and colleagues reported a novel opsin in the melanophores, brain, and eye of Xenopus laevis, the African clawed frog (Provencio, Jiang, De Grip, Hayes, & Rollag, 1998). They named this photopigment melanopsin and later showed that it is expressed by cells in the inner retinas of primates and mice (Provencio et al., 2000). Soon after, in seminal publications, the Berson and Yau laboratories demonstrated that a subgroup of retinal ganglion cells contains melanopsin and is intrinsically photosensitive (Berson, Dunn, &

Takao, 2002; Hattar, Liao, Takao, Berson, & Yau, 2002). These intrinsically 1 photosensitive retinal ganglion cells (ipRGCs) are able to capture light and signal their brain targets independent from rod and cone inputs and are the focus of this dissertation.

Intrinsically photosensitive retinal ganglion cells: functional and anatomical diversity

As a class, the defining characteristic of ipRGCs is their possession of the photopigment melanopsin that allows them to capture light. Melanopsin can be found in the plasma membranes of their somata, dendrites, and axons at a peak effective density of approximately 240 molecules per square micron (Bellingham, Whitmore, Philp, Wells,

& Foster, 2002; Hattar et al., 2002). For comparison, the density of photopigment found in rods and cones is 25,000 molecules per square micron of membrane, 105 times higher than melanopsin in ipRGCs (Do et al., 2009). Melanopsin does not play a role in plasma membrane stability, as experiments on melanopsin knockout rodents have demonstrated that the morphology of ipRGCs lacking melanopsin is not altered (Lucas et al., 2003).

Any attempt to summarize the anatomy of ipRGCs would be incomplete without addressing the morphological and physiological diversity within these neurons. Six subclasses (M1-M6) of ipRGCs are known, each with its unique anatomical attributes.

M1 and M2 ipRGCs are relatively well-characterized; but the features of the more recently discovered M3, M4, M5, and M6 cells remain under investigation. Most of the original experiments that parsed out these subtypes were conducted in mice, but corresponding cells exist in the human retina, save for M5 and M6 ipRGCs (Hannibal,

Christiansen, Heegaard, Fahrenkrug, & Kiilgaard, 2017).

The ipRGCs first identified and recorded by the Berson and Yau groups were M1 cells. Like all ipRGCs, M1 cells are rare, constituting approximately 1% of rodent RGCs 2 and 0.2% of human RGCs (Dacey et al., 2005; Hattar et al., 2002). Their small (~15 µM) somata are found primarily in the retinal ganglion cell (RGC) layer of the retina, but a few are displaced in the inner nuclear layer (INL) (Dumitrescu, Pucci, Wong, & Berson, 2009;

Hattar et al., 2002). M1 ipRGCs make up for their sparse numbers with expansive dendrites that span the entire retina and allow each individual cell to maximize light capture and bipolar cell input (Berson, Castrucci, & Provencio, 2010). Although these dendrites stratify in the OFF sublamina of the inner plexiform layer (IPL), they synapse primarily with ON bipolar cells (Dumitrescu et al., 2009; Grunert, Jusuf, Lee, & Nguyen,

2011; Hoshi, Liu, Massey, & Mills, 2009). M1 cells send axons exclusively to non-image- formation brain centers (Hu, Hill, & Wong, 2013). Specifically, they provide 80% of ipRGC innervation to the suprachiasmatic nucleus (the circadian master clock) and 45% of ipRGC innervation to the outer shell of the olivary pretectal nucleus (the pupillary light response nexus) (Baver, Pickard, Sollars, & Pickard, 2008). Interestingly, M1 ipRGCs projecting to the olivary pretectum express the transcription factor Brn3b, but those projecting to the suprachiasmatic nucleus do not (S. K. Chen, Badea, & Hattar, 2011).

The implications for this difference are unclear, but genetically altered mice bred to lack

Brn3b have severe anatomical and functional abnormalities, including defects in the pupillary light response and photoentrainment of circadian rhythms (Badea, Cahill,

Ecker, Hattar, & Nathans, 2009).

The light-response characteristics of M1 ipRGCs are well studied. In addition to having the highest concentration of melanopsin photopigment in plasma membranes (Do et al., 2009), they possess a relatively depolarized resting membrane potential and are slow to repolarize after firing action potentials (Hu et al., 2013). As a result, their intrinsic responses to light stimulation are more robust and have a lower threshold than other subclasses (T. M. Schmidt & Kofuji, 2009; Xue et al., 2011; Zhao, Stafford, Godin, King, 3

& Wong, 2014). M1 cells rely heavily on this intrinsic light response, as mice lacking melanopsin photopigment show a greater reduction in light-evoked spiking than a group of wild-type mice whose ON signaling pathways are pharmacologically blocked (T. M.

Schmidt & Kofuji, 2010, 2011). Finally, their lack of center-surround receptive fields makes them unsuitable for image formation but bolsters their ability to act as irradiance detectors (Zhao et al., 2014).

Like M1 ipRGCs, the anatomy and function of M2 cells are well-characterized.

Their large (~20 µM) cell bodies populate the entire extent RGC layer of the retina in numbers equal to M1 cells (Baver et al., 2008; Berson et al., 2010; Ecker et al., 2010).

Unlike the M1 subclass, the dendrites of M2 ipRGCs intricately arborize in the ON sublamina of the IPL, where they receive excitatory input from ON bipolar cells (Berson et al., 2010; Ecker et al., 2010; T. M. Schmidt & Kofuji, 2009, 2010). M2 axons provide approximately 20% of ipRGC innervation to the suprachiasmatic nucleus and 55% to the olivary pretectum (Baver et al., 2008). M2 cells express less melanopsin in their plasma membranes than M1 cells, resulting in a relative reduction in their light sensitivity and an attenuated light response (T. M. Schmidt & Kofuji, 2009). Thus, M2 cells depend on integrating excitatory synaptic input from bipolar cells to supplement their intrinsic light responses (T. M. Schmidt & Kofuji, 2010, 2011).

The M3 subclass of ipRGCs is anatomically distinct from M1 and M2 cells. There are fewer M3 ipRGCs than M1 and M2 cells, and their dendrites do not span the entire retina (Berson et al., 2010; Viney et al., 2007). The most notable anatomical difference, however, is that M3 cells are bistratified, sending their dendrites to both the ON and OFF sublamina of the IPL where both receive excitatory signals from ON bipolar cells (Berson et al., 2010; T. M. Schmidt & Kofuji, 2011). The intrinsic photoresponses of M3 cells are

4 similar to those of M2 cells in that they are relatively weak and depend on supplementary input from bipolar cells (T. M. Schmidt & Kofuji, 2010, 2011).

Less is known about the M4, M5, and M6 subclasses of ipRGCs. Melanopsin expression is so low in all three that traditional melanopsin immunostaining techniques cannot identify these cells (Ecker et al., 2010; Quattrochi et al., 2019). M4 and M5 cells both send dendrites to the ON sublamina of the IPL (T. M. Schmidt & Kofuji, 2011), while

M6 cells are bistratified (Quattrochi et al., 2019). All three types have weak intrinsic light responses and depend on ON bipolar cell input to drive their signaling to downstream targets (Quattrochi et al., 2019; Zhao et al., 2014). The dorsal lateral geniculate nucleus is one of those downstream targets for M4 and M6 ipRGCs, which raises the possibility that they contribute in some manner to image formation (M. E. Estevez et al., 2012;

Quattrochi et al., 2019). The fact that both have ON center and OFF surround vision fields provides further evidence of a role in image formation for these neurons.

Phototransduction in ganglion cell photoreceptors

Phototransduction is defined as the translation of electromagnetic radiation into electrophysiological signals. The phototransduction cascade in ipRGCs is similar to that observed in the rhabdomeric photoreceptors of invertebrates, such as Drosophila, in that light capture causes the cells to depolarize (Berson et al., 2002; Hattar et al., 2002). This parallel is perhaps to be expected, given the strong genetic homology between invertebrate opsins and melanopsin (Koyanagi, Kubokawa, Tsukamoto, Shichida, &

Terakita, 2005; Provencio et al., 2000). Rods and cones employ a distinct phototransduction cascade that results in light-evoked hyperpolarization (Yau & Hardie,

2009).

The activation of a G-protein receptor by opsin-bound retinal is the basis for ipRGC phototransduction. The details of this process continue to be worked out. Briefly, light absorption by melanopsin causes the chromophore 11-cis-retinal to realign into all- trans-retinal (Rollag, 2008). The now active photopigment triggers a cascade that includes Gq protein activation of phospholipase C-β (PLCβ) (Graham et al., 2008;

Terakita et al., 2008; Xue et al., 2011). In turn, PLCβ opens a TRPC6/7 heterodimeric channel through a process that is not fully understood (Xue et al., 2011). In Drosophila, active PLCβ induces membrane depolarization through the secondary messengers phosphatidylinositol 4,5-bisphosphate (PIP2), inositol triphosphate (IP3), and diacylglycerol (DAG) (Hardie & Raghu, 2001). An opened TRP channel in ipRGCs allows for Na+ and Ca2+ influx that induces membrane depolarization (Hartwick et al.,

2007; Sekaran et al., 2007; Sekaran et al., 2005; Warren, Allen, Brown, & Robinson,

2003). The number of spikes fired by ipRGCs positively correlates with irradiance levels

(Dacey et al., 2005; Tu et al., 2005), resulting in more spiking in response to bright lights than dim lights, and the subsequent flow of calcium into ipRGCs via voltage-gated calcium channels correlates to their spiking rate (Hartwick et al., 2007).

Just as the phototransduction cascade of ipRGCs is distinct from rods and cones, so too are their photoresponses. Three features of the intrinsic ipRGC photoresponse will have prominent roles in this dissertation. The first is its insensitivity to light. The low density of melanopsin expressed by ipRGCs and its high threshold for activation necessitate a bright light to evoke spiking (Do et al., 2009). A bright irradiance of a light pulse of 1011-12 photons/s/cm2, at a wavelength near ipRGC peak sensitivity, is necessary for in vitro melanopsin activation (Dacey et al., 2005). This value corresponds to approximately 2 × 1013 photons/s/cm2 at the corneal surface (Lucas et al., 2003). ipRGCs employ several compensatory mechanisms to overcome this insensitivity (Do et 6 al., 2009). First, they are capable of signaling single photon capture due to signal amplification along the phototransduction cascade. Second, they hold a resting membrane potential close to action-potential threshold, priming them to respond to absorption of even a single photon. Finally, they are able to effectively sum photon captures over the course of extended integration time, 8 seconds or longer. These features, combined with the known resistance of ipRGCs to photobleaching (Sexton,

Golczak, Palczewski, & Van Gelder, 2012), could underlie how ipRGCs can sustain light- evoked spiking over the course of hours (Wong, 2012).

A second distinct characteristic of the ipRGC photoresponse is that they are very sluggish. Their response dynamics are 20 times slower than rods and 100 times slower than cones (Berson et al., 2002; Do et al., 2009). As a result, ipRGCs are not able to faithfully encode abrupt changes in stimulus intensity and keep firing action potentials for seconds after the offset of a light stimulus (Berson et al., 2002; Do et al., 2009). This sluggishness can be combated, however, as bright irradiances reduce the latency of the ipRGC photoresponse (Berson et al., 2002). A positive sequela of these characteristics is that ipRGCs are surprisingly sensitive to steady lights (Dacey et al., 2005; Do et al.,

2009; Wong, Dunn, Graham, & Berson, 2007), a trait important for tracking gradual changes in irradiance throughout the course of the day to regulate circadian rhythms and to control baseline pupil size.

Finally, ipRGCs have a unique spectral sensitivity curve. Work conducted on both rodents and primates place the peak sensitivity of melanopsin at approximately 480 nm, solidly in the blue portion of the visual spectrum (Bailes & Lucas, 2013; Berson et al., 2002; Dacey et al., 2005; Newman, Walker, Brown, Cronin, & Robinson, 2003). For reference, this value is between the spectral peaks of human short wavelength cones

(420 nm) and rods (498 nm) (Bowmaker & Dartnall, 1980). Long wavelength light is 7 commonly thought to be ineffectual at producing functional changes in the non-imaging- forming aspects of vision that are under the purview of ipRGCs, such as the photoentrainment of circadian rhythms (Papamichael, Skene, & Revell, 2012). Chapter 3 of this dissertation challenges this notion, however.

External modulation of ipRGC function

Ganglion cell photoreceptors are not isolated from the myriad of external modulators that exist in retina. Although studies using physical and pharmacological techniques to isolate the photoresponses of ipRGCs are useful in learning about the mechanisms that distinguish them from their non-photosensitive counterparts, they are limited in their ability to quantify the effects that external influences have on how these neurons signal their brain targets. This dissertation will deal with one such retinal modulator, dopamine, and two environmental conditions, repeated light exposure and traumatic brain injury, in which ipRGC neuromodulation may be influenced or altered. A brief overview of some of the other major external modulators of ipRGC function will help contextualize the work of this dissertation.

Like other neurons, ipRGCs are subject to diurnal variations in function. ipRGC influence over serum melatonin levels increases in the pre-dawn hours as the spectral sensitivity of circadian photoentrainment shifts to shorter wavelengths (Figueiro,

Bullough, Parsons, & Rea, 2005). Within ipRGCs, this shift corresponds with melanopsin mRNA’s peaking in the early subjective night (Sakamoto et al., 2005; Sakamoto, Liu, &

Tosini, 2004) and an increase in melanopsin protein expression in darkness (Hannibal,

Georg, Hindersson, & Fahrenkrug, 2005). The functional result of these protein changes is a modest increase in firing rate of ipRGCs when exposed to bright light in the early night (Weng, Wong, & Berson, 2009). Although ipRGCs play a key role in the 8 photoentrainment of circadian rhythms through their projections to the suprachiasmatic nucleus (Goz et al., 2008; Guler et al., 2008; Hatori et al., 2008), they do not express mPer1 (Witkovsky et al., 2003), an important internal clock gene. Thus, circadian modulation of these cells likely arises from an external source. Dopaminergic amacrine cells may be this source because they do express core clock genes and may be autonomously rhythmic (Ruan, Zhang, Zhou, Yamazaki, & McMahon, 2006;

Witkovsky et al., 2003). They are known to synapse directly with ipRGCs via the release of dopamine and γ-aminobutyric acid (GABA) (Belenky, Smeraski, Provencio, Sollars, &

Pickard, 2003; Contini & Raviola, 2003; Liao et al., 2016; Viney et al., 2007; Vugler et al.,

2007).

Neurotransmitters and neuromodulators within the retina also influence ipRGC function. Since ipRGCs are the least sensitive of the three photoreceptor classes, they depend on the integration of signaling from rods and cones to augment their intrinsic photoresponses (Goz et al., 2008; Guler et al., 2008; Hatori et al., 2008). Thus, ipRGCs receive the majority of their excitatory synaptic input through glutamate release from ON bipolar cells (Jusuf, Lee, Hannibal, & Grunert, 2007; Wong et al., 2007). OFF and rod bipolar cells may make small glutamatergic contributions, as well (Ostergaard, Hannibal,

& Fahrenkrug, 2007; Wong et al., 2007).The effect that these synaptic connections have on ipRGCs is not small, as ipRGCs are five times more sensitive to light when their synapses with bipolar cells remain intact than during synaptic blockade (Wong et al.,

2007).

Amacrine cells balance excitatory input from bipolar cells by providing inhibitory signals to ipRGCs. The fact ipRGCs have more synapses with amacrine cells than bipolar cells points toward the importance of inhibitory signaling in shaping their intrinsic photoresponses (Belenky et al., 2003; Jusuf et al., 2007). Inhibitory ion channels drive 9 most of these synapses, but gap junctions have also been observed between ipRGCs and certain amacrine cells (L. P. Muller, Do, Yau, He, & Baldridge, 2010). GABA and glycine released from amacrine cells are the primary vehicles of inhibitory input to ipRGCs (Perez-Leon, Warren, Allen, Robinson, & Brown, 2006; Wong et al., 2007).

They achieve their inhibitory effect by opening chloride channels in the cell membrane of ipRGC, hyperpolarizing the cell. Whereas excitatory drive from bipolar cells is sustained during light exposures, the inhibitory drive to ipRGCs is transient (Perez-Leon et al.,

2006). Just because inhibitory input is brief does not mean that it is insignificant, as light- evoked spiking from ipRGCs becomes stronger when amacrine signaling is pharmacologically blocked (Wong et al., 2007).

Adenosine is a third neuromodulator that has an inhibitory effect on ipRGC signaling. Retinal adenosine levels increase after dark adaptation and during the night

(Ribelayga & Mangel, 2005). Activation of adenosine A1 receptors expressed by ipRGCs dampens light-evoked signaling in these cells (Sodhi & Hartwick, 2014). Unlike GABA- and glycine-mediated inhibition, adenosine attenuates the ipRGC photoresponse by reducing intracellular concentrations of cyclic adenosine monophosphate, an important secondary messenger within all RGCs (Vaquero, Pignatelli, Partida, & Ishida, 2001). Of direct relevance to this dissertation, adenosinergic modulation of the ipRGC photoresponse during the night may diminish the prolonged pupil constriction that persists after a bright light stimulus is extinguished (Zele, Feigl, Smith, & Markwell,

2011). As will be covered in chapters 3 and 4, this aspect of the pupillary light response is driven by ipRGCs (Gamlin et al., 2007).

Amacrine cells do not deliver exclusively inhibitory signals to ipRGCs. Starburst amacrine cells release acetylcholine (ACh) at light onset and offset (Famiglietti, 1983;

Masland & Mills, 1979; Vaney, 1984). Accordingly, low-frequency (3–10 Hz) flickering 10 lights are potent stimuli for driving ACh release within the retina (Masland & Livingstone,

1976; Massey & Neal, 1979; Neal, Cunningham, Hutson, & Semark, 1992; O'Malley &

Masland, 1993). ACh interacts with metabotropic muscarinic receptors that are found throughout the RGC layer of the retina (Baldridge, 1996; Jensen & Daw, 1984; M.

Schmidt, Humphrey, & Wassle, 1987; Strang, Renna, Amthor, & Keyser, 2010). Sodhi and Hartwick recently demonstrated that light-evoked spiking can be elicited by activation of cholinergic receptors on ipRGCs (Sodhi & Hartwick, 2016). The ACh- induced signaling was sluggish and sustained in nature, more similar to the intrinsic photoresponses of ipRGCs than to their transient glutamatergic responses. It may play a role in driving ipRGC spiking in response to dim, flickering stimuli that are unable to drive the intrinsic light responses of these cells.

Dopamine is also an amacrine-derived neuromodulator that may affect ipRGC spiking. As discussed in the next chapter of this dissertation, activation of dopamine D1 receptors expressed by ipRGCs causes complex changes to their electrophysiology

(Van Hook, Wong, & Berson, 2012). Some of these changes are inhibitory in nature; others are excitatory. The results presented here suggest that the excitatory effects outweigh the inhibitory ones in multielectrode array recordings of flat-mounted, synaptically intact rat retinas.

Dissertation objectives

The overall objective of this dissertation is to quantify how ipRGC function can be influenced by retinal neuromodulation and then to explore conditions in which ipRGC modulation may occur or be altered. The effect that the neuromodulator dopamine has on the signaling of ipRGCs and on the aspects of the pupillary light response under their control will be a common theme in all three of its experiments. Chapter 2 will utilize rat 11 retinas in vitro to examine whether the activation of dopamine D1 receptors on ipRGCs changes their spiking responses to bright, blue flickering light stimuli. Chapter 3 will analyze the human pupillary light responses to a battery of flickering light stimuli and determine whether prior light exposure influences the contribution of ipRGCs to the pupillary light response. Finally, chapter 4 will investigate the pupillary light responses to blue and red flickering lights in human subjects suffering from post-traumatic brain injury photophobia and compare the results to age- and gender-matched controls.

Chapter 2: Dopamine D1 receptor-mediated enhancement of ipRGC

photoresponses

The ability to maintain functional vision over a wide range of lighting conditions is a crucial component of the vertebrate visual system. Human vision is effective on both a summer afternoon and an overcast night, a ten-log-unit difference in irradiance, due to a process called light adaptation, which optimizes the visual system to process vast amounts of photic data (Boff, Kaufman, & Thomas, 1986). Pupillary constriction and dilation account for a 1.2-log-unit reduction and increase, respectively, in light entering the eye (Perlman & Normann, 1998), leaving the remaining nine-log-unit adaptation to physiological processes within the retina. These processes are found throughout the retina and involve many of the cells that constitute the neurosensory retina and its support structure. The neurotransmitter dopamine plays a significant role in coordinating light adaption throughout the retina, from the photoreceptors to the ganglion cells. It does so in part by manipulating both the ON and OFF signaling pathways of the retina.

The existence of these two pathways is a prime example of parallel processing within this structure (Wassle, 2004). The OFF pathway preserves the polarity of glutamatergic signaling from photoreceptors, resulting in increased activity in OFF retinal ganglion cells

(RGCs) in the dark. Contrarily, the ON pathway inverts the glutamatergic signal from photoreceptors, causing ON RGCs to fire action potentials during light exposures. There remains much to be discovered about how dopamine affects the light-evoked photoresponses of ganglion cell photoreceptors. The goal of the second chapter of this dissertation is to test the hypothesis that the activation of dopamine D1 receptors on 13 intrinsically photosensitive retinal ganglion cells (ipRGCs) alters the responses of these neurons to subsequent light exposures.

Light adaptation within photoreceptors

Light adaptation in the retina begins within the outer-retina photoreceptors, the primary generators of the electrophysiological signals that are the basis for image formation. Both in this chapter and the next, the effects of rod and cone adaptation can be seen in the extracellular recordings of ipRGC action potentials and the ipRGC- mediated aspects of the pupillary light response, respectively. Four negative feedback mechanisms facilitate photoreceptor light adaption. Before they can be understood, however, a review of basic rod-cone phototransduction is necessary. Lagnado provides a comprehensive evaluation of this process (Lagnado, 2000). Briefly, in a dark-adapted state, high levels of cyclic guanosine monophosphate (cGMP) within the outer segment of a rod or a cone open ligand-gated Na+ and Ca2+ channels in its cellular membrane, allowing for an inward flow (the “dark current”) of these cations (Yau & Baylor, 1989). As a result, photoreceptors are relatively depolarized in the dark. When light reaches the photoreceptor’s outer segment, it is absorbed by an opsin protein, such as rhodopsin in rods (Alpern, 1971; Wald & Clark, 1937). The chromophore 11-cis retinal is bound to the opsin and straightens to its all-trans configuration after light absorption. When the now- activated photopigment contacts the G-protein transducin, the conversion of guanosine diphosphate (GDP) to guanosine-5'-triphosphate (GTP) stimulates the dissociation of transducin’s α subunit from the rest of the protein. The α subunit then disinhibits the γ subunit of phosphodiesterase six (PDE6), revealing the active site of this second protein.

Activated PDE6 hydrolyzes cGMP, reducing its intracellular concentration. Low concentrations of cGMP close the inward ligand-gated Na+ and Ca2+ channels, but Ca2+ 14 and K+ efflux continue via Na+ and Ca2+-K+ exchange channels, resulting in hyperpolarization of the photoreceptor (Baylor, Lamb, & Yau, 1979a; Cervetto, Lagnado,

Perry, Robinson, & McNaughton, 1989; Yau & Nakatani, 1985). Thus, rods and cones are relatively depolarized in the dark and hyperpolarized in the light. This hyperpolarization diffuses throughout the cell, including its synaptic ending where it reduces the release of glutamate (Baylor, Lamb, & Yau, 1979b).

The first mechanism of light adaptation inherent in photoreceptors is also the simplest. Bleaching of the photopigment occurs when a bright stimulus activates a large portion of the photopigment in its outer segment. Activated photopigment cannot capture photons, raising the threshold of subsequent stimulus detection (Alpern & Pugh, 1974).

In other words, a bleached photopigment needs a brighter light stimulus to achieve a given level of photon absorption, effectively reducing the gain of the host neuron (Agular,

1956; Cornwall & Fain, 1994; Rushton, 1956). In rods, all the Na+ and Ca2+ channels in the outer segment are closed when only ten percent of rhodopsin is in its bleached state

(Baylor, Nunn, & Schnapf, 1984; Rushton, 1965). In this saturated state, the rod cannot become any more hyperpolarized, prohibiting it from encoding additional photon capture, although recent evidence suggests that rods may continue to function at photopic light levels higher than that previously thought (Pahlberg et al., 2017; Tikidji-Hamburyan et al., 2017). The cone opsins are optimized to operate in high-light levels and are thus more resistant to bleaching than rhodopsin. The photopigments in human cones are half-bleached at high irradiances, approximately 105 phots/s/mm2, or 4.3 log trolands

(Hollins & Alpern, 1973; Rushton & Henry, 1968; Valeton & van Norren, 1983). Unlike rods, cones do not saturate (Alpern, Rushton, & Torii, 1970; Boynton & Whitten, 1970;

Burkhardt, 1994; Hood, Ilves, Maurer, Wandell, & Buckingham, 1978; Shevell, 1977).

Three additional mechanisms of light adaptation in photoreceptors aim to return the neuron to its dark-adapted, depolarized state. Modulation of intercellular calcium concentrations plays a significant role in all three (Matthews & Watanabe, 1988; Pugh &

Lamb, 1990). First, the recovery of a rod or cone from a light stimulus depends in part on the activity of guanylate cyclase, the enzyme that synthesizes cGMP from GTP. The calcium-binding protein called guanylate cyclase activating protein (GCAP) stimulates this process when intracellular calcium concentrations are low (Gorczyca, Gray-Keller,

Detwiler, & Palczewski, 1994; Lolley & Racz, 1982). Specifically, calcium inhibits the activity of GCAP (Dizhoor et al., 1995; Palczewski et al., 1994), limiting the synthesis of new cGMP in the dark. When a light exposure decreases the concentration of intracellular calcium, however, GCAP is free to stimulate the activity of guanylate cyclase, raising cGMP levels within the cell. As a result, the cGMP-gated channels in the rod or cone’s plasma membrane open, and the neuron depolarizes, which partially restores its ability to respond to light.

Second, the enzyme rhodopsin kinase facilitates the second calcium-mediated light adaptation process (C. K. Chen, Inglese, Lefkowitz, & Hurley, 1995; Gorodovikova,

Senin, & Philippov, 1994; Kawamura, 1993). This enzyme phosphorylates photo- activated rhodopsin, reducing its ability to interact with transducin and drive phototransduction. Another binding protein called recoverin modifies the activity of rhodopsin kinase. When light levels are low, recoverin inhibits the activity of rhodopsin kinase to keep rhodopsin in its activated state for as long as possible. Contrarily, the drop in intracellular calcium levels that accompanies brighter light exposure disengages recoverin from rhodopsin kinase, allowing for the phosphorylation of activated rhodopsin.

The final result is an increase in cGMP concentrations and a recovery of the membrane potential to a more depolarized state. 16

Finally, a third calcium-binding protein called calmodulin mediates light adaptation in rods at the site of the cGMP-gated membrane channels (Haynes & Stotz,

1997; Hsu & Molday, 1993; Nakatani, Koutalos, & Yau, 1995). Calmodulin facilitates the increased affinity of cGMP-gated channels to cGMP when calcium concentrations are low during light exposure. As a result, what little cGMP that remains during light exposure is able to more effectively bind to the channels and restore some cation influx.

Although the ipRGC phototransduction cascade is much different than that of rods and cones and may or may not depend on the retina pigment epithelium (RPE) for regeneration of its photopigment (Tu et al., 2006; Zhao, Pack, Khan, & Wong, 2016), these ganglion cell photoreceptors also exhibit light adaptation. The melanopsin photopigment itself is highly resistant to bleaching (Sexton et al., 2012). The exact mechanism is unknown, but several groups hypothesize that melanopsin is a bistable molecule, where certain wavelengths of light drives phototransduction while others drive photopigment regeneration. Wong and colleagues have shown that an adapting light stimulus can reduce the amplitude of the isolated ipRGC photoresponse to overlaid light pulses by over 1.5 log units over the course of approximately 5 minutes (Wong, Dunn, &

Berson, 2005). In response to a continuous light stimulus, the amplitude of the ipRGC photocurrent slowly increases to a steady level but, in a sign of adaptation, then relaxes from a transient peak (Do & Yau, 2013). The exact mechanisms for this adaptation are unknown; however, processes that involve the deactivation of melanopsin via its phosphorylation by protein kinase A (Blasic, Brown, & Robinson, 2012; Blasic, Lane

Brown, & Robinson, 2012) or its binding with arrestin (Cameron & Robinson, 2014) have been proposed.

Dopamine release in the retina

Light adaptation does not exclusively occur in the photoreceptors. Biochemical mechanisms throughout the entire retina optimize the function of the visual pathway to function in bright environments. This global effect has been termed “network adaptation”

(Dowling, 2012). Dopamine, a neurotransmitter found throughout the central nervous system, plays a large role in regulating those processes. Its interaction with ganglion cell photoreceptors is the primary focus of this dissertation chapter.

A special class of interplexiform cells called dopamine amacrine cells generate dopamine and release it in the retina (Dowling & Ehinger, 1975, 1978; Laties &

Jacobowitz, 1966). The primate retina contains approximately 7,500 dopamine amacrine cells (<1% of all amacrine cells), with the greatest density in a 3 mm ring around the fovea, a pattern similar to the distribution of rods (Mariani, Kolb, & Nelson, 1984;

Popova, 2014). The processes of dopamine amacrine cells arborize in the distal-most layer of the retina’s inner plexiform layer (D. Q. Zhang, Zhou, & McMahon, 2007). Here, they receive input from a variety of retinal neurons. Historically, giant bi-stratified cone

ON bipolar cells were thought to provide the primary innervation into dopamine amacrine cells through ribbon synapses (Mariani, 1989; Mariani & Hokoc, 1988; Myhr, Dong, &

McReynolds, 1994; Qiao, Zhang, Ribelayga, Zhong, & Zhang, 2016). Recent evidence in mouse, however, suggests that rods play a larger role in dopamine release in the retina than previously thought (Munteanu et al., 2018; Perez-Fernandez et al., 2019; Zhao,

Wong, & Zhang, 2017). How this finding translates to the primate retina is unclear.

Dopamine amacrine cells receive important inhibitory input from γ-aminobutyric acid

(GABA)-containing amacrine cells (Hokoc & Mariani, 1987; Mariani & Caserta, 1986;

Marshburn & Iuvone, 1981; Newkirk, Hoon, Wong, & Detwiler, 2013). It has also been demonstrated that ipRGCs synapse on at least some dopamine amacrine cells and can 18 provide direct excitatory input via glutamatergic stimulation of α-amino-3-hydroxy-5- methyl-4-isoxazolepropionic acid (AMPA)-type receptors (D. Q. Zhang, Belenky, Sollars,

Pickard, & McMahon, 2012; D. Q. Zhang et al., 2008). Whether signaling from ipRGCs actually drives dopamine release from dopamine amacrine cells remains up for debate

(Dkhissi-Benyahya et al., 2013; Perez-Fernandez et al., 2019; Prigge et al., 2016).

Light exposure drives dopamine release within the retina. Retinal dopamine levels are strongly correlated with environmental light levels (Iuvone, Galli, Garrison-

Gund, & Neff, 1978; Melamed, Frucht, Lemor, Uzzan, & Rosenthal, 1984), with brighter stimuli eliciting more dopamine release (Godley & Wurtman, 1988; Mills et al., 2007;

Perez-Fernandez et al., 2019). Pertinent to the studies described in this dissertation, repeated pulses of light exposures are more efficacious at driving dopamine release than similar continuously applied lights (Dubocovich & Weiner, 1981; Kramer, 1971).

There is also a well-established diurnal pattern to the release of dopamine within the retina that is independent of environmental irradiance. Dopamine levels within the retina increase during the daytime hours when light adaption is necessary and decrease at nighttime (S. E. Doyle, McIvor, & Menaker, 2002; Jackson et al., 2012). The neurohormone melatonin is hypothesized to play a role in mediating this circadian effect

(S. E. Doyle, Grace, McIvor, & Menaker, 2002; S. E. Doyle, McIvor, et al., 2002;

Dubocovich, 1983).

Once released, dopamine propagates though the retina through two primary routes. First, synaptic vesicles containing dopamine accumulate at the distal ends of dopamine amacrine cell processes (L. A. Levin & Adler, 2011) and are released into the synaptic clefts formed with adjacent amacrine and bipolar cells (Mariani, 1989). Second and perhaps more importantly for this work, dopamine amacrine cells can release dopamine into the extracellular space at non-synaptic sites through a poorly understood 19 mechanism (Bjelke et al., 1996). As a paracrine substance, dopamine propagates throughout the retinal milieu via volume conduction (Witkovsky, Nicholson, Rice,

Bohmaker, & Meller, 1993), which is defined as the transmission of a biological signal from a defined origin to widespread targets (Rutkove, 2007).

Physiological effects of retinal dopamine

The target of retinal dopamine is any one of five G-protein-coupled receptors

(Gingrich & Caron, 1993). These receptors (labeled D1-5) can be found throughout the retina (Firth, Morgan, & Boelen, 1997; Nguyen-Legros, Simon, Caille, & Bloch, 1997;

Veruki, 1997; Veruki & Wassle, 1996), including on rods and cones, horizontal cells, bipolar cells, amacrine cells, and retinal ganglion cells. Activation of these receptors produce an assortment of functional sequelae, but some generalities can be made.

Activated D1 receptors interact with the enzyme adenylate cyclase to mediate the conversion of adenosine triphosphate (ATP) to 3', 5'-cyclic adenosine monophosphate

(cAMP) and pyrophosphate (Masson, Mestre, & Blin, 1993). Contrarily, activated D2-5 receptors inhibit the activity of adenylate cyclase, limiting intracellular cAMP production

(Cohen, Todd, Harmon, & O'Malley, 1992; Qu, Fertel, Neff, & Hadjiconstantinou, 1988;

Sokoloff & Schwartz, 1995).

Manipulation of cAMP concentrations within retinal neurons produces cell- specific outcomes designed to preserve the visual pathway’s ability to function during bright light stimulation. In the outermost regions of the retina, dopamine acting through

D5 receptors prevents photoreceptors from shedding their outer-segment discs (La Vail,

1976; Reme, Wirz-Justice, Aeberhard, & Rhyner, 1984) and promotes RPE-facilitated phagocytosis of discs that are shed (Edwards & Flaherty, 1986a, 1986b; Guha et al.,

2012). It does not change the membrane potentials of photoreceptors but does 20 depolarize horizontal cells, making them more receptive to rod-cone signaling (Hedden

& Dowling, 1978). More notably, dopamine uncouples the gap junctions that connect horizontal cells (Kaneko, 1971; Piccolino, Neyton, & Gerschenfeld, 1984), shrinking their receptive fields (Lasater & Dowling, 1985). Smaller receptive fields in horizontal cells translate to improved spatial resolution and contrast sensitivity during photopic vision

(Lasater & Dowling, 1985; A. J. Zhang, Jacoby, & Wu, 2011).

The effects of dopamine on bipolar cells and amacrine cells are complex. Rod bipolar cells lack D1 receptors (Farshi, Fyk-Kolodziej, Krolewski, Walker, & Ichinose,

2016; Veruki & Wassle, 1996), but cone bipolar cells express them (Nguyen-Legros et al., 1997). Dopamine facilitates the transmission of the OFF signal from photoreceptors to retinal ganglion cells. Recent work has shown that dopamine stimulation of D1 receptors in the inner retina attenuates the inhibitory input that OFF cone bipolar cells receive from glycinergic amacrine cells (Mazade, Flood, & Eggers, 2019). Furthermore,

Xiao and colleagues have demonstrated that dopamine reduces the latency of the OFF signal as it propagates through the inner retina (Xiao, Zhang, Gong, & Liang, 2014).

These findings make it clear that the visual system prioritizes preservation of OFF signals during bright-light conditions, facilitating the detection of targets that are darker than the background. In ON bipolar cells, dopamine stimulation of D1 receptors sensitizes the cell to modulation by horizontal-cell-derived GABA acting on GABAA receptors that facilitate detection of spatial details in bright environments (Chaffiol, Ishii,

Cao, & Mangel, 2017). It also dampens ON bipolar cell sodium channel activity, reducing the light-evoked excitatory postsynaptic potential (Ichinose & Lukasiewicz, 2007). These alterations allow bipolar cells to faithfully transmit light responses from photoreceptors to

RGCs.

Amacrine cells are additional targets of retinal dopamine released by dopamine amacrine cells (Mariani, 1989). In an important negative feedback loop, activation of D2 autoreceptors on dopamine amacrine cells down regulate the amount of dopamine these cells release in response to light (Dubocovich & Weiner, 1981; Scatton, Bischoff, Dedek,

& Korf, 1977; Veruki, 1997). Likewise, dopamine inhibits the release of glycine (Pycock

& Smith, 1983) and GABA (Calaza, de Mello, & Gardino, 2001) from amacrine cells found throughout the inner retina. This inhibition of these inhibitory neurotransmitters will be significant in the analysis of the results of the experiments presented here. Perhaps in its most important function, dopamine facilitates the transition from scotopic to photopic by modulating the pathways that rods use to communicate with retinal ganglion cells. The AII amacrine cells link the rod pathway to the cone pathway by receiving synaptic input from ON rod bipolar cells and providing excitatory innervation into ON cone bipolar cells and inhibitory innervation via glycine into OFF cone bipolar cells during light exposure (Bloomfield & Dacheux, 2001; Dacheux & Raviola, 1986). Acting through D1 receptors, dopamine uncouples the gap junctions amongst AII amacrine cells and ON cone bipolar cells (Bloomfield, Xin, & Osborne, 1997; Hampson, Vaney, &

Weiler, 1992) and possibly disrupts the synaptic communication from AII amacrine cells to OFF cone bipolar cells (Xia & Mills, 2004). The result of these physiological changes is a shift from rod-dominated vision in dim environmental light to cone-dominated vision in bright environments.

The effects that dopamine has on RGCs remains an emerging topic and is the focus of this dissertation chapter. In a brief overview, most evidence points toward dopamine having an inhibitory effect on electrophysiological currents within RGCs. In cat, dopamine attenuates the rod-driven light responses in ON and OFF RGCs, independent of its effects on AII amacrine cells (F. Muller, Wassle, & Voigt, 1988). In 22 rabbit, (Daw, Brunken, & Parkinson, 1989; Jensen & Daw, 1984, 1986), rat (Hayashida et al., 2009), cat (Straschill & Perwein, 1969; Thier & Alder, 1984), carp (Glickman,

Adolph, & Dowling, 1982), and turtle (Liu & Lasater, 1994) dopamine reduces the maintained, current-driven spiking of rod- and cone-driven ON RGCs. These effects in

RGCs are driven, at least in part, by D1 receptor activation of protein kinase A (PKA), which manipulates the function of voltage-gated channels within the cell (Hayashida et al., 2009; Vaquero et al., 2001). Approaching the question from a different direction, studies that prevented dopamine release from amacrine cells report augmented responses to brightness in rabbit (Haeggendal & Malmfors, 1965) and goldfish (Z. S. Lin

& Yazulla, 1994). Similar to what has been reported in bipolar cells, these data in RGCs suggest that dopamine reduces the current in RGCs as a means of gain control.

In comparison to other retinal neurons, relatively little is known about how dopamine modulates the light responses of ipRGCs. Sakamato and colleagues provided the first evidence that dopamine modulates the function of ipRGCs (Sakamoto et al.,

2005). They found that dopaminergic stimulation of D2 receptors located on ipRGCs mediates light-driven production of melanopsin over the course of four hours. Van Hook and colleagues then investigated the question of whether dopamine can act directly on synaptically isolated ipRGCs to modulate their intrinsic light responses (Van Hook et al.,

2012). Using voltage-clamp techniques in rat, they first found that stimulation of D1 receptors with the agonist SKF 38393 attenuated the amplitude of light-evoked currents in ipRGCs, similar to the reported effect in regular RGCs. This effect has since been attributed to the dopamine-driven phosphorylation of melanopsin by protein kinase A

(Blasic, Brown, et al., 2012). Second, they reported that ipRGCs exposed to SKF exhibit higher levels of background spiking compared to control ipRGCs due to an associated

23 rise in their resting membrane potential. Since ipRGCs depolarize in response to light

(Berson et al., 2002), dopamine brings ipRGCs closer to their action potential threshold.

Purpose

The two effects of dopamine on ipRGCs found by Van Hook et al. (2012) would appear to counteract each other, and so the influence that extracellular dopamine has on light-evoked action potentials of ipRGCs in situ remains unclear. This dissertation chapter aims to fill this conceptual gap by utilizing retinal flat-mounts on multielectrode arrays to record the spiking activity of intact ipRGCs in the presence of dopaminergic D1 receptor agonists and antagonists.

Methods

This study adhered to the tenants of the Association for Research in Vision and

Ophthalmology Statement for the Use of Animals in Ophthalmic and Vision Research.

The Ohio State University Institutional Animal Care and Use Committee approved all procedures. Before the start of the investigation, the author received training from Ohio

State University Laboratory Animal Resources personnel on the proper handling of laboratory animals. All animal sacrifices were conducted during early in their subjective daytime (around 9:00 AM, local time) in order to control for diurnal variations in melanopsin expression in ipRGCs (Hannibal et al., 2005; Sakamoto et al., 2005;

Sakamoto et al., 2004).

Multielectrode array (MEA) recordings from rat retinas

Adult female Long Evans rats (Charles River, Wilmington MA), housed under a

12/12 h light/dark cycle, were killed by decapitation after isoflurane overexposure (by 24 inhalation in a sealed container), and their eyes were enucleated. Adult rats were the appropriate model for the experiments described here for two reasons. First, the melanopsin-driven intrinsic light responses of ipRGCs are present from birth, but the rod- and cone-driven light responses in regular ganglion cells do not occur until later in development (Hannibal & Fahrenkrug, 2004; Sekaran et al., 2005; Tu et al., 2005).

Recording from adult rat retinas allowed the activity of ON RGCs, OFF RGCs, and ipRGCs to be analyzed, providing a more complete picture of how dopamine modulates the activity of the inner retina. Second, dopamine amacrine cells do not fully develop until 20 days after birth (Kato, Nakamura, & Negishi, 1980) and dopamine release in the rat retina does not mature until 16 days after the eyes open, or 30 days after birth

(Morgan & Kamp, 1982). The animals used here all were over one month old and therefore had the retinal circuitry in place to drive endogenous dopamine release in the retina.

The euthanasia and enucleations of all animals occurred under standard room lighting conditions, and retinal dissections for the experiments testing a dopamine agonist were performed under a white microscope light. Dissections for the experiments using a dopamine antagonist were performed under direct red light (625 nm; DiCon LED,

Richmond CA) and dim background room light to minimize rod/cone photobleaching in order to facilitate endogenous dopamine release in the retina. All eyes were hemisected, and the posterior eyecup was pinned down in a Sylgard-coated (Dow Corning, Midland

MI) dish containing Hibernate-Complete (AB) medium (Brain Bits, Springfield IL). The vitreous was removed with a pair of curved forceps by placing the forceps underneath the vitreous and pulling up from the optic nerve head. Using a fine paintbrush, each retina was transferred to a glass coverslip and trimmed to obtain square retinal pieces, which were placed ganglion cell-side down in a chamber with a 60-electrode array (30 25

μm diameter, 200 μm inter-electrode spacing; Multi-Channel Systems, Reutlingen

Germany) and anchored with a weighted, circular nylon mesh (ALA Scientific

Instruments, Farmingdale NY). The first array-mounted retina was then placed onto a heated stage that was continuously superfused (1 mL min−1 flow rate), via a peristaltic pump (Gilson, Middleton WI), with Ames medium (Sigma-Aldrich, St Louis MO) supplemented with 10 mM Hepes (pH 7.4; Sigma-Aldrich) that was bubbled with 100% oxygen and pre-heated through a perfusion cannula (PH-01, Multi-Channel Systems).

The temperature of the Ames medium in the chamber was 32–34°C. The other retinal pieces (3-4 retinal pieces were prepared from the two eyes) were stored on the array in

Hibernate medium and maintained in the dark until the recordings from the other retinas were completed. After transfer to the stage, which occurred under dim red light (GBX-2

Safelight filters; Kodak, Pittsburgh PA), the retinas were perfused with the heated, oxygenated Ames medium for 30 min in the dark prior to the first light exposure.

A light-emitting diode (LED) illumination system (DiCon LED) generated a bright

(2.83 x 1014 photons/s/cm2), blue (peak λ = 465 nm, FWHM = 22 nm) light that flickered

(0.10 Hz; 5 s on, 5 s off) for 2 min. This blue light was chosen based on previous work showing that rat ipRGCs are most sensitive to 480 nm light (Berson et al., 2002). The flicker frequency matched that used to drive the human pupillary light response in the experiments described in chapters 3 and 4 of this dissertation. Furthermore, ipRGCs possess the ability to track 0.1 Hz light stimuli but not those that flicker at higher frequency (Walch et al., 2015). A fiber optic light guide delivered this stimulus to the

MEA stage from above. Light incident on the array-mounted retina first passed through the weighted nylon mesh and then hit the top rod/cone layer before traveling through the retina to the ganglion cells. Electrophysiologically active retinas received eight separate light exposures. After the 30-minute dark adaptation period that preceded the first 26 flickering light, 15 minutes of darkness separated each of subsequent seven light exposures. Pharmacological agents were introduced to the Ames medium before exposures 4, 6, 7, and 8. Figure 2.1 outlines the experimental protocol.

MEA voltage recordings were amplified and digitized at 25 kHz using a PC-based

A/D interface card and MCRack software (Multi-Channel Systems). The signals were bandpass filtered with cutoffs at 200 Hz and 3 kHz. A detection threshold of –5 standard deviations (SD) from baseline electrical activity was used to identify spike waveforms that were then separated from the continuous data. Clustered spike data were sorted into individual units with Offline Sorter software (Plexon Inc., Dallas TX). Specifically, spikes that appeared simultaneously on 80% of the channels were removed as noise artifacts, and remaining waveforms were aligned to the global minimum. The waveforms were represented in a 3D feature space, and the optimum number of spike clusters was determined using a T-distribution Expectation-Maximization algorithm (degrees of freedom multiplier = 10). The waveforms were then further sorted using an iterative K- means algorithm, with a threshold of 2 SD used to remove outlier waveforms. Using

NeuroExplorer software (Nex Technologies, Madison AL), the number of spikes occurring in 1 s bins was counted, and these data were used to generate spike frequency graphs and total counts of the light-evoked spikes.

Two criteria identified ipRGCs. First, candidate cells had to fire ≥200 spikes during the 145 seconds following the onset of the first light pulse of the third baseline light exposure (third overall exposure; Fig. 2.1C). Second, they had to fire ≥200 spikes during the 145 seconds following the beginning of the first pulse of light during the sixth overall light exposure (Fig. 2.1F), which was presented in the presence of a synaptic- blocker cocktail that silenced rod/cone-driven signaling. Based on initial visual inspections of the data, these criteria identified units exhibiting robust and prolonged 27 light responses characteristic of ipRGC photoresponses. These criteria likely biased the greater selection of M1-type ipRGCs in the data analysis, as the M1 subtype exhibits the most robust light-evoked responses in mouse (Ecker et al., 2010; T. M. Schmidt & Kofuji,

2009; Zhao et al., 2014) and in rat (Reifler et al., 2015). A subset of ipRGCs did not begin to fire ≥200 spikes until the ON- and OFF-blocker cocktail was first introduced into the Ames medium (Fig. 2.1F). This population of cells likely had more robust responses with the synaptic blocker cocktail present due to a reduction of inhibitory input from amacrine cells (Wong et al., 2007). Their spiking data were only included in the analysis of ipRGC responses to light in the presence of the synaptic-blocker cocktail. They were not included in the grouped data for the responses recorded with the synaptic-blocker cocktail absent.

Separate criteria were used for identification of ON RGCs. First, visual inspection of the candidate cell’s spiking raster plot had to reveal a consistent light response while the flickering light was on and minimal activity while the light was off. Second, the candidate cell had to fire ≥150 total spikes while the 12 pulses of blue flickering light were on. Finally, it could not fire more than 75 spikes while the flickering lights were off or in presence of the synaptic-blocker cocktail. These criteria yielded a population of ON cells that demonstrated sustained, consistent responses while the lights were on and minimal activity while the lights were off.

Pharmacology and solution preparation

On the day of each experiment, I prepared fresh solutions of the dopamine receptor agonist and antagonist. Stock solutions of the synaptic blockers that were previously prepared, aliquoted, and stored at −80°C were thawed as necessary. Each compound was perfused onto the retinas for 5 min prior to light stimulation and removed 28 from circulation immediately after the last pulse of the flickering light stimulus. To investigate dopamine signaling pathways, D1 receptor agonist (±)-1-phenyl-2,3,4,5- tetrahydro-(1H)-3-benzazepine-7,8-diol hydrobromide (SKF 38393 hydrobromide) and

D1 receptor antagonist (R)-(+)-7-chloro-8-hydroxy-3-methyl-1-phenyl-2,3,4,5-tetrahydro-

1H-3-benzazepine hydrochloride (SCH 23390 hydrochloride) were obtained from Tocris

Bioscience (Minneapolis MN).

Melanopsin-based light responses were isolated in the intact adult retinas by superfusing a cocktail of synaptic blockers onto the retinas to inhibit rod/cone-driven signaling (Wong et al., 2007). Glutamatergic signaling was inhibited using 100

μM metabotropic glutamate receptor agonist L-(+)-2-amino-4-phosphonobutyric acid (L-

AP4; Tocris) and 25 μM AMPA receptor antagonist 2,3-dioxo-6-nitro-1,2,3,4- tetrahydrobenzo[f] quinoxaline-7-sulfonamide (NBQX; Tocris). As detailed above, light responses recorded in the adult retinas in the presence of Ames medium containing this blocker cocktail were deemed to be the melanopsin-dependent responses of ipRGCs.

Data analysis

Preparation of final graphs and statistical comparisons were performed using

Sigmaplot 14 software (Systat Software; San Jose CA). The overall light‐evoked spike counts of ipRGCs were quantified by adding the number of spikes that occurred during the 145 seconds following the beginning of the first pulse for each light exposure. The total spike counts of ON RGCs were collected while the flickering blue light was on. All data are expressed as the mean ± standard error of the mean (SEM). There were few

(usually < 3) light‐sensitive units recorded from each retina, allowing responses from individual cells to be matched across the different conditions (before, during, and after drug treatment). Significant differences in the spike counts obtained from the adult retina 29 recordings were therefore determined using one‐way repeated‐measures analysis of variance (RM ANOVA) and Student-Newman-Keuls post hoc testing. When the data were not normal, the non-parametric Freidman RM ANOVA on ranks was used.

Figure 2.1. Schematic representation of in vitro retinal recordings. Array-mounted rat retinas were exposed to eight flickering (0.10 Hz) blue light stimuli designed to elicit ganglion cell signaling. Throughout the protocol, the intensity of the blue (λ = 470 nm) lights was 2.83 x 1014 photons/s/cm2. A) In light presentation 1, a 30-minute dark adaptation period preceded a 2-minute light exposure. B-C, E) In light presentations 2-3

31 and 5, a 15-minute dark adaptation period preceded a 2-minute light exposure. D, F-H)

In light presentations 4 and 6-8, pharmacological agents were introduced into the Ames medium after a 15-minute dark adaptation period. They were then given 5 minutes to circulate into the MEA well during which time the array-mounted retina sat in darkness, followed by exposure to the standard 2-minute flickering light stimulus.

Results

Stimulation of D1 receptors increases light-evoked spiking in ON RGCs

The MEA-mounted rat retinas were exposed to 12 pulses of flickering (0.1 Hz) blue light. Spikes from individual units were counted during treatment with SKF 38393

(100 µM in Ames medium) and compared to baseline and recovery conditions during which the retinas were superfused with Ames medium alone. SKF 38393 (SKF) was chosen as the dopamine receptor agonist due to its high affinity for the D1 receptors

(Sibley, Leff, & Creese, 1982) that are found throughout the retina’s retinal ganglion cell layer (Hayashida et al., 2009), including ipRGCs (Van Hook et al., 2012).

The RGC types recorded by the MEA cannot be pre-selected, as it is dependent on the cells that end up adjacent to the electrodes when the whole-mount retinas are placed on top of the array. Thus, ON- and OFF-type RGCs, along with ipRGCs, were recorded from during the experiments and analyzed separately. The density of ipRGC somata in an example rat retina relative to the density of the electrodes on the arrays used in these experiments is illustrated in Figure 2.2. This visual comparison shows that only a minority of electrodes is expected to be adjacent to an ipRGC soma during any giving recording. In addition, not all electrodes will make tight enough adhesions with adjacent ganglion cells due to factors such as residual vitreous presence and sheathing of the neurons by glial cells. Furthermore, the rod/cone-driven light responses in some

RGCs may have been absent due to photoreceptor bleaching that occurred during the dissection. All of these factors, along with the strict criteria for the inclusion of light- responding units in the data analysis (see Methods), underlie why only a few (generally

<5) light-responding RGCs were captured in a given experiment.

A recording from an example ON RGC (Fig. 2.3) illustrates the effect that SKF had on spike firing in these cells. During the baseline condition, the cell responded 33 faithfully to the blue flickering light stimulus, firing action potentials when the light pulses were on but not when they were off (Fig. 2.3A). The addition of SKF to the Ames solution resulted in more frequent spiking when the lights were on (Fig. 2.3B), and this effect persisted during the subsequent recovery experiment (Fig 2.4C). The addition of the ON- and OFF-pathway blockers L-AP4 and NBQX eradicated all of the cell’s light responses, confirming that the spiking was driven by rod/cone photoreceptors, rather than being melanopsin-driven (Fig. 2.3D). L-AP4 is a metabotropic glutamate receptor agonist that abolishes the ON response pathway by stimulating the G-protein-linked mGlu6 receptors found on the dendrites of ON bipolar cells (Hensley, Yang, & Wu, 1993; Nomura et al.,

1994; Slaughter & Miller, 1981). Activation of the mGlu6 cascade causes the cell to hyperpolarize, just as it would in darkness (Dhingra et al., 2000; Nawy, 1999). NBQX is a selective and competitive AMPA receptor antagonist that suppresses the OFF response pathway by preventing OFF bipolar cells from receiving synaptic communication from outer retinal photoreceptors (Yu & Miller, 1995). It also blocks the synaptic transmission from bipolar cells to amacrine cells and RGCs (Yu & Miller, 1995). Whereas mGlu6 receptors invert the glutamatergic signal coming from rods and cones (i.e. they cause

ON bipolar cells to hyperpolarize when photoreceptors depolarize), activation of ionotropic AMPA receptors allow OFF bipolar cells to depolarize when photoreceptors depolarize, which is in the dark (DeVries, 2000).

Figures 2.3E-F show the mean data obtained from all recorded ON RGCs (N = 5 retinas; n = 12 ON RGCs), demonstrating that there was a significant increase (p <

0.001; RM ANOVA with Student-Newman-Keuls) in ON RGC spiking with the addition of

SKF to the Ames medium, compared to baseline. Spiking remained enhanced throughout the recovery condition, resulting in no statistical difference (p = 0.92) in total

34 spikes between the SKF condition and the recovery, although it was trending back to baseline levels. The synaptic blockers all but eradicated light responses (p < 0.001).

Stimulation of D1 receptors agonist increases light-evoked spiking in ipRGC

ipRGCs were identified with the criteria defined in the Methods, and the total spikes that occurred during the entire 145 seconds following the onset of the first light pulse of the blue flickering light stimulus were counted during the same baseline-SKF- recovery-blockers test protocol described above.

A recording from a representative ipRGC demonstrates the effect that SKF had on ipRGC spiking (Fig 2.4). During the early pulses of the baseline condition the cell accurately encoded the pulses of the blue flickering light stimulus but began to lose temporal resolution and fire indiscriminately starting around the halfway point of the light exposure (Fig. 2.4A). These sluggish temporal characteristics are a hallmark of the ipRGC photoresponse (Berson et al., 2002; Hartwick et al., 2007). The introduction of

SKF to the Ames solution resulted in more frequent spiking both when the lights were on and when they were off, resulting in the early loss of temporal resolution of the flickering stimulus (Fig. 2.4B). Spiking then decreased modestly during the recovery period (Fig

2.4C). Notice that the sporadic spiking before the onset of the first light pulses was high in both the SKF and recovery conditions. This observation is consistent with a previous report that SKF increases the background spiking of ipRGCs (Van Hook et al., 2012).

Finally, the cell’s light response persisted in the presence of the ON- and OFF-pathway blockers L-AP4 and NBQX (Fig. 2.4D). The photoresponse with the blockers present looked different than when the ON- and OFF-pathways were intact, exhibiting a very sluggish onset and no discernment of the temporal characteristics of the flickering light.

When total spiking was summed in the population of identified ipRGCs (N = 4 retinas; n = 6 ipRGCs), a similar pattern emerged. As Figures 2.5A-B show, the addition of SKF to the Ames medium elicited a significant increase (p = 0.019; RM AMOVA with

Student-Newman-Keuls) in ipRGC spiking, compared to baseline. During the subsequent recovery condition there was a significant decrease (p = 0.020) in spiking compared to the SKF condition. Spiking in the presence of L-AP4 and NBQX continued at a rate similar to baseline (p = 0.744) even though those compounds blocked rod-cone input into ipRGCs. Diminished photoreceptor signaling to the inhibitory amacrine cells that subsequently dampen ipRGC photoresponses could underlie the robust intrinsic responses of the ipRGCs in the presence of synaptic blockers (Wong et al., 2007).

After establishing that the presence of extracellular SKF increased the total number of spikes in ipRGCs over the course of the entire flickering blue stimulus, I next addressed the time course of this enhancement and whether the spiking increase occurred more during or after each light pulse. SKF caused increased spiking in ON

RGCs while the lights were on, and the same result was found in ipRGCs (Fig. 2.5C).

Specifically, the presence of SKF caused a significant increase (p = 0.004, Friedman repeated measures analysis of variance on ranks) in ipRGC spiking during the periods the lights were on versus the baseline condition, the recovery condition (p < 0.001), and in the synaptic-blockers condition (p = 0.037). Also during the light-off intervals between the pulses, ipRGC spiking was significantly increased with SKF present compared to baseline (p = 0.029, RM AMOVA with Student-Newman-Keuls; Fig. 2.5C). Dissimilar to what was found during the light pulses, ipRGC spiking in the dark did not significantly decrease in the recovery (p = 0.111) or synaptic-blockers’ (p = 0.638) conditions but remained elevated.

A second question regarding SKF-mediated enhancement of ipRGC was whether the effect was more prevalent at the beginning or the end of the 2-minute flickering light stimulus. To answer this question, spiking evoked by each individual pulse was quantified both when the light was on (5-second duration) and during the following 5 seconds of darkness. Figure 2.6 displays these data on a pulse-by-pulse basis for each of the test conditions. In general, darker circles represent spiking while the lights were on, and lighter circles while the lights were off. Overall, there was no significant difference (all had overall p > 0.05, Friedman RM ANOVA on ranks) in the amount of spiking across the individual pulses within the overall light stimulus for any of the test conditions (baseline, SKF, recovery, and synaptic blockers). Visual inspection of the data suggests that, for each test condition, spiking remained relatively stable after each light pulse (lighter circles). This observation is consistent with melanopsin’s known resistance to photobleaching (Sexton et al., 2012). During each light pulse (darker circles), however, there seemed to be a subtle decrease in spiking during the early pulses (e.g. pulses 1-4) of the baseline (Fig. 2.6A) and SKF (Fig. 2.6B) test conditions.

Rod and cone adaptation was the likely source of this change. In Figures 2.3A-C, this effect can be visualized as fewer spikes during the final pulse of blue light compared to the first.

The fact that SKF increased ipRGC spiking in the periods of darkness between the light pulses suggested that the effect is at least in part independent of rod-cone input into ipRGCs. To more rigorously test the impact that SKF had on their intrinsic photoresponse, a second population of cells (N = 5 retinas; n = 9 ipRGCs) was examined exclusively in the presence of synaptic blockers (L-AP4 [100 µM] and NBQX

[25 µM]). When ipRGCs were isolated from the ON- and OFF-pathways, the effect of

SKF on spiking did not occur immediately (Figs. 2.7A-B). There was no difference in 37 spiking (p = 0.341, RM ANOVA) between the baseline condition (synaptic blockers only) and SKF condition (synaptic blockers with SFK). Interestingly, however, spiking significantly increased (p < 0.001) during the recovery condition (synaptic blockers only), compared to the SKF condition. Pulse-by-pulse analysis of total spiking (including the time during each 5-second light pulse and the 5 s of following darkness) showed that there was no progressive change in ipRGC activity during each test condition (Fig.

2.7C): baseline (p = 0.737, Friedman RM ANOVA on ranks), SKF (p = 0.140), and recovery (p = 0.098).

Antagonism of D1 receptors does not affect light-evoked spiking in ON RGCs

The addition of a dopamine receptor agonist (SKF) enhanced light-evoked spiking in ON RGCs and ipRGCs compared to baseline. I next examined whether a dopamine receptor antagonist had the opposite effect (i.e. dampen ON RGC and ipRGC spiking). Identification of ON RGCs followed the same criteria employed in the SKF experiments and total spikes that occurred during the flickering blue light stimulus were counted with SCH 23390 (100 µM) present in the Ames medium, and compared to baseline and recovery conditions. SCH 23390 (SCH) was chosen as the dopamine receptor antagonist due to its high affinity for the D1 receptor (Bourne, 2001). It is commonly used in vision research (Ventura, de Mello, & de Melo Reis, 2013).

Representative spike rasters in Figure 2.8 show one effect that SCH was observed to have on ON RGCs. During baseline testing the cell encoded the flickering light, firing action potentials during the light pulse but not after (Fig. 2.8A). The addition of SCH to the Ames medium increased ON RGC firing during light stimulation while retaining good temporal resolution (Fig. 2.8B). Spiking decreased during the recovery condition (Fig. 2.8C) and ceased in the presence of the synaptic blockers L-AP4 and 38

NBQX (Fig. 2.8D). In this cell, SCH enhanced ON RGC spiking in a similar fashion to

SKF, the D1 receptor agonist. Not all of the ON cells responded to the SCH with a heightened light response, however. Other cells demonstrated a marked decrease in light-evoked spiking in the presence of SCH.

Evidence for heterogeneous light responses in the presence of SCH can be found in the mean data from the entire cohort of ON RGCs (N = 6 retinas; n = 16 ON

RGCs). Although the overall mean spike counts were higher with SCH present (Figs.

2.8E-F), there was no statistical difference in total spiking between any of the three test conditions (p = 0.299, RM ANOVA). The wide error bars point to the variable responses obtained in these experiments, especially when SCH was present in the Ames medium.

Effect of D1 receptor antagonism on light-evoked spiking in ipRGCs

During the experiments conducted without synaptic blockers, only two ipRGCs met the 200-spike criteria outlined in the methods (N = 2 retinas; n = 2 ipRGCs). As a result of this small sample size, no statistical tests were conducted to seek differences in ipRGC spiking between test conditions. Nevertheless, visual examination of Figure 2.9 can give some insight into how SCH affected these two cells.

First, the cells maintained a light response in the presence of synaptic blockers, confirming that they were ipRGCs (Figs. 2.9A-B). Second, the addition of SCH to the

Ames medium caused a small increase in ipRGC spiking, compared to baseline, with a drop in spiking detected during the recovery condition. These observations were similar to those produced by the cohort of ON RGCs exposed to SCH. When spiking was parsed between that which occurred when the flickering lights were on and when they were off, the pattern was identical (Fig. 2.9C): the presence of SCH led to a modest increase in the number of light-evoked spikes. However, no firm conclusions can be 39 made about the effect that SCH had on ipRGC signaling without synaptic blockers present until the sample size is increased.

A larger sample size (N= 3 retinas; n = 5 ipRGCs) was collected for the SCH experiments conducted exclusively in the presence of synaptic blockers. There were no statistical differences in the amount of ipRGC spiking between any of the three test conditions (p = 0.144, RM ANOVA): baseline (synaptic blockers only), SCH with synaptic blockers, and recovery (synaptic blockers only). Interpretation of this negative result must be made with caution; Figures 2.10A-B reveal a trend towards a decrease in ipRGC spiking when SCH was added to the Ames solution already containing the synaptic blockers. There was a rebound effect in the following recovery experiment.

Potential reasons for the lack of a statistical difference in ipRGC spiking between the

SCH test and baseline and recovery tests will be evaluated in the discussion. Finally, pulse-by-pulse analysis of ipRGC spiking did not uncover a significant change in action potential frequency during the course of 12 pulses of bright blue light under any of the test conditions (Fig. 2.10C): baseline (p = 0.090, Friedman RM ANOVA on ranks), SCH

(p = 0.126), and recovery (p = 0.277).

Dopamine modulation of OFF RGCs

Recordings from two OFF RGCs (using the identification criteria described in the

Methods) were also collected, one each during the SKF and SCH experiments. The OFF

RGC recording in Figure 2.11A shows that this cell fired action potentials when the light stimulus was off but ceased when the light was on. Similar to what was seen in ON

RGCs and ipRGCs, the addition of SKF to the Ames medium caused the cell to spike more, but the effect was small (Fig. 2.11B). Spiking modestly decreased during the recovery condition (Fig. 2.11C). The OFF RGC recorded from during the SCH 40 experiments displayed a steady increase in spiking compared to baseline (Fig. 2.12A) when the SCH was added to the Ames medium (Fig. 2.12B) and in the subsequent recovery experiment (Fig. 2.12C).

Figure 2.2. Comparison of ipRGC density in rat retina to density of electrodes on MEA array. A) Unmarked image montage of a portion of rat retina immunostained (green- labelled cells) for melanopsin (see Sodhi & Hartwick, 2014 for details) to identify ipRGCs. B) Marked image montage of the same retina, highlighting the ipRGC somata.

C) Example overlay of this montage with an image of an MEA used in this work, showing four ipRGCs touching MEA electrodes in this orientation (distance between adjacent electrodes = 200 µm). D) Example overlay of images at a different orientation, showing nine ipRGCs touching MEA electrodes. Moving the electrode grid systematically in 100

µm increments across this image montage, the maximum number of electrode-contacted ipRGCs was 9 (image in D), but in 20 (43%) of the 46 array alignments examined, the number of ipRGC-electrode contacts was two or less. (Immunohistochemistry and data collection performed by Jacsen Luthy, student in the Hartwick lab.) 42

Figure 2.3. Group data showing effect of D1 dopamine receptor agonist on ON RGCs.

MEA-recorded spiking activity in an ON RGC exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light in the presence of A) Ames medium alone (baseline), B) 100 μM SKF 38393 in Ames, and C) Ames alone

(recovery). D) The addition of the synaptic blockers L-AP4 (100 μM) and NBQX (25 μM) eliminated the light response. This effect was quantified with E) firing frequency (error bars removed for clarity) and F) mean spike counts in a population of robust ON RGCs

(N = 5 retinas; n = 12 ON RGCs) exposed to the same flickering light. The addition of the

SKF increased the total amount of spikes that occurred while the lights were on, and spiking remained high during the recovery condition. Synaptic blockers confirmed that the cells were ON RGCs. Blue bars represent the bright, flickering blue light. * p < 0.05 and NS = not significant (p > 0.05), RM ANOVA with Student-Newman-Keuls or

Friedman RM ANOA on ranks.

Figure 2.4. Representative spike rasters showing effect of D1 dopamine receptor agonist on an ipRGC. MEA-recorded spiking activity in an ipRGC exposed to flickering light in the presence of A) Ames medium alone (baseline), B) 100 μM SKF 38393 in

Ames, and C) Ames alone (recovery). D) The persistence of a light response in the presence of the synaptic blockers L-AP4 (100 μM) and NBQX (25 μM) confirmed that the cell was an ipRGC. Blue bars represent the bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light. Flickering light duration: 2 min.

Figure 2.5. Group data showing effect of D1 dopamine receptor agonist on ipRGCs. A)

Mean spike counts and B) firing frequency (error bars removed for clarity) in a population 45 of ipRGCs (N = 4 retinas; n = 6 ipRGCs) exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light. The addition of SKF 38393 (100

μM) to the Ames medium elicited an increase in total ipRGC spiking. This effect was washed out during the subsequent light exposure. C) This pattern of ipRGC signaling was largely preserved when spiking activity during (light ON) and after (light OFF) exposures were analyzed separately. The persistence of a light response in the presence of the synaptic blockers L-AP4 (100 μM) and NBQX (25 μM) confirmed that these cells were ipRGCs. Blue bars represent the bright, flickering blue light. * p < 0.05 and NS = not significant (p > 0.05), RM ANOVA with Student-Newman-Keuls or

Friedman RM ANOVA on ranks.

Figure 2.6. Pulse-by-pulse effect of D1 dopamine receptor agonist on ipRGCs. Total ipRGC (N = 4 retinas, n = 6 ipRGCs) spiking evoked during (ON) and after (OFF) each pulse of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz for 2 min) blue (465 nm) light in the presence of A) Ames medium alone (baseline), B) 100 μM SKF 38393 in

Ames, C) Ames alone (recovery), and D) L-PA4 (100 μM) and NBQX (25 μM). Although the effect was not statistically significant, there appeared to be modest, progressive decrease in spiking while the lights were on in the baseline (p = 0.075, Friedman RM

ANOVA on ranks) and SKF conditions (p = 0.227). Spiking during and after light exposure remained consistent during all other test conditions.

Figure 2.7. Effect of D1 dopamine receptor agonist on pharmacologically isolated ipRGCs. A) Mean total spike counts, B) total firing frequency (error bars removed for 48 clarity), and C) pulse-by-pulse spike counts in a population of ipRGCs (N = 5 retinas; n =

9 ipRGCs) isolated from ON and OFF pathways (with 100 μM L-AP4 and 25 μM NBQX) and exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue

(465 nm) light. The addition of SKF 38393 (100 μM) to the Ames medium elicited a delayed increase in total ipRGC spiking, not manifesting until the recovery condition.

Blue bars represent the bright, flickering blue light. * p < 0.05 and NS = not significant (p

> 0.05), RM ANOVA with Student-Newman-Keuls or Friedman RM ANOVA on ranks.

Figure 2.8. Effect of D1 dopamine receptor antagonism on ON RGCs. MEA-recorded spiking activity in an ON RGC exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light in the presence of A) Ames medium alone

(baseline), B) 100 μM SCH 23390 in Ames, and C) Ames alone (recovery). D) The addition of the synaptic blockers L-AP4 (100 μM) and NBQX (25 μM) eliminated the light response. This effect was quantified with E) firing frequency (error bars removed for clarity) and F) mean spike counts in a population of robust ON RGCs (N = 6 retinas; n =

16 ON RGCs) exposed to the same flickering light. The addition of the SCH did not have a statistically significant effect on ON RGC spiking that occurred while the lights were on.

Synaptic blockers confirmed that the cells were ON RGCs. Blue bars represent the bright, flickering blue light. NS = not significant (p > 0.05), RM ANOVA.

Figure 2.9. Effect of D1 dopamine receptor antagonism on ipRGCs. A) Mean spike counts and B) firing frequency (error bars removed for clarity) in a small population of 51 ipRGCs (N = 2 retinas; n = 2 ipRGCs) exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light. C) A modest increase in ipRGC spiking was largely preserved when activity during (light ON) and after (light OFF) exposures were analyzed separately. The persistence of a light response in the presence of the synaptic blockers L-AP4 (100 μM) and NBQX (25 μM) confirmed that these cells were ipRGCs. Blue bars represent the bright, flickering blue light.

Figure 2.10. Effects of D1 dopamine receptor antagonism on pharmacologically-isolated ipRGCs. A) Mean total spike counts, B) total firing frequency (error bars removed for 53 clarity), and C) pulse-by-pulse spike counts in a population of ipRGCs (N = 3 retinas; n =

5 ipRGCs) isolated from ON and OFF pathway (with 100 μM L-AP4 and 25 μM NBQX) and exposed to 2 mins of a bright (2.83 x 1014 photons/s/cm2), flickering (0.10 Hz) blue

(465 nm) light. The addition of SCH 23390 (100 μM) to the Ames medium elicited a decrease in ipRGC spiking that trended toward statistical significance. Blue bars represent the bright, flickering blue light. NS = not significant (p > 0.05), RM ANOVA.

Figure 2.11. Representative spike rasters showing effects of D1 dopamine receptor agonist on an OFF RGC. MEA-recorded spiking activity in an OFF RGC exposed to flickering light in the presence of A) Ames medium alone (baseline), B) 100 μM SKF

38393 in Ames, and C) Ames alone (recovery). Blue bars represent the bright (2.83 x

1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light. Flickering light duration: 2 min.

Figure 2.12. Representative spike rasters showing effects of D1 dopamine receptor antagonist on an OFF RGC. MEA-recorded spiking activity in an OFF RGC exposed to flickering light in the presence of A) Ames medium alone (baseline), B) 100 μM SCH

23390 in Ames, and C) Ames alone (recovery). Blue bars represent the bright (2.83 x

1014 photons/s/cm2), flickering (0.10 Hz) blue (465 nm) light. Flickering light duration: 2 min.

Discussion

Ganglion cell photoreceptors signal environmental light levels to brain centers that mediate the photoentrainment of circadian light levels and the pupillary light response (Hattar et al., 2006). Sustained spiking responses to light exposures that can persist for many seconds after the light offset is a hallmark of their physiology (Do et al.,

2009). Extracellular modulators are known to modify this unique light response. For example, the neuromodulator adenosine is present in the retina in the dark (Ribelayga &

Mangel, 2005) and shortens the duration of ipRGC action potential firing evoked by bright light stimulation (Sodhi & Hartwick, 2014). Furthermore, flickering lights or moving gratings stimulate starburst amacrine cells to release the neurotransmitter acetylcholine into the retina (Masland & Mills, 1979; O'Malley & Masland, 1993) where it elicits ipRGC spiking independent of direct light stimulation (Sodhi & Hartwick, 2016). Here, dopamine is shown to alter the light-evoked signaling of ipRGCs. Specifically, the total number of spikes fired by ipRGCs significantly increased following the activation of D1 receptors present on these cells and throughout the retina. This finding provides further evidence that a neuromodulator released into the retina in response to light stimulation can alter the signaling of ganglion cell photoreceptors. This finding may have implications for understanding how the pupillary light response, and other non-image-forming functions, may exhibit changes in its response parameters over the course of repeated light exposures.

Origins of the D1 receptor-mediated enhancement of ipRGC signaling

The principal result of this study was that bath application of the D1 receptor agonist SKF 38393 (100 µM) significantly (p < 0.05) increased the total number of spikes fired by ipRGCs in response to stimulation with a flickering (0.10 Hz), bright (2.83 x 1014 57 photons/s/cm2) blue (470 nm) light (Fig. 2.5A). This effect may have been mediated through D1 receptors located on the ipRGCs themselves, changes to the extrinsic signals ipRGCs received from other retinal neurons, or both.

Evidence for both mechanisms can be found by comparing the light responses of ipRGCs in the presence and absence of synaptic blockers. ipRGCs make synaptic contacts with ON bipolar cells (Belenky et al., 2003) and transmit synaptically-mediated signals originating from rods and cones to their various brain targets (Dacey et al., 2005;

Perez-Leon et al., 2006; Wong et al., 2007). With synaptic blockers absent, ipRGC spiking increased during the light pulses in the presence of SKF, compared to baseline and recovery experiments (Fig. 2.5C). This effect can also be seen when ipRGC spiking was sorted in a pulse-by-pulse fashion, as the spiking that occurred during each individual light pulse was visibly higher in the SKF condition (dark circles of Fig. 2.6B) than during the light pulses of the baseline (dark circles of Fig. 2.6A) and recovery (dark circles of Fig. 2.6C) conditions.

These findings open the possibility that dopamine increases synaptic input into ipRGCs by acting on an up-stream retinal neuron. The fact that dopamine is known to reduce inhibitory signals in the inner retina supports this scenario. Specifically, GABA released from amacrine cells (Chun & Wassle, 1989) impedes the influx of Ca2+ into bipolar cells via stimulation of GABAC receptors (Heidelberger & Matthews, 1991;

Matthews, Ayoub, & Heidelberger, 1994). The result is a reduction in glutamate release from the terminals of bipolar cells synapsing with RGCs (Lukasiewicz & Werblin, 1994).

Dopamine acting at D1 receptors on cone bipolar cells reduces the sensitivity of the

GABAc receptor through cAMP-mediated phosphorylation of that receptor (Wellis &

Werblin, 1995). Consequently, the presence of dopamine in the inner retina increases the influx of Ca2+ into bipolar cells, which in turn augments their glutamatergic signaling 58 to RGCs (Heidelberger & Matthews, 1994; Hull, Li, & von Gersdorff, 2006; Wellis &

Werblin, 1995). Dopamine’s modulation of GABAC receptors produces a different effect on the bipolar cell than its modulation of GABAA receptors (i.e. closes sodium channels to enhance center-surround and dampen the photocurrent; see Introduction). This effect is not limited to the ON system. Glycine released from amacrine cells is another source of inhibition to bipolar cells, especially OFF bipolar cells (Eggers, McCall, & Lukasiewicz,

2007). Recent work by the Eggers laboratory has shown that OFF bipolar cell signaling to RGCs was augmented in the presence of SKF 38393, the same D1 receptor agonist used here (Mazade et al., 2019). This result matched previous reports of a dopamine- mediated reduction in the latency of bipolar-RGC signaling (H. Li & Liang, 2013; Xiao et al., 2014) and was likely mediated by a reduction of glycine release from AII amacrine cells rather than a direct effect on the bipolar cells.

Although dopamine appears to increase the excitatory signaling from ON bipolar cells to ipRGCs, it may also act directly on the latter to modify their intrinsic photoresponse. During the 5-second periods of darkness between the repeated light pulses, rod-cone driven signaling is expected to have ceased, with the melanopsin- mediated spiking responses of ipRGCs continuing. The post-illumination persistence of ipRGC spiking is well-established in the literature (Berson et al., 2002; Hartwick et al.,

2007; Hattar et al., 2002) and clearly illustrated in the spike rasters of Figures 2.4A-D.

Activation of D1 receptors significantly enhanced ipRGC spiking during the intervals of darkness that separated the repeated light exposures, compared to baseline testing, in the absence of synaptic blockers (Fig. 2.5C). This effect can also be seen in the pulse- by-pulse spiking data. Overall, the spiking that occurred after each individual light pulse was visibly higher in the SKF condition (light circles of Fig. 2.6B) than during the light pulses of the baseline (light circles of Fig. 2.6A) and recovery (light circles of Fig. 2.6C) 59 conditions. Unlike the spiking that occurred when the lights were on (dark circles in same figures), the spiking that occurred after each light presentation remained constant throughout the entire 2-minute test.

The effects of dopamine on ipRGCs that have been previously reported are somewhat contradictory. First, dopamine was shown to inhibit ipRGC photocurrents in isolated rat ipRGCs as measured through voltage-clamp patch recordings in which the cells were clamped at hyperpolarized potentials (Van Hook et al., 2012). Similar to what has been observed in regular RGCs (Hayashida et al., 2009; Vaquero et al., 2001), this inhibition could be mediated through the phosphorylation of the melanopsin photopigment by cAMP-dependent protein kinase A (Blasic, Brown, et al., 2012).

However, Van Hook and colleagues also showed that exposure of ipRGCs to SKF elicited an increase in the holding current (and decrease in input resistance) measured during voltage-clamp recordings, indicating an inward current was being activated by the treatment. This result is consistent with the increased membrane depolarization and background spiking they also observed when current-clamp or cell-attached

(extracellular) recording techniques were employed on the cultured ipRGCs. A more depolarized resting membrane potential could be expected to increase ipRGC light sensitivity and prolong response duration, resulting in enhanced ipRGC spiking responses to light.

Activation of D1 receptors is associated with an increase in internal cAMP levels through the stimulation of the enzyme adenylate cyclase. Sodhi and Hartwick demonstrated that forskolin, a compound that directly stimulates adenylate cyclase to raise intracellular cAMP levels (Dunn et al., 2006), increases the response duration and the total number of spikes fired by light-stimulated ipRGCs (Sodhi & Hartwick, 2014).

Conversely, the reduction in intracellular cAMP that occurs with the activation of A1 60 adenosine receptors results in attenuated photoresponses in ipRGCs (Sodhi & Hartwick,

2014). A similar attenuation of ipRGC light responses was observed through the activation of μ-opioid receptors with [D-Ala2, MePhe4, Gly-ol5]-enkephalin (DAMGO)

(Cleymaet et al., 2019). These opioid receptors are also linked to adenylate cyclase inhibition and reductions in intracellular cAMP. Taking the results of these two papers together with the finding of Van Hook et al. (2012) linking D1 receptor activation to changes in ipRGC membrane potential provides a possible underlying mechanism for the results of the current study. Stimulation of D1 receptors with SKF activates adenylate cyclase, resulting in cAMP build-up within ipRGCs and subsequent depolarization of the membrane potential, and this ultimately leads to more robust light-evoked spiking by these neurons. The inhibitory effect on melanopsin-driven photocurrents, noted by Van

Hook et al. (2012), appears to be outweighed or negated when the ipRGCs are maintained in a preparation with intact ON and OFF signaling pathways, as done in the experiments here.

The downstream pathway through which cAMP mediates its effects on light- evoked ipRGC spiking was not addressed in the current study. Sodhi and Hartwick proposed that cAMP-dependent protein kinase A (PKA) mediates the effect through its interaction with one of three potential targets (Sodhi & Hartwick, 2014). The first targets are voltage-gated calcium channels (VGCCs). These channels open after the large depolarization that accompanies ipRGC action potential firing to allow calcium ions to

+ flow into the neuron (Hartwick et al., 2007). In turn, calcium ions alter the activation of K channels and thus change the electrophysiological output of the neuron (Sah & Faber,

2002). PKA is known to phosphorylate VGCCs in cardiac and skeletal tissues (Catterall,

2000; Gao et al., 1997; Hosey, Chien, & Puri, 1996; McDonald, Pelzer, Trautwein, &

Pelzer, 1994). In ipRGCs, it is possible that SKF-driven increases in VGCC 61 phosphorylation may produce an increase in Ca2+ influx and a parallel decrease in K+ efflux. These two mechanisms would lead to membrane depolarization of the ipRGC and more light-evoked spiking. Evidence supporting a role for VGCCs and K+ channels in mediating the downstream effects of cAMP was reported in a recent study that investigated the effect of the μ-opioid receptor agonist DAMGO on light-evoked ipRGC spiking (Cleymaet et al., 2019).

Canonical transient receptor potential channels (TRPCs) are the second potential downstream targets of cAMP-activated PKA within ipRGCs. There is anatomical and physiological evidence that TRPCs, and especially TRPC6/7, are the light-gated channels in ipRGCs (Hartwick et al., 2007; Sekaran et al., 2005; Warren, Allen, Brown, &

Robinson, 2006; Xue et al., 2011). Kwan and colleagues have found consensus phosphorylation sites on these channels (Kwan, Huang, & Yao, 2004), making them a likely target of SKF-driven PKA. The effects of TRPC phosphorylation remains uncertain, as there is conflicting evidence that phosphorylation of TRPC6 by PKA promotes (Shen et al., 2011), inhibits (Horinouchi et al., 2012), or does not affect (Hassock, Zhu, Trost,

Flockerzi, & Authi, 2002) the Ca2+ current that it carries. Further characterization of

TRPC6/7 receptors is necessary to more completely evaluate their potential role in mediating the enhancing effect that SKF had on ipRGC photoresponses in the current study.

Hyperpolarization-activated cyclic nucleotide-gated (HCN) channels are the third possible targets of SKF-stimulated PKA production. ipRGCs are known to express these channels (Van Hook & Berson, 2010). In neurons throughout the retina, including RGCs, they stabilize the overall resting membrane potential and contribute to post-spike rebound depolarization by carrying an inward cation current (S. C. Lee & Ishida, 2007).

Dopamine-mediated increases in cAMP facilitate the opening of HCN channels in 62 regular RGCs by interacting with cAMP-specific binding domains located on the channel itself (L. Chen & Yang, 2007; Ulens & Siegelbaum, 2003). Whether alteration of HCN channel function affects ipRGCs under physiological conditions remains unknown; however, as Van Hook and Berson have shown, blockade of HCN channels does not alter the intrinsic light responses of ipRGCs (Van Hook & Berson, 2010).

The effect that D1 receptor activation has on the intrinsic photoresponse of ipRGCs was also studied here by blocking glutamatergic input to and from bipolar cells with L-AP4 (100 µM) and NBQX (25 µM). Under these conditions, SKF did not have an immediate effect on ipRGC signaling. As Figure 2.7A shows, SKF did not elicit significantly more (p > 0.05) spiking compared to baseline, but there was significantly more (p < 0.05) spiking during the recovery condition than the SKF condition. The exact reason for this late response is unclear. One possibility is that dopamine does not elicit more ipRGC spiking without the synergistic effects of bipolar cell input to drive up intracellular cAMP levels during light exposure (Vaquero et al., 2001). In this scenario, the increased spiking detected in the recovery period (synaptic blockers only) may be a result of a “ramp-up” effect due to a gradual rise in internal cAMP levels evoked by the repeated light exposures that was independent from D1 receptor activation. The experiments conducted in the presence of synaptic blockers were conducted after the experiments conducted without the synaptic cocktail. As a result, five exposures to the bright blue flickering light preceded the first experiment conducted in the presence of synaptic blockers. These repeated light exposures likely drove up endogenous dopamine levels (Godley & Wurtman, 1988; Iuvone, Galli, Garrison-Gund, et al., 1978) and drove down extracellular adenosine levels (Ribelayga & Mangel, 2005), both of which resulted in increased intracellular cAMP levels (Sodhi & Hartwick, 2014; Vaquero et al., 2001). Thus, by the time the experiments with synaptic blockers occurred, a 63 ceiling effect may have limited the effect that SKF had on ipRGC spiking due to already high intracellular cAMP levels from the previous six light exposures. Future experiments on the effects that D1 receptor agonists have on ipRGC signaling may consider applying the synaptic-blocker cocktail at the beginning of the experiment, before significant ramp- up in internal cAMP occurs.

A second explanation for the significant increase in spiking during the recovery experiment in the presence of synaptic blockers is that SKF had a direct but delayed effect on the physiology of pharmacologically isolated ipRGCs. Evidence in the results presented here argue against this possibility. D1 receptor activation elicited an immediate increase in light-evoked ipRGC spiking before the application of the synaptic cocktail.

This enhancing effect was washed-out in the following experiment conducted in Ames solution alone; there was no delayed effect. Liu and Lasater similarly found that dopamine-mediated changes to regular RGCs were washed out after approximately eight minutes of superfusion with Ringer solution (Liu & Lasater, 1994).

Blockade of D1 receptors does not alter light-evoked ipRGC spiking

If a D1 receptor agonist enhances ipRGC spiking in response to a bright blue flickering stimulus, then it is reasonable to hypothesize that a D1 receptor antagonist would impede it. The results presented in this study did not confirm this hypothesis.

However, only two ipRGCs from two retinas were recorded without synaptic blockers during the SCH experiments (Figs. 2.9A-C). This small sample size was prohibitive to making conclusions about the effect that the D1 receptor antagonist had on ipRGCs under these conditions.

Recordings from a larger population of ipRGCs were obtained in the presence of the synaptic cocktail (N= 3 retinas; n = 5 ipRGCs). Statistically, there was no difference 64

(p > 0.05) between the number of light-evoked spikes between the baseline condition (in

Ames solution only), the D1 receptor antagonist condition (SCH 23390 in Ames), and the recovery condition (in Ames only). Figure 2.10A clearly shows, however, a trend towards a decrease in spiking in the presence of SCH. Reduced spiking was observed in four out of the five recorded cells during the SCH treatment. The fifth cell, however, increased its light-evoked spiking during the bath application of SCH. It is possible that increasing the sample size to beyond two cells (without synaptic blockers) and five cells (with synaptic blockers) will result in a statistically significant decrease in ipRGC spiking in the presence of SCH and these experiments are planned in the future.

A second explanation emerges from the fact that extracellular endogenous dopamine must be present for the D1 receptor antagonist to have an effect. Since rods and cones are the primary mediators of dopamine release in the retina (Perez-

Fernandez et al., 2019; Qiao et al., 2016), the ON- and OFF-pathway synaptic blockers used to isolate the intrinsic photoresponse of ipRGCs likely kept endogenous dopamine concentrations low. Thus, it is possible that a lack of endogenous, extracellular dopamine masked the results of D1 receptor blockade. Two factors likely mitigated this problem. First, ipRGCs are known to signal directly to dopamine amacrine cells (Belenky et al., 2003; Prigge et al., 2016), and this signaling is resistant to NBQX blockade of

AMPA receptors (D. Q. Zhang et al., 2008). Therefore, these cells likely received light- evoked stimulation even though the rod/cone-driven signals to these cells were blocked.

Second, just as in the SKF experiments, the SCH experiments conducted in the presence of the synaptic cocktail occurred after five experiments without the cocktail.

Extracellular endogenous dopamine and intracellular cAMP levels were likely high heading into these experiments due to the repeated exposures to the bright, flickering lights (Godley & Wurtman, 1988; Iuvone, Galli, Garrison-Gund, et al., 1978; Vaquero et 65 al., 2001). Whether these levels were high enough to allow SCH to have a measurable effect on the ipRGC photoresponse cannot be answered with this battery of experiments, but the potential ceiling effect observed during the SKF experiments conducted in the presence of synaptic blockers suggests they were.

Activation of D1 receptors enhances light-evoked spiking in ON RGCs

The D1 receptor agonist SKF did not only enhance spiking in synaptically intact ipRGCs but also in sustained ON RGCs. Figure 2.3F demonstrates that ON RGCs fired significantly more (p < 0.05) action potentials in the presence of extracellular SKF than during baseline conditions. Spiking remained high during the recovery condition and ceased in the presence of synaptic blockers. This finding in ON RGCs is important because it bolsters the theory that dopamine, at least in part, affects ipRGC signaling through mechanisms beyond modification of the intrinsic phototransduction pathways that occur within those cells. In other words, the changes seen in ipRGC spiking in the presence of SKF are one aspect of an overall change in inner-retina signaling.

The recent literature provides two possible explanations for this SKF-induced increase in ON RGC spiking during the repeated light pulses. The first is a dopamine- mediated reduction of inhibition signaling (via phosphorylation of GABAc receptors) in the inner retina, as discussed earlier in this discussion. The second is activation of D1 receptors may make ON RGCs more excitable to glutamatergic stimulation by bipolar cells in a manner similar to that hypothesized to occur in ipRGCs. Although several examples of dopamine’s attenuating the electrophysiological current of RGCs were provided in the Introduction, there is recent evidence that activation of D1 receptors on rat RGCs enhances their temporal summation (Cui et al., 2017). Specifically, dopamine may modulate the excitatory signal integration within RGC dendrites by suppressing 66 inward-rectifying potassium (Kir) channels and HCN channels through a cAMP and protein kinase A pathway. These channels are known to play important roles in regulating a neuron’s receptiveness to external signals (L. Chen, Yu, Zhao, & Yang,

+ 2004; Kase & Imoto, 2012). Furthermore, Kir channels mediate resting K transmission through the cellular membrane and thus play an important role in setting the resting membrane potentials (Podda, Riccardi, D'Ascenzo, Azzena, & Grassi, 2010). The Cui group also found inhibition of Kir channels through the dopamine-cAMP pathway altered the resting membrane potential of RGCs enough to make them more excitable (L. Chen

& Yang, 2007; Q. Li et al., 2017).

Blockade of D1 receptors has no effect on light-evoked spiking in ON RGCs

Unlike the significant effect that a D1 receptor agonist had on the number of spikes fired by ON RGCs during the repeated light exposures of a bright blue flickering light, bath application of the D1 receptor antagonist SCH 23390 (100 µM) had no significant effect (P > 0.05; Fig. 2.8F). Inspection of individual recordings showed some

RGCs exhibited substantially more firing during the application of SCH (for example, see

Figs. 2.8A-D), yet spiking in others almost completely ceased. The net result was no significant overall difference. This dichotomy between ON RGC responses to SCH is not novel. Liu and Lasater noted that exogenous dopamine elicits a reduced photocurrent in most isolated turtle ON RGCs, an increased photocurrent in others, and no change in photocurrent in yet a third group (Liu & Lasater, 1994). They hypothesized that these differences arose from dopamine’s having different effects on different subtypes of

RGCs.

When the effects of SKF and SCH on light-evoked spiking in ON RGCs are considered in tandem, they may give insight into the global effects that dopamine has on 67 the function of the inner retina. Hayashida and colleagues have shown that SCH reverses the inhibitory effect that exogenous dopamine has on current-induced ON RGC spiking in cells physically isolated from the retina using immuno-panning techniques

(Hayashida et al., 2009). The results are different, however, when light-evoked ON RGC spiking is recorded from intact retinas using MEA. In this setting, ON RGCs are able to receive input from bipolar and amacrine cells, changing the outcome. As shown above, the D1 receptor agonist SKF causes an increase in ON RGC spiking, not a decrease.

This finding suggests that the extra-cellular excitatory effects outlined in the last section outweigh the intrinsic inhibitory effect on electrophysiological current that Hayashida and colleagues demonstrated in their isolated rat ON RGCs. Likewise, any enhancement of the internal current induced by the blockade of D1 receptors did not outweigh the reduced inhibition, as here SCH did not modulate the light responses of ON RGCs.

In total, these results suggest that dopamine has paradoxical effects on light responses in the inner retina. Some of them reduce the gain of internal photocurrents so that they do not become overwhelmed by the high volume of signaling originating from the outer retina. Others increase the signaling from these same cells in order to provide downstream targets with accurate photic data. In the case of synaptically intact ON

RGCs and ipRGCs, action potentials are the signals that are transmitted to the brain targets innervated by these neurons. Thus, any alternation in light-evoked spiking is the output response that would ultimately influence the functions regulated by these photoreceptors, including the pupillary light response, which will be the focus of the next two dissertation chapters.

Modulation of D1 receptors on OFF RGCs

Although this study was unable to draw conclusions on the effect that dopamine has on OFF RGCs, other groups have. Anatomically, dopamine amacrine cells extend processes into the OFF sublamina of the inner plexiform layer (Famiglietti & Kolb, 1976), but exactly how these cells receive light-evoked signals from bipolar cells remains unelucidated (Witkovsky, 2004). The Jensen laboratory has published several manuscripts on the physiological effect that dopamine has on OFF RGCs. This group first reported that dopamine increases the maintained signaling of OFF RGCs (Jensen &

Daw, 1984, 1986) and then found that the blockade of D1 dopamine receptors with SCH

23390, the same D1 receptor antagonist used in the present study, attenuates their center-surround mechanism (Jensen, 1989, 1991). These experiments were conducted with patch-clamp methodology. A recent study using MEA recordings of intact retinas from mice that were genetically altered to lack dopamine in their retinas found that light response duration decreased under dark-adapted conditions but increased under light- adapted conditions in comparison to wild-type animals (Sprinzen, Risner, & McMahon,

2014). These results for OFF RGCs match with the previously reported effects that dopamine has on OFF bipolar cells (see Introduction) and further emphasize the importance of maintaining the integrity of the OFF signal during bright-light conditions.

Conclusion

In this chapter, I demonstrated that activation of D1 receptors on ipRGCs and ON

RGCs with SKF 38393 results in increased light-evoked spiking from both neuronal types. Previous works using patch-clamp techniques on isolated RGCs have shown that activation of D1 receptors reduces electrophysiological currents in these cells, but that effect is outweighed in intact retinas by the other sequelae of D1 activation: an increase 69 in excitatory input from bipolar cells and a heightened excitability of the RGCs themselves. The fact that these results were not exactly replicated in ipRGCs in the presence of synaptic blockers suggest that intact glutamatergic signaling pathways are an essential component of any D1-mediated enhancement. Regardless of the exact mechanism, stimulation of D1 receptors causes ON and ipRGCs to fire more action potentials that are in turn received by their downstream brain targets. This finding may have functional implications for the aspects of vision mediated by ganglion cell photoreceptors, including the photoentrainment of circadian rhythms (Hattar et al., 2003;

Panda et al., 2002; Ruby et al., 2002) and the pupillary light response (Barnard et al.,

2004; Gamlin et al., 2007; Lucas et al., 2003).

Chapter 3: Photopotentiation of the human pupillary light response after

stimulation with red and blue flickering lights

Chapter 2 of this dissertation demonstrated that intra-retinal dopamine can alter the signaling of intrinsically photosensitive retinal ganglion cells (ipRGCs), as measured using in vitro electrophysiological techniques. Measuring the interaction between dopamine and ipRGCs in an intact human eye, where light conditions can vary and no precise measurements of intra-retinal dopamine levels exist, poses unique challenges.

Electroretinography (ERG) offers one approach. This form of visual electrophysiology employs extracellular electrodes, usually placed at or near the cornea in human subjects, to measure the light-evoked electrical currents of rods and cones, bipolar cells, and amacrine cells (Perlman, 1995). Using silent substitution techniques that take advantage of the principle of univariance to selectively stimulate one type of photoreceptor while preventing other photoreceptors from responding to the stimulus (O.

Estevez & Spekreijse, 1982), Fukuda and colleagues have reported that it is possible to isolate ipRGC photoresponses from those of rods and cones in ERGs obtained from healthy patients (Fukuda, Higuchi, Yasukouchi, & Morita, 2012; Fukuda, Tsujimura,

Higuchi, Yasukouchi, & Morita, 2010). ERGs may hold future diagnostic potential, as the amplitude of the ipRGC photoresponse has been shown to be reduced in a small cohort of advanced glaucoma subjects (Kuze et al., 2017). However, the need for expensive and perfectly calibrated equipment, the presence of light-filtering macular pigments

(Spitschan & Woelders, 2018), and the incorrect assumption that cone spectral sensitivities are uniform across individuals (Neitz & Jacobs, 1990; V. C. Smith & 71

Pokorny, 1995) currently act as obstacles for using ERGs as a common and reliable measure of in vivo ipRGC function in human subjects.

Quantification of the ipRGC-mediated aspects of the pupillary light response may be a better approach to assessing the function of these neurons in the living human eye.

This chapter will investigate how repeated light exposures, which likely increase retinal dopamine levels, change the pupillary light response and alter the function of ipRGCs.

Anatomical evidence for ipRGC contributions to the pupillary light response

The midbrain’s olivary pretectal nucleus (OPN) is a key brain region involved in mediating the pupillary light response. This structure receives direct input from the retina

(M. J. Young & Lund, 1998) and is the first nucleus located along the pupillary light response pathway (Trejo & Cicerone, 1984). The neuronal activity of the OPN reflects environmental light levels (Clarke & Ikeda, 1985a, 1985b). That is, its neurons begin firing with light onset, stop firing with light offset, and fire more frequently when the light is bright than when it is dim. Intriguingly, the OPN may contribute to the photoentrainment of circadian rhythms (Krout, Kawano, Mettenleiter, & Loewy, 2002;

Miller, Obermeyer, Behan, & Benca, 1998; Prichard et al., 2002). A role for the OPN in regulating sleep-wake cycles is perhaps consistent with the fact that it receives input from ipRGCs (Hattar et al., 2006; Hattar et al., 2002), whose role in circadian rhythm photoentrainment is well-established (Hattar et al., 2003; Panda et al., 2002; Ruby et al.,

2002).

In fact, ipRGCs play an important role in relaying signals related to environmental irradiance levels to the OPN. They provide significant axonal innervation to both its anatomical outer shell and core (Hattar et al., 2006; Hattar et al., 2002; Morin,

Blanchard, & Provencio, 2003). Both M1 and M2 ipRGCs send axons to the OPN in 72 rodents (Baver et al., 2008; Hattar et al., 2006; Hattar et al., 2002) and primates (Liao et al., 2016). Of all ipRGC subclasses, M1 cells are the most sensitive to light (Xue et al.,

2011; Zhao et al., 2014) and possess large dendritic fields (T. M. Schmidt & Kofuji,

2011). Although less sensitive to light than M1 cells, M2 cells fire action potentials at a higher rate (T. M. Schmidt & Kofuji, 2009) and seem to be more heavily influenced by the activity of bipolar cells (T. M. Schmidt & Kofuji, 2010). Taken together, these two classes of ipRGCs effectively relay both melanopsin- and rod/cone-initiated signals to the OPN, allowing the OPN to maintain, over the course of many hours, appropriate pupillary constriction for the amount of environmental light present.

In an anatomical curiosity, melanopsin has been identified in the anterior segment of the eye. First, melanopsin-containing cells are found in the iris of mice and other nocturnal mammals (Xue et al., 2011). They likely only contribute minimally to the pupillary light response, however, because there is little difference in magnitude between the direct and consensual pupillary light responses to a unilateral light stimulus (S.

Grozdanic et al., 2003; Zhu et al., 2007). Second, melanopsin is also found in the murine cornea along the nerve fibers from the trigeminal nucleus, but the function of the photopigment here is not clear (Delwig et al., 2018; Matynia et al., 2016).

Functional evidence for ipRGC influence over the pupillary light response

In addition to the anatomical evidence, there is extensive functional evidence that these ipRGCs contribute to pupil dynamics. The earliest studies were conducted on non- primate mammals, especially rodents. Mice (Semo et al., 2003; Whiteley, Young,

Litchfield, Coffey, & Lund, 1998) and canines (S. D. Grozdanic, Matic, Sakaguchi, &

Kardon, 2007), both blind from outer retinal disease, maintain a pupillary light response despite the loss of rods and cones. Similarly, mice genetically modified to lack functional 73 rods and cones retain pupil function in the presence of bright lights (Barnard et al., 2004;

Hattar et al., 2003; Lucas, Douglas, & Foster, 2001; Lucas et al., 2003). Rodents bred to lack melanopsin, on the other hand, show normal pupil responses to dim lights (likely rod-driven) but show deficits at high irradiances (Lucas et al., 2003; Panda et al., 2003).

These studies suggest that ipRGCs contribute to pupil responses to bright lights but not to dim ones, consistent with the relative light insensitivity to light of ipRGCs as demonstrated in in vitro studies (Do & Yau, 2010).

Once it became clear that ipRGCs contribute to the pupillary light response in non-primate mammals, evidence emerged that the same is true in non-human primates.

Gamlin and colleagues published the seminal paper on the topic (Gamlin et al., 2007).

They observed that the macaque pupil constricted to the onset of both bright long and short wavelength stimuli, as expected. Pupil re-dilation after the offset of the bright long wavelength light occurred immediately and swiftly. However, after the bright short wavelength light turned off the pupil remained constricted for several seconds. This sustained pupil constriction after light exposure was hypothesized to be due to ipRGC input into the pupillary light response, because it matched the known spectral and temporal properties of melanopsin phototransduction. Namely, ipRGCs lethargically encode light onset and offset (Berson et al., 2002; Hartwick et al., 2007), are most sensitive to short wavelength light (Dacey et al., 2005; Newman et al., 2003), and are best stimulated by bright light (Berson et al., 2002; Dacey et al., 2005; Do et al., 2009). It was possible that S-cones, not ipRGCs, were the source of this sustained, post- illumination constriction. To rule out this possibility, the Gamlin group made intravitreal injections of the compounds L-AP4 (l-2-amino-4-phosphobutyrate) and CNQX (6-cyano-

7-nitroquinoxaline-2,3-dione) in order block the ON- and OFF-pathways in the macaque retina. Even after the blockade of rod- and cone-driven retinal circuits, the primates 74 demonstrated a pupillary light reflex to a bright, short wavelength stimulus that persisted after stimulus offset. Thus, this work definitively established the intrinsic responses of ipRGCs in contributing to the primate pupillary light response.

The influence of ipRGCs on the pupillary light response has also been demonstrated in human subjects. In addition to the work on macaques described above, the Gamlin group has also shown, in human subjects, that 10 s of short wavelength light can elicit a stronger sustained pupillary response at both five and thirty seconds after stimulus offset than a similar long wavelength stimulus (Gamlin et al., 2007; Kankipati,

Girkin, & Gamlin, 2010). Just as in the macaque, this difference in pupil re-dilation was ascribed to sustained ipRGC firing after stimulus offset. Appropriately, blue-blocking filters eliminate the differences in post-illumination pupil constriction between red and blue lights (Ishikawa, Onodera, Asakawa, Nakadomari, & Shimizu, 2012). Sustained pupillary constriction after blue light offset has good intra-subject repeatability (Herbst,

Sander, Milea, Lund-Andersen, & Kawasaki, 2011).

Additional evidence for ipRGC involvement in the human pupillary light response comes from these neurons’ role in photoentraining circadian rhythms. ipRGCs provide innervation to the suprachiasmatic nucleus, the body’s master clock (Hannibal &

Fahrenkrug, 2004; Hattar et al., 2002; Hattar et al., 2003). In turn, the suprachiasmatic nucleus helps to regulate the release of melatonin, a hormone associated with sleep- wake cycles, from the pineal gland (Saper, Lu, Chou, & Gooley, 2005). Appropriately,

479 nm wavelength light, which is almost identical to the peak spectral sensitivity of melanopsin, has been shown to be optimal in melatonin suppression and subsequent photoentrainment of circadian rhythms (Papamichael et al., 2012; Prayag, Najjar, &

Gronfier, 2019). Tying together melatonin regulation and pupil function, the amount of sustained pupillary constriction after short wavelength stimulus offset positively 75 correlates with serum melatonin levels (Munch, Leon, Crippa, & Kawasaki, 2012). Thus, patients who have an inner retinal disease that can compromise ipRGCs, such as glaucoma, often report difficulties with sleep (Agorastos et al., 2013), but those with severe outer retinal disease, such as retinitis pigmentosa, typically do not (Zaidi et al.,

2007).

Evidence also indicates that the ipRGC influence over the human pupillary light reflex is not limited to sustained pupil constriction after light exposure, but that ipRGCs also contribute to pupil constriction during prolonged light exposures. Increasing the intensity of a continuous light stimulus in a stepwise manner causes increased pupillary constriction if the light is blue but not if the light is red (R. S. Young & Kimura, 2008).

This finding is consistent with blue-light-sensitive ipRGCs’ resistance to adaptation (Do &

Yau, 2013) and ability to integrate photic data over many hours (Wong, 2012). In a shorter protocol, a single pulse of bright blue light elicits more pupil constriction than a single pulse of red that is photometrically matched for cone stimulation (Herbst et al.,

2011). This difference is presumably due to ipRGCs and rods contributing to the pupillary light response during the blue light but not during the red light. From this collection of studies on human subjects, it is clear that the pupil’s behavior, both during and after light exposure, needs to be considered when evaluating ipRGC contribution to the pupillary light response.

Photoreceptor contributions to the pupillary light response

It has been known since the discovery of melanopsin and ipRGCs, that rods, cones, and ipRGCs all contribute to the full dynamic range of the normal pupillary response to light (Lucas et al., 2003). Since then, work on animals and humans has

76 better established how each of the three photoreceptor types present in the mammalian retina influences the pupillary light response.

As introduced above, the signature of ipRGC influence over the pupillary light response is sustained constriction after the offset of a light stimulus. Many groups use the pupillary light response as a metric to quantify ipRGC impact on the pupil response.

The intensity, spectral make-up, and temporal characteristics of the light source dictate the magnitude of ipRGC input. ipRGC-mediated pupillary responses are best elicited when the stimulus has a wavelength of 480 nm, matching melanopsin’s wavelength of maximum sensitivity (Gamlin et al., 2007; Kardon et al., 2009). Murine models suggest that the minimum irradiance (measured at the corneal plane) necessary for ipRGC contribution is around 1011.5 photons/s/cm2 for light around 480 nm (Lall et al., 2010;

Lucas et al., 2003). In human subjects, the likely minimum irradiance, approximately 1012 photons/s/cm2 at wavelengths near melanopsin’s peak sensitivity, matches well with the mouse data (Barrionuevo et al., 2014; Park et al., 2011). Like ipRGC phototransduction

(Berson et al., 2002; Hartwick et al., 2007), the ipRGC-mediated pupillary light reflex reacts lethargically to light onset and offset. A good example of this sluggishness can be seen when a bright blue square-wave light stimulus (designed to maximize ipRGC signaling) drives the pupillary light response (Tsujimura & Tokuda, 2011). In this scenario, the pupil response is not a square wave, but rather a sinusoid with slow pupil re-dilation. When the light stimulus is presented as a sinusoid, however, the pupil response is able to follow suit. Likewise, the latency period for the ipRGC contribution to the pupillary light response is a lengthy 0.8-1.8 seconds (Barrionuevo et al., 2014; Lall et al., 2010; McDougal & Gamlin, 2010). All these data point toward ipRGCs being ideally situated to mediate post-illumination pupil constriction.

Rods and cones have a more immediate impact on the pupillary light response than ipRGCs. Their contribution is not insignificant, mediating 80 percent or more of pupillary constriction in response to light (Do & Yau, 2010; Jain et al., 2016). They encode rapidly oscillating light stimuli (Hecht & Shlaer, 1936; Lowenstein & Loewenfeld,

1958) and exhibit a latency period below that of the iris dilatory muscle, which is approximately 220 ms (Ellis, 1981; Lowenstein & Loewenfeld, 1958). Thus, it is not surprising that the outer retina photoreceptors are likely responsible for the initial, rapid pupillary response seen shortly after light onset (Adhikari, Zele, & Feigl, 2015; Kostic et al., 2016; McDougal & Gamlin, 2010; Park et al., 2011). In effect they mask the relative lethargy of ipRGCs (Gooley et al., 2012). When the stimulus is flickering, outer retinal photoreceptors drive the immediate, rapid pupil responses to the light pulses, but contribute little to post-illumination constriction (Barrionuevo et al., 2014; Gooley et al.,

2012).

Rods are the main conduit for the pupillary light response under dim conditions.

Specifically, rods likely dominate the pupillary light response below ipRGC threshold

(Barrionuevo et al., 2014; Keenan et al., 2016; McDougal & Gamlin, 2010; Park et al.,

2011). This effect can be readily observed in the pupil’s constriction in response to dim and red and blue lights (Kardon et al., 2009). When photometrically-matched red and blue lights have intensities under 1012 photons/cm2/s, the blue light elicits more pupillary constriction than the red light due to the influence of rods, which have a peak sensitivity around 498 nm (Bowmaker & Dartnall, 1980). Intriguing evidence in mouse suggests that rods, whose photoresponses are typically described as transient, provide essential input into ipRGCs during exposure to a slowly flickering light source (Ostergaard et al.,

2007; Schroeder et al., 2018). It follows, then, that although ipRGCs are the major source of the sustained pupillary constriction that occurs immediately after the offset of a 78 blue light, rods may also make a small contribution (Adhikari, Feigl, & Zele, 2016;

McDougal & Gamlin, 2010).

Cones also provide important input into the pupillary light response. They are the likely mediators of the immediate pupil response to light stimuli that either quickly saturate rods or are outside the spectral sensitivity range of rods or, both (Barrionuevo et al., 2014; Gooley et al., 2012). Under these conditions, L- and M-cones provide excitatory input into the pupillary light response, while S-cones provide inhibitory input

(Barrionuevo & Cao, 2016; Cao, Nicandro, & Barrionuevo, 2015; Spitschan, Jain,

Brainard, & Aguirre, 2014). These signals from cones combine with melanopsin-driven signals in a linear fashion to drive the pupil (Barrionuevo & Cao, 2016; Barrionuevo et al., 2014). Cones are able to drive ipRGC signaling, independent of the latter’s intrinsic photoresponse, via ON bipolar cells (Wong et al., 2007). Cone influence over the pupillary light response attenuates with stimulation by progressively dimmer lights, but never fully ceases (Park et al., 2011). At light levels below the threshold for ipRGC activation, S-cones make a small but detectable contribution to the initial pupillary light response (Allen, Brown, & Lucas, 2011). Unlike rods, cones do not contribute to the sustained pupillary light response in humans under these conditions (Adhikari et al.,

2016).

Prior light exposures alter pupillary light responses

In the conditions of daily life, the retina is not dark adapted for long periods, and the eye does not receive calibrated light stimuli. Rather, the eye, retina, and ipRGCs are bombarded by lights whose spectral and temporal characteristics are continuously changing. Very few light exposures occur without the influence of the preceding one lingering in the visual system. Thus, experiments that attempt to characterize ipRGC 79 input into the pupillary light response using either brief pulses or prolonged exposures of monochromatic light are likely limited in their ability to draw conclusions that are applicable to how the cells function in the natural environment. Understanding this methodological restriction, several studies have investigated the effects that prior light exposures have on the pupillary light reflex.

Four recent studies investigated the effects that repeated light exposures have on the human pupillary light response. Gooley and colleagues demonstrated flickering

(0.1 to 4 Hz) green lights elicited greater pupillary constriction than a similarly bright, continuous light (Gooley et al., 2012). They attributed this effect to photoreceptors dark- adapting during the period of darkness that separated the light pulses. Vartanian and colleagues showed the same effect in humans with blue lights. They were also able to demonstrate in mouse that their flickering lights enhanced in vitro ipRGC signaling, compared to a continuous light (Vartanian, Zhao, & Wong, 2015). In a third example, Ba-

Ali and colleagues demonstrated that both flickering (0.10 Hz) red and blue lights elicited more sustained pupillary constriction than irradiance-matched, continuous lights (Ba-Ali,

Lund-Andersen, Ahmadi, & Brondsted, 2017). This effect was larger for the red light than the blue light, and the authors hypothesized that cones have a larger role in the sustained pupillary light response than previously thought. Finally, Lee and colleagues demonstrated that the time between single, sequential pulses of blue and green lights is an important variable in predicting the overall pupil constriction to the second (S. Lee,

Muto, Shimomura, & Katsuura, 2017). Specifically, pulses separated by >500 ms elicited more pupillary constriction during the second (green) pulse than those separated by

<500 ms. These results of these four studies point toward flickering stimuli having an enhancing effect on the pupillary light reflex as the stimulus progresses in its duration.

Other groups have investigated the effect that adapting lights have on the pupillary light reflex. In mice, a white adapting light presented immediately prior to a bright, brief blue light pulse elicited nearly 1.5 times more pupil constriction to the latter than if no adapting stimulus were present (Zhu et al., 2007). Joyce and colleagues demonstrated in human subjects that sustained pupil constriction after exposure to a 1 s pulse of blue light increases when a pre-stimulus adapting field of blue, cyan, or green light precedes it (Joyce, Feigl, & Zele, 2016). Increasing the brightness and duration of the adapting field augments this effect. Mure and colleagues have shown that a long wavelength (>515 nm) adapting light presented before 5 minutes of continuous red light causes a 28% increase in sustained pupillary constriction in human subjects (Mure et al.,

2009). They also demonstrated that, unlike the results from Joyce and colleagues, a short wavelength adapting light (< 515) attenuates pupil constriction during the subsequent red light. This difference in results may be partially explained by the two vastly different second lights used in the studies (i.e. short versus long duration, and red versus blue). For the purposes of this dissertation, I will refer to the enhancement of the pupillary light reflex by preceding light exposure as photopotentiation.

Purpose

My master’s thesis attempted to maximize ipRGC input into the human pupillary light response by utilizing a stimulus that slowly flickered (0.10 Hz for 1 min) between darkness and bright red and blue lights (Yuhas, 2014). There, I demonstrated, on a small cohort of healthy subjects (n = 6), that this red-blue flickering stimulus was able to enhance pupil constriction both during and after light exposure in a manner consistent with the pupillary photopotentiation. Even more surprising was that the pupillary responses to the red lights changed more than those to the blue lights, over the course 81 of the stimuli. By the end of the 2 minute-long flickering light stimuli, the pupil traces elicited by the red lights were indistinguishable from those of the blue lights. Responses to either the blue or red stimuli exhibited signs of ipRGC influence: sustained pupil constriction after light offset and stable constriction while the stimulus lights were on.

The purpose of this dissertation chapter is to characterize and explore in more detail how repeated light exposures may modulate ipRGC input into the human pupillary light response. Questions addressed in this dissertation include: 1) do flickering light stimuli need to alternate between long and short wavelength lights to elicit maximum pupillary photopotentiation; 2) can flickering stimuli gradually increase ipRGC contributions to the pupillary light response at irradiance levels below melanopsin threshold for single light pulses; and 3) what is the time course of pupillary photopotentiation in terms of its onset and its persistence?

Methods

This study followed the tenants of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) in Biomedical Sciences at The Ohio State

University (OSU). Subjects freely gave their informed consent to participate in the investigation prior to data collection. Every subject was ≥18 years old, and all females reported no current pregnancy (confirmed or suspected). In order to neutralize the effects of any diurnal rhythm for ipRGC influence over the pupillary light reflex (Zele et al., 2011), all pupil testing began late in the morning or early in the afternoon.

Subject recruitment and eligibility

A cohort of young, healthy subjects was recruited from the faculty, staff, and students of the OSU College of Optometry. At the first of two study sessions, all 82 participants were screened for three clinically-based inclusion criteria. Namely, potential study subjects were required to be 1) 45 years of age or younger; 2) achieve at least

20/25 distance visual acuity (as measured on a wall-mounted Bailey-Lovie chart) using habitual refractive error correction; and 3) have an open anterior chamber angle, as judged by a licensed optometrist using the Van Herick technique. In this method, an optic section of light is projected at a 60-degree angle on the cornea near the limbus.

The depth of the anatomical angle casts a slit-shaped shadow on the iris between the light incident on the cornea and the light incident on the iris. A wide shadow indicates a deep angle (i.e. safe for dilation), and a narrow shadow indicates a shallow anatomical angle (i.e. high risk of angle closure with dilation). Estimates of anatomical angle depth using the Van Herick technique correlate with measurements of the anterior chamber angle between the posterior face of the cornea and iris using anterior chamber optical coherence tomography (Larsen, Luraas, & Lundmark, 2013).

Participants meeting these clinical screening criteria were then questioned about their medical and ocular history. Any individual meeting one of the following exclusion criteria was dismissed from the study before pupil testing: 1) a diagnosis of any form (i.e. secondary, closed-angle, or primary open-angle) of glaucoma or a history of optic nerve disease other than glaucoma (e.g. optic neuritis); 2) a history of ocular surgery (e.g. trabeculectomy) or current medication use (e.g. cholinergic agonists/antagonists) known to profoundly affect pupillary function or shape; 3) a history of traumatic brain injury requiring hospitalization; 4) a history of unreactive pupil responses; 5) a history of retinopathy (e.g. diabetic retinopathy, retinal detachment); 6) the presence of significant cataract (any posterior sub-capsular opacification, cortical spoking, or nuclear sclerosis graded > 1+) at last eye examination; or 7) known refractive error greater than a spherical equivalent of ± 6 diopters. 83

First study session

All eligible subjects gave informed consent to participate in the study prior to pupil testing. One drop of 1.0% tropicamide was instilled in the subject’s left eye. Pupil dilation ensured that the retinal illuminance remained constant throughout the light stimulus presentation. Un-dilated pupils exhibit less sustained pupillary constriction after the offset of blue light stimuli than dilated pupils (Ba-Ali et al., 2017; Nissen, Sander, &

Lund-Andersen, 2011). This effect is likely due to the reduction in light reaching relatively insensitive ipRGCs due to the un-dilated pupil constricting in response to light onset (Do et al., 2009).

The pupillometry protocol began 30 minutes after drop instillation, with the subject spending the last 20 minutes with the room lights extinguished and with a patch placed over his or her left eye. This length of time is sufficient to dark adapt both the rods and cones (Asakawa et al., 2019; Rushton, 1963) and ipRGCs (Wong et al., 2005).

Once the dark adaption period ended, the subject removed the patch and positioned him- or herself in front of the custom-built pupilometer described below. An adjustable chinrest ensured that the subject was properly aligned in front of the instrument and allowed for stability throughout the pupil testing protocol.

Four experimental trials of flickering light stimuli were presented to the subject’s dilated left eye while an infrared camera captured the consensual pupillary light response of the right eye. These trials were designed to elicit changes in the pupillary light response over the course of repeated light challenges. The first trial (Fig 3.1A) occurred immediately after the end of the initial 20-minute dark adaptation period and consisted of a bright red (7 x 1013 photons/s/cm2) or blue (1 x 1013 photons/s/cm2) light that slowly flickered (0.10 Hz; 5 seconds on, 5 second off) for one minute (6 total pulses 84 of light). This irradiance level for the blue light, but not for the red light, has been shown to be bright enough to elicit the ipRGCs signature (i.e. sustained pupil constriction after light offset) in the pupillary light responses of young people (Yuhas, 2014). Half of the recruited cohort received the red light, and the other half received the blue light. The next three light trials (Fig. 3.1B-D) were presented in a computer-randomized order.

Sequentially, each comprised a 20-minute dark adaptation period, a 2-minute “priming” light stimulus that flickered at 0.10 Hz for 2 minutes (12 total pulses), a 5-minute dark adaption period, and a 1-minute “test” light stimulus that flickered at 0.10 Hz. The priming stimuli consisted of a red flickering light (7 x 1013 photons/s/cm2), a blue flickering light (1 x 1013 photons/s/cm2), and a flickering light that alternated between darkness and red and blue lights in an alternating fashion (henceforth referred to as

“red-blue alternating”). Each study participant received all three of these priming lights after a 20-minute dark adaption period. Then, following a 5-minute dark adaptation period after the priming stimulus, each subject received either the red or the blue test stimulus. The test stimulus used on each subject was identical to the stimulus used in the first trial and did not change throughout the entire protocol. That is, whichever light

(red or blue) the subject received during the first trial was the same light he or she received as the test stimulus after the three different priming stimuli.

Second study session

The second study session followed the first by at least 24 hours. Visual acuities were again measured to ensure at least acuities of 20/25 at distance in each eye through habitual refractive error correction, and the subject confirmed that there were no changes to his or her medical or ocular histories. Once these screening procedures were complete, the left eye was dilated with 1.0% tropicamide. 85

The pupil testing protocol of the second session was identical to the first (Fig.

3.2A-D), save for two important alterations. First, the irradiance of both the priming and test stimuli was reduced to 7 x 1011 photons/s/cm2 for the red lights and 1 x 1011 photons/s/cm2 for the blue lights. Note that these irradiances, as measured at the corneal plane, are below the threshold for ipRGC activation (Do & Yau, 2010). There were two reasons for testing a 2-log-unit reduction in stimulus irradiance. First, it ensured that a ceiling effect was not limiting the magnitude of pupil photopotentiation elicited by the brighter lights of the first session. Put another way, the bright lights of the first session may have maxed out the iris’s ability to constrict, masking an increase in pupillary constriction over time. Second, by starting below threshold for ipRGC contribution to the pupillary light response, one could determine whether a light that would be too dim to keep the pupil relatively constricted after light offset (i.e. the

‘signature’ indicating an ipRGC contribution to the pupillary light response) with a single pulse could do so after repeated exposures over a relatively short time period. The second alteration to the pupil testing protocol was that a fifth trial was added to the end of the session (Fig 3.2E). After a 5-minute dark adaptation period, a two-pulse, red-blue light stimulus was presented at the same (brighter) light irradiances used during the first experimental session. This final, brighter light presentation caused more pupil constriction than any of the preceding, dimmer lights. As expected, the magnitude of pupil constriction roughly matched the maximum pupil constriction recorded during the first session, allowing for a direct comparison of the normalized pupil size data collected in the second session (dim lights) with those collected in the first session (bright lights).

Pupil testing apparatus

The apparatus was located in a dark, window-less room and was separated from the control computer by a black curtain. Light stimuli were generated using a custom- made optical system that was based on pupillometers previously described in the literature (Kankipati et al., 2010; Yuhas, Shorter, McDaniel, Earley, & Hartwick, 2017).

This device employed an extended Maxwellian-view system that optimizes retinal illumination. In a traditional Maxwellian-view system, a single convex lens places an image of a light source at the pupillary plane of an observer (Westheimer, 1966). None of the light reaching the pupil is obstructed by the iris, allowing for a complete, intense image of the light source. Traditional Maxwellian-view systems depend on exact placement of the pupil and the secondary focal point of the lens and a monocular viewing system to deliver. Extended Maxwellian-view systems employ two convex lenses of equal power, separated by a distance of double their focus lengths (Beer,

MacLeod, & Miller, 2005). A diffuser is placed after the second light source. Instead of a single image placed at the pupil, an extended image is placed in the region around the pupil. This system sacrifices some of the intensity and saturation of a traditional

Maxwellian-view system but allows for more flexibility in pupil location.

Specific to the device used in this study, blue light (peak λ = 465 nm, dominant λ

= 470 nm, FWHM = 22 nm) and red light (peak λ = 635 nm, dominant λ = 625 nm,

FWHM = 17 nm) were generated using a light-emitting diode (LED) illumination system

(DiCon LED, Richmond CA) and then delivered to the left eye. For retinal illumination values (in trolands), see Table 3.1. Two 3” x 3” Fresnel lenses (Edmund Optics,

Barrington NJ) with 3” focal lengths were separated by a distance of 6” on an optical bench. Light stimuli generated by a light-emitting diode (LED) illumination system (DiCon

LED, Richmond CA) were transmitted through a fiber optic light guide (Edmund Optics), 87 with the end of the guide placed at the focal point (3” away) of the first Fresnel lens. A 5° holographic diffuser (Edmund Optics), attached to the back surface of the second

Fresnel, produced a uniform stimulus with a visual angle of approximately 53 degrees. A barrier extended from the lens’ housing unit to the subject’s nose in order to minimize exposure of the subject’s right eye to the light stimuli. Light irradiance at the corneal plane 3” from the second lens was determined using an optical power meter (Newport,

Irvine CA). Irradiance was adjusted through computer control (LightControl software,

DiCon LED). Under infrared illumination (880 nm, SFH 484; Siemens, Washington DC), a Sony (Tokyo Japan) HDR-XR500V high-definition digital video camera was focused on the right eye and adnexa and recorded (under infrared conditions) the subject’s response to the light stimuli at a rate of 30 frames per second. To stabilize accommodation, the subject fixated on the dim, red glow of the infrared bulb positioned near the camera in front of the right eye. Figure 3.3 provides a schematic diagram of this pupil testing apparatus.

Video recording processing and pupil size normalization

Video recordings from the pupilometer were imported into MATLAB software

(MathWorks, Natick MA). Here, a custom program determined pupil size, in pixels, for each frame in the video (see Appendix A). The raw pupil size data were copied into

Excel (Microsoft, Redmond WA) where they were averaged before the onset of the first light pulse to determine the ‘‘baseline pupil size’’ for each subject. Blink artifacts were also removed (see Appendix A). The smallest pupil area measurement for each subject during the entire experimental session (all four or five light trials) was identified and used as the ‘‘minimum pupil size.’’ All other pupil measurements were then normalized using the following equation: 88

퐵푎푠푒푙푖푛푒 푃푢푝푖푙 푆푖푧푒 − 푀푒푎푠푢푟푒푑 푃푢푝푖푙 푆푖푧푒 푁표푟푚푎푙푖푧푒푑 푃푢푝푖푙 퐶표푛푠푡푟푖푐푡푖표푛 = 푥 100 퐵푎푠푒푙푖푛푒 푃푢푝푖푙 푆푖푧푒 − 푀푖푛푖푚푢푚 푃푢푝푖푙 푆푖푧푒

Thus, for each subject, 0% represented the average baseline pupil size and

100% represented the maximum pupil constriction achieved within each experimental session. Figure 3.4 provides a visual schematic of how the raw pupil size data were normalized within a study session for one individual. For the mean data, normalized pupil measurements for each subject were averaged at each time point collected. These averaged normalized pupil responses were plotted with error bars representing standard error of the mean (SEM). As the time-point at which the minimum pupil size occurred varied from individual to individual, the mean pupil size, averaged across subject groups, did not reach 100% at any time point.

Analysis of normalized pupillary data

The normalized pupil data were analyzed in two separate fashions. First, decay functions were utilized to analyze pupil re-dilation characteristics. Cones, and to a lesser extent rods, likely drive the initial pupil constriction after the onset of the bright-light stimuli (~1013 photons/s/cm2) used in this study (Allen et al., 2011; Barrionuevo et al.,

2014; Gooley et al., 2012). ipRGCs have a relatively long latency period before they begin firing action potentials after light exposure (Berson et al., 2002; Fukuda et al.,

2012), so they likely contribute little to the initial stages of pupil constriction (Gooley et al., 2012). These sluggish cells, however, do keep firing action potentials for long after the offset of a light stimulus (Berson et al., 2002), which means they maintain pupil constriction for many seconds after light offset (Gamlin et al., 2007; Kankipati et al.,

2010; McDougal & Gamlin, 2010). Thus, quantifying the kinetics of pupil re-dilation after bright light exposure may provide an avenue for studying ipRGC function. Here, the

89 normalized recovery in pupil size during the 5 seconds following each light pulse of the bright and dim stimuli were fit with a simple exponential decay equation (y = e–bx) to determine the rate of re-dilation over this timeframe. Blinks or squinting caused a poor fit between several pupil traces and decay equations. Thus, traces whose R2 values (a measure of conformity between the pupil trace and the exponential decay equation) were more than two standard deviations from the mean R2 value of all the pulses within a given light test were excluded from analysis on account of their poor fit to the decay equation. For analysis, pulses of light were grouped into two-pulse periods, starting with period one containing pulses one and two (Fig. 3.4B). As a result, the three priming stimuli (red, blue, and red-blue alternating) had six periods. The average pupillary decays of the six (priming stimuli) light periods were then compared within a given test parameter (i.e. the bright red priming stimulus) using repeated measures analysis of variance (RM ANOVA) with Holm-Sidak post hoc testing.

Second, quantification of total pupillary light constriction during a given time period was used to analyze the contribution of all photoreceptors (rods, cones, and ipRGCs) to the pupillary light response. As mentioned above, the ipRGCs have a relatively long latent period before becoming electrophysiologically active after light exposure. Thus, it takes 0.8-1.8 seconds of light exposure before they begin to contribute to the primate pupillary light response (Barrionuevo et al., 2014; Lall et al.,

2010; McDougal & Gamlin, 2010). The 5-second pulses used in this study were longer than this latency period, so ipRGCs likely contributed to the sustained pupil responses both when the bright lights utilized in this study were on and off. As a result, quantification of pupil constriction both while the lights are on and while the lights are off may be useful in quantifying ipRGC contributions to the pupillary light response, rather than just studying the re-dilation characteristics. Of course, the rods and cones also 90 contribute to the pupillary light response when lights turn on, but steps were taken to control this variable by setting the irradiances of the blue and red stimuli to elicit essentially equal pupil constriction during the first 2 seconds of light stimulation. Within the flickering stimuli utilized here, each pulse of light could be described as a 10 second epoch, which included the 5 seconds when the light was on and the following 5 seconds while the light was off. During this 10-second period, the normalized pupil constriction data were summed to generate a total constriction value for each pulse of light. Similar to the analysis of the pupil decay after light offset, pulses of light were grouped into two- pulse periods, starting with period one containing pulses one and two. The average pupil constriction of the six light periods of the priming stimuli were then compared within a given test parameter (i.e. the bright red priming stimulus) using RM ANOVA with Holm-

Sidak. Additionally, peak pupil constriction (defined as the mean constriction that occurred during a 5-second light pulse) was calculated for each pulse of light in order to quantify pupil adaptation. These data were non-parametric and thus compared with RM

ANOVA on ranks with Student-Newman-Keuls post hoc testing.

Figure 3.1. Schematic representation of the pupil testing protocol during the first session. Healthy subjects (N = 12) sat for four trials of light stimulation designed to elicit pupillary light responses that varied over time. Throughout the protocol, the irradiances of the red and blue lights was 7 x 1013 photons/s/cm2 and 1 x 1013 photons/s/cm2, respectively. A) In trial 1 half of the subjects received a 1-minute red “test” stimulus after a 20-dark adaptation period, and the other half received a 1-minute “blue” test stimulus.

B-D) Trials 2-4 were presented in a randomized order and consisted of a 20-minute dark adaptation period and a 2-minute “priming” stimulus of either red, blue, or red-blue alternating light. Then, after an intervening 5-minute dark adaptation period, half of the subjects received a red flickering “test” stimulus, and the other half a similar blue stimulus. Whichever colored “test” stimulus a subject received in trial 1 (red or blue) was the same “test” stimulus he or she received in trials 2-4.

Figure 3.2. Schematic representation of the pupil testing protocol during the second session. Healthy subjects (N = 12) sat for five trials of light stimulation designed to elicit pupillary light responses that varied over time. For trials 1-4, the irradiances of the red and blue lights was 7 x 1011 photons/s/cm2 and 1 x 1011 photons/s/cm2, respectively. A)

In trial 1 half of the subjects received a 1-minute red “test” stimulus after a 20-dark adaptation period, and the other half received a 1-minute “blue” test stimulus. B-D) Trials

2-4 were presented in a randomized order and consisted of a 20-minute dark adaptation period and a 2-minute “priming” stimulus of either red, blue, or red-blue alternating light.

Then, after an intervening 5-minute dark adaptation period, half of the subjects received a red flickering “test” stimulus, and the other half a similar blue stimulus. Whichever colored “test” stimulus a subject received in trial 1 (red or blue) was the same “test” stimulus he or she received in trials 2-4. E) Trial 5 was the same for every subject and presented last. It comprised a 5-minute dark adaptation period followed by 5-second pulses of red and blue light, which were separated by a 5-second period of darkness.

The irradiances of these final red and blue lights was equated to those used in session 1

(red 7 x 1013 photons/s/cm2 and blue 1 x 1013 photons/s/cm2) in order to facilitate the comparison of normalized pupil sizes between the two sessions.

Light Stimulus Photons/s/cm2 Scotopic Trolands Photopic Trolands

Bright Blue Light 1 x 1013 1.88 x 1010 1.01 x 1010

Bright Red Light 7 x 1013 7.30 x 108 3.58 x 1010

Dim Blue Light 1 x 1011 1.88 x 108 1.01 x 1008

Dim Red Light 7 x 1011 7.30 x 106 3.58 x 1008

Table 3.1. Retinal illumination caused by the red and blue light stimuli utilized on healthy human subjects. Since most of the participants were young, an assumed dilated pupil size of 7 mm was used in the troland calculations (A. T. Smith, 1991). Photons/s/cm2 were measured at the corneal surface.

Figure 3.3. Schematic of the extended-Maxwellian view system utilized in this study.

The Fresnel lenses and LED source were contained within an opaque housing unit, which is not depicted.

Figure 3.4. Normalization of pupil size fluctuation evoked by flickering light stimuli. A)

Example trace of change in pupil area (in pixels) for a subject stimulated with 1x1013 blue light flickering at 0.1 Hz (5 s on and 5 s off) for 1 minute. Inset: Images illustrate pupil size in this subject before (1) light exposure and at maximum constriction (2). B)

The data was normalized, with the average baseline pupil size set at 0% and the maximum constriction (smallest pupil size) set at 100%. Finally, two pulses were grouped into periods for pupil constriction and pupil re-dilation (i.e. pupil decay) analysis.

Results

Subject recruitment and demographics

Twelve (N = 12) subjects were recruited and all subjects completed both experimental sessions. Half of the subjects (n = 6) received the red “test” stimuli during both session 1 (bright lights) and session 2 (dim lights). The other half (n = 6) received the blue “test” stimuli during both sessions.

Matching the demographics of the OSU College of Optometry, the entire cohort comprised young (mean ± SEM age: 27.6 ± 1.66 years), mostly female (58% female) individuals. No subject reported any ocular disease or any systemic disease that could have reasonably affected the pupillary light response. All participants achieved a visual acuity of at least 20/20 in each eye using habitual refractive error correction and had received a recent comprehensive eye examination.

Bright priming stimuli: pupil traces and total pupil constriction

In the first study session, each subject (N =12) sat for three bright “priming” light stimuli, red, blue, and red-blue alternating, which were presented in random order to his or her dilated left eye after a 20-minute dark adaption period. A commercial video camera recorded the consensual pupillary light response of the right eye under infrared conditions. Figure 3.5 displays mean (± SEM) changes in pupil size evoked by these flickering (0.10 Hz), 2-minute light exposures. Pupil size was normalized within each individual to the maximum pupil constriction (denoted as 100%) measured during any of the light trials during the session, so larger numbers on the y-axis represent smaller pupil areas.

The red lights used during this test session had a greater irradiance (7 x 1013 photons/s/cm2) than the blue lights (1 x 1013 photons/s/cm2) in order to elicit roughly 98 equal pupil constriction in response to the first light pulse in each trial. Previous work on young, healthy subjects established these intensities. This balance can be seen in the first pulse of the red and blue lights in Figure 3.5A. Notice that the red (red trace) and blue (blue trace) stimuli elicit essentially equal pupil constriction during their first light pulses. Rods and cones drive the first two seconds of the pupillary light response

(Barrionuevo et al., 2014; McDougal & Gamlin, 2010). Thus, the fact that the bright red and blue lights used here elicited equal pupil constriction throughout their entire first pulses indicated that they were initially matched for rod-cone input.

After the first pulses of red and blue lights turned off, the pupil responses to each began to deviate. The pupil re-dilated quickly after the red light turned off and returned to nearly 80% of its baseline size. These same dynamics were not seen after the offset of the first pulse of blue light. Instead, the pupil remained relatively constricted once the blue light turned off and recovered to only approximately 60% of baseline size. This general pattern continued during the 11 subsequent pulses of the red and blue lights, with two important exceptions. First, after repeated light exposures, the pupil did not constrict as much while the red light was on. This effect manifested visually in Figure

3.5A with the peaks of the red traces sagging below those of the blue traces and can be seen starting with the second light pulse. Second, starting after the second pulse of light, the pupil began to re-dilate slower with successive red-light pulses. Thus, the pupil progressively remained more constricted after each pulse of red light. By the eleventh and twelfth pulses of red light, the pupil re-dilation (finishing around 60% of baseline) was similar to that seen after the offset of the blue light pulses. This change in re-dilation kinetics suggested that the ipRGC contribution to the pupillary light reflex may have increased after repeated presentations of bright red light, a wavelength that they are known to be relatively insensitive to when presented as a single pulse (Berson et al., 99

2002). Similar changes in the pupillary light responses to the blue flickering stimulus were observed, but to a lesser extent.

The alternating bright red and blue light stimulus evoked more pupil re-dilation after the offset of the first red light pulse than after the first blue light pulse (Fig. 3.5B).

Starting with the second pulse of red light (the third pulse of the stimulus), however, the pupil stayed progressively more constricted after each presentation of red light so that by the end of the stimulus there was little difference in pupil re-dilation kinetics during the offset of the red and blue lights. This effect of repeated red-blue light presentation on pupillary re-dilation after the red-light pulses was similar to that described above for the red-light only stimulus. Contrarily, the pupil responses during (not after) the red-light presentations of the red-blue alternating stimulus were different from those elicited by the red-light only stimulus. In the latter, the pupil constricted less and less during each sequential red light pulse, but in the former, peak pupil constriction remained stable over time during the red light pulses. For the alternating stimulus, the end result was that the pupil’s total response (constriction and re-dilation) to the red light was almost indistinguishable from its response to the blue light by the end of the presentation.

The changes to the pupillary light response evoked by the blue lights of the red- blue alternating stimulus were less dramatic than those elicited by the red lights. During each subsequent blue pulse, there was a slight decline in peak pupil constriction.

Conversely, the pupil appeared to remain more constricted after the offset of each subsequent pulse of blue light. These changes over time to the pupillary light response were similar to those detected in the blue only stimulus.

For each of the three bright priming stimuli (red, blue, and red-blue alternating), mean pupil constriction was calculated across the 120 seconds of the entire flickering stimulus (Fig. 3.5C). As expected, the blue stimulus evoked significantly (p < 0.001; RM 100

ANOVA) more total pupil constriction than the red stimulus. The red-blue alternating stimulus also caused the pupil to be more constricted overall than the red-light only stimulus (p < 0.001). There was no statistical difference (p = 0.731) between the blue- light only and the red-blue alternating stimuli. To ensure that these results were not the product of an order effect, the total constriction of the first, second, and third priming stimuli presented to the subjects, regardless of light composure, were compared. There was no statistical difference (p = 0.969, RM ANOVA with Holm-Sidak) in total pupil constriction (mean ± SEM) between the priming light stimuli presented first (68.6 ±

1.82%), second (68.1 ± 0.90%), or third (68.6 ± 1.68%).

Bright priming stimuli: changes to the pupillary light reflex after repeated light exposures

The pupillary light responses to bright flickering red, blue, and red-blue alternating light stimuli, each comprised of 12 pulses of light, were grouped into 6 pairs for further analysis. The first “period” comprised pulses 1 and 2; the second, pulses 3 and 4; the third, pulses 5 and 6; the fourth, pulses 7 and 8; the fifth, pulses 9 and 10; and the sixth, pulses 11 and 12. Total pupil constriction and pupil re-dilation values (“pupil decay”; mean ± SEM) were averaged for the two pulses of light that constituted each period outlined above. Peak pupil constriction values were similarly calculated for pulses

2 and 12.

A change in pupillary dynamics over the course of the red light trial can be seen in Figure 3.6A. The pupil decay value of the sixth period was significantly (p < 0.001; RM

ANOVA) slower than the first period (lower numbers correspond to slower pupil decays).

Figure 3.7A illustrates this effect. There was more sustained pupil constriction (i.e. slower pupil re-dilation) after the offset of the eleventh and twelfth pulses than after the offset of the first and second pulses. To better visualize these changes in pupil re- 101 dilation dynamics, which are the aspects of the pupillary light response most influenced by ipRGCs (Gamlin et al., 2007), the pupil traces of the 5 seconds after each light offset were renormalized and replotted (Figure 3.7D). This presentation of the data reinforces the observation that pupil re-dilation was slower during the sixth period of red-light exposure than during the first period. A change in pupil re-dilation kinetics was not isolated to the sixth period (Fig. 3.6A). Starting with the fourth period (light pulses 7 and

8), the pupil decay was significantly slower (p = 0.009) than the first period, as was the decay in period 5 (pulses 9 and 10; p = 0.014). The lower decay values seen after later pulses of the red stimulus meant that there was more sustained pupil constriction after light offset during the last three periods.

Unlike pupil decay, total pupil constriction elicited by the red stimulus remained flat from period 1 to period 6 (Fig. 3.6D). There was no statistically significant change in total pupil constriction between the first period and any of the following periods (p =

0.464; RM ANOVA). This result may be surprising, considering the significant slowing of pupil re-dilation seen in Figure 3.6A. Pupil adaptation seen at the top of the pupil traces in Figure 3.7A may explain it. Qualitatively, there appeared to be less peak pupil constriction while the eleventh and twelfth pulses of red light (lighter traces) were on than when the first and second pulses (darker traces) were on. Accordingly, quantification of mean pupil constriction that occurred during the 5-second light pulses revealed significantly less (p < 0.001; RM ANOVA on ranks with Student-Newman-

Keuls) peak pupil constriction during pulse 12 (81.4 ± 2.61%) than during pulse 2 (87.8 ±

0.891%). The two competing pupil dynamics over the course of the red stimulus (i.e. progressively less pupil constriction when the lights were on but progressively slower pupil re-dilation when the lights were off) resulted in little net change in the total mean

102 pupil constriction measured during the sixth period (dashed reference line in Fig. 3.7A) versus the first period (solid reference line).

Similar to the red-only stimulus, the blue-only stimulus elicited significant slowing of pupil decay (Fig. 3.6B), starting with the fourth period (p = 0.025; RM ANOVA) and continuing to the fifth (p = 0.004) and sixth periods (p = 0.003). This change in pupil dynamics seemed to originate from a decrease in pupil constriction during the light pulses, rather than a slowing of pupil re-dilation after them. As Figures 3.7B and 3.7E demonstrate, there was only a modest slowing of pupil re-dilation rate at the end of the stimulus (period 6; lighter traces) compared to the beginning (period 1; darker traces).

Furthermore, there was clear pupil adaptation when the light pulses of the sixth period

(lighter traces) were on, compared to the first period (darker traces). It follows that peak pupil constriction during pulse 12 (86.0 ± 2.16%) was significantly lower (p = 0.039; RM

ANOVA on ranks) than during pulse 2 (90.9 ± 1.07. Just as in the red stimulus, these two dynamics balanced each other in such a way that there was no significant difference

(p = 0.191) in total mean pupil constriction between period 1 (solid reference line in

Figure 3.6B) and period 6 (dashed reference line) or any other period.

Repeated exposures to red-blue alternating lights also evoked changes in pupil re-dilation rates, but on an accelerated timeline. Like the red and blue only stimuli, the alternating red-blue stimulus elicited a significant decrease in pupil decay rate from the first period to the sixth period (p < 0.001; RM ANOVA; Fig. 3.6C). However, this significant slowing of pupil re-dilation began much sooner than in the blue or red stimuli, starting with the second period (p = 0.003) and continuing to the third (p < 0.001), fourth

(p = 0.002), and fifth (p < 0.001) periods. Figures 3.7C suggests that the red light pulses likely drove this rapid change. Pupil re-dilation slowed considerably after the red pulse of the sixth period, as compared to after the red pulse of the first period. In fact, when the 103 first pulse of the sixth (red) period was compared to the second pulse of the first and sixth periods (both blue), there was little apparent difference in pupil constriction or re- dilation between the three light pulses (Fig. 3.7F). The pupillary response to the final red light pulse had become indistinguishable from those of the blue light pulses.

The bright red-blue alternating stimulus also had a unique effect on pupil constriction. There was significantly more total pupil constriction during the second (p =

0.007; RM ANOVA), third (p = 0.020), fourth (p = 0.021), fifth (p = 0.027), and sixth

(0.025) periods than in the first period (Fig. 3.6F). The reason for this difference can be found in Figure 3.7C. Not only did this stimulus enhance the sustained pupil constriction after the blue and especially the red pulses of period 6, but it also prevented substantial adaptation of pupil constriction while the red light was on. The peak pupil constriction during the light exposures of pulse 12 (88.89 ± 1.76%) was statistically similar (p =

0.960) to that of pulse 2 (91.7 ± 0.520%). The result of the changes in the pupillary light response elicited by the repeated light exposures of the alternating red-blue stimulus was that total pupil constriction increased during the sixth period (dashed reference line in Fig. 3.7C) compared against the first period (solid reference line).

To summarize, each subsequent pulse of the alternating red-blue stimulus not only rapidly slowed pupil re-dilation but also increased total pupil constriction. This robust enhancing effect on the pupillary light response is a prime example of pupillary photopotentiation and was not seen during the other two priming light stimuli.

Bright test stimuli: persistence of photopotentiation effect

These results showed that repeated light exposures can change the pupillary light reflex over the course of a two-minute test. To evaluate the question of how long the effect persisted, the subjects were returned to the pupillometer for a second round of 104 light exposures (the “test” stimuli) 5 minutes after the end of each of the three priming stimuli. This 5-minute break was spent in the dark. Six of the subjects (n = 6) received a one-minute, red flickering (0.01 Hz) light, and the other six (n = 6) a similar blue flickering light. The irradiances of these lights were the same as the ones used in the priming stimuli. The responses were compared to the responses recorded during the first trial of the study session (see Figure 3.1).

Figure 3.8A displays the normalized pupil traces for the red test stimuli. The red trace represents the pupillary light responses elicited by the flickering red stimulus that followed the initial 20-minute dark adaptation period, the pink trace after the red priming stimulus, the dark red trace after the blue priming stimulus, and the purple trace after the alternating red-blue priming stimulus. The four traces overlapped almost completely, and there were no statistical differences (p = 0.984; RM ANOVA) in total pupil constriction during the entire course of the 60-second test between the flickering red lights presented after the 20-minute dark adaptation and any of the flickering red lights presented after the priming stimuli (Fig 3.8C).

Pupil re-dilation kinetics also recovered after the 5-minute dark interval. There was no statistical difference (p = 0.607; RM ANOVA) in normalized pupil decay between the first period of the red stimuli that occurred after the dark adaptation period (0.238 ±

0.0271), the red priming stimulus (0.236 ± 0.0178), the blue priming stimulus (0.220 ±

0.0158), and the red-blue alternating priming stimulus (0.251 ± 0.00421). Likewise, there was no statistical difference between the decay value of the first period of the red test stimulus and the first period of the red priming stimulus (p = 0.653) or the first period of the red-blue alternating priming stimulus (p = 0.059).

A similar pattern can be found in the blue test stimuli (Fig 3.8B). The blue trace represents the pupillary light reflex elicited by the flickering red stimulus that followed the 105 initial 20-minute dark adaptation period, the light blue trace after the red priming stimulus, the dark blue trace after the blue priming stimulus, and the blue-green trace after the alternating red-blue priming stimulus. Similar to the red flickering stimuli, there was substantial overlap of the pupil traces during the initial pulse of blue light. As a result, there was no statistical difference (p = 0.196; RM ANOVA) in total pupil constriction between the flickering blue lights presented after the 20-minute dark adaptation and any of the flickering blue lights presented after the priming stimuli (Fig.

3.8C). Just as in the red test stimuli, there was no statistical difference (p = 0.329; RM

ANOVA) in normalized pupil decay between the first period of the blue stimuli that occurred after the dark adaptation period (0.199 ± 0.0184), the red priming stimulus

(0.195 ± 0.0110), the blue priming stimulus (0.172 ± 0.00541), and the red-blue alternating priming stimulus (0.213 ± 0.0382). There was also no statistical difference between the decay value of the first period of the blue test stimulus and the first period of the blue priming stimulus (p = 0.088) or the first period of the red-blue alternating priming stimulus (p = 0.059).

Dim priming stimuli: pupil traces and total pupil constriction

All the subjects (N = 12) returned for the second study session and sat for the same test protocol presented during the first study session, with two alterations. First, the red and blue lights used were 2 log units dimmer than those used in the first study session. The red lights had an irradiance of 7 x 1011 photons/s/cm2, and the blue lights 1 x 1011 photons/s/cm2. Second, at the end of the protocol, single pulses of bright red (7 x

1013 photons/s/cm2) and bright blue (1 x 1013 photons/s/cm2) lights were presented five seconds apart after a 5-minute dark adaptation period. These brighter lights allowed the

106 dimmer lights used during the rest of the second study session to be normalized to the same lights used during the first study session.

During the first pulses of the dim red and blue priming stimuli (Fig. 3.9A), it was apparent that the red and blue stimuli no longer caused equal pupil constriction.

Specifically, the first blue pulse elicited more pupil constriction than the first red pulse, likely due to rod input into the pupillary light response (Kardon et al., 2009). This trend continued for the following 11 pulses of light. Modest pupil adaption occurred during each successive red and blue light pulse, resulting in less and less maximal pupil constriction while each was on. This effect can be visualized in Figure 3.9A as a progressive decrease in the peak pupil constrictions to the red and blue lights.

Interestingly, this same adaptation was not seen while the lights were off. Instead, there appeared to be more sustained pupil constriction after the offset of each successive red and blue light, resulting in the bottom of the traces moving steadily upward after each pulse of light.

The dim red-blue alternating light stimulus evoked similar pupillary responses

(Fig. 3.9B). The blue pulses of light evoked more pupil constriction while they were on than the red lights. There was progressively less pupil constriction during each pulse of red and especially blue light, but progressively more pupil constriction after each pulse.

The increase in sustained pupil constriction after the offset of the alternating red-blue lights was less pronounced that that after the red and blue light only stimuli, however.

For each of the three dim priming stimuli (red, blue, and red-blue alternating), total pupil constriction was calculated across the 120 seconds of the entire flickering stimulus, 5 seconds during and the 5 seconds after each of the 12 light presentations

(Fig. 3.9C). The blue stimulus evoked significantly more (p = 0.017; RM ANOVA) total pupil constriction than the red stimulus. There was no statistical difference between the 107 red and red-blue alternating stimuli (p = 0.227) or between the blue and red-blue alternating stimuli (p = 0.148).

Dim priming stimuli: changes to the pupillary light reflex after repeated light exposures

Once again, the 12 pulses of the dim, flickering red, blue, and red-blue alternating stimuli were grouped into six periods of two pulses for analysis. The first

“period” comprised pulses 1 and 2; the second, pulses 3 and 4; the third, pulses 5 and 6; the fourth, pulses 7 and 8; the fifth, pulses 9 and 10; and the sixth, pulses 11 and 12.

Total pupil constriction and pupil decay values (mean ± SEM) were averaged for the two pulses of light that constituted each period outlined above. Peak pupil constriction values were similarly calculated for pulses 2 and 12.

Figure 3.10A demonstrates that repeated exposures to the dim red stimulus significantly slowed pupil re-dilation by the sixth period (p < 0.001; RM ANOVA).

Consistently, the pupil traces of Figure 3.11A and the renormalized pupil decay curves of

Figure 3.11D show more rapid pupil re-dilation after the pulses of period 1 (darker traces) than after the pulses of period 6 (lighter traces). This significant difference was first seen during period 2 (p = 0.002) and continued through period 3 (p < 0.001), period

4 (p < 0.001) and period 5 (p < 0.001). An equally dramatic change in the total constriction was not replicated when total pupil constriction was measured during each time period (Fig. 3.10D). Periods 2, 3, 4, 5, and 6 did not evoke significantly more pupil constriction than period 1 (p = 0.15; RM ANOVA). Just as in the bright red stimulus, there was significantly less (p < 0.001; RM ANOVA on ranks) peak pupil constriction during pulse 12 (58.2 ± 4.67%) than during pulse 2 (69.0 ± 3.28%). Thus, the pupil adaptation while the dim red lights of periods 2-6 were on (see the reduction in pupil

108 constriction at the top of the lighter traces in Figure 3.11A) canceled out any increase in sustained pupil constriction after the lights turned off.

The dim blue stimulus caused similar changes to the pupillary light response.

Figure 3.10B shows a significant slowing of pupil re-dilation that began during pulse 2 (p

< 0.001; RM ANOVA) and continued through pulses 3-6 (all p < 0.001). Figure 3.11E mirrors this result. On the other hand, Figure 3.10E demonstrates that overall pupil constriction did not change over the course of the test (p = 0.171; RM ANOVA). It is clear in Figure 3.11B that the gains in sustained pupil constriction after light offset seen in the later periods were negated by adaptation of the peak pupil constriction while the lights of the same periods were on. It is therefore not surprising that pulse 12 (68.5 ±

3.08%) elicited significantly less (p = 0.005; RM ANOVA on ranks) peak pupil constriction than pulse 2 (78.6 ± 2.63%).

Unlike its bright counterpart, the pupillary light response to the dim red-blue alternating light was similar to the dim red and blue stimuli. A significant slowing of pupil re-dilation again began during pulse 2 (p = 0.006; RM ANOVA) and continued through pulse 3-6 (all p <0001; Fig 3.10C). Interestingly, this effect did not appear to be as robust in the dim red-blue alternating stimulus as the single-color stimuli (Figs. 3.11C and

3.11F). Once again, the overall pupil constriction did not change over the course of the test (p = 0.459; Fig. 2.10F). Pupillary adaptation likely drove this negative result, as there was significantly less (p = 0.036; RM ANOVA on ranks) peak pupil constriction during pulse 12 (69.1 ± 4.10%) than during pulse 2 (79.0 ± 2.63%).

Dim test stimuli: persistence of photopotentiation effect

Figure 3.12A displays the normalized pupil traces for the dim red test stimuli. The red trace represents the pupillary light reflex elicited by the flickering red stimulus that 109 followed the initial 20-minute dark adaptation period, the pink trace after the red priming stimulus, the dark red trace after the blue priming stimulus, and the purple trace after the alternating red-blue priming stimulus. The four traces overlap almost completely, and there was no significant effect of the priming stimuli on total pupillary constriction five minutes later (p = 0.971; RM ANOVA; Fig. 3.12C).

Pupil re-dilation kinetics also recovered after the 5-minute dark interval. There was no statistical difference (p = 0.704; RM ANOVA) in normalized pupil decay between the first period of the dim red stimuli that occurred after the dark adaptation period (0.372

± 0.0312), the dim red priming stimulus (0.301 ± 0.0530), the dim blue priming stimulus

(0.366 ± 0.0853), and the dim red-blue alternating priming stimulus (0.308 ± 0.0370).

Likewise, there was no statistical difference between the decay value of the first period of the dim red test stimulus and the first period of the dim red priming stimulus (p =

0.058) or the first period of the dim red-blue alternating priming stimulus (p = 0.355).

A similar pattern was found in the blue test stimuli (Fig. 3.12B). The blue trace represents the pupillary light reflex elicited by the flickering red stimulus that followed the initial 20-minute dark adaptation period, the light blue trace after the red priming stimulus, the dark blue trace after the blue priming stimulus, and the blue-green trace after the alternating red-blue priming stimulus. Similar to the red flickering stimuli, there was substantial overlap of the pupil traces during the initial pulse of blue light. There was no statistical difference (p = 0.469; RM ANOVA) in total pupil constriction during the entire 60-second test between any of the test blue light stimuli presented after either the

20-minute dark adaptation or the priming stimuli (Fig. 3.12C). Just as in the dim red test stimuli, there was no statistical difference (p = 0.294; RM ANOVA) in normalized pupil decay between the first period of the dim blue stimuli that occurred after the dark adaptation period (0.267 ± 0.0294), the dim red priming stimulus (0.259 ± 0.0206), the 110 dim blue priming stimulus (0.223 ± 0.0193), and the dim red-blue alternating priming stimulus (0.281 ± 0.0220). There was also no statistical difference between the decay value of the first period of the dim blue test stimulus and the first period of the dim blue priming stimulus (p = 0.051) or the first period of the dim red-blue alternating priming stimulus (p = 0.355).

111

Figure 3.5. Pupillary light response to bright flickering red and blue lights in healthy subjects. A-B) Traces of mean (± SEM; gray shading) pupil size (normalized; 100% = smallest measured pupil area in experimental session and 0% = baseline pupil area) elicited

112 by twelve pulses of A) red, blue, or B) red-blue alternating lights (bars at top indicate light was on) flashed at 0.10 Hz in healthy subjects (n =12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1013 photons/s/cm2, and the red lights of the red and red-blue alternating stimuli had an irradiance of 7 x 1013 photons/s/cm2. C) Mean (± SEM) normalized pupil constriction over the course of the entire twelve pulses of the red, blue, and red-blue alternating stimuli. *P < 0.05. NS = not significant. RM ANOVA.

113

Figure 3.6. Changes in pupil decay and constriction over the course of bright flickering light stimuli. A-C) Mean (± SEM) values of pupil decay measured after light offset during six periods of flickering A) red, B) blue, and C) red-blue alternating light stimuli in healthy subjects (n = 12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1013 photons/s/cm2, and the red lights of the red and red-blue

114 alternating stimuli had an irradiance of 7 x 1013 photons/s/cm2. Higher decay values indicate faster pupil re-dilation after the offset of the two light pulses in each period. D-F)

Mean (± SEM) values of normalized pupil constriction (100% = smallest measured pupil area in experimental session and 0% = baseline pupil area) elicited over the course of the same six periods of the same bright flickering light stimuli. Each period contained two pulses of light and covered 20 seconds of time. *p < 0.05. RM ANOVA, periods 2-6 compared against period 1.

115

Figure 3.7. Comparison of pupil characteristics between the first and sixth periods of the bright light stimuli. A-C) Traces of mean (error bars omitted for clarity) pupil size

(normalized) elicited by the first two pulses (period 1; darker shades) and by the last two

(period 6; lighter shades) of A) red, B) blue, or C) red-blue alternating lights flashed at

0.10 Hz in healthy subjects (n =12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1013 photons/s/cm2, and the red lights of the red and red-

116 blue alternating stimuli had an irradiance of 7 x 1013 photons/s/cm2. The solid and dashed horizontal lines represent the mean pupil constriction during the first and sixth periods, respectively. D-F) In order to better visualize the recovery rate of pupil re- dilation after stimulation with the three different priming light stimuli, the pupil constriction data collected in the five seconds after the lights in (A-C) turned off were averaged together and re-normalized so that at x = 0, y = 1. The two pulses of period 1 are the darker shades (solid line = 1st overall light pulse, dashed line = 2nd overall pulse), and the two pulses of period 6 are the lighter shades (solid line = 11th overall pulse, dashed line

= 12th overall pulse). The gray shaded areas are the SEM.

117

Figure 3.8. The effects of previous light exposure on the pupillary light response to bright flickering red and blue lights in healthy subjects. A-B) Traces of mean (± SEM; gray shading) pupil size (normalized) elicited by six pulses of A) red or B) blue lights flashed 118 at 0.10 Hz in healthy subjects (red light n = 6, blue light n = 6). The blue lights had an irradiance of 1 x 1013 photons/s/cm2, and the red lights had an irradiance of 7 x 1013 photons/s/cm2. The red and blue stimuli were presented either after a 20-minute dark adaptation period, 5 minutes after the red priming stimulus, 5 minutes after the blue priming stimulus, or 5 minutes after the red-blue alternating priming stimulus. C) Mean (± SEM) normalized pupil constriction over the course of the entire six pulses of the red and blue test stimuli after each of the four preceding lighting conditions. NS = not significant. RM ANOVA.

119

Figure 3.9. Pupillary light response to dim flickering red and blue lights in healthy subjects. A-B) Traces of mean (± SEM; gray shading) pupil size (normalized; 100% = smallest measured pupil area in experimental session and 0% = baseline pupil area) elicited 120 by twelve pulses of A) red, blue, or B) red-blue alternating lights (bars at top indicate light was on) flashed at 0.10 Hz in healthy subjects (n =12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1011 photons/s/cm2, and the red lights of the red and red-blue alternating stimuli had an irradiance of 7 x 1011 photons/s/cm2. C) Mean (± SEM) normalized pupil constriction over the course of the entire twelve pulses of the red, blue, and red-blue alternating stimuli. *P < 0.05. NS = not significant. RM ANOVA.

121

Figure 3.10. Changes in pupil decay and constriction over the course of dim flickering light stimuli. A-C) Mean (± SEM) values of pupil decay measured after light offset during six periods of flickering A) red, B) blue, and C) red-blue alternating light stimuli in healthy subjects (n =12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1011 photons/s/cm2, and the red lights of the red and red-blue

122 alternating stimuli had an irradiance of 7 x 1011 photons/s/cm2. Higher decay values indicate faster pupil re-dilation after the offset of the two light pulses in each period. D-F)

Mean (± SEM) values of normalized pupil constriction (100% = smallest measured pupil area in experimental session and 0% = baseline pupil area) elicited over the course of the same six periods of the same dim flickering light stimuli. Each period contained two pulses of light and covered 20 seconds of time. *p < 0.05. RM ANOVA, periods 2-6 compared against period 1.

123

Figure 3.11. Comparison of pupil characteristics between the first and sixth periods of the dim light stimuli. A-C) Traces of mean (error bars omitted for clarity) pupil size

(normalized) elicited by the first two pulses (period 1; darker shades) and by the last two

(period 6; lighter shades) of A) red, B) blue, or C) red-blue alternating lights flashed at

0.10 Hz in healthy subjects (n =12). The blue lights of the blue and red-blue alternating stimuli had an irradiance of 1 x 1011 photons/s/cm2, and the red lights of the red and red- 124 blue alternating stimuli had an irradiance of 7 x 1011 photons/s/cm2. The solid and dashed horizontal lines represent the mean pupil constriction during the first and sixth periods, respectively. D-F) In order to better visualize the recovery rate of pupil re- dilation after stimulation with the three different priming light stimuli, the pupil constriction data collected in the five seconds after the lights in (A-C) turned off were averaged together and re-normalized so that at x = 0, y = 1. The two pulses of period 1 are the darker shades (solid line = 1st overall light pulse, dashed line = 2nd overall pulse), and the two pulses of period 6 are the lighter shades (solid line = 11th overall pulse, dashed line

= 12th overall pulse). The gray shaded areas are the SEM.

125

Figure 3.12. The effects of previous light exposure on the pupillary light response to dim flickering red and blue lights in healthy subjects. A-B) Traces of mean (± SEM; gray shading) pupil size (normalized) elicited by six pulses of A) red or B) blue lights flashed

126 at 0.10 Hz in healthy subjects (red light n = 6, blue light n = 6). The blue lights had an irradiance of 1 x 1011 photons/s/cm2, and the red lights had an irradiance of 7 x 1011 photons/s/cm2. The red and blue stimuli were presented either after a 20-minute dark adaptation period, 5 minutes after the red priming stimulus, 5 minutes after the blue priming stimulus, or 5 minutes after the red-blue alternating priming stimulus. C) Mean (± SEM) normalized pupil constriction over the course of the entire six pulses of the red and blue test stimuli after each of the four preceding lighting conditions. NS = not significant. RM ANOVA.

127

Discussion

The results presented in this chapter indicate that prior light exposure to flickering lights can alter the aspects of the human pupillary light response thought to be under the control of ganglion cell photoreceptors. These data add to the growing body of knowledge that describes how ipRGCs influence the visual system and open new lines of research into their relationship with the photic environment.

Pupillary photopotentiation: bright stimuli

The repeated exposures of bright flickering red, blue, and red-blue alternating lights had a very specific effect on the human pupillary light response. Namely, the rate of pupil dilation slowed with sequential exposures to the flickering lights. It is well established that ipRGC input to the pupillary light response keeps the pupil constricted after the offset of a bright light (Gamlin et al., 2007; Hattar et al., 2003; Lucas et al.,

2003). Thus, an increase in ipRGC contribution to the pupillary light response may cause the pupil to progressively re-dilate more slowly with repeated light challenges. I term this effect ‘photopotentiation.’

The bright blue stimulus serves as a marker for consistent ipRGC contribution to the pupillary light response. Although there was a statistically significant (p < 0.05) slowing of the pupil re-dilation rate, starting in period 4 (Fig. 3.6B), Figure 3.7E shows this effect was potentially driven by a modest but progressive reduction of pupil constriction during the light pulses rather than a slowing of pupil re-dilation after them.

This result suggests that blue-light sensitive ipRGCs (Berson et al., 2002; Dacey et al.,

2005) contributed to the pupillary light response for the duration of the 2-minute test. The fact that the bright blue stimulus caused more (p < 0.05) total pupil constriction over the course of the entire 120 s test than the bright red stimulus (which likely did not evoke 128 immediate ipRGC contributions to the pupillary light response) bolsters this conclusion

(Fig. 3.5C). It also fits with the known ability of ipRGCs to resist light adaptation and bleaching (Sexton et al., 2012) and count photons over extended durations (Wong,

2012). These temporal characteristics have recently been demonstrated in the human pupillary light response to pulses of light using silent substitution techniques (McAdams,

Igdalova, Spitschan, Brainard, & Aguirre, 2018).

It is well established that a single pulse of bright blue light will elicit more sustained pupil constriction after its offset than a similarly intense red light (Kankipati et al., 2010; Park et al., 2011), likely due to ipRGC input into the former but not the latter

(Gamlin et al., 2007). Figure 3.5A replicates this result here by demonstrating that the pupil re-dilated slower after the offset of the first pulse of the bright red light (red trace) than after the first pulse of the bright blue light (blue trace). With time, however, the rate of pupil re-dilation after the red pulses slowed, so that toward the end of the 2-minute test, there was little difference in sustained pupil constriction after the offset of the red and blue stimuli. Figures 3.7A and 3.7D highlight this progressive slowing of pupil re- dilation. Similar to the bright blue stimulus, this effect became statistically significant (p <

0.05) starting with the fourth time period (pulses 7 and 8; Fig. 3.6A).

Ba-Ali and colleagues have reported an increase in pupil constriction that occurs over the course of exposure to a bright red flickering stimulus (Ba-Ali et al., 2017). They attributed this effect to enhanced cone input to the post-illumination pupil response and cited as evidence a study on genetically altered mice that cones may contribute to prolonged pupil constriction in mice (Kostic et al., 2016). However, there is ample evidence in humans that shows that cones have little influence over pupil constriction after light exposure (Adhikari et al., 2016; Gooley et al., 2012). Here, I observed a significant (p < 0.05) decrease in peak pupil constriction between pulses 2 and 12 of the 129 bright red stimulus, indicating cone adaptation not enhancement. This result matches with a previous report that cones are susceptible to greater adaptation than rods or ipRGCs and that their dominance of the pupillary light response is only transient

(McDougal & Gamlin, 2010). It is unlikely that cones recover immediately after light offset and begin to contribute to post-illumination constriction. An alternative interpretation of the data presented here is that the contribution of ipRGCs to the pupillary light reflex increases after repeated light exposures. Whereas a single pulse of bright red light is unable to drive ipRGC input into the human pupillary light response, multiple presentations of that same light might.

The bright red-blue alternating stimulus evoked rapid pupillary photopotentiation.

In contrast to the bright red and blue single-color stimuli, the bright red-blue alternating stimulus significantly (p < 0.05) slowed pupil re-dilation rates, starting with the second time period (Fig. 3.6C). This effect can be seen in the traces of Figures 3.5B and 3.7F, where the sustained pupil constriction occurring after the offset of the red lights becomes indistinguishable from that occurring after the blue lights. The kinetics of pupil re-dilation rapidly retarded as the bright red-blue alternating stimulus progressed, but the pupil constriction was maintained while the pulses were on. There was no significant (p >

0.05) reduction in peak pupil constriction between pulses 2 and 12, unlike the red and blue stimuli. Figure 3.7C illustrates this point: the bright red-blue alternating stimulus kept the peak pupil constriction high throughout the 2-minute test. One interpretation is there was less cone adaptation in response to the alternating stimulus, while another interpretation is that there was a greater contribution of ipRGCs to the pupil responses that counteracts some of the cone adaptation. Given the rapid reduction in pupil re- dilation rates and steady peak pupil constriction throughout the test, the bright red-blue alternating stimulus was the only stimulus able to significantly (p < 0.05) increase total 130 pupil constriction (Fig. 3.6F). This result demonstrates the overwhelming effect that this stimulus had on the pupillary light response. As discussed below, the red-blue alternating stimulus may be able to recruit greater ipRGC input into the pupillary light response as compared to single-color stimuli.

Pupillary photopotentiation appears to be a relatively transient effect. After 5- minute dark adaptation periods, bright red and blue lights elicited similar (p > 0.05) amounts of total pupil constriction regardless of the preceding priming stimuli (Fig. 3.8C).

Furthermore, the pupil decay values of the first period of all the test stimuli (bright and dim) were not statistically different (p > 0.05) from the first period of their preceding priming stimuli; indicating that that pupil re-dilation kinetics recovered during the preceding darkness. Blumenthaler and colleagues have preliminary evidence to suggest that pupillary photopotentiation lasts for longer than 5 minutes, but their subjects did not spend the entire 5-minute interval in the dark (Blumenthaler, Hartwick, & Mutti, 2018).

The final minute before the next light presentation was spent looking at a liquid crystal display screen that was dimly backlit. Even this dim light exposure before the following flickering stimuli may have had a photopotentiating effect.

Pupillary photopotentiation: dim stimuli

Repeated light exposures to bright lights altered the pupillary light responses in these human subjects. The question then arose whether dim flickering stimuli could change the pupillary light responses over time. To answer this question, the subjects returned for a second session with the same protocol as the first, save that the stimuli were two long units dimmer than those used in the bright flickering lights. At an irradiance of ~1011 phots/s/cm2, these lights were below the known threshold for ipRGC activation (Lall et al., 2010; Lucas et al., 2003; Park et al., 2011). 131

The dim red, blue, and red-blue alternating stimuli all caused a similar, surprising change in pupil re-dilation kinetics. Although the red and blue lights no longer caused equal initial pupil constriction (Fig. 3.9A), the rate of the pupil re-dilation that followed the first pulses of dim red and blue light were nearly identical (Figs 3.9A-B). This similarity in re-dilation kinetics indicated that these stimuli were indeed below the irradiance threshold needed for ipRGC stimulation. However, after each pulse of light, the post- illumination pupil response became greater (i.e. slower re-dilation rate). For all three stimuli, the reduction in pupil re-dilation rate became significant (p < 0.05) starting in period 2 (Figs. 3.10A-C). Figures 3.11D-F illustrate that this effect was most pronounced in response to the flashing red and blue lights. Similar to the bright stimuli, a 5-minute dark adaptation period washed out this photopotentiation effect (Fig. 3.12C).

Rods may contribute to the changes seen here. They are known to dominate the pupillary light responses to lights with irradiances below ipRGC threshold (Kardon et al.,

2009; Park et al., 2011), and contribute to the very initial stages (i.e. within 2 s) of post- illumination sustained pupil constriction (Adhikari et al., 2016). The influence of rods cannot fully explain my results, however, as repeated exposures to the dim red stimulus elicited slowing of pupil re-dilation over the same time course as the dim blue stimulus.

Also, the sustained pupil constriction lasted for the entire 5-second interval of darkness between each pulse, not just the first two seconds. An alternative explanation for these results is that repeated exposures to dim lights can recruit the input of ipRGCs to the pupillary light response. The flickering nature of the stimuli used here could have gradually elicited a low-level response from the ganglion cell photoreceptors that kept the pupil slightly more constricted after light offset at the end of the stimulus versus the beginning. It fits with the known ability of ipRGCs to sum photic input over relatively long periods of time (Wong, 2012). 132

Why this change happened quickly in all three dim stimuli but slowly in the bright red and blue stimuli is unclear. One possibility is that the significant (p < 0.05) adaptation of the peak pupil constriction observed in all three stimuli (see the tops of the traces in

Figs. 3.11A-C for examples) may have influenced the pupil decay results. A progressive reduction in peak constriction would cause the pupil to begin the post-illumination re- dilation process from an already relatively re-dilated state. Thus, it may not have been necessarily more sustained pupil constriction that caused the decay values to slow, but less maximal pupil constriction. Figures 3.11A-C suggest that this effect cannot completely explain my results, as there was visibly more sustained pupil constriction after the offset of the light pulses of period 6 (lighter traces) than period 1 (darker traces). Adaptation of the peak pupil constriction while the light pulses were on did not appear to reduce the sustained constriction that occurred after they turned off.

Etiology of pupillary photopotentiation

Repeated light exposure caused an enhancement of the aspects of the pupillary light response under the control of ganglion cell photoreceptors. This effect was best seen with stimuli that would normally not drive ipRGCs contributions to the pupillary light response, such as the dim red or blue stimuli or the bright red stimulus used in these experiments. I have termed this phenomenon photopotentiation, and its exact physiological etiology remains a question for future studies. Melanopsin bistability, dopaminergic manipulation of ganglion cell photoreceptors, and cyclic adenosine monophosphate (cAMP) build up in ganglion cell photoreceptors are three possible ipRGC-mediated mechanisms that underlie photopotentiation.

Some have theorized that melanopsin is a bistable molecule due to its strong resistance to bleaching (Sexton et al., 2012; Zhu et al., 2007). A short wavelength light 133 drives phototransduction in a bistable photopigment by isomerizing it into an intermediate form, but long wavelength light has the ability to regenerate the chromophore in photobleached opsin without an independent isomerase intermediate

(Rollag, 2008). If melanopsin were bistable, short wavelength light would drive phototransduction and create all-trans retinaldehyde. Long wavelength light would drive it back to 11-cis retinaldehyde. The fact that bistability is frequently seen in invertebrate rhabdomeric opsins, to which melanopsin is related, supports bistability in playing a role in mediating pupillary photopotentiation (Sexton et al., 2012). Further support comes from studies that have shown that prior exposure to long wavelength light enhances pupillary constriction to short wavelength light and that short wavelength induces photoentrainment of free running cycles in rodents (Mawad & Van Gelder, 2008; Mure et al., 2009; Mure, Rieux, Hattar, & Cooper, 2007).

There is considerable evidence against melanopsin bistability, however. IpRGC- controlled melatonin suppression receives no additional benefit from simultaneous long and short wavelength light administration (Papamichael et al., 2012). Another study demonstrated that prior long wavelength light enhances the pupillary response to short wavelength light, but this response could not be replicated in vitro on a multielectrode array (Mawad & Van Gelder, 2008). An electrophysiological experiment on pharmacologically isolated ipRGCs found that a long wavelength priming stimulus provides no enhancement of ipRGC photoresponse (Enezi et al., 2011). Another set of recordings under conditions that preserved synaptic input demonstrated that a flickering blue stimulus can elicit enhanced ipRGC signaling compared to a continuous light source (Vartanian et al., 2015), and no long wavelength priming stimulus was necessary.

Similar evidence can be found in the pupil traces here. First, the bright red-blue alternating stimulus elicited pupillary photopotentiation after both the red and blue light 134 pulses. If bistability were driving photopotentiation, then the enhancement would only be associated with only the blue pulses. If the difference in spectral sensitivity between active melanopsin and its intermediate were small enough for both to be driven by short wavelength light, then photopotentiation would likely be detected with a blue-only stimulus. The opposite was observed. Pupil photopotentiation occurred primarily during the bright red stimulus. Here, there was no alternation between long and short wavelengths to drive and regenerate melanopsin, yet photopotentiation occurred anyway. These results indicate that another physiological process must mediate photopotentiation.

The neurotransmitter dopamine is released into the retina during high environmental light levels and may play an important role in pupil photopotentiation. ipRGCs receive synaptic input from dopaminergic amacrine cells (Jusuf et al., 2007;

Ostergaard et al., 2007). Dopamine imparts a diverse array of the changes to ipRGC physiology, two of which have importance here. First, dopamine interaction with ipRGC

D1 receptors elicits a depolarization of their normal resting potential (Van Hook et al.,

2012) and alters inhibitory signaling within the inner retina (Mazade et al., 2019). Since ipRGCs depolarize in response to light (Berson et al., 2002; Hartwick et al., 2007), dopamine-driven depolarization and inhibition reduction brings them closer to their action potential threshold. In other words, dopamine may cause a change in ipRGC light sensitivity, shifting the activation threshold to a dimmer light level. Thus, it is possible that the presence of dopamine in the retina may enhance ipRGC spiking to light stimuli that would otherwise not drive their photoresponses, such as a long wavelength dim light).

The second important effect that dopamine has on ipRGCs is that it modulates cAMP levels in ipRGCs. Specifically, dopamine increases cAMP levels in goldfish retinal 135 ganglion cells (Vaquero et al., 2001). In addition to dopamine, excitatory synaptic input from ON bipolar cells drives up this compound within ipRGCs (Wong et al., 2007). cAMP is an important secondary messenger in ipRGCs. Through cAMP-dependent protein kinase A (Cass et al., 1999), the presence of cAMP phosphorylates ion channels within the cell, resulting in an enhancement of the intrinsic ipRGC photoresponse. Sodhi and

Hartwick demonstrated this effect in vitro when they increased ipRGC signaling by introducing 8-Br-cAMP (a cAMP analog) into the cellular environment (Sodhi & Hartwick,

2014). Similar results can be found in Chapter 2 of this dissertation, where a D1 receptor agonist enhanced ipRGC signaling. Thus, the presence of intra-retinal dopamine may not only cause ipRGCs to respond to stimuli that would normally not drive ipRGC photoresponses, but it may also increase the strength of ipRGC signaling to above- threshold stimuli. These characteristics match the effect that repeated light exposures have on ipRGC-mediated aspects of the pupillary light response.

The flickering stimuli used here were likely efficient at driving up dopamine and cAMP levels within the retina. Gooley and colleagues have shown that a flickering green stimulus causes more total pupillary constriction than a similar continuous one (Gooley et al., 2012). They attributed this effect to the recovery of cones during the intermittent dark periods. That same rod and cone recovery likely occurred during the dark periods of the flickering stimuli used here. Even though the outer-retina photoreceptors adapted to most of the present light sources, their adaptation would be expected to be greater if the lights were on continuously. Thus, rods and cones may have been able to effectively drive up cAMP levels in ipRGCs by stimulating a rise in intra-retinal dopamine through their bipolar-mediated connections with dopaminergic amacrine cells (Zhao et al., 2017).

Interestingly, ipRGCs also provide input into dopaminergic amacrine cells (D. Q. Zhang et al., 2012). Unlike bistability, a potential role for dopamine and cAMP underlying 136 photopotentiation does not depend on the alternation between certain wavelengths and intensities, but only on repeated rod and cone input into ipRGCs and dopamine amacrine cells. The fact that the bright red and all three dim flickering stimuli of this study were able to elicit at least modest signs of photopotentiation support this hypothesized mechanism.

The bright red-blue alternating stimulus would have been especially effective at driving up cAMP and dopamine levels for two reasons. First, it may have given the rods,

S-cones, and ipRGCs an extended period to recover between light stimulations. The bright red light primarily drove the M- and L-cones; and the blue light the rods, L-cones,

M-cones, S-cones, and ipRGCs. By alternating between red and blue lights, the rods, S- cones, and ipRGCs could have received a full 15 seconds of recovery time between exposures of blue light. In contrast, there would be only 5 seconds of recovery time during the use of the single-color stimuli. Second, the red-blue alternating stimulus likely recruited all photoreceptor types to help maximize retinal dopamine levels. The dim red- blue alternating stimulus had a more muted effect on photopotentiation because it was less able to drive rod and cone input into ipRGCs and dopaminergic amacrine cells. As a result, all three of the dim stimuli did drive pupillary photopotentiation, but the red-blue alternating stimulus provided no additional efficacy over the single-color ones.

Alternatively, iris fatigue rather than an increased ipRGC contribution could underlie the pupil photopotentiation observed in these results. Repeated electrical stimulation of dissected rat irises causes an initially forceful contraction, but tension gradually diminishes during the course of stimulation (Narita & Watanabe, 1981). It is possible that fatigue in the iris dilator muscle or associated effector pathways contributed to the slowing of iris re-dilation after multiple pulses of light. However, aspects of these results suggest that it was not the primary underlying mechanism for the 137 photopotentiation effect. First, although the rate of pupil re-dilation slowed with each light pulse, the rate of constriction remained brisk. If clinically meaningful pupil fatigue altered the light response, then both constriction and re-dilation may be expected to slow.

Second, the change in re-dilation rates was not uniform across chromatic conditions.

The bright red-blue alternating test elicited large, rapid changes in re-dilation rates, but the red and blue stimuli evoked smaller, slower changes. Iris fatigue would be expected to equally affect each condition.

Conclusion

This study demonstrated that exposures to flickering lights are able to alter the human pupillary light response. Flickering lights elicited progressively slower pupil re- dilation after the offset of each pulse, a process that I termed photopotentiation. A bright flickering light that alternated between red and blue pulses elicited the largest photopotentiation response, but single-color red and dim stimuli were able to elicit it, as well. The exact mechanism of photopotentiation remains unknown, but I propose that ipRGCs play a critical role. The following chapter will examine the pupillary light responses to red and blue flickering lights in a population of photophobic traumatic brain injury patients.

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Chapter 4: Observer-perceived light aversion behavior and light-evoked pupil

responses in photophobic subjects with TBI

This dissertation has shown that external modulators can influence the signaling of intrinsically photosensitive retinal ganglion cells (ipRGCs). Specifically, a dopamine receptor agonist will enhance in vitro ipRGC spiking, as recorded with a multi-electrode array, in response to repeated light pulses. In healthy human subjects, flickering light stimuli, especially those that alternate between red and blue light, photopotentiate the ipRGC-driven aspects of the pupillary light response. Taken together, these results indicate that stimuli that are initially below threshold for stimulating an ipRGC contribution to the pupillary light response may be capable at reaching threshold with repeated stimulation. Evaluation of this change in ipRGC sensitivity caused by repeated light exposures may have diagnostic applications in the clinic.

ipRGC function in ocular disease

The in vivo function of ipRGCs, as measured by the quantification of post- illumination pupillary constriction, has been investigated in individuals with many different ocular diseases. Sustained pupillary constriction after light offset, the hallmark of ipRGC contribution to the pupillary light response, is preserved in outer retinal degenerations, such as retinitis pigmentosa, where the inner retina is relatively spared

(Kardon et al., 2009, 2011; Park et al., 2011). Interestingly, another group has shown that early- and late-stage macular degeneration, an outer retinal disease that compromises the retinal pigment epithelium and rods and cones, impairs the ipRGC- 139 mediated aspect of the pupillary light response (Maynard, Zele, & Feigl, 2015; Maynard,

Zele, Kwan, & Feigl, 2017). It is unclear why ipRGC function is preserved in one outer retinal disease, retinitis pigmentosa, and impaired in another, macular degeneration.

One possibility is that outer-retinal cell death may disrupt the arborization of ipRGC dendrites in the inner plexiform layer (Esquiva, Lax, & Cuenca, 2013). One might predict that optic neuropathies are associated with a reduction in post-illumination pupillary constriction, but this hypothesis does not consistently hold true. Sustained pupillary constriction after the offset of a blue light is not affected in non-arteritic optic neuropathy

(Herbst et al., 2013), dominant optic atrophy (Kawasaki, Collomb, Leon, & Munch,

2014), and Leber’s hereditary optic neuropathy (La Morgia et al., 2010). Likewise, animal models of glaucoma suggest that, compared to other retinal neurons, ipRGCs are relatively resistant to the degeneration induced by high intraocular pressure (R. S. Li et al., 2006; Q. Zhang et al., 2013). In glaucoma patients, the pupillometry results are varied. There is evidence that the ipRGC-mediated aspects of the pupillary light response are preserved in glaucoma (Zhou, Davis, Spitze, & Lee, 2014), but this result is not universally reported (Feigl, Mattes, Thomas, & Zele, 2011; Kankipati, Girkin, &

Gamlin, 2011; Najjar et al., 2018).

Why is ipRGC function spared in optic nerve degenerations that result in the loss of regular ganglion cells? One explanation may be that ipRGCs themselves may be resistant to axonal damage. Several animal models support this view (R. S. Li et al.,

2006; Perez de Sevilla Muller, Sargoy, Rodriguez, & Brecha, 2014; Robinson &

Madison, 2004; Q. Zhang et al., 2013). Another reason may be that the pupillometry methodologies used in these investigations were not able to detect subtle alterations in ipRGC function. In all of these studies, single pulses of red and blue lights of varying duration were employed to compare the magnitude of post-illumination pupillary 140 constriction. More sustained constriction after the offset of the blue light than the red light was interpreted as unaltered ipRGC functioning. The problem with this approach is that symptoms may not be elicited by laboratory stimuli. Human beings are not exposed to single flashes of light as they navigate their environment; instead, they encounter constantly changing spatial and temporal stimuli. Thus, a test that better replicates this environment, or at least its temporal modulations, may have greater sensitivity to detect functional deficits in ipRGCs caused by ocular disease. While flickering red and blue light stimuli are also relatively artificial, the temporal dynamics of these stimuli may better reflect those prevalent under natural conditions and therefore may be more able to elicit alterations in ipRGC function. That is to say, ipRGCs within a diseased eye may function normally in response to a single light exposure but be unable to adapt to repeated light challenges. This dissertation has already shown that repeated light exposures, which are expected to drive up retinal dopamine levels, photopotentiate post-illumination sustained pupil constriction in vivo. Furthermore, stimulation of dopamine D1 receptors with an agonist elicits greater light-evoked ipRGC spiking in rat retinas in vitro. I speculate that ocular and neurological disease may alter this photopotentiation response to repeated light stimuli, thereby increasing the diagnostic potential for measurements of the change in pupillary light responses over time. In this fourth chapter, flickering red and blue stimuli were utilized to repeatedly challenge the ability of ipRGCs to send signals to the brain centers that control the pupillary light response, in subjects who report difficulty adapting to photic environments.

Epidemiology and classification of traumatic brain injury

The Centers for Disease Control define a traumatic brain injury (TBI) as a disruption in brain function that is caused by an external force such as a bump, blow, jolt, 141 or penetrating wound to the head. TBI is not a rare condition in the United States.

According to Taylor and colleagues, physicians diagnosed a TBI in 2.5 million emergency department (ED) visits and 282,000 hospitalizations in 2013 (Taylor,

Greenspan, Xu, & Kresnow, 2015). From 2007 to 2013, the number of these encounters increased by 47% and 2.5%, respectively. In a separate study of TBI visits to EDs in the

United States from 2006 through 2010, Marin and colleagues reported that the rate of increase in TBI-related ED visits was eight-fold more than the rate of increase in total ED visits (Marin, Weaver, Yealy, & Mannix, 2014). Children disproportionally contributed to this rise in incidence, as sport- and recreation-associated TBIs more than doubled in individuals younger than 20 years of age between 2001 and 2012 (Coronado et al.,

2015). Falls are the leading cause of TBI, accounting for 47% of all TBI-related ED visits, hospitalizations, and deaths (Taylor et al., 2015). It is unsurprising that the elderly

(individuals over 65 years old) and the very young (individuals younger than 14 years old) are especially susceptible to suffer a TBI as the result of a fall (Cancelliere,

Coronado, Taylor, & Xu, 2017; Taylor et al., 2015). Across all age groups, strikes to the head account for 15% of all TBI-related ED visits, hospitalizations, and deaths, and are the second leading cause of TBI (Taylor et al., 2015). Motor vehicle accidents are the third most prevalent cause, resulting in 14% of all TBI-related ED visits, hospitalizations, and deaths (Taylor et al., 2015). In its most severe form, TBI leads to over 50,000 annual deaths in the United States (Coronado et al., 2011).

Clinicians and researchers typically classify TBIs as mild, moderate, or severe.

Individuals who suffer from a moderate or severe TBI lose consciousness for an extended period of time (>30 minutes) after injury, experience prolonged amnesia (>24 hours), and exhibit limited verbal and motor responses (Teasdale & Jennett, 1974).

Fortunately, the majority (70%-90%) of TBI cases are classified as mild (Cassidy et al., 142

2004). These patients experience brief (<30 min) or no loss of consciousness, limited posttraumatic amnesia, and minimally altered verbal and motor responses (Teasdale &

Jennett, 1974). The adjective ‘‘mild,’’ however, is not meant to describe the severity of the consequences that occur after the injury, and a significant minority of persons with mild TBI (mTBI) can exhibit cognitive, emotional, behavioral, and physical impairments that can persist for many months or years (H. S. Levin & Diaz-Arrastia, 2015).

Ocular manifestations of TBI

Despite the lack of clinically detectable damage to the eye and the maintenance of good visual acuity, the posttraumatic ocular symptoms of even the mild classification of TBI can be debilitating. Accommodative dysfunction, oculomotor deficits, reading difficulties, and visual field defects are all among signs and symptoms associated with mTBI (Alvarez et al., 2012; Capo-Aponte, Urosevich, Temme, Tarbett, & Sanghera,

2012; Goodrich, Flyg, Kirby, Chang, & Martinsen, 2013; Kapoor & Ciuffreda, 2002).

Photophobia can be particularly disruptive to activities of daily living and is a common sequela of mTBI (Alvarez et al., 2012; Capo-Aponte et al., 2012; Magone, Kwon, & Shin,

2014; Waddell & Gronwall, 1984). Photophobic patients may remain light-averse for months or even years after their injury (Bohnen, Twijnstra, Wijnen, & Jolles, 1991;

Jonsson, Lidvall, & Malhammar, 1967; Magone et al., 2014; Waddell & Gronwall, 1984), and recovery may be incomplete (Truong, Ciuffreda, Han, & Suchoff, 2014). There has yet to be longitudinal data reported for this population that establishes the frequency at which photophobia symptoms improve, worsen, or remain stable over time.

Photophobia, sometimes called photoallodynia, is classically defined as a pathological state in which normally non-noxious light induces or exacerbates pain within the eye or head (Lebensohn, 1951; Matynia et al., 2012). Behavioral aversions, such as 143 squinting (Aboshiha et al., 2017; Lebensohn, 1951), tearing (Lebensohn, 1951), blinking

(Blackburn et al., 2009; Nahar, Sheedy, Hayes, & Tai, 2007), and excessive eye movements (Y. Lin et al., 2015) commonly accompany the perceived discomfort. From a clinical perspective, anterior segment diseases that incur ocular inflammation, such as herpes simplex keratitis (Darougar, Wishart, & Viswalingam, 1985) and iridocyclitis

(Griffin, 1989), are often associated with photophobia; however, pathology anywhere along the visual pathways can elicit a painful response to light. Thus, photophobia has been reported in a variety of ocular and neurological conditions that do not incur ocular inflammation, including achromatopsia (Aboshiha et al., 2017) and migraine (Cucchiara,

Datta, Aguirre, Idoko, & Detre, 2015; Drummond, 1986; Hay, Mortimer, Barker, Debney,

& Good, 1994; Vanagaite et al., 1997).

The diagnosis of photophobia associated with mTBI and migraine remains a challenge for clinicians. Photophobic patients may not always present light aversion as a primary complaint when their clinician takes a case history (Katz & Digre, 2016). A simple inquiry about heightened sensitivity to light is the most common diagnostic tool but is likely insensitive (Evans, Seifert, Kailasam, & Mathew, 2008; Vanagaite et al.,

1997). A questionnaire-based scale of photophobia is more accurate in diagnosing photophobia than a single, binary question in migraine patients (Cucchiara et al., 2015).

Several similar questionnaires have been developed for TBI patients but have yet to gain widespread clinical acceptance (Capo-Aponte et al., 2012; Laukkanen, Scheiman, &

Hayes, 2017; Waddell & Gronwall, 1984). Standardized, objective clinical assessments do not currently exist (Truong et al., 2014). As the incidence of mTBI is rising

(Cancelliere et al., 2017; Marin et al., 2014; Taylor et al., 2015), the need for clinical procedures to diagnose and monitor photophobia is significant.

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Pathophysiology of TBI-related photophobia

The physiological etiology of TBI-related photophobia has long remained elusive

(Digre & Brennan, 2012), but evidence in rodent models has begun to support the role of ganglion cell photoreceptors. In rats Noseda and colleagues recently showed that a small group of retinal ganglion cells project to pain centers in the posterior thalamus which are also innervated by the trigeminovascular pathway (Noseda et al., 2010). The nociceptive neurons in this thalamic region are believed to play a critical role in migraine headache signaling, and therefore this study provided evidence for an anatomical pathway that links light stimulation to pain sensation. Intriguingly, these retinal neurons were ipRGCs. As previously described, this small subset of RGCs expresses the photopigment called melanopsin, and are thus able to encode light stimulation independent of rod and cone input through bipolar cells (Berson et al., 2002; Hattar et al., 2002). ipRGCs function primarily as irradiance detectors, signaling to the brain information about the amount of light present in the environment (Do & Yau, 2010).

Additional evidence for the contribution of ipRGCs to photophobia exists in the light aversion behaviors of nocturnal rodents. Neonatal rats, which lack outer retinal function, demonstrate light avoidance before the maturation of their rods and cones, and this avoidance behavior is eradicated when melanopsin, the photopigment of ipRGCs, is genetically eliminated (Johnson et al., 2010). Similarly, adult mice genetically lacking functional outer photoreceptors maintain light aversion behavior (Matynia et al., 2015;

Semo et al., 2010; Thompson et al., 2010), as long as melanopsin-containing ipRGCs are left functionally intact. As one exception, however, Göz and colleagues reported that the targeted destruction of ipRGCs with saporin did not alter light aversion behavior in adult mice (Goz et al., 2008). However, saporin is not effective at eliminating the entire ipRGC population (Ingham, Gunhan, Fuller, & Fuller, 2009); therefore, the maintenance 145 of light aversion in this study could have been due to a surviving small population of ipRGCs.

Evidence for the role of ipRGCs in TBI-related photophobia is also emerging in human subjects. Anatomically, the retinothalamic track described in rodents above can be imaged in human subjects using diffusion magnetic resonance imaging tractography

(Maleki, Becerra, Upadhyay, Burstein, & Borsook, 2012). Functionally, blue light is the most efficient part of the visible light spectrum at eliciting squinting in three healthy subjects (Stringham, Fuld, & Wenzel, 2003). This finding makes sense in light of the fact that blue-light-sensitive ipRGCs are known to contribute to brightness perception in humans (Brown et al., 2012; Zele, Adhikari, Feigl, & Cao, 2018). Furthermore, light stimulation affects blind migraineurs with severe outer retina degeneration, but not blind migraineurs who had undergone enucleation (Noseda et al., 2010). This finding is consistent with the premise that ipRGCs, presumed to remain functional in the former individuals but not the latter, provided the photic signals that exacerbated the migraine headaches. There is recent electrophysiological evidence in sighted migraineurs that an exaggerated, rod-driven response in bipolar and amacrine cells is the main contributor to migraine-related photophobia (Bernstein et al., 2019); but this explanation does not match the perceptual evidence that bright white, blue, amber, and red lights all exacerbate migraine headaches, equally (Noseda et al., 2016). Electroretinography in mTBI subjects does not show the same amplified response in extra-photoreceptor neurons (Freed & Hellerstein, 1997), suggesting that an alternative hypothesis is necessary to explain recalcitrant photophobia in this patient population.

One such alternative hypothesis for why photophobia occurs after head trauma is that ipRGCs, and their axons in the retinohypothalamic tract, are among the CNS neurons affected by mTBI. The rapid coup-countercoup movement of the brain during 146 this type of injury deforms the brain and precipitates shear stress on central nervous system neurons and their supporting glial cells (Meythaler, Peduzzi, Eleftheriou, &

Novack, 2001; L. Zhang, Yang, & King, 2004). Diffuse axonal injury within the central nervous system is a consistent sequela of shear stress and is hallmarked by damage to the long, thin axons of the brainstem, cerebral cortex, thalamus, and corpus callosum (H.

Adams, Mitchell, Graham, & Doyle, 1977; J. H. Adams et al., 1989; Scheid, Preul,

Gruber, Wiggins, & von Cramon, 2003). Neurons whose axons are compromised by the diffuse axonal injury of a TBI are hypothesized to become leakier to calcium ions and thereby exhibit more depolarized membrane potentials (McAllister, 2011). The susceptibility of ipRGCs to the shearing forces of TBI is unknown. If ipRGCs became more depolarized after TBI, however, one could expect that these cells would respond to dimmer light as their resting potential would be closer to the threshold for spike firing. In other words, this theory suggests that ipRGCs become “hypersensitive” to light stimuli in some patients with mild TBI. It should be noted that this potential effect of a TBI on the resting membrane potentials of ipRGCs is similar to that elicited by the activation of ipRGC D1 receptors by dopamine (Van Hook et al., 2012) and is consistent with the in vitro dopamine experiments described earlier in this dissertation. The enhanced stimulation of the thalamic pain centers innervated by ipRGCs could then underlie the increased discomfort (photophobia) experienced by these patients upon exposure to standard ambient light levels.

Novel approaches to the clinical detection of TBI-related photophobia

Of the visual consequences linked to mTBI, there is perhaps the least consensus on the best clinical practice guidelines for assessing photosensitivity or light intolerance.

Masked observer grades of videos and photographs have had merit in the diagnosis, 147 classification, and management of several ocular diseases. For example, the Ocular

Hypertension Treatment Study used stereo-photographs of the optic nerve head to assess whether ocular hypertension had converted to primary open-angle glaucoma

(Feuer et al., 2002). Efron and colleagues validated a grading system of contact lens complications that utilized a series of photographs to stage damage to the conjunctiva, cornea, limbus, and eyelid (Efron, Morgan, & Katsara, 2001). Cataract surgeons currently rely on the Lens Opacities Classification System III images produced by The

Longitudinal Study of Cataract Study Group to anticipate difficulties during phacoemulsification of nuclear cataract (Bencic, Zoric-Geber, Saric, Corak, & Mandic,

2005; Chylack et al., 1993). The literature is currently devoid of any systematic approach for the classification of TBI-related photophobia using a formalized grading scale.

Furthermore, it is unknown whether eye care practitioners could use such a scale to detect photophobia in their TBI patients. Thus, this investigation first seeks to fill these gaps in the literature by testing whether experienced clinicians can distinguish subjects with self-reported TBI-associated photophobia based on the observation of video recordings of their responses to bright, flashing red and blue lights.

Assessment of the pupillary light response in TBI patients may be an objective method of quantifying their photophobia. In addition to innervating the pain centers of the posterior thalamus, ipRGCs also signal irradiance information to other brain targets such as the olivary pretectal nucleus, a critical component of the pupillary light reflex circuit

(Hattar et al., 2002). Their unique contribution to the human pupil light response makes it possible to assess their function in the living human eye. To briefly review, ipRGCs are most sensitive to blue light and can continue to fire for many seconds after light offset

(Do & Yau, 2010). These cells contribute to the sustained pupillary constriction that is especially apparent after the eye is exposed to bright blue light stimuli (Gamlin et al., 148

2007; Kankipati et al., 2010; Park et al., 2011). If an alteration in the input from ipRGCs to thalamic pain centers underlies TBI-associated photophobia, ipRGC input to the olivary pretectal nucleus would also likely be altered in these individuals. The second goal of this work, therefore, is to test the hypothesis that ipRGC function is altered in participants with mild TBI and photophobia by assessing and comparing pupillary responses to blue and red light. If the hypothesis proves correct, the sustained constriction after blue light stimulation will be more pronounced in participants with mTBI. Such pupil testing strategies could potentially have considerable clinical value as objective tests used for the diagnosis and monitoring of this debilitating condition that affects mTBI patients.

Methods

This study followed the tenants of the Declaration of Helsinki and was approved by the Institutional Review Board (IRB) in Biomedical Sciences at The Ohio State

Subject recruitment and initial eligibility

Two cohorts were initially enrolled. Individuals suffering from TBI-related photophobia and age- and gender-matched control subjects were recruited from patients seen at the Rehabilitation Services at the OSU Wexner Medical Center and at the OSU

College of Optometry as a result of direct contact or in response to posted pamphlets and recruitment posters. In addition, the study was posted to ResearchMatch

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(researchmatch.org), a free and secure registry of volunteers interested in research studies. A phone screening was performed on all interested TBI and control participants to determine study eligibility as either a TBI or a control subject.

During the screenings, potential TBI participants were identified if (1) they were

18 years of age or older, (2) they reported having at least one head injury that had occurred a minimum of 6 months previously, and (3) they reported lights being more bothersome since the injury. Those who met all three criteria were invited to attend the initial study visit. Similarly, potential control participants were identified if (1) they were

18 years of age or older, (2) they reported no previous head injuries, (3) they had a comprehensive eye examination at the OSU College of Optometry (or were willing to have one prior to the study visit), and (4) they were not told at that examination they had any eye condition that could not be corrected with glasses. Those who met all four criteria were invited to attend the initial study visit.

First study visit: TBI and control subjects

TBI and control subjects sat for up to two study sessions. At the first session, distance visual acuity with habitual refractive error correction was measured in both eyes. Macular pigment is known to abate disability glare in healthy subjects (Stringham,

Garcia, Smith, McLin, & Foutch, 2011; Stringham & Hammond, 2007). In order to ensure that a deficit in ocular pigment density did not account for the photophobia in the TBI subjects, macular pigment optical density in the right eye was assessed using a commercial device (QuantifEye; Zeavision, Chesterfield MO). This machine employs a flicker psychophysical methodology to measure macular pigment optical density (Bone &

Landrum, 2004; van der Veen et al., 2009). Briefly, the patient observes a small light stimulus that alternates between a test wavelength that is absorbed by macular pigment 150

(around 460 nm) and a reference wavelength that is not absorbed (around 540 nm). The amount of blue light necessary to create the appearance of a null (i.e. not flickering) stimulus corresponds to the density of carotenoids present in the macula. Macular pigment ocular density measurements were collected at each study session. One-way

ANOVA sought differences between the mean values of the two study groups.

Next, the verbally-administered Ohio State University Traumatic Brain Injury

Identification Method (OSU TBI-ID; Short Form) established each participant’s TBI exposure. This instrument is a validated survey that provides an indicator of the lifetime

TBI history (Corrigan & Bogner, 2007). Every subject was then given a six-statement survey designed to query their light sensitivity and its impact on their daily lifestyle.

TBI subjects then underwent a comprehensive eye examination by a licensed optometrist that included assessments of extraocular muscle movements, binocular vision, refractive status, external eye health, intraocular pressure, visual fields (using automated perimetry), and internal eye health (dilated fundus examination). The eye examinations were performed to rule out alternative etiologies (e.g. corneal, lens, or retinal pathologies; angle-closure glaucoma; and uncorrected refractive error) for the photophobia separate from TBI.

TBI subjects were eligible to return for a second study visit if they (1) scored 2 or

3 on the OSU TBI-ID survey (indicative of mTBI), (2) had a best-corrected visual acuity of 20/30 or better in each eye, (3) did not have a measurable afferent pupillary defect,

(4) did not have strabismus, (5) had an intraocular pressure ≤21 mm Hg, (6) had no active corneal pathology (e.g. dry eye and infectious and inflammatory disease), (7) had no current or previous history of optic nerve or retinal disease, (8) had no history of neurodegenerative diseases (e.g. Parkinson’s and multiple sclerosis), and (9) had a reliable (≤20% fixation losses and ≤25% false positives and negatives) threshold visual 151 field (30-2, Humphrey HFA II; Zeiss, San Diego CA) with no more than 10% of points depressed below 0.5% on the pattern deviation plot. A total of 28 TBI participants were recruited for the first session, of which 24 were deemed to meet the eligibility criteria after completing the first visit. Four TBI participants were excluded from attending the second session due to either retinal pathology, the presence of a significant visual field defect, or an OSU TBI-ID score of 4 or 5 (moderate or severe TBI). A summary of the

OSU TBI-ID scores, number of experienced TBIs, and the underlying cause of the most recent TBI for the TBI participants are listed in Table 4.1. The median duration since last

TBI for this cohort was 24.5 (range: 6-164) months. A previous study of pupillary function in TBI subjects showed that there is no difference in the pupillary light response of subjects with a history of a single TBI and those with multiple TBIs (Truong & Ciuffreda,

2016); therefore, the TBI subjects in the present investigation were not delineated based on the number of TBIs reported on their OSU TBI-ID survey.

In contrast to TBI participants, a score of 0 or 1 (no TBI or improbable TBI) on the

OSU TBI-ID was required for the control participants to return for the second study visit.

All had a best-corrected visual acuity of 20/30 or better in each eye. These participants did not undergo a full eye examination at the visit, but had had a recent (within 6 months) examination at the OSU College of Optometry as part of their screening eligibility. The previous eye examination records were reviewed to confirm that similar ocular health eligibility criteria (points 3 to 9 in preceding paragraph) that described for the TBI participants were met. All 12 recruited control participants were deemed eligible and returned for the second study session.

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Second study visit: TBI and control subjects

Twenty-four TBI and 12 control participants returned for the second study session, 2 to 4 weeks later. The testing protocol at this second visit was identical for both subject groups. Visual acuities and macular pigment optical density were again measured, and the photophobia survey was re-administered. The left eye was dilated

(1.0% tropicamide), and the pupil testing reported here occurred approximately an hour later. During the intervening period, additional testing was performed using a commercial pupillometer (RAPDx; Konan Medical, Irvine CA); their data are not included in the current report. After these initial pupil tests, participants were positioned in a custom- made optical system (described below) for two trials of pupil testing, with each one preceded by 10 minutes of dark adaptation. In the tests, the participants’ left eyes were stimulated with three pulses of both red (peak λ = 635 nm, dominant λ = 625 nm, FWHM

= 17 nm) and blue (peak λ = 465 nm, dominant λ = 470 nm, FWHM = 22 nm) light that flashed at 0.1 Hz (5 sec on, 5 sec off). For retinal illumination values (in trolands), see

Table 4.2. In half of the participants, blue light was tested first, whereas the red-light stimulus was tested first in the other half.

To assess whether TBI subjects perceived the red and blue light stimuli as being brighter and more uncomfortable than their controls, all TBI and control subjects used a simple, five-point verbal scale to judge the brightness and discomfort of each individual pulse of red or blue light used in the pupillometry protocol. A grade of 1 represented minimal brightness and/or discomfort, and a grade of 5 extreme brightness and/or discomfort. Subjects were told that a score of ‘5’ represented a light that subjects would have pulled back from the apparatus if it had been on longer than 5 seconds; otherwise, subjects used the grading scale at their own discretion.

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Pupil testing apparatus

The custom pupillometer utilized in this investigation has already been detailed in chapter 3 of this dissertation. Briefly, two 3” x 3” Fresnel lenses (Edmund Optics,

Barrington NJ) with 3” focal lengths were separated by a distance of 6” on an optical bench. Light stimuli generated by a light-emitting diode (LED) illumination system (DiCon

LED, Richmond CA) were transmitted through a fiber optic light guide (Edmund Optics), with the end of the guide placed at the focal point (3” away) of the first Fresnel lens. A 5° holographic diffuser (Edmund Optics) attached to the back surface of the second Fresnel and produced a uniform stimulus with a visual angle of approximately 53 degrees. A barrier extended from the lens’ housing unit to the subject’s nose in order to minimize exposure of the subject’s right eye to the light stimuli. Light irradiance at the corneal plane 3” from the second lens was determined using an optical power meter (Newport,

Irvine CA). Irradiance was adjusted through computer control (LightControl software,

For the TBI and control subjects, the light irradiances of the LED-generated stimuli were determined at the corneal plane using an optical power meter (Newport,

Irvine, CA). The irradiance of the blue stimulus was 1 x 1013 photons/s/cm2, and the irradiance of the red stimulus was 7 x 1013 photons/s/cm2. These irradiances were chosen based on previous empirical studies in the laboratory on young, healthy participants that showed the initial pupil constrictions evoked by these stimuli are 154 approximately equal, whereas the pupil constriction that persists post-illumination is more pronounced for the blue stimulus. This latter feature is characteristic of the involvement of ipRGCs in the pupil responses to the blue light, as these neurons can continue to fire after light offset (Berson et al., 2002; Gamlin et al., 2007; Hattar et al.,

2002).

Pupil data and statistical analysis

Video recordings from the commercial pupillometer were imported into Image J software (National Institutes of Health, Bethesda MD). An observer who was masked to the subject group (TBI subjects or control subjects) associated with each video recording used the circle tool in this software to manually place a circle around the perimeter of the pupil in the image at a rate of 2 Hz. Raw pupil measurements (pupil area in pixels) before the light exposures were averaged to determine the ‘‘baseline pupil size’’ for each subject. The smallest pupil area measurement for each subject during the entire experimental session (including both trials with the red and blue stimuli) was identified and used as the ‘‘minimum pupil size.’’ All other pupil measurements were then normalized using the following equation:

Thus, for each subject, 0% represented the average baseline pupil size and

100% represented the maximum pupil constriction achieved within the experimental session. For the mean data, normalized pupil measurements were averaged at each time point collected. These averaged normalized pupil responses were plotted with error bars representing standard error of the mean (SEM). As the time-point at which the

155 minimum pupil size occurred varied from individual to individual, the mean pupil size, averaged across subject groups, did not reach 100% at any time point.

The normalized pupil constriction data was translated to the frequency domain by performing a fast Fourier transform (FFT) of the data in Excel (Microsoft, Redmond,

WA), enabling the average amplitude of the pupil fluctuation (constriction and dilation) at

0.1 Hz to be calculated objectively. To quantify the inter-individual variability in pupil responses, the absolute difference from the mean group data was calculated at each data point collected in the individual trials. The total variance from the mean was then summed for each individual pupil trace, with mean data calculated for both subject groups. Intra-group comparisons were made with paired t-tests and inter-group comparisons with t-tests.

Subjective brightness data and statistical analysis

Subject-generated grades of discomfort evoked by each of the three pulses of red or blue light were averaged, creating a single red or blue discomfort grade for each participant. The Wilcoxon signed rank test compared median grades within a cohort (e.g. the control subjects’ grades of the red stimulus versus the control subjects’ grades of the blue stimulus). The Mann–Whitney rank sum test compared median grades between cohorts (e.g. the control subjects’ grades of the red stimulus versus the TBI subjects’ grades of the red stimulus). For the photophobia survey given at the beginning of each study session, mean responses between the TBI and control subjects were compared with one-way ANOVA.

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Pupil recording session: student subjects

While examining the videos of the TBI and control subjects offline during the pupil size analyses, it was noted that some subjects blinked, teared, and squinted in response to each pulse of light. Others offered little to no reaction to the same lights.

The question of whether the responses were greater in the subjects with TBI-associated photophobia became the impetus for a secondary study. To develop a more standardized grading scale, a group of healthy university students who had received a recent comprehensive eye examination and reported no ocular or neurological disease were recruited. On the day of testing, each individual achieved at least monocular 20/25 visual acuity at distance, with or without habitual refractive error correction.

Students sat for a single pupil recording session. Like the TBI and control subjects, one drop of 0.5% tropicamide (Akorn, Lake Forest IL) was instilled into the left eye to minimize the effect of pupil constriction on retinal illumination. Dilation occurred

30 minutes before testing, including a 20-minute dark adaptation period. Once dilated and dark-adapted, the student subjects were placed behind the same pupillometer used for the TBI and control subjects. Similar light stimuli were presented to the student cohort, but there were two notable differences in the flickering stimuli: 1) the light was one log unit brighter (red light 7x1014 photons/s/cm2, blue light 1x1014 photons/s/cm2); and 2) the test duration was longer, lasting 1 minute (6 total light pulses). For the retinal illumination (in trolands) of these stimuli, see Table 4.2. These differences in the stimuli were introduced in order to elicit a range of light aversion responses in a group of young, healthy individuals. Pupil size data were analyzed in the same manner as those collected from the TBI and control subjects.

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Light aversion grading

Five masked observers (optometrists by training) graded light aversion behavior in the videos of the students’ pupil responses using a five-point scale. The observers were instructed to give lower grades to students who responded minimally to the light pulses and higher grades to subjects who demonstrated prominent light aversion behavior, including excess blinking, tearing, and fixation losses. Grades were based on the content of the entire recording, not on each pulse, individually.

The raw data were analyzed to find recordings where the judges largely agreed

(based on average ± SEM grade) on the severity of light aversion behavior contained within. Videos receiving an average grade near an integer (1-5) with a low standard error were then reviewed for quality and consistency of responses across all six pulses of light. This process produced five representative videos, one for each grade on the five- item scale. Each video was then edited down to a synoptic 10-second clip for easy reference. Finally, brief descriptions of pertinent behaviors, including blinking, squinting, tearing, and fixation losses, were written for the five videos.

Four masked observers used these representative images and descriptions for the 5-point scale to assess the pupil recordings of the TBI and control subjects for light aversion behavior.

Light aversion grades data and statistical analysis

The agreement for the light aversion behavior grades between the four (TBI and control cohorts) or five (student cohort) observers was quantified with an intra-class correlation coefficient (MedCalc, Ostend Belgium), a statistical measurement of consistency. Inter-observer concordance was calculated by dividing the number of videos that received the same grade from all observers by the total amount of videos 158 graded. The median difference in the light aversion grades between TBI and control subjects and between colored stimuli in all three subject groups were made using the

Wilcoxon signed rank or Mann–Whitney rank sum tests.

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Number of OSU TBI-ID Short Subject Cause of Most Recent TBI Reported TBIs Form Score 101ǂ 1 MVA 5 102ǂ 3 MVA 3 103+ 1 Strike/blow to head 2 104 4 Fall 3 105 3 Fall 3 106ǂ 3 Assault 5 107 5 MVA 3 108 1 Athletics 2 109 1 Fall 2 110 1 MVA 3 111 1 Strike/blow to head 3 112 2 MVA 2 113ǂ 1 Strike/blow to head 3 114 12 Strike/blow to head 3 115 8 Strike/blow to head 3 116 2 Strike/blow to head 2 117 7 MVA 3 118 3 Fall 3 119 4 MVA 3 120 5 Fall 3 121 4 Strike/blow to head 3 122+ 4 Strike/blow to head 3 123 4 Assault 3 124 2 Strike/blow to head 3 125 1 Blast injury 3 126 3 Strike/blow to head 3 127 7 Strike/blow to head 2 128 2 Fall 2

Table 4.1. Head injury history of TBI subjects. OSU TBI-ID score of 0 = no TBI; 1 = improbable TBI; 2 = mild TBI (no loss of consciousness); 3 = mild TBI (loss of consciousness); 4 = moderate TBI; 5 = severe TBI. All control subjects scored 0 or 1 on OSU TBI-ID. ǂ Due to inclusion/exclusion criteria, these subjects did not participate in the second session of pupil testing. + Unable to complete pupil testing protocol (blepharospasm, head withdrawal from instrument). Motor vehicle accident

(MVA).

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Light Stimulus Photons/s/cm2 Scotopic Photopic Trolands Trolands TBI/Control 1 x 1013 1.88 x 1010 1.01 x 1010 Blue Light TBI/Control Red 7 x 1013 7.30 x 108 3.58 x 1010 Light Student Blue 1 x 1014 1.88 x 1011 1.01 x 1011 Light Student Red 7 x 1014 7.30 x 109 3.58 x 1011 Light

Table 4.2. Retinal illumination caused by the red and blue light stimuli utilized on the

TBI, control, and student subjects. Since most of the participants were young, an assumed dilated pupil size of 7 mm was used in the troland calculations (A. T. Smith,

1991). Photons/s/cm2 were measured at the corneal surface.

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Results

Subject enrollment and demographics

After finishing the first study visit, which included a comprehensive eye examination, 24 of the 28 TBI participants met eligibility criteria and returned for a second study visit for pupil testing. One TBI subject (103) was unable to complete pupil testing during the second session due to excessive head movements, which included pulling away from the instrument. As a result, subjective brightness grades were also not collected from this individual. Another’s (TBI subject 122) pupil data was unusable due to blepharospasm, but subjective brightness grades were collected. All 12 of the matched control subjects successfully completed the entire two-session protocol.

The age, gender, and race demographics of the 24 TBI participants were well matched with those for the 12 control participants, and there were no significant differences in the quantifiable clinical findings for the two groups (Table 4.3). All TBI and control participants were corrected to a visual acuity of at least 20/20 in either eye, except for one TBI subject (124) with a best-corrected acuity of 20/25 in the left eye. For the TBI participants, the mean ± SEM deviation recorded in the 30-2 threshold visual field tests for TBI subjects was -1.6 ± 0.4 dB OD and -2.2 ± 0.4 dB OS. Clinical examinations revealed no gross abnormalities in ocular health as assessed using standard eye examination procedures. There were considerable differences, however, in the number and type of medications taken by the TBI participants, relative to controls

(Table 4.4).

A third cohort of 20 healthy students (n = 20) was also recruited to assist in the development of a grading scale for assessing light aversion behavior. One student withdrew from the study, with 19 subjects completing the protocol. Table 4.5 compares key demographics between the student group and the TBI and control groups. 162

Unsurprisingly, the students were significantly younger than the TBI and control subjects. Light adaption can become delayed with age (Sturr, Kelly, Kobus, & Taub,

1982); therefore, the observer-generated light aversion grades of the TBI subjects were not compared to those of the students. There were no differences in refractive error or gender factors between the TBI, control, and student cohorts. Ethnicity information, ocular posture (cover test), visual field sensitivity, and optic nerve C/D ratio were not collected from the students.

Subjective photophobia

All 24 TBI participants who sat for the second study session reported an increase in photosensitivity since their most recent TBI (screening criteria for study entry), and this change was quantified in their responses to a six-statement survey. By self-report, the TBI participants indicated that the situations reflected in the statements were applicable to them more frequently than did the control participants (Fig. 4.1). The survey was administered during both the first and second study visits, and the responses for each subject group were consistent across the two visits. Additionally, 20 of the 28

TBI subjects reported not being able to be outside without a dark filter in their spectacles. Half of these subjects also had to wear a filter indoors, which was consistent with the answers obtained in the surveys. No control or student subjects were observed wearing a filter while indoors.

In addition to completing the photophobia survey, TBI and control subjects judged their discomfort to each pulse of the red and blue light stimuli used in the pupil response experiments with a five-point verbal scale. A grade of one represented a seemingly dim light, and five a very bright one. The three grades (one for each pulse) were averaged into a single, composite discomfort grade for each participant. As one 163

TBI subject was not able to remain in the custom pupillometer during light presentations, the data shown Figure 4.2 compiles the individual discomfort grades from 23 TBI subjects. All 12 control subjects provided discomfort grades.

The discomfort grades elicited from the three red pulses did not differ significantly from those from the blue pulses in both the TBI (p = 0.22; Wilcoxon) and control subjects

(p = 0.25). When the responses of the TBI subjects were compared to the matched control subjects, the subjective discomfort scores given by the TBI subjects for the red (p

= 0.017; Mann–Whitney) and blue (p < 0.001) stimuli were significantly higher than those given by the control subjects. This finding is consistent with the photophobia reported by the TBI subjects and with the results of the administered photophobia questionnaire.

Macular pigment ocular density

Macular pigment ocular density levels were assessed at both study visits using the QuantifEye screening device. All 24 TBI and all 12 control participants were able to complete the heterochromatic flicker sensitivity test. As Figure 4.3 demonstrates, the readings across the two visits were consistent with each other within each subject group.

There was no significant difference in macular pigment ocular density between the TBI and control groups (p = 0.42; one-way ANOVA).

Pupillary light response amplitude: TBI and control subjects

The pupil recordings from 2 of the 24 TBI subjects that sat for the second study session were unusable, as previously described. Thus, comparisons of the data obtained for the other 22 TBI participants and 12 control participants were performed.

Mean changes in pupil size evoked by the flashing red and blue light stimuli in the TBI and control participants over time are displayed in Fig. 4.4. Pupil size was 164 normalized within each individual to the maximum pupil constriction (denoted as 100% constriction) measured during either trial, so larger numbers on the y-axis represent smaller pupil areas. In response to these two colored light stimuli, the largest separation between the traces occurred toward the end of each 5-second light pulse and during the subsequent 5 seconds of darkness when the pupil was re-dilating (Fig. 4.4A, B). ipRGCs are most sensitive to blue light (peak at ~480 nm; Berson et al., 2002), and the red light was presented at an irradiance that was designed to be photometrically matched to the blue light (in accord, the cone-driven pupil constriction during first 2 s of light stimulation was essentially equal for the two light stimuli) while being below ipRGC stimulation thresholds. The slower re-dilation associated with the blue light stimuli was indeed consistent with a greater contribution of the more sluggish ipRGCs to these pupil responses, relative to those evoked by the red light stimuli. The pupil responses to the red light also served as an important internal control, as differences in the blue light- evoked responses between the two subject groups could have been due to a TBI-related alteration in the autonomic innervation of the iris. Such an effect should alter pupil responses to both the blue and red light, so the relative difference in responses to the two colored lights served as the primary indicator of ipRGC stimulation.

For each trial, the Fourier-derived amplitude was determined at the fundamental frequency of the flashing light stimulus (0.1 Hz). This method, which involved fast Fourier transforms (FFT) of the data, enabled the average amplitude of the pupil fluctuation during the 30-second trials to be calculated objectively. Similar to the amplitude of a sine wave, this measure provides an estimate of half the difference between the averaged maximum minus minimum values for a given waveform. In either the TBI or control subject groups, the amplitude of the pupil fluctuation evoked by the blue flashing light stimuli was significantly (p ≤ 0.01; paired t-tests) smaller than that evoked by the red 165 flashing light stimuli (Fig. 4.4E). Thus, the separation between the red and blue pupil traces evident in Fig. 4.4A and B were reflected in the Fourier-transformed data, and this difference can be used to quantify the relative ipRGC contribution in the pupil responses to the two colored stimuli.

Comparing the pupil responses to either the blue light (Fig. 4.4C) or red light (Fig.

4.4D) between the TBI and control subject groups, there was considerable overlap in these traces of pupil size. In accord, there was no significant difference in the FFT amplitudes of pupil responses to either the red (p = 0.88; t-test) or blue (p = 0.59) light stimuli between the two subject groups. These results indicate that the relative ipRGC contribution to the pupil responses evoked by the specific flashing stimuli used in these trials was not detectably altered in the participants with mTBI-associated photophobia.

Pupillary light response variability: TBI and control subjects

Although the analysis of the mean data indicated that the subject groups, as whole cohorts, had similar pupil responses, an examination of the individual pupil traces suggested that there was more response variability in the TBI participants’ data. This was evident by plotting all the individual traces for the TBI (Fig. 4.5A, B) and control (Fig.

4.5C, D) participants with the mean normalized pupil size (solid black line) plus and minus two standard deviations (dotted black lines). A consistently larger area between the standard deviation boundaries was evident in the TBI participants. To quantify the increased variability in the pupil responses of the TBI participants, the average pupil size determined for the cohort was subtracted from the pupil size recorded in each individual trace. This difference was calculated at each time-point during the trial, and then the absolute values were summed over the duration of the recording to obtain an average deviation for each individual. A subject with a pupil size trace that closely mimicked the 166 mean data for the subject’s cohort would have a low average deviation, whereas a subject with a pupil trace located near the borders defined by the two standard deviation limits would have a relatively high average deviation. Comparing these average deviation values for the control participants (n = 12) and TBI participants (n = 22) indicated that there was significantly (p ≤ 0.01; t-test) more variation in the pupil responses to blue light in the participants with TBI-related photophobia (Fig. 4.5E).

Variation in the pupillary light response to flickering red and blue lights were compared to the responses of TBI and control subjects on the photophobia questionnaire and to the subjective discomfort grades elicited by red and blue lights. The participants with pupil responses that greatly differed from the mean group response were expected to report more photophobia than those with results similar to the mean.

The correlations between the total score on the photophobia survey (Fig. 4.1) and the average deviation for the pupil responses to blue (R2 = 0.066; p = 0.25) or red light (R2 =

0.002; p = 0.84) were not statistically significant. Likewise, the correlations between composite discomfort grades and the average deviation for the pupil responses were not significant for both the blue (R2 = 0.010; p = 0.66) and red (0.004; p = 0.78) light. The homogeneity of the survey responses and discomfort grades among the participants with

TBI likely limited the potential of finding such correlations, however.

Light aversion behavior: students

As the above pupil recordings were processed and analyzed, it became apparent that the light aversion behavior of TBI subjects was not as uniform as their subjective discomfort grades elicited by the red and blue lights. Some participants squinted, teared, and blinked to the lights, but others did not flinch. This observation lead to the question of whether masked observers could consistently perceive differences in the light 167 aversion behavior in these two groups, using the videos of the ocular responses used for the pupil response analyses.

To answer this question, an additional cohort of university students was recruited to assess inter-observer agreement on grading light aversion behaviors and to generate representative images that captured the spectrum of these responses. Using signs such as blinking, tearing, squinting and fixation losses, five clinically trained observers judged light aversion behavior in video recordings of pupil responses to bright, flickering red or blue stimuli of one-minute duration using a five-point scale (1 = minimal light aversion behavior, 5 = severe). As Fig. 4.6A demonstrates, the observers achieved good agreement across the student cohort, and the interclass correlation coefficient (ICC), a statistical measurement of consistency, between graders was strong (r = 0.74 [95% CI:

0.63, 0.84]). Perfect concordance between the observers occurred in 7.9% of the recordings.

When the observer’s grades were analyzed for differences between groups, students exhibited significantly more (p = 0.007, Wilcoxon Signed Rank Test) light aversion behavior in response to blue light than to red light (Fig. 4.6B). Males (median:

3.00 [interquartile range: 1.25]) were also graded as displaying significantly greater (p =

0.027, Mann-Whitney Rank Sum Test) light aversion behavior than females (2.00 [2.75]).

Note that there is a considerable range of light aversion responses to these light stimuli within this group of healthy, young subjects.

Light aversion behavior grading scale

The data obtained from the student cohort led to the development of a standardized scale for grading light aversion behavior. Specifically, an example video for each score on the five-point grading scale was selected based on the mean score each 168 video received. For example, the recording portraying the mildest score on the scale (1) received a mean ± standard error of the mean score of 1.0 ± 0.0 from the observers.

Likewise, the recordings matched with an itemized score of two (1.8 ± 0.20), three (3.6 ±

0.51), four (4.6 ± 0.24) and five (4.8 ± 0.20) represented a consensus grade from the observers. Written descriptions were then added to the videos, highlighting the salient behaviors (blinking, tearing, squinting, and fixation loss) associated with each video.

Table 4.6 compiles the standardized grading scale; and Figure 4.7 displays representative, still images from the videos.

Light aversion behavior: TBI and control subjects

Four observers used this standardized video grading scale to judge perceived light aversion behavior in the videos obtained from the TBI subjects and their matched controls. Two videos (one red and one blue) were judged from each of the 23 TBI subjects able to successfully complete the second study session (46 total videos). All 12 of the matched control subjects successfully completed the protocol, resulting in 24 video recordings.

Figure 4.8A shows that observers did not find a significant difference between

TBI and control subjects in their responses to either the red (p = 0.36, Mann–Whitney) or blue (p = 0.37) light stimuli. In correlating the TBI subjects’ subjective discomfort scores to the observers’ grades for light aversion, a higher subjective grade (i.e. the perception of a brighter or more uncomfortable stimulus) for a given stimulus was significantly associated with a higher light aversion grade (r2 = 0.100, p = 0.032; linear regression).

This relationship was not observed in the data obtained from the matched control subjects (r2 = 0.050, p = 0.30).

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Further analysis of the data revealed that TBI subjects were significantly more (p

< 0.001, Wilcoxon) averse to the blue stimulus than the red stimulus (Fig. 4.8A). This difference was not perceived in the control subjects (p = 0.53). No difference was detected between the two genders (males: 2.00 median [2.00 interquartile range], females 2.00 [1.00]; p = 0.19, Mann-Whitney Rank Sum Test) in the combined TBI and control groups.

Finally, all of the video recordings of the TBI and matched control subjects (70 total videos) were combined to test inter-observer agreement on the severity of light aversion behavior in each video. The four observers achieved strong agreement (Figure

4.8B). Utilization of the standardized grading scale led to a modest increase in ICC between graders (r = 0.78 [95% CI: 0.67, 0.85]) over the less structured scale used to judge the student videos. Inter-observer perfect concordance also increased to 29%.

Pupillary light response amplitude: students

Evidence of ipRGC influence over the pupillary light response was sought in the student cohort. Students sat for the same pupillometry protocol as the TBI and control subjects, with two significant exceptions. First, the red and blue lights used to drive the pupillary light response were one log unit brighter (red light: 7 x 1014 phots/s/cm2; blue light: 1 x 1014 phots/s/cm2) than those used on the TBI and control subjects (red light: 7 x

1013 phots/s/cm2; blue light: 1 x 1013 phots/s/cm2). Second, the duration of the test was

30 seconds longer for the student subjects (60 seconds) than the TBI and control subjects (30 seconds). Since the amplitude of the pupillary light responses of the TBI and control subjects did not differ from each other for both the red and blue lights (refer back to Fig. 4.4E), their data for each chromatic condition were combined into one aggregate cohort for comparison against the pupil responses of the student subjects. 170

The aggregated pupil traces of the TBI and control subjects did not differ from their individual traces. That is, equivalent pupillary constriction between red and blue during the first light pulse was followed by more pupillary constriction evoked by the blue light than the red light after each subsequent pulse (Fig. 4.9A). Despite the differences in stimuli length and intensity, the pupil traces of the student subjects shared these same characteristics (Fig. 4.9B). The difference in pupillary fluctuation (overall constriction and re-dilation) between the red and blue lights were larger for the students than for the TBI and control subjects because the brighter blue stimulus used on the students was likely more effective at driving the intrinsic photoresponse of ipRGCs (Do et al., 2009).

Fast Fourier transforms again quantified the amplitude of pupil fluctuation in response to the red and blue lights (Fig. 4.9C). As expected, the amplitude of the pupil fluctuation in the pooled TBI-control group evoked by the blue flashing stimulus was significantly (p = 0.0001; paired t-test) smaller than that evoked by the red flashing stimulus. The brighter blue stimulus used on the students also elicited significantly (p <

0.0001; paired t-test) less pupillary fluctuation than the brighter red stimulus. These results verified that the light stimuli used on all three cohorts were sufficiently bright to elicit the intrinsic ipRGC photoresponse.

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TBI Controls p-value Age (years) 43.3 (2.34) 42.6 (4.46) 0.87

Sex (percent female) 58.3% F 58.3% F 0.56

Ethnicity 0.63 Caucasian 75% 67% African American 17% 17% Native American 8% 16%

Visual acuity (logMAR) OD 0.0 (0.0) 0.0 (0.0) 1.00 OS 0.004 (0.004) 0.0 (0.0) 0.52

Refractive error (D) OD (spherical equivalent) -0.94 (0.35) -1.42 (0.76) 0.93 OS (spherical equivalent) -1.03 (0.35) -1.30 (0.74) 0.81

Cover Test (Δ; + is exo) Distance 0.42 (0.29) 0.67 (0.45) 0.52 Near 4.71 (1.31) 3.75 (1.51) 0.66

Optic Nerve (C/D ratio) OD 0.28 (0.02) 0.29 (0.03) 0.60 OS 0.26 (0.02) 0.29 (0.03) 0.26

Table 4.3. Demographics of TBI and control subjects. Age, visual acuity, refractive error, cover test, and optic nerve cup-to-disk (C/D) ratio values are means (SEM). These non- parametric values were compared using Mann-Whitney rank sum tests. Sex and ethnicity are represented as percent of cohort and these values were compared using chi-squared tests for association.

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Medication Class TBI Controls Analgesic 12 (50) 1 (8) Anti-diabetic 1 (4) 1 (8) Anti-infective 4 (17) 0 (0) Anti-rheumatic 1 (4) 0 (0) Cardiovascular 10 (42) 2 (17) CNS/Mental health 18 (75) 2 (17) Digestive 9 (38) 0 (0) No medication 1 (4) 2 (17) Nutritional supplement 4 (17) 3 (25) Reproductive/Hormone 7 (29) 1 (8) Respiratory 7 (29) 4 (33) Sleep aid 6 (25) 0 (0)

Table 4.4. Medications prescribed to the TBI and control subjects during the study.

Values represent the number of subjects taking at least one medication in the designated class, with percent of the cohort in parentheses.

Students TBI Controls p-value

Age (yrs) 23.0 (3.00) 43.0 (16.0) 38.0 (30.3) <0.001 Gender (% female) 42.1% F 56.5% F 58.3% F 0.570 Refractive error (D) OD (spherical -1.00 (4.00) -0.50 (1.38) -0.19 (2.94) 0.432 equivalent) OS (spherical -2.00 (4.25) -0.50 (1.75) -0.13 (44.09) 0.348 equivalent)

Table 4.5. Demographics of student study group. Age and refractive error are medians

(with interquartile range). The non-normally distributed data were compared with

Kruskal-Wallis one-way analysis of variance on ranks. Gender is represented as a percent of the cohort and was compared between the three groups with the Marascuilo procedure.

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Figure 4.1. Effects of photophobia on daily activities of subjects. At both study visits, TBI

(“Cases,” n = 24) and control (n = 12) subjects were read six statements and asked to reply whether each situation applied to them never, sometimes, occasionally, frequently, or always. These answers were given grades 0 to 4, respectively. The responses by the

TBI subjects to each of the six statements were significantly higher than those by the control subjects. **p ≤ 0.01; one-way ANOVA, Holm-Sidak post hoc test. The statements on the survey were as follows: (1) I find indoor lighting levels in public places to be uncomfortably bright, (2) I find indoor fluorescent lighting to be bothersome, (3) I try to avoid light at home (e.g. close curtains, dim lights), (4) I find outdoor light (sunlight) to be uncomfortably bright, (5) My light sensitivity interferes with my daily activities, (6) Light causes me to have prolonged discomfort (headaches) even after light exposure stops.

174

Figure 4.2. Self-reported discomfort grades of the TBI and control groups. In addition to recording pupil responses, TBI (“Cases,” n = 12) and control (n = 23) subjects reported the subjective discomfort of each five-second pulse of the red and blue flickering stimuli with a simple, five-item verbal grading scale. Light exposures that caused no discomfort received a low grade (starting with 1), and those that caused high discomfort received a high grade (ending with 5). Grades across the three pulses were then averaged to create one brightness grade for each subject. Each box represents the interquartile range, and the internal line the median. The whiskers represent the 90th and 10th percentiles, and the filled circles are the 95th and 5th percentiles. *p < 0.05 (Mann–

Whitney Rank Sum Test), **p < 0.001 (Mann–Whitney Rank Sum Test), NS = no significance (Wilcoxon Signed Rank Test).

175

Figure 4.3. Macular pigment optical density (MPOD) measurements. At both study visits, MPOD was measured in TBI (“Cases,” n = 24) and control (n = 12) subjects using a commercial device (QuantifEye) that tests heterochromatic flicker sensitivity. There was no significant (NS; p = 0.4, one-way ANOVA) difference in MPOD between the two subject groups.

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Figure 4.4. Pupil responses to flashes of red or blue light. (A) Traces of mean (± SEM) pupil size (normalized; 100% = smallest measured pupil area in experimental session and 0% = baseline pupil area) elicited by three pulses of blue or red light (bars at top indicate light was on) flashed at 0.1 Hz in the TBI (“Cases,” n = 22) and (B) control subjects (n = 12). The blue light stimuli had an irradiance of 1 x 1013 photons/s/cm2 and the red light stimuli had an irradiance of 7 x 1013 photons/s/cm2. (C) Same traces with the data for the blue and (D) red light stimuli plotted together to aid comparison of the two subject groups. (E) Mean (± SEM) amplitude of the fluctuation (at 0.1 Hz) in the pupil responses to the flashing lights, as determined by fast Fourier transforms (FFT) of the

177 data. **p ≤ 0.01, NS = not significant. Intra-group comparisons with paired t-tests and inter-group comparisons with t-tests.

178

Figure 4.5. Inter-individual variability of pupil responses. (A) Blue lines represent traces of normalized pupil responses evoked by blue light stimuli in individual TBI (“Cases”) and

(B) control subjects. (C) Similar traces of pupil responses to red light stimuli in individual

TBI and (D) control subjects. Solid black lines in (A) to (D) represent mean data shown in Fig. 4.4. Dotted black lines signify two standard deviations from the mean data. Note the higher variability in the data for the TBI subjects. (E) The inter-individual variation was further quantified by measuring the absolute difference (in % pupil constriction) between each individual trace and the mean data at every time-point. The mean sum

179 difference (TSEM) per individual is plotted. **p ≤ 0.01, NS = not significant. Inter-group comparisons with t-tests.

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Figure 4.6. Light aversion grading of the student group. (A) Five observers graded light aversion behavior in the video recordings used to create the pupil traces in Figure 4.9

(red and blue conditions pooled, n = 38). Each video was graded as whole, not individual pulses. In total, students reacted to six pulses (each 5 seconds in duration) of both red and blue light. Videos containing minimal light aversion behavior received a low grade

(starting with 1), and those containing severe light aversion behavior received a high grade (ending with 5). Circles constitute individual grades, and the hashed lines with accompanying number are the mean of each column. (B) Pooled light aversion grades to the red (n = 95) and blue (n = 95) stimuli. *p < 0.05 (Wilcoxon Signed Rank Test).

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Grade Description 1 Minimal reaction to stimulus

2 Increased blinking or mild tearing No squinting or fixation losses

3 Increased blinking with the presence of mild squinting and/or mild tearing No large fixation losses

4 Increased blinking with moderate squinting and/or moderate tearing Infrequent, large fixation losses

5 Increased blinking with severe squinting and/or severe tearing Frequent, large fixation losses

Table 4.6. Standard video grading scale. Five clinically trained observers graded light aversion behaviour in video recordings of the pupil light response in a cohort of healthy students (n =19) on a 1-5 scale (1 = negligible light aversion, 5 = severe light aversion).

Recordings receiving a consensus grade near an integer were reviewed for quality and edited down to a representative 10-second clip. Written descriptions were then created for each clip and used to help guide the assignment of light aversion grades to the videos of the TBI and control subjects (see Figure 4.8). Representative, still images of the synoptic videos can be found in Figure 4.7.

182

A B C

D E

Figure 4.7. Representative, still images from the standardized videos described in Table

4.6. Five clinically trained observers graded light aversion behaviour in video recordings of the pupil light response in a cohort of healthy students (n =19) on a 1-5 scale (1 = negligible light aversion, 5 = severe light aversion). Recordings receiving a consensus grade near an integer were reviewed for quality and edited down to a representative 10- second clip. Five syntonic video clips were created, one each for (A) grade 1, (B) grade

2, (C) grade 3, (D) grade 4 and (E) grade 5.

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Figure 4.8. Light aversion grades of the TBI and control groups. (A) Four observers

(Graders 1 to 4 from Figure 4.6), using the standardized, five-item grading scale outlined in Table 4.6 and Figure 4.7, did not find differences in light aversion behavior between

TBI (“Cases”) and control subjects in response to the red (pooled data; TBI n = 92, controls n = 48) and blue (pooled data; TBI n = 92, controls n = 48) flickering stimuli. (B)

They did, however, maintain strong intra-class agreement when judging the same recordings (TBI and control pooled; n = 70 videos). Circles constitute individual grades, and the hashed lines with accompanying numbers are the means of the columns. *p <

0.001 (Wilcoxon Signed Rank Test). NS = no significance. Mann–Whitney Rank Sum

Test for TBI versus control subjects and Wilcoxon Signed Rank Test for inter-group comparison of red versus blue stimuli.

184

Figure 4.9. Pupil responses to flashes of red or blue light. (A) Traces of mean (± SEM) pupil size (normalized; 100% = smallest measured pupil area in experimental session 185 and 0% = baseline pupil area) elicited by pulses of blue (irradiance of 1x1013 phots/s/cm2; bars at top indicate light was on) or red light (7x1013 phots/s/cm2) flashed at

0.1 Hz for 30 seconds in the cohort that contained both the case and control subjects from the TBI study (n = 36). (B) The pupil experiments were repeated in the student group (n = 19) using longer (60 seconds) and brighter (blue 1x1014 phots/s/cm2; red

7x1014 phot/s/cm2) stimuli. (C) Mean (±SEM) amplitude of the fluctuation (at 0.1 Hz) in the pupil responses to the flashing lights, as determined by fast Fourier transforms of the data. **p < 0.001 (paired t-tests).

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Discussion

Summary of light-induced discomfort and light aversion grades

It became apparent while initially analyzing videos of the pupillary light response to flashing red and blue lights in subjects suffering from recalcitrant photophobia after mTBI that some subjects blinked, teared, and squinted in response to the lights; but others showed no response. Given the current lack of reliable diagnostic tools for photophobia after mTBI, the question arose of whether masked clinicians could reliably distinguish these individuals based on light aversion behavior evident in videos of their ocular responses to light stimuli. The literature was not only unable to address whether eye care practitioners could observationally detect photophobia in their TBI patients, but it was also devoid of any systematic approach for the classification of TBI-related photophobia using a formalized grading scale. Thus, this dissertation chapter first sought to fill these gaps in the literature by testing whether experienced clinicians can distinguish subjects with self-reported TBI-associated photophobia based on the observation of video recordings of their responses to bright, flashing red and blue lights.

To address this question, a group of healthy student subjects underwent a pupil- testing protocol that was similar to that used on the photophobic TBI subjects. Five masked observers rated light aversion behavior in these pupillary recordings. Using a simple, five-point scale, the observers exhibited good agreement (r = 0.74; 7.9% perfect concordance); and a more standardized, five-point scale for grading light aversion behavior was developed (see Table 4.6 and Fig. 4.7). Using this scale, four observers judged light aversion behavior in the video recordings of the ocular responses of the TBI and matched control subjects to the red and blue light stimuli, again with strong inter- observer consistency (r = 0.78; 29% perfect concordance). Although the TBI subjects scored higher on the photophobia survey (Fig. 4.1) and subjectively gave higher 187 brightness scores for the red and blue flashing lights as compared to the matched controls (Fig. 4.2), the masked observers did not judge light aversion behavior to be any different in the two subject groups (Fig. 4.8A). These results indicate that masked assessments of videos of dark-adapted ocular responses (blinking, tearing, squinting, and eye movements) to light stimuli will not reliably identify subjects that report being afflicted with TBI-associated photophobia.

Disconnect between post-TBI photophobia symptoms and signs

This disconnect between symptoms and signs may be due to the larger effects of

TBI on the central nervous system. The diffuse axonal injury associated with TBI that may encompass the axons of intrinsically photosensitive retinal ganglion cells also affects neuropsychiatric brain centers. Specifically, a TBI often compromises an important nodal point in the subcortical circuit that connects the frontal lobe to the limbic system, resulting in the patient’s inability to produce reflexive social behaviors and/or to simultaneously self-monitor and self-correct them (Arnould, Rochat, Azouvi, & Van der

Linden, 2013). Thus, it is unsurprising that sixty percent of TBI cases suffer from at least some form of post-injury apathy or flat effect (Kant, Duffy, & Pivovarnik, 1998). Although the TBI subjects in this study reported that light is bothersome during their daily activities and graded the light stimuli used here to cause significant discomfort, they may have been unable to produce the light aversion behaviors that one might expect to observe.

One alternative explanation of the negative result considered was that the light stimulus may not have been bright enough to stimulate the intrinsic photoresponse of these neurons. In addition to communicating with the pain centers of the posterior thalamus, ganglion cell photoreceptors signal to the olivary pretectal nucleus that mediates the pupillary light response (Hattar et al., 2002). The pupillometry data here 188 showed that both the brighter (14 log units) and relatively dimmer (13 log units) red and blue lights presented to the student and combined TBI-control cohorts, respectively, produced pupil traces containing evidence of ganglion cell photoreceptor influence (Fig.

4.9). Specifically, after each offset of the red stimulus, the pupil re-dilated more quickly and completely than after the offset of the corresponding blue light pulse. This sustained post-illumination pupil response to the blue stimulus is consistent with the involvement of blue-light-sensitive (peak sensitivity around 480 nm), sluggish ganglion cell photoreceptors that continue to fire action potentials after the cessation of the light stimulus (Berson et al., 2002; Hattar et al., 2002). The difference in pupil fluctuation amplitude between the red and blue lights was larger for the students than for the TBI and control subjects because the brighter blue stimulus used for the student cohort was likely more effective at driving spiking within these neurons (Do et al., 2009).

Further support for a role for ipRGCs in the pathophysiology of post-TBI photophobia can be found in the observers’ grades of perceived light aversion behaviors. Both the student and TBI subjects were graded as being more averse to their respective blue lights than their red lights. Moreover, we found a linear correlation between the light aversion grade and subjective brightness grade in TBI subjects

(subjective discomfort was not measured in students). These data are in agreement with reports that the peak spectral sensitivity of the squinting response to a bright stimulus occurs around 500 nm (blue-green light), which is not spectrally distant from my blue stimulus or the peak sensitivity of melanopsin, the photopigment of ipRGCs (Provencio et al., 2000; Stringham et al., 2003). TBI subjects did not subjectively grade the blue light to be more uncomfortable than the red light, but that is likely due to a ceiling effect from the fact that they found all lights to be significantly brighter than their control counterparts. 189

Unlike the TBI and student subjects, the video observers did not grade the matched control subjects to be more light-averse to blue light than red light. It is possible that age-related changes to the ocular media influenced these results. The crystalline lens yellows with age, allowing progressively less blue light through to the retina with each passing year (Boettner & Wolter, 1962). As the student subjects were statistically and clinically younger than the TBI and control subjects (Table 4.5), more blue light likely reached their retinas than TBI or control subjects, resulting in more aversion to blue light than red light. The TBI and control subjects were similarly aged, but ipRGC sensitivity may have been altered in the TBI subjects due to their head injury. Thus, it is possible the blue light passing through the yellowed lens was sufficiently bright to elicit light aversion behavior in TBI subjects but not in the matched control subjects with normal ipRGC function. Furthermore, the blue and red lights used to stimulate the pupil response in the student group were one log unit brighter than those presented to the TBI and control subjects. Since ipRGCs are relatively insensitive to dim light (Do et al.,

2009), the brighter lights likely evoked a larger disparity in the ipRGC photoresponse between the blue (high photoresponse) and red (low photoresponse) lights than the dimmer lights given to the control subjects. This larger difference manifested as more perceivable light aversion behavior to the blue light than the red light in the students.

A second alternative explanation for why the masked observers did not grade significant differences in the light aversion responses of the two subject groups is that the TBI subjects may have overstated their level of photophobia. Steps were taken to confirm light sensitivity within this group. The six-item survey designed to query the impact of photophobia on daily lifestyle was able to detect a significant difference (p <

0.01) between TBI and control subjects for each question. Additionally, 20 of the 28 TBI subjects reported not being able to be outdoors without a dark filter in their spectacles. 190

Half of these subjects also had to wear a filter indoors. Tinted spectacles and hats are known to decrease the symptoms of photophobia in mTBI subjects (Clark, Hasselfeld,

Bigsby, & Divine, 2017). Rose-tinted lenses (FL-41; peak absorption at ~500 nm) are particularly useful in reducing signs and symptoms of photophobia in subjects with benign essential blepharospasm and may provide a similar benefit for patients suffering from post-TBI photophobia (Blackburn et al., 2009; Herz & Yen, 2005). No control subjects were observed wearing a filter while indoors.

Finally, the five-point clinical grading scale employed here may have been too coarse to discriminate differences in perceived light aversion behavior. Bailey and colleagues contest that the concordance, which is the frequency of perfect agreement between independent observers, of paired comparisons should not exceed 37% to achieve moderate discriminative sensitivity (Bailey, Bullimore, Raasch, & Taylor, 1991).

Fine scales, such as the ones long employed by cataract surgeons to judge the severity of nuclear, cortical, and posterior subscapular cataracts (Adamsons, Taylor, Enger, &

Taylor, 1991; Chylack et al., 1993; West, Munoz, Wang, & Taylor, 1993), increase the sensitivity to change in the clinical parameter being assessed. Although interclass correlation between the observers of the student group (r = 0.74) and the TBI and control cohort (r = 0.78) in the present study were robust, perfect concordance values were 7.9% for the former and 29% for the latter. These values suggest at least moderate discriminative sensitivity of the five-point scale used to assess photophobic behavior.

Alternative clinical measurements of photophobia

Although clinically-trained observers could grade light aversion responses in recordings of the pupillary light reflex with good inter-observer consistency and with concordance consistent with acceptable discrimination, the results presented here 191 suggest that clinicians cannot diagnose or follow post-TBI photophobia by observing the behavior of a patient to light stimulation. An objective diagnostic marker is necessary.

The photophobia literature describes several attempts to develop one. One simple approach is to subject the patient to increasing light levels in an ascending staircase pattern until the patient voices discomfort (Bohnen et al., 1991; Drummond, 1986). In addition to being harsh on the patient, this method suffers from weak agreement between threshold discomfort levels and subjective complaints of photophobia in migraineurs (Vanagaite et al., 1997) and TBI patients (Waddell & Gronwall, 1984).

Electromyography (EMG) has been used extensively to measure the electrophysiological activity of the orbicularis oculi in response to glaring stimuli as a proxy for light. Groups investigating both healthy and post-TBI subjects have found a strong correlation between subjective photophobia thresholds and the magnitude of

EMG responses to stimuli of varying intensity, size, and background illumination

(Berman, Bullimore, Bailey, & Jacobs, 1996; Stringham et al., 2003). Nevertheless, EMG faces several obstacles to clinical adoption. First, the retina’s electrophysiological response to the light and the blink reflex can lead to noisy data (Murray, Plainis, &

Carden, 2002). Second, light levels of glare illuminance are required to raise the EMG response than the subjective response, possibly leading to undue patient discomfort

(Berman, Bullimore, Jacobs, Bailey, & Gandhi, 1994). Third, the need for surface electrodes and the skin preparation that they require are not conducive to clinical efficiency or patient comfort. Similar to EMG, electrooculography (EOG) measures the involuntary eye movements resulting from glaring stimuli with good correlation to the subjective evaluation of discomfort (Y. Lin et al., 2015). Unfortunately, it also shares the same drawbacks. Finally, Aboshiha and colleagues have recently shown that video measurement of palpebral aperture size in achromatopsia is a feasible method of 192 quantifying light aversion behavior (Aboshiha et al., 2017). This procedure has since been used on three TBI subjects to determine their photophobia thresholds (Aguilar et al., 2018). Its effectiveness at identifying photophobia in a large cohort of TBI subjects has yet to be determined. The present results that show photophobic TBI subjects do not blink, squint, or tear more than matched control subjects may limit its diagnostic utility.

Measurement of the pupillary light response to stimuli bright enough may provide an objective, reliable measurement of photophobia. It is independent of the neuropsychiatric circuits and is simple to test. Pupil size and photophobia have been studied in healthy and migraine subjects. In healthy subjects, the pupil diameter is related to the intensity and size of the light stimulus, not to the subjective degree of discomfort elicited by the same stimulus (Hopkinson, 1956). In photophobic migraineurs, however, pupil size is relatively decreased during an active attack (Drummond, 1986, 1987). It was previously hypothesized that the iris, specifically iris hippus, plays a significant role in the pathogenesis of photophobia (Hopkinson, 1956; Stringham et al., 2011). Howarth and colleagues have refuted this theory with their report that iris hippus decreases with increasing illumination and that it does not differ between glaring and non-glaring conditions (Howarth, Heron, Greenhouse, Bailey, & Berman, 1993). The behavior of the pupil in subjects with post-TBI photophobia was heretofore unstudied and formed the basis for the second part of this investigation.

Macular pigment density in TBI and control subjects

The hypothesis that ipRGCs are among the CNS neurons affected by mild TBI, resulting in a persistent depolarization of resting membrane potential of these photoreceptive neurons, was next tested by comparing the ipRGC-mediated post- illumination pupil response in participants with TBI-associated photophobia to healthy 193 controls. If the hypothesis was correct, the signal relayed by ipRGCs in response to a given irradiance level would be more robust and comparable to an uninjured ipRGC’s response to higher irradiance light.

An alternate etiology of photophobia was first ruled out in the TBI subjects.

Ocular pigment density is significantly correlated to glare disability and photostress recovery (Stringham & Hammond, 2008, 2007). Two steps were taken to ensure that this variable did not confound the assessment of the pupillary light response. First, the extended Maxwellian-view light delivery system utilized in this study subtended a large visual angle (53°). Macular pigment is densest near the fovea but rapidly dissipates in the peripheral retina (Snodderly, Brown, Delori, & Auran, 1984). As a result, macular pigment optical density is less likely to affect large-angle lights that stimulate both the central and peripheral retina, like the ones employed here, than relatively small, macula- only light sources (Stringham, Fuld, & Wenzel, 2004). It is not surprising, then, that large-angle light sources cause greater discomfort than those that subtend small angles

(Berman et al., 1994). Interestingly, this spatial pattern of photophobia is similar to that of ipRGC dendritic fields (Dacey et al., 2005). Second, macular ocular pigment was measured with a commercially available device. The values recorded for both subject groups were consistent with those reported previously for healthy populations (Ciulla et al., 2001; de Kinkelder et al., 2011). No difference in macular pigment density was observed between TBI and control subjects (Fig. 4.3), suggesting that this physiological parameter did not contribute to the photophobia experienced by this population of mTBI participants.

194

Comparison of pupil fluctuation between TBI and control subjects

More sustained pupil constriction after blue light pulses was predicted in TBI participants than in control participants. In response to flashing light stimuli, this would manifest as smaller amplitudes in blue light-evoked pupil fluctuation as quantified through Fourier analysis. The final results did not support this hypothesis. Consistent with previous reports (Gamlin et al., 2007; Kankipati et al., 2010; Park et al., 2011), blue light stimuli elicited less pupillary fluctuation (that is, more sustained post-illumination pupillary constriction) than red stimuli of equated irradiance in control participants (Fig.

4.4A). A similar finding was observed in the TBI participants (Fig. 4.4B). There was no difference, however, in the amplitude of the pupil constriction/re-dilation evoked by the flashing red or blue light stimuli in the two subject groups (Fig 4.4C-D), as demonstrated by pupillary fluctuation amplitude analysis using fast Fourier transforms (Fig. 4.4E).

A simple quantification of the mean pupillary light response to single-color red and blue lights may not be the ideal measurement of ipRGC function after a TBI.

Evidence for this proposition can be found in the fact that the pupil responses to the flashing red and blue stimuli in the TBI participants were not homogeneous (Fig. 4.5A-

D). Specifically, TBI subjects had more variation in their pupil responses than control subjects (Fig. 4.5E). As a statistically significant difference (p < 0.05) was only observed in response to the blue stimuli, irregular ipRGC function other than enhanced light sensitivity cannot be ruled out. Such heterogeneity is seen in sleep and circadian disturbances in TBI participants, which represent other functions regulated by ipRGCs

(Hattar et al., 2003). Some individuals with TBI report difficulty with sleep initiation and maintenance (Al-Ameri, Mohsin, & Abdul Wahid, 2019; Beetar, Guilmette, & Sparadeo,

1996; Ouellet, Beaulieu-Bonneau, & Morin, 2006; Parcell, Ponsford, Rajaratnam, &

Redman, 2006), and others report excessive sleepiness (Englander, Bushnik, Oggins, & 195

Katznelson, 2010; Ponsford et al., 2012; Sommerauer, Valko, Werth, & Baumann,

2013). Rather than a ‘‘hypersensitivity’’ to light, it is possible that other functional changes in ipRGCs, such as altered light/dark adaptation, are responsible for abnormal perception of brightness and sleep patterns in this study’s photophobic TBI subjects.

The results of the present study can be contrasted with those of Truong and

Ciuffreda (Truong & Ciuffreda, 2016). They utilized single light pulses of varying durations, intensities, and spectral compositions to compare the pupillary light reflex between mTBI subjects and matched control subjects. Similar to the results presented here, their TBI subjects demonstrated more pupil response variability to bright blue pulses of light than their control subjects did. Dissimilar to the results presented here, they recorded more sustained pupillary constriction at six seconds after the offset of a blue light in their TBI subjects than their control subjects. Park and colleagues report that pupillary constriction measured six seconds after light offset is ideal for capturing ipRGC influence over the pupillary light response (Park et al., 2011). Thus, one might conclude that Truong and Ciuffreda provide evidence that ipRGC function is hyperactive after TBI.

This interpretation must be made with caution. The problem with the six-second approach to measuring ipRGC influence over the pupillary light response is that it depends on a single data point. Both Truong and Ciuffreda and the present study have shown that healthy subjects exhibit large amounts of variability in their pupillary light responses and that TBI subjects demonstrate even more. A mean-based quantification of pupil size dependent on a single time point is susceptible to undue influence by outlying or noisy individual data. Thus, any conclusion about the in vivo function of ipRGCs based on one-time measurement of pupil size in a population known to have variable pupillary light responses is perilous. The approach employed here of quantifying the pupillary light response based on fast Fourier transforms depends on many 196 sequential measurements of pupil size. It is therefore more robust to variations in individual pupillary responses at any given time point. A weakness of fast Fourier transforms is that they cannot capture changes in the pupillary light response over time.

Future approaches to overcome this limitation will be addressed in the next section.

Interpretation of pupil results

The results of pupillometry on photophobic TBI subjects using flickering single- color red and blue lights invite two different conclusions. First, a confounding factor may have obfuscated differences in the pupillary light response between the two groups. One such factor could be that the mTBI subjects did not have the injury that they claimed. In other words, it is possible that some of the TBI participants enrolled in the study were misclassified as mild or had no diagnosis of TBI. Although the OSU TBI-ID is a validated instrument for the classification of TBI (Corrigan & Bogner, 2007), each subject’s medical records were not available to cross-reference the OSU TBI-ID score with their initial Glasgow coma score. The possibility of misdiagnosis is low, however, because most TBI subjects were recruited from reputable clinics that manage high volumes of head injury patients, namely the OSU Wexner Medical Center Rehabilitation Services and the OSU College of Optometry.

Another confounding factor may be that TBI subjects reported taking more systemic medications, especially CNS modulating agents, than control participants

(Table 4.4). Many of these pharmaceuticals have anti-cholinergic side effects, including uneven pupil dilation (Naicker, Anoopkumar-Dukie, Grant, & Kavanagh, 2017). As a result, it is possible that medicinal alterations of the autonomic nervous system increased the variability of the pupillary light reflexes in the TBI participants. However, a statistically significant increase in the pupil response variability of TBI subjects was 197 detected in response to the blue stimulus only, not to the red. One would expect that alterations in the autonomic innervation would have resulted in the pupil responses to the red and blue light stimuli being affected equally.

A second interpretation of the pupillometry results is that the etiology of TBI- related photophobia is not universally (i.e. in all participants) due to an alteration in ipRGC light sensitivity. If only the mean pupil data generated from the red and blue flickering stimuli were considered, then this would be the logical conclusion. But the fact that there was significantly more variation in the pupillary light response to the blue light in the TBI subjects than the control subjects is evidence against this overtly simple deduction. My interpretation of these results, then, is that a simple comparison of the pupil’s sustained response to red or blue pulses of light is insufficient to serve as a direct marker for ipRGC disruption after mTBI. TBI subjects are known to have difficulties adapting to changing light conditions. For example, it is known that TBI subjects have a relatively elevated dark adaptation threshold compared to controls, suggesting an abnormality in retinal or cortical gain control (Du, Ciuffreda, & Kapoor, 2005). Thus, a more sophisticated test may be necessary to better quantify the ability of ipRGC to adapt to repeated light challenges.

The flickering stimulus that alternates between red and blue lights that was introduced in the previous chapter has promise to fulfill this role. There is evidence that

TBI subjects show more pupillary photopotentiation (i.e. more total pupil constriction at the end of a series of red-blue alternating light pulses than at the beginning) than matched control subjects (Shorter, 2015). This dissertation has hypothesized that pupillary photopotentiation in healthy subjects is a result of increased ipRGC input into the pupillary light response. A bright light that flashes between red and blue lights causes rapid, robust photopotentiation, possibly due to its theorized ability to drive up 198 dopamine levels in the retina. Intraretinal dopamine depolarizes the resting membrane potentials of ipRGCs (Van Hook et al., 2012). If the resting membrane potential of ipRGCs is further raised after the diffuse axonal damage of a TBI (McAllister, 2011), then it follows that TBI subjects would show more pupillary photopotentiation in response to a red-blue alternating light stimulus than matched control subjects. In this scenario, ipRGCs would send exaggerated environmental irradiance data to the thalamic pain centers. Future in vitro electrophysiology experiments on TBI-damaged ipRGCs will be necessary to test the synergistic effect that dopamine and calcium-permeable cell membranes have on the signaling of these neurons.

Conclusions

First, this chapter demonstrated that clinically-trained observers could grade light aversion responses in recordings of the pupillary light reflex with good inter-observer consistency and with concordance consistent with acceptable discrimination. Although the TBI subjects reported both the red and blue stimuli to be subjectively more uncomfortable than their controls, the observers could not grade significant differences in perceived aversion behavior between the two groups. Pupil recordings confirmed that the blue stimuli were sufficiently bright to elicit an intrinsic photoresponse in the ganglion cell photoreceptors that are the potential conduits of exaggerated environmental irradiance to the thalamic pain centers. These results stressed the need for an objective method to diagnose and follow post-TBI photophobia.

Second, this chapter proposed an objective method to detect post-TBI photophobia. Specifically, it hypothesized that the ipRGC component of the human pupillary light response would be more robust in TBI participants with photophobia as compared to healthy normal participants. Contrary to this hypothesis, there was no 199 significant difference in the ipRGC-mediated sustained pupillary constriction between the two populations. TBI subjects did, however, demonstrate more variation in their responses to the blue stimulus, potentially signifying alternative ipRGC dysfunction, including dysfunctional adaptation to repeated light challenges.

200

Chapter 5: Conclusions

Dopamine D1 receptor-mediated enhancement of ipRGC photoresponses

I first examined whether the stimulation of dopamine D1 receptors on intrinsically photosensitive retinal ganglion cells (ipRGCs) alters their light-light spiking. A previous paper on the topic suggested that dopamine has seemingly contradictory effects on these neurons. Specifically, using patch-clamp techniques, Van Hook and colleagues reported that activation of D1 receptors attenuates the photocurrent of ipRGCs, but raises their resting membrane potential closer to action potential threshold (Van Hook et al., 2012). Activation of D1 receptors also likely increases cAMP levels within ipRGCs

(Vaquero et al., 2001), which in turn may induce stronger photoresponses through intracellular signaling cascades (Sodhi & Hartwick, 2014).

The primary finding of chapter 2 was that SKF 38393-mediated activation of dopamine D1 receptors significantly increased the light-evoked spiking of synaptically intact ipRGCs (Fig. 2.5A-B). Compared to baseline conditions, more robust spiking occurred both during the light pulses of the bright, blue flickering stimulus and during the immediate post-illumination period (Fig. 2.5C). When a cocktail of synaptic blockers isolated ipRGCs from glutamatergic signaling, SKF did not induce more ipRGC spiking compared to baseline (Fig. 2.7A-B). However, there was a significant increase in spiking during the recovery condition after the addition of SKF to the Ames medium. This suggested that intact ON- and/or OFF-pathway signaling may be necessary for dopamine to enhance ipRGC signaling. The blockade of D1 receptors with an antagonist

201

(SCH 23390) caused a non-significant trend toward decreased light-evoked spiking in pharmacologically isolated ipRGCs (Fig. 2.10A-B). A small sample size prohibited the statistical analysis of ipRGC activity in the presence of SCH when the ON and OFF pathways remained intact. For this reason and the possibility that endogenous dopamine levels were not high in the rat retinas due to the stresses of dissection and the diluting effects of superfusion, conclusions about the effects of the D11 receptor antagonist cannot be as securely drawn as the effects of the D1 receptor agonist.

Significance

Historically, research on dopamine’s interaction with RGCs has focused on its modulation of currents linked to changes RGC membrane potential. An array of studies on multiple species has demonstrated that activation of RGC D1 receptors diminishes electrophysiological currents within these cells (Glickman et al., 1982; Jensen & Daw,

1984, 1986; Liu & Lasater, 1994; F. Muller et al., 1988; Thier & Alder, 1984), likely through a cAMP/PKA-mediated pathway (Hayashida et al., 2009; Vaquero et al., 2001).

Without this dopamine-mediated reduction in electrophysiological gain in response to bright-lighting conditions, daylight would likely overwhelm the signaling of the cells and would impair the behaviors that they mediate (Haeggendal & Malmfors, 1965; Iuvone,

Galli, Garrison-Gund, et al., 1978; Z. S. Lin & Yazulla, 1994).

Recent works have challenged the idea that gain reduction in bipolar cells and

RGCs due to a reduction of their electrophysiological currents is the singular role of dopamine in the retina. They provide evidence that dopamine also amplifies other electrophysiological activities within the inner retina. For example, the Eggers laboratory has demonstrated that activation of the D1 receptors located on bipolar cells enhances bipolar cells’ signaling to RGCs (Mazade et al., 2019). Similarly, Cui and colleagues 202 have shown that dopamine lengthens the temporal summation of non-photosensitive

RGCs and heightens their excitability through depolarization of their resting membrane potentials (Cui et al., 2017). This second effect matches well with dopamine’s known effect on the resting membrane potentials of ipRGCs (Van Hook et al., 2012). The results presented in the first study of this dissertation add to this growing canon of evidence that the excitatory effects of dopamine may outweigh its inhibitory effects. This finding appears to be true for both ipRGCs and ON RGCs (Fig. 2.3E-F).

When the circuitry of the retina remains intact, activation of D1 receptors on ipRGCs intensifies their signaling to downstream targets in the brain by increasing the number of action potentials that they fire in response to light. Thus, stimuli that are effective at driving dopamine release in the retina, such as bright (Godley & Wurtman,

1988; Mills et al., 2007), flickering (Dubocovich & Weiner, 1981; Kramer, 1971) lights, may alter the aspects of vision that ipRGCs affect. Photoentrainment of circadian rhythms is one such function. As the pre-synaptic neurons that innervate the suprachiasmatic nucleus (SCN) (Hattar et al., 2006), ipRGCs are essential to this process (Panda et al., 2002; Ruby et al., 2002). During sleep, repeated exposures to short but bright pulses of blue or white light over the course of one hour induce a phase delay in dim light melatonin onset (Figueiro, Bierman, & Rea, 2013; Zeitzer, Fisicaro,

Ruby, & Heller, 2014). It is possible that the increase in retinal dopamine concentrations that likely accompany these repeated light exposures mediate, at least in part, this effect. In another example, signaling by ipRGCs contributes to feelings of alertness in humans (Lockley et al., 2006). Repeated exposure to a bright, flashing red and blue light stimulus may enhance this effect through a dopamine-mediated increase in ipRGC signaling (C. Doyle, Yuhas, & Hartwick, 2017). Likewise, chapter three of this

203 dissertation established that the same light stimulus photopotentiates aspects of the human pupillary light response thought to be under the control of ipRGCs.

Future directions

Dopamine may not be the only endogenous neuromodulator to increase the spiking of ganglion cell photoreceptors. Retinopetal axons from the tuberomammilary nucleus of the posterior hypothalamus deliver histamine to the retina during the night in nocturnal animals (Prast, Dietl, & Philippu, 1992; Steininger, Alam, Gong, Szymusiak, &

McGinty, 1999) and during the day in diurnal animals (Vanni-Mercier, Gigout, Debilly, &

Lin, 2003). Histamine inhibits light-evoked spiking of ON RGCs (Akimov, Marshak,

Frishman, Glickman, & Yusupov, 2010) and inhibits dopaminergic amacrine cells under dark-adapted conditions (Weber & Schlicker, 2001). Sodhi has presented preliminarily data that suggest that histamine enhances the light-evoked spiking of ipRGCs (Sodhi,

2015), potentially via activation of Gs-coupled H2 receptors that may stimulate the cAMP/PKA signaling pathway. I possess a small amount of preliminary data that do not replicate this result. When I applied histamine dihydrochloride (100 µM) to whole- mounted rat retinas, I did not observe an increase in in vitro ipRGC spiking. Histamine is highly diffusible within the retina. The lack of response I observed could be due to the fact that I superfused the retinas with histamine for only 5 minutes before light exposure, which might not long enough to allow for sufficient histamine build-up within the MEA well. Sodhi needed 20 minutes of profusion with histamine to observe an effect. I also conducted my histamine experiments after the conclusion of my dopamine experiments.

That means over three hours of exposures to bright lights and neuromodulating compounds preceded them. The retina may not have been viable by the time the

204 histamine experiments began. The role of histamine in ipRGC modulation needs further investigation.

Pituitary adenylate cyclase-activating peptide (PACAP) is another neuromodulator that may enhance the ipRGC photoresponse. ipRGCs that project to the

SCN contain PACAP in their axons (Hannibal & Fahrenkrug, 2004; Hannibal,

Hindersson, Knudsen, Georg, & Fahrenkrug, 2002). Activation of PAC1 receptors stimulates the cAMP/PKA signaling pathway through a Gs-coupled mechanism (Vaudry et al., 2009), similar to dopamine. Intriguingly, some ipRGC axons terminate within the retina instead of projecting to the brain (Joo, Peterson, Dacey, Hattar, & Chen, 2013; D.

Q. Zhang et al., 2012; D. Q. Zhang et al., 2008). Thus, during light stimulation, these collaterals may release PACAP onto other PACAP-containing ipRGCs, resulting in the potentiation of ipRGC signaling. This positive-feedback loop would be separate from the dopamine pathway due to its independence from rod-cone signaling. Currently, this pathway is just hypothesized, but future investigations have the potential to further develop it.

Photopotentiation of the human pupillary light response after stimulation with red and blue flickering lights

The second aim of this dissertation was to characterize how prior light exposure alters the human pupillary light response. Prior exposure to adapting lights is known to heighten the pupillary light response in rodents and humans (Joyce et al., 2016; Mure et al., 2009; Zhu et al., 2007). Flickering lights have a similar effect on the human pupillary light response, as several groups have reported more pupil constriction in response to repeated light exposures than in response to a continuous light exposure of the same duration (Ba-Ali et al., 2017; Gooley et al., 2012; Vartanian et al., 2015). In my master’s 205 thesis, I demonstrated that a stimulus that flickered between darkness and both red and blue lights potentiates a sustained pupil constriction that persists after light offset

(Yuhas, 2014). ipRGCs are thought to drive this aspects of the primate pupillary light response (Gamlin et al., 2007).

The main result of chapter 3 was that repeated light exposures progressively slow pupil re-dilation, resulting in more post-illumination pupil constriction. As expected, the bright (~1013 photons/s/cm2) stimulus that flickered (0.10 Hz) between darkness and red (625 nm) and blue (470 nm) lights elicited progressively more pupil constriction during and after each sequential light pulse (Fig. 3.5B). Surprisingly, the bright single- color flickering red and blue stimuli also caused a progressive slowing of pupil re-dilation after the offset of each “on” light pulse (Fig. 3.6A-B). I interpret this result as being consistent with escalating ipRGC contribution to the pupillary light response over the stimulus presentation time. Surprisingly, this pupil potentiation effect was also seen in response to the flickering dim (~1011 photons/s/cm2) stimuli. Although these red, blue, and red-blue alternating stimuli were all initially too dim to recruit ipRGC contributions to the pupillary light response, the rate of pupil re-dilation slowed with each successive light pulse (Fig. 3.10A-C). Although single pulses of long-wavelength or dim stimuli are not able to drive ipRGC contributions to the human pupillary light response, I interpret my results as suggesting that multiple pulses of the same lights may drive them. If true, this conclusion suggests that ipRGCs have a larger dynamic range of light sensitivity than previously thought.

Significance

These results from chapter 3 demonstrate that repeated light exposures alter the human pupillary light response. I previously proposed an ipRGC-based mechanism that 206 involved the light-evoked release of retinal dopamine. If this is correct, how, then, do the outcomes from chapter 3 compare with the results from the in vitro dopamine experiments of chapter 2? It is difficult to replicate in rat the rapidly potentiating effect that the bright red-blue alternating light has on the human pupillary light response because rats have only two cones types, one with a peak spectral sensitivity at 509 nm

(Radlwimmer & Yokoyama, 1998) and the other with a peak at 358 nm (Yokoyama,

Radlwimmer, & Kawamura, 1998). Therefore, I instead investigated the effect of a dopamine D1 receptor agonist (SKF) on ipRGCs.

Two pieces of evidence support dopamine’s role in pupillary photopotentiation. I will use the pupillary photopotentiation elicited by the bright red-blue alternating stimulus in these examples because it was the most robust. First, both the bright red-blue alternating stimulus and SKF appeared to have caused an increase in ipRGC activity both during and after light presentations. Specifically, the bright red-blue alternating light enhanced pupil constriction when its light pulses were both on and off (Figs. 3.6C and

3.6F), and SKF caused more light-evoked spiking in ipRGCs both during and after the repeated exposures to bright blue light (Fig. 2.5C). Second, the time course of pupillary photopotentiation onset fits with the known characteristics of dopamine release and action in the retina. Pupillary photopotentiation in response to the bright red-blue alternating light manifested quickly (within four pulses, 40 s) and remained until the end of the 2-minute stimulus. Flickering lights rapidly (within 30 s) drive up retinal dopamine levels (Iuvone, Galli, Garrison-Gund, et al., 1978), with more dopamine released during each subsequent light pulse within the 30 seconds (Kramer, 1971). Once in the retina, dopamine’s action upon in vitro RGCs follows a similar time course as pupillary photopotentiation: approximately 10 s for onset and 2 min for peak (Liu & Lasater, 1994).

Thus, it is feasible that the bright red-blue alternating stimulus in the present 207 experiments drove up retinal dopamine levels, and that dopamine affected ipRGC function within the 2-minute test duration.

Contrarily, two pieces of evidence may argue against a role for dopamine in pupillary photopotentiation. First, a brief dark adaptation period (5 min) washed out the pupillary photopotentiation effect (Fig. 3.8). Work in rabbits suggests that dopamine dissipates to pre-light-stimulation levels over the course of 30-60 min in light-adapted animals, and approaching 6 hours in dark-adapted animals (Iuvone, Galli, Garrison-

Gund, et al., 1978; Iuvone, Galli, & Neff, 1978). In vitro recordings from RGCs propose a shorter recovery time course, however. For example, my in vitro experiments were all separated by 15-minute dark adaptation periods, so it is unclear whether the dopamine’s enhancing effect was still present after five minutes. It did recover after 15 minutes, however (Fig. 2.5A). This time-course value fits with some reports of the time it takes to recover from the effects of D1 receptor modulation (Liu & Lasater, 1994; Van Hook et al.,

2012), but is shorter than other reports (Jensen & Daw, 1984). The relatively short recovery period of in vitro experiments may be a result of the constant superfusion of the bathing medium used to keep the dissected retina alive during testing. Dissipation of retinal dopamine levels may take longer in vivo. Second, the bright red-blue alternating stimulus drove more pupil constriction with each light pulse (Fig. 3.6F), but in vitro ipRGC spiking did not increase with each pulse, even in the presence of SKF (Fig.

2.6B). The fact that the dissected retinas were bathed in SKF for five minutes before the beginning of light exposures could explain this difference, however. In the SKF condition, in vitro spiking started high, and stayed high, because D1 receptors were stimulated during the entire duration of the test. A similar effect was seen in the pupillary light response to the bright blue stimulus that: pupil constriction started high and stayed high

(Fig. 3.6E), presumably due to ipRGC contributions throughout. 208

In total, I believe that these results point toward a role for ipRGCs and dopamine in the physiology that drives pupillary photopotentiation. They do not exclude the contribution of other neuromodulators, however. Roles for histamine or PACAP in the mediation of pupillary photopotentiation remain an intriguing possibility. Future work utilizing genetically modified animals with altered ipRGC or dopamine amacrine cell function may shed light on these possibilities.

Future directions

In data presented in chapter 3, the pupillary photopotentiation effect was reversed within a 5 minute dark adaptation period. I performed my experiments on a custom pupillometer that comprised a LED light source and an infrared camera. A recent protocol that employed a commercial pupilometer that uses a backlit liquid crystal display (LCD) to drive the pupillary light response reported different results. Namely, pupillary photopotentiation remained intact over a 5-minute dark adaptation period when the subject viewed the dark but back-lit screen during its final minute (Blumenthaler et al., 2018). This exposure to the dim light of the LCD screen immediately before pupil testing may play an important role in this finding. A dim, uniform background light is known to elicit a tonic excitation of ON RGCs (Farrow et al., 2013; F. Muller et al., 1988).

Since ipRGCs receive input from ON bipolar cells (Dumitrescu et al., 2009; Grunert et al., 2011; Hoshi et al., 2009), they likely experience a similar excitatory effect that drives up intracellular cAMP concentrations (Vaquero et al., 2001). This compound is known to enhance the photoresponses of ipRGCs (Sodhi & Hartwick, 2014). Additionally, exposure to a dim background light potentiates dopamine release in the retina

(Kolbinger & Weiler, 1993). As shown in chapter 2, dopamine increases the light-evoked spiking of ipRGCs, possibly through a cAMP-mediated pathway. Thus, the dim 209 background light of the commercial pupilometer may drive up cAMP levels within ipRGCs before the first pulse of a bright light stimulus occurs, preserving the pupillary photopotentiation effect through the dark adaptation period. Future studies are necessary to further develop this hypothesis, which is important for comparing results between the two pupil testing approaches.

Observer-perceived light aversion behavior and light-evoked pupil responses in photophobic subjects with TBI

The final aim of this dissertation was to determine whether a mild traumatic brain injury (mTBI) could enhance ipRGC signaling in a manner similar to activation of dopamine D1 receptors, and similar to repeated exposures of red and blue light pulses.

Based on the assumptions that 1) ipRGCs project to the pain centers of the posterior thalamus (Maleki et al., 2012; Noseda et al., 2010) and 2) that the shear forces associated with a mTBI may increase their resting membrane potentials via an increased influx of calcium cations (McAllister, 2011), I hypothesized that the sustained pupil constriction after blue light stimulation will be more pronounced in photophobic mTBI subjects than age- and gender-matched control subjects. In a parallel investigation, I also asked whether clinically trained observers could distinguish subjects with self- reported TBI-associated photophobia based on the observation of video recordings of their responses to bright, flashing red and blue lights.

The clinical research study outlined in chapter 4 produced two important outcomes. First, there was no difference in the amplitude of the pupil constriction/re- dilation evoked by flashing red or blue light stimuli between photophobic mTBI subjects and matched control subjects (Fig 4.4). Second, masked assessments of videos of dark- adapted ocular responses (blinking, tearing, squinting, and eye movements) to light 210 stimuli could not reliably identify subjects that report being afflicted with mTBI-associated photophobia (Fig. 4.8A). Although these negative results did not conform to the foundational hypotheses of this investigation, they are important for clinicians and researchers.

Significance

It is my clinical experience that observing light-aversion behavior in patients with corneal or uveal disease is a straightforward endeavor. For example, patients with a corneal ulcer or anterior uveitis will invariably squint, tear, blink, and lose fixation during pupil assessment and slit lamp biomicroscopy. In severe cases involving the cornea, the instillation of a topical anesthetic, such as proparacaine ophthalmic solution, is necessary to complete even simple tasks, like measuring visual acuities. The results of this study suggest that the same is not true of patients suffering from photophobia due to

TBI. Clinicians cannot simply observe their behavior during the eye examination to assess their level of light-induced discomfort. The lack of widely accepted instruments to quantify TBI-associated photophobia limits the ability of the clinician not only to diagnose the condition but also to manage it over time as the patient emerges from the symptoms of the initial injury. This void is a public health concern, as in 1982 head injuries drained an estimated $12.5 billion from the United States economy in direct and indirect costs

(Grabow, Offord, & Rieder, 1984). The economic impact of TBI in 2019 certainly eclipses this figure.

I anticipated that the simple pupillometry experiments described in chapter 4 would provide a solution for this diagnostic and management dilemma. Although there was no difference in the mean pupillary light response between the TBI and control subjects, the TBI subjects did manifest more variability (Fig. 4.5). This finding suggests 211 the possibility that a TBI alters the function of ipRGCs. Shorter has provided preliminary evidence that TBI impairs the ability of ipRGCs to adapt to a flickering red and blue light

(Shorter, 2015). Specifically, using the same TBI and control subjects as studied here, he demonstrated significantly more pupillary photopotentiation in the TBI subjects than in the control subjects. This finding agrees with their self-reported inability to adapt to the lighting in both indoor and outdoor environments (Fig. 4.1).

Future directions

Employing a rodent model of mTBI to assess the in vitro signaling of ipRGCs is a logical next step in this line of research. Based on these results in human subjects, it would be reasonable to expect more light-evoked ipRGC spiking in the injured animals than uninjured controls. Furthermore, as an extension of the central nervous system, the retina is an attractive platform for the detection of other neurodegenerative diseases.

During embryonic development, the retina arises from diencephalic neural ectoderm and shares the vascularization patterns of the brain (London, Benhar, & Schwartz, 2013;

Ong et al., 2013; Patton et al., 2005; Remington, 2005). The typically transparent media of the eye allow for direct evaluation of the neuronal and vascular tissue of the retina and, by extension, of the brain. Researchers have only begun to capitalize on these unique developmental and anatomical qualities to unlock the retina’s diagnostic potential in the diagnosis and management of neurodegenerations. Thus, there are many unanswered questions regarding how neurological disorders such as Alzheimer’s disease as well as chronic traumatic encephalopathy, which is an emerging neurodegeneration that may be linked to repeated TBIs, may manifest in the retina. In future studies, I plan to investigate whether examination of the living human eye can facilitate the detection of these neurodegenerations, which can currently only be 212 diagnosed after death due to a paucity of established biomarkers (Beydoun et al., 2015;

Frost et al., 2013; Gavett et al., 2011; Huber, Alosco, Stein, & McKee, 2016).

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Appendix: Automated processing of pupil recordings

The MATLAB code used to measure pupil size is provided below:

In plain language, this program received a video file in .AVI format and sequentially read and analyzed each frame, from the first frame to the last frame.

Specifically, it turned the original color image into a black-and-white image based on a threshold that could be adjusted by the user. This threshold was manipulated to turn the pupil from a black circle into a white circle while causing everything else in the recording to turn black. When applied correctly, this procedure resulted in a white circle (the pupil) against a black background. Then, the program filled in any missing areas within the circle that might have been caused by the eye’s Purkinje images. Finally, it drew a circle around the now white and full pupil. The area of this circle was the main output of the program. When the eye was closed and no pupil was visible, the program reported the

253 outcome as “not a number.” Frame-by-frame, these area measurements were copied into an Excel document for the removal of blink artifacts and normalization.

Blinks were removed with a two-part approach. First, all the “not a number” outputs (created when the pupil was not visible) were identified and removed from the data set. Then, the following Excel function removed the pupil measurement artifacts created when the eyelid was opening and closing at the beginning and end of a blink, respectively: =IF(OR(ABS(D2-D1)>300,ABS(D2-D3)>300),NA(),D2). This function removed any data point that was 300 pixels different than the data point immediately preceding it and the point immediately following it. This pixel amount was chosen based on the fact that when the eyelid moved open or shut, the area of the visible pupil changed very quickly, much quicker than during light-induced pupillary constriction and dilation. After trial and error, 300 pixels was found to be a reliable threshold for differentiating between pupil size changes due to a blink and those due to the normal light response. Once all the blinks were eliminated from the data set, pupil size was normalized according to the method outlined in the main body of this dissertation.

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