The Role of the Mesocortical Dopaminergic Pathway in the Processing of Chronic Pain Signals

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2020-04-27 The role of the mesocortical dopaminergic pathway in the processing of chronic pain signals

Huang, Shuo

Huang, S. (2020). The role of the mesocortical dopaminergic pathway in the processing of chronic pain signals (Unpublished doctoral thesis). University of Calgary, Calgary, AB. http://hdl.handle.net/1880/111917 doctoral thesis

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The role of the mesocortical dopaminergic pathway in the processing of chronic pain signals

Shuo Huang

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF DOCTOR OF PHILOSOPHY

GRADUATE PROGRAM IN NEUROSCIENCE

CALGARY, ALBERTA

APRIL, 2020

Abstract

Chronic pain is a debilitating condition which is prevalent in terminal diseases and aged

populations. Pain medications are frequently ineffective for chronic use due to resistance to

treatment. This is because the pathophysiology, especially cerebral mechanisms of chronic pain

is not fully understood. The processing of chronic pain signals is mainly through the cortical

areas, the limbic system, and the nucleus accumbens in the brain, which outputs affect

downstream targets exerting top-down control. These brain areas mediate emotional and

salience-related processing of pain signals, forming the ‘pain matrix’. The ‘pain matrix’ refers to

the brain regions mediating different functions such as valance, salience, emotion, and memory

that are able to interact with each other to allow pain perception to emerge. The ‘pain matrix’

also process reward information. Signals from pain and reward converge in the ‘pain matrix’and

dopamine modulates the emotional and salience aspects of both. The medial prefrontal cortex

(mPFC) is a cortical region that controls many executive functions such as attention, working

memory, and learning. The mPFC is involved in pain perception, and undergoes plasticity during

development of chronic pain. The PFC receives dopaminergic inputs from the ventral tegmental

area (VTA), forming the mecoscortical pathway. The mesocortical circuit modulates neuronal plasticity in the mPFC. This modulation has been shown to affect working memory and aversion; however, whether and how the VTA-mPFC dopaminergic inputs are involved in chronic pain remains incompletely understood. This PhD dissertation examines the hypothesis that VTA dopaminergic neurons undergo plasticity during chronic pain states, and projections from these neurons to the mPFC modulate chronic pain-associated behaviours. Dopaminergic subpopulations of both the lateral and medial VTA were defined by action potential firing patterns. However, plasticity induced by neuropathic chronic pain only resides in specific

dopaminergic subpopulations. In addition, dopaminergic subpopulations of lateral and medial

VTA are differentially altered after induction of neuropathic pain. Using optogenetic approaches

to selectively target dopaminergic inputs to the mPFC, we found that phasic activation of VTA-

mPFC dopaminergic inputs reduced mechanical hypersensitivity during neuropathic pain states.

Photostimulation of dopamine input to the mPFC also induced a preference for photostimulation-

paired context only in mice with neuropathic pain. Fiber photometry imaging of calcium signals

demonstrated that dopamine enhances the activity of mPFC neurons projecting to the

ventrolateral periaquductal gray, a crucial downstream target for top-down regulation of pain

states. Altogether, this study indicates an important modulatory role of mesocortical dopamine in cerebral chronic pain signaling.

Acknowledgements

I would like to first thank my supervisor Dr. Gerald Zamponi for your encouragement and support to my scientific ideas, and for providing me an excellent platform to conduct research.

Without you I would not be able to develop independent and critical thinking. Thank you for all your advices throughout my PhD. I am also very thankful to have Dr. Stephanie Borgland to be my co-supervisor. Thank you for always supporting me with your expertise and all the insightful inputs. The critical attitude to science and warm heart to people are what I learned from you and will carry on in the rest of my career.

I am very grateful to my committee Dr. Jaideep Bains and Dr. Tuan Trang for your constructive suggestions for this project, and your generous advices on my academic and non- academic problems. I also felt very lucky to know Dr. William Cole who was an external examiner of my candidacy, but gave me invaluable support till the end of my PhD.

Many thanks to Dr. Zizhen Zhang, Eder Gambeta, Nathan Godfrey, Shi Chen Xu, Lina

Chen, Catherine Thomas, Dr. Said M'Dahoma, Dr. Vinicius Gadotti, and other lab members from

the Zamponi and Borgland lab for your contribution to this study, and to Dr. Tamas Fuzesi and

Dr. Leonardo Molina from the HALO Optogenetics Platform for your technical support. Without

you this study would not happen.

I would like to acknowledge the Hotchkiss Brain Institute and my funding agencies

Alberta Innovates-Health Solutions and University of Calgary Eyes High Strategy for supporting through my PhD study, and providing me precious academic opportunities.

Finally, thanks to all the obstacles and failures, without which I could not become a better scientist.

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Dedication

To my parents, who gave me wings and let me fly. Thank you for always being there, with unconditional love.

To Zixiang, who shared all the up and down times with me through the end of my PhD.

Acknowledging collaborators

This work involves collaboration with Dr. Zizhen Zhang, Eder Gambeta, Nathan Godfrey, Shi

Chen Xu, Lina Chen, Catherine Thomas, Dr. Said M'Dahoma, Dr. Vinicius Gadotti and other lab members from the Zamponi and Borgland lab.

ZZ participated in designing the fiber photometry experiments, and conducted PAG microinjections. EG performed the CPP experiments, analyzed the data, and drafted the method section for CPP. Voltammetry recordings were conducted by SCX. Voltammetry data were analyzed by SCX and organized by CT and SH. CT provided essential technical support and drafted the method section for voltammetry. NG did voltage-clamp recordings for validation of

ChR2 function in the VTA, and analyzed the data. LC performed double immunochemistry for c- fos and CamKII. SM contributed to RT-PCR and data analysis. VG performed Complete

Freund's Adjuvant and PBS injections. SH performed all other experiments.

*This thesis contains three manuscripts (with permissions from the publishers):

1. Dopaminergic modulation of pain signals in the medial prefrontal cortex: challenges and perspectives. Neurosci. Lett. 2018 Nov 29. PMID: 30503912, Huang S, Borgland SL, Zamponi

GW. (Introduction, with modifications)

2. Peripheral nerve injury-induced alterations in VTA neuron firing properties. Molecular Brain.

PMID: 31685030. Huang, S, Borgland, SL, Zamponi, GW. (Chapter Three)

3. Dopaminergic inputs from the ventral tegmental area into the medial prefrontal cortex modulate neuropathic pain associated behaviors in mice. Cell Reports. Submitted. Huang S,

Zhang Z, Gambeta E, Xu SC, Thomas C, Godfrey N, Chen L, M'Dahoma S, Borgland SL, and

Zamponi GW. (Chapter Four)

Abbreviations

ACC: anterior cingulate cortex

Amy: amygdala aCSF: artificial cerebrospinal fluid

AHP: afterhyperpolarization potential

AP (figures and table only): action potential

AP (text-for injection locations): anterior posterior

ATP: adenosine triphosphate

BDNF: brain-derived neurotrophic factor

BLA: basolateral amygdala cAMP: cyclic adenosine monophosphate

Cav: voltage-gated calcium channel

CaMKII: calcium/calmodulin-dependent protein kinase II

CCI: chronic constriction injury

ChR2: channelrhodopsin-2

CFA: complete Freund's Adjuvant

Con (in the figures): contralateral

CPA: conditioned place aversion

CPP: conditioned place preference

CTB: cholera toxin B subunit

D1R: dopamine D1 like receptors

D2R: dopamine D2 like receptors

DAT: dopamine transporter

DIO: double-floxed inverted open reading frame dLight: fluorescent dopamine sensor

DPA: dynamic plantar aesthesiometer

DREADDs: designer receptors exclusively activated by designer drugs

DV: dorsal ventral

EEG: electroencephalogram

EPSC: excitatory postsynaptic current fMRI: functional magnetic resonance imaging

F-I: frequency-current

FSCV: fast-scan cyclic voltammetry

GABA: gamma aminobutyric acid

GABAR: gamma aminobutyric acid receptor

GCaMP: genetically encoded green fluorescent protein-based calcium sensor

Gs: stimulative regulative G-protein

Gi/o: inhibitory regulative G-protein

Hipp: hippocampus

HCN: hyperpolarization-activated cyclic nucleotide–gated

Ih: hyperpolarization-activated current

INaP: persistent sodium current

IPSC: inhibitory postsynaptic current

Ips (in the figures): ipsilateral

Kir: inward-rectifier potassium ion channel

LHb: lateral habenula

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ML: medial lateral mPFC: medial prefrontal cortex mRNA: messenger RNA

NAc: nucleus accumbens

NAcc: nucleus accumbens core

NMDA: N-Methyl-d-aspartic acid or N-Methyl-d-aspartate

NMDAR: N-Methyl-d-aspartic acid or N-Methyl-d-aspartate receptor

NS (in the figures): not statistically significant

NSAID: non-steroidal anti-inflammatory drug

PAG: periaqueductal gray

PBS: phosphate-buffered saline

PKA: protein kinase A

PKC: protein kinase C

PL: prelimbic

RCaMP: red-fluorescent genetically encoded calcium indicators

ReaChR: red-activatable channelrhodopsin

RT-PCR: reverse transcription polymerase chain reaction

SEM: standard error of the mean

SNI: spared nerve injury vlPAG: ventrolateral periaqueductal gray

VTA: ventral tegmental area

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Table of Contents

Abstract ...... i

Acknowledgements ...... iii

Dedication ...... iv

Acknowledging collaborators ...... v

Abbreviations ...... vi

Table of Contents ...... ix

List of Tables ...... xiii

List of Figures and Illustrations ...... xiii

Chapter One: Introduction ...... 1

1.1 General Introduction ...... 1 1.2 Pain and Chronic Pain ...... 2 1.2.1 Pain and pain pathway ...... 2 1.2.2 Chronic pain ...... 3 1.2.3 Pain models...... 4 1.3 Pain and reward systems have shared features ...... 5 1.3.1 The overlapped brain circuits between pain and reward signaling ...... 5 1.3.2 Dopaminergic neurons in the VTA ...... 5 1.3.3 Dopamine signaling in reward and aversion in the VTA ...... 7 1.4 The mPFC as a target of treatment for chronic pain ...... 8 1.4.1 The mPFC and pain induced plasticity ...... 8 1.4.2 Descending modulation of pain from the mPFC ...... 9 1.4.3 Subregions of mPFC and the prelimbic area ...... 9 1.4.4 Plasticity in the mPFC prelimbic area and other subregions during chronic pain states ...... 11 1.5 Neuronal plasticity in the mPFC can be modulated by dopamine signaling ...... 12 1.6 Mesolimbic pathway as a target of treatment for chronic pain ...... 13

1.6.1 Existing studies on mesocorticolimbic dopaminergic pathways related to pain ...... 13 1.6.2 Potential mechanisms for dopamine modulation of pain signals in the mPFC ...... 14 1.6.3 Complexity in studying dopamine modulation of the mPFC ...... 15 1.7 Executive functions of the mPFC in pain signal processing and the role of dopamine ...... 17 1.7.1 Roles of cognitive functions of mPFC in pain signaling ...... 17 1.7.2 Mechanisms for dopamine modulation of attention to affect chronic pain ...... 18 1.7.3 Mechanisms for dopamine modulation of working memory and learning to affect chronic pain ...... 19 1.8 Summary ...... 20 Chapter Two: Material and Methods ...... 25

2.1 Animals ...... 25 2.2 Spared nerve injury (SNI) neuropathic pain model ...... 25 2.3 Mechanical withdrawal threshold ...... 26 2.4 Conditioned place preference (CPP) ...... 26 2.5 Fast-scan cyclic voltammetry (FSCV) ...... 27 2.6 Electrophysiology...... 29 2.7 Fiber photometry ...... 31 2.8 Optogenetics ...... 32 2.9 Microinjection ...... 33 2.10 Optrode implantation...... 33 2.11 Immunohistochemistry ...... 34 2.12 Reverse transcription polymerase chain reaction (RT-PCR) ...... 35 2.13 Statistics ...... 35 Chapter Three: Peripheral Nerve Injury-induced Alterations in VTA Neuron Firing

Properties ...... 39

3.1 Abstract ...... 39 3.2 Introduction ...... 39 3.3 Results ...... 41 3.3.1 Nerve injury-induced changes in the total medial and lateral VTA neuron population 41 3.3.2 Injury induced changes in the firing behavior of lateral VTA neurons subtypes ...... 42 3.3.3 Injury induced changes in the firing behavior of medial VTA neurons subtypes ...... 44

3.4 Discussion ...... 45 Chapter Four: Dopaminergic Inputs from the Ventral Tegmental Area into the Medial

Prefrontal Cortex Modulate Neuropathic Pain Associated Behaviors in Mice ...... 66

4.1 Abstract ...... 66 4.2 Introduction ...... 67 4.3 Results ...... 68 4.3.1 VTA dopaminergic neurons anatomically and functionally connect to the PL mPFC 68 4.3.2 Phasic but not tonic activation of dopaminergic terminals in the mPFC reduces nerve injury-induced mechanical hypersensitivity ...... 70 4.3.3 Phasic activation of dopaminergic terminals in the mPFC affects conditioned place preference ...... 72 4.3.4 Nerve injury does not induce changes in dopamine signaling in the mPFC at mRNA levels ...... 72 4.3.5 Nerve injury does not change D1R and NMDA interaction in the mPFC ...... 73 4.3.6 Phasic activation of VTA-mPFC inputs increases neuronal activity of mPFC-vlPAG projecting neurons ...... 74 4.4 Discussion ...... 76 Chapter Five: Discussion ...... 103

5.1 Summary of findings ...... 103 5.2 Heterogeneity in VTA dopaminergic population related to pain ...... 104 5.2.1 Anatomic, functional, and electrophysiological characterization of lateral and medial VTA dopaminergic neurons...... 104 5.2.2 Lateral VTA dopaminergic subpopulation ...... 105 5.2.3 Medial VTA dopaminergic neurons heterogeneity ...... 106 5.3 Mesocortical modulation of pain signals ...... 107 5.3.1 Nerve injury-induced plasticity in the mPFC ...... 107 5.3.2 Analgesic effect of mesocortical dopamine ...... 108 5.3.3 Mechanisms underlying dopamine modulation of pain signalling in the mPFC ...... 109 5.4 Limitations and caveats ...... 110 5.4.1 Whether VTA dopaminergic subpopulation that show plasticity to nerve injury project to the mPFC ...... 110 5.4.2 Whether nerve injury-induced plasticity in the medial VTA relates to behavioral observations ...... 111

5.4.3 Whether an enhanced activity of mPFC output neurons is required of dopamine modulation of neuropathic pain related behaviour ...... 112 5.4.4 Whether dopamine modulation of neuropathic pain-associated behaviour also exist in female mice ...... 113 5.5 Future directions ...... 113 5.6 Significance ...... 115 References ...... 118

Chapter Six: Appendices ...... 134

6.1 Neuronal plasticity during acute and chronic inflammatory pain states ...... 134 6.1.1 Introduction of experimental design ...... 134 6.1.2 Photostimulation induced temporal c-fos expression in the mPFC ...... 134 6.1.3 Photostimulation altered inhibitory/excitatory input ratio to mPFC pyramidal neurons ...... 135 6.1.5 Experimental design for studying CFA-induced plasticity in the mPFC ...... 136 6.1.6 Behavioural evaluation of CFA-induced thermal and mechanical hypersensitivity .. 137 6.1.7 CFA treatment altered action potential threshold but not other cell excitability properties in mPFC pyramidal neurons ...... 137 6.1.8 Investigation of hemispheric lateralization in pain signal processing in the mPFC (CFA model)...... 138 6.1.9 Conclusion ...... 139

xii

List of Tables

Table 2-1. Key Resources ...... 37

Table 6-1. Action potential voltage threshold and cell membrane input resistance in PFC pyramidal neurons from left paw versus right paw injected mice...... 165

List of Figures and Illustrations

Figure 1-1. The shared neuronal circuit between pain and reward systems...... 21

Figure 1-2. Anatomy of the mPFC...... 22

Figure 1-3. Neuronal circuits in the prelimbic area of the mPFC...... 23

Figure 1-4. Dopamine modulation of PFC neuronal activity...... 24 Figure 3-1. Biocytin labeling in the internal recording solution allows post-hoc recovery of recording locations...... 49 Figure 3-2. Electrophysiological characterization of lateral and medial VTA dopaminergic neurons...... 51

Figure 3-3. Mechanical withdrawal threshold in SNI and SHAM mice...... 53

Figure 3-4. Electrophysiological properties of lateral and medial VTA dopaminergic neurons isolated from in SHAM versus SNI groups...... 55 Figure 3-5. Electrophysiological characterization of three subpopulations of lateral VTA dopaminergic neurons...... 57 Figure 3-6. Action potential firing patterns of different dopaminergic neuronal subpopulations in the lateral and medial VTA without and with synaptic blockers...... 59 Figure 3-7. Electrophysiological properties of Type 1 and 3 lateral VTA dopaminergic neuron subpopulations in SHAM and SNI groups...... 61

Figure 3-8. Electrophysiological properties of ventral-dorsal VTA dopaminergic neurons...... 63

Figure 3-9. Electrophysiological properties of dopaminergic neuronal subpopulations in the medial VTA in SHAM and SNI groups...... 65

Figure 4-1. VTA dopaminergic neurons anatomically and functionally connect to the PL mPFC .... 82

Figure 4-2. Photostimulation-triggered dopamine release in the NAc...... 84 Figure 4-3. Phasic but not tonic activation of dopaminergic terminals in the mPFC reduces nerve injury-induced hypersensitivity...... 86

Figure 4-4. Validation of ChR2 expression and function in DAT-ChR2 mice ...... 89

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Figure 4-5. Phasic activation of dopaminergic terminals in the mPFC of mice with neuropathic pain induces preference to the photostimulation-conditioned context ...... 91

Figure 4-6. Nerve injury did not induce change in dopamine signaling at mRNA level in the mPFC...... 93

Figure 4-7. The effect of D1 agonist SKF82197 on NMDA current amplitude in mPFC pyramidal neurons...... 95

Figure 4-8. Nerve injury did not affect D1R and NMDA interaction in the mPFC...... 97

Figure 4-9. Phasic activation of VTA dopaminergic terminals increases spontaneous activity of mPFC-vlPAG projecting neurons...... 99

Figure 4-10. Supplementary traces and statistics for fiber photometry ...... 102

Figure 6-1. Photostimulation on the right paw induced c-fos expression in the left mPFC 3 h after stimulation...... 141

Figure 6-2. Photostimulation induced c-fos expression in the mPFC 1 h after stimulation...... 143 Figure 6-3. C-fos expression was not observed in the mPFC 24 h after photostimulation on the right paw...... 145 Figure 6-4. C-fos signal from photostimulation partially overlapped with parvalbumin neurons in the mPFC...... 147

Figure 6-5. C-fos expression in the thalamus induced by photostimulation...... 149

Figure 6-6. Validation of spontaneous IPSC and EPSC at 0 mV and -70 mV respectively...... 151 Figure 6-7. Photostimulation altered the ratio of spontaneous inhibitory/excitatory input onto mPFC layer V pyramidal neurons...... 153

Figure 6-8. Photostimulation did not alter spontaneous EPSC or IPSC amplitudes in mPFC layer V pyramidal neurons...... 155

Figure 6-9. Photostimulation did not alter cell excitability of mPFC layer V pyramidal neurons. .. 157 Figure 6-10. CFA treatment significantly dropped the thermal and mechanical threshold of the ipsilateral paws 7 days after injection...... 159

Figure 6-11. CFA treatment slightly increased the cell excitability of mPFC layer V pyramidal neurons...... 161

Figure 6-12. Hemispheric lateralization was not observed in the mPFC pyramidal neurons when comparing right versus left paw injected mice...... 163

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Chapter One: Introduction

1.1 General Introduction

Pain is an unpleasant sensory and emotional experience associated with actual or potential tissue

damage (Bonica, 1979). Pain relies on both the peripheral pain signaling pathway and the central

nervous system. Pain is the integration of nociception with consciousness and emotion. This

involves complex brain networks including, but not limited to, the thalamus, the medial

prefrontal cortex (mPFC), the nucleus accumbens (NAc), the insula, the somatosensory cortex,

the amygdala, and the periaqueductal gray (PAG) (Aggleton et al., 1980; Gabbott et al., 2005;

Vertes, 2004). The mPFC plays key roles in many executive functions such as attention, judgement, and working memory (Miller and Cohen, 2001). The rodent mPFC can be further subdivided into infralimbic, prelimbic and anterior cingulate cortex (ACC), which show similar connectivity patterns as in the primate frontal cortex (Hoover and Vertes, 2007; Ongur and Price,

2000; Sesack et al., 1989; Wise, 2008). Studies in the past two decades have revealed a significant role of the mPFC in pain perception (Apkarian et al., 2004; Seminowicz and

Moayedi, 2017; Treede et al., 1999). It has been observed that the mPFC undergoes plasticity during chronic pain (Metz et al., 2009; Zhang et al., 2015). Such changes have been shown to be causal rather than being a consequence of chronic pain states (Kiritoshi et al., 2016; Lee et al.,

2015).

Dopaminergic neurons of the ventral tegmental area (VTA) have been intensively investigated because of their role in encoding motivationally relevant appetitive stimuli.

However, many studies have implicated dopamine in aversion and pain (Bromberg-Martin et al.,

2010; Budygin et al., 2012; Lammel et al., 2012). Dopaminergic neurons in the VTA make reciprocal projections with the mPFC (Berger et al., 1976; Carr and Sesack, 2000a, b) and play

1 important modulating roles in fine tuning the neuronal activities and inducing plasticity

(Durstewitz et al., 2000; Popescu et al., 2016). Behavioral studies have documented effects of dopaminergic modulation in the mPFC in working memory and attention (Fletcher et al., 2007;

Granon et al., 2000). Photostimulation of the VTA dopaminergic input to the mPFC elicits anxiety-like behaviour (Gunaydin et al., 2014). Furthermore, anxiogenic drugs or stressful stimuli increase dopamine release in the mPFC (Abercrombie et al., 1989; Moghaddam et al.,

1990; Thierry et al., 1976). Given the modulatory roles of dopamine in the mPFC, and that the mPFC regulates chronic pain signals, we hypothesized that the mesocortical dopaminergic pathway also modulates the processing of chronic pain signals.

1.2 Pain and Chronic Pain

1.2.1 Pain and pain pathway

Pain is an essential alert system for the body to avoid. People who lack Nav1.7 do not have a functional pain system and thus frequently injure themselves (Cox et al., 2006; Peddareddygari et al., 2014). Most pain signals start from nociceptive sensation at the free nerve endings in the periphery, and travel to dorsal root ganglion neuronal cell bodies through Aδ- and c-fibers before reaching the spinal cord. Nociceptive signals are then processed and relayed along ascending pain pathways (spinothalamic, spinoreticular, and spinomesencephalic) to the brain (Kandel et al., 2000; Willis Jr, 1985), where pain arises. Pain signals in the brain are processed in a network including the thalamus, the somatosensory cortex, the mPFC, the NAc and the amygdala

(Aggleton et al., 1980; Apkarian et al., 2011; Bingel et al., 2002; Davis et al., 1995; Gabbott et al., 2005; Vertes, 2004). The output from the pain signaling network modulates spinal nociceptive relay neurons via a descending pathway including the PAG, the locus coeruleus, and the nucleus raphe-magnus (Kandel et al., 2000).

1.2.2 Chronic pain

The pain signaling system can undergo maladaptive changes due to disease and trauma, leading to chronic pain. The pain pathway is not static. It undergoes plasticity from the periphery to the

CNS, at the levels of ion channels, receptors, synaptic connections and axodendritic rewiring

(Braz et al., 2014; Brown and Weaver, 2012; Kuner and Flor, 2016; Lazniewska and Weiss,

2014; Zamponi et al., 2015). For example, the expression of different types of voltage-gated calcium channels such as L-types and T-types is dysregulated under chronic pain conditions, and this contributes to the development of hypersensitivity in the ascending pain pathway (Fossat et al., 2010; Garcia-Caballero et al., 2014; Park and Luo, 2010; Zamponi et al., 2009). Plasticity in the CNS not only happens in neurons, but also resides in changes in the microenvironment. For example, there is an increased adenosine triphosphate (ATP) level after peripheral nerve injury.

ATP acts on P2X receptors and modulates the glia-neuronal work locally, or circulates in the cerebrospinal fluid as a signaling molecule (Inoue et al., 2005; Khakh and North, 2012; Masuda et al., 2016).

Plasticity in pain signaling is important. It allows the nervous system to filter needless inputs under normal physiological conditions, and enhance pain signaling under abnormal or neuropathic conditions. However, this plasticity can be maladaptive, leading to chronic pain

(Brown and Weaver, 2012). Chronic pain patients experience a poor quality of life, due to both physiological and psychological reasons (Nicholl et al., 2009). In addition, due to the inherent plasticity, the pain signaling pathways adapt quickly to analgesic drugs such as morphine

(Allouche et al., 2014; Trang et al., 2015), making chronic pain treatment a challenging task.

Despite a great amount of work that has been carried out, the exact cause for chronic pain still remains unknown. This is probably due to the complexity of plasticity in pain signaling system

such that multiple mechanisms at various levels may interact with each other to result in chronic

pain.

1.2.3 Pain models

Various pain models have been used to study the mechanistic causes of pain (Burma et al.,

2016). Researchers use acute pain models such as tail pinch. For studying chronic pain

conditions, neuropathic (e.g. ligating the spinal nerves, SNL; spared nerve injury, SNI; chronic

constriction injury, CCI) and inflammatory (e.g. Complete Freund's adjuvant, CFA) pain models

are commonly used. There are also other pain models including cancer pain and visceral pain

(Gregory et al., 2013). Different pain models have different behavioural phenotypes and involve different mechanisms. For example, a distinct c-fos expression pattern has been demonstrated in rat brains from a tail-pinch acute pain and a sciatic nerve ligation chronic pain model (Narita et al., 2003; Smith et al., 1997). Though neuropathic pain shares mechanisms with inflammatory pain (Chessell et al., 2005; Jarvis et al., 2007), it was demonstrated that the expression of cannabinoid receptor CB2 increased in response to neuropathic, but not inflammatory pain

(Zhang et al., 2003). Another study showed that deletion of nociceptor-derived BDNF reduced inflammatory pain, but had no effect on neuropathic pain development (Zhao et al., 2006).

Elucidating differences between mechanisms that are involved in different pain models will contribute to the knowledge of how acute pain transforms into chronic pain state, and how inflammation induces neuropathic pain. The present study focuses on pain related mechanisms in the VTA-mPFC mesolimbic pathway. This study is partially based on a previous study from our lab describing plasticity in the mPFC induced by SNI (Huang et al., 2019a; Zhang et al., 2015).

In preliminary experiments, possible CFA induced changes in the mPFC was tested, yet no

significant change in neuronal excitability was observed (see appendices Figure 6-11). Thus, the

SNI neuropathic pain model was chosen for the present study.

1.3 Pain and reward systems have shared features

1.3.1 The overlapped brain circuits between pain and reward signaling

The idea of interactions between pain and reward systems was first raised in the 1980s (Le

Magnen et al., 1980). It has been suggested that that relief from pain is in itself rewarding

(Navratilova et al., 2012). However more recent evidence indicated that although pain and reward signals interact, they are processed independently via a shared neuronal circuit, including, but not limited to the mPFC, NAc, amygdala, and hippocampus (Brischoux et al., 2009; Leknes and Tracey, 2008). The mPFC is a hub for both pain and reward systems (Ferenczi et al., 2016;

Wiech et al., 2008). The mPFC sends projections to the NAc, amygdala, and hippocampus

(Gabbott et al., 2005; Jay and Witter, 1991; Sesack et al., 1989), modulating the valence,

emotional component, memory consolidation and extinction of both painful and rewarding

experiences (Figure 1-1). Responding to acute pain can be considered a motivated survival

behaviour and similar to feeding and sexual behaviour, it requires activation of the

mesocorticolimbic system (Apkarian et al., 2005; Creac'h et al., 2000; Mantz et al., 1989;

Stemkowski et al., 2016b). However, plasticity in the mPFC have also been linked to the

development of chronic pain and addiction (Apkarian et al., 2011; Koob and Le Moal, 2005;

Metz et al., 2009; Robinson and Kolb, 1997).

1.3.2 Dopaminergic neurons in the VTA

The VTA is one of the main reservoirs of dopaminergic neurons, and is well known for its role

of mediating motivated behaviours (Halbout et al., 2019; Nishino et al., 1987) and has also been implicated in mediating noxious stimuli (Bromberg-Martin et al., 2010). Dopaminergic neurons

in the VTA receive afferent inputs from many brain regions, including but not limited to the

mPFC, the NAc, the amygdala, the lateral habenula (LHb), the laterodormal tegmentum, and the dorsal raphe (Beier et al., 2015; Carr and Sesack, 2000b). These brain areas receive sensory

inputs carrying motivational and aversive signals from the periphery, integrate and pass the

signals to the VTA to modulate activity of dopaminergic neurons. Thus under chronic pathology

conditions such as neuropathic pain, constant afferent input with salient and valent information

of pain and pressure for pain relief can lead to plasticity in dopaminergic neurons of the VTA.

This plasticity was observed in brain imaging studies in human subjects showing that neuronal

activity in the VTA is compromised under chronic pain conditions (Loggia et al., 2014). Changes

also occur in animal models. Increased microglia activity in the VTA was reported in animals

with peripheral nerve injury, which further impaired downstream dopamine transmission (Taylor

et al., 2015).

Dopamine plays an important role in modulating reward and motivational signaling via

mesocortical (VTA-PFC) and mesolimbic (VTA-NAc) dopaminergic pathways (Kandel et al.,

2000). VTA dopaminergic neurons are heterogeneous. They are located in different VTA

subregions and have distinct electrophysiological properties depending on the projections. For

example, as reported by Lammel and colleagues (Lammel et al., 2014), the lateral VTA projects to the lateral shell of the NAc; and the medial VTA projects to the NAc core, the NAc medial

shell, the basolateral amygdala (BLA), and the mPFC. Dopaminergic neurons in the medial VTA

lack a hyperpolarization-activated current (Ih), have a high firing frequency and adapting action potentials to a 100-200 pA ramp, have a broader action potential shape and high AMPA/NMDA ratio. For comparison, dopaminergic neurons in the lateral VTA have a prominent Ih (Zhang et

al., 2010), and have a lower action potential frequency, narrow action potential shape, and lower

AMPA/NMDA ratio.

1.3.3 Dopamine signaling in reward and aversion in the VTA

In the VTA, dopamine neuronal activity is the key mediator for reward prediction error and reward learning (Schultz, 1998). Dopaminergic neurons in the VTA fire spontaneously under basal conditions, and show increased and phasic activity towards novel or highly salient rewarding events. When the reward is linked with predictors, the increase in the activity of dopaminergic neurons only occurs during expectation, but not during the actual rewarding period, and lack of expected reward can silence the dopaminergic neurons. It was originally proposed that dopamine encodes a prediction error only with appetitive stimuli (Mirenowicz and

Schultz, 1996), however recent evidence indicates that subpopulations of VTA dopamine neurons may also encode responses to aversive stimuli (de Jong et al., 2019) . A painful stimulus

(foot pinch) can induce elevated activity in ventral VTA dopaminergic neurons (Brischoux et al.,

2009). Furthermore, subpopulations of dopaminergic neurons respond differentially to rewarding, noxious (foot shock) or aversive (air puff) stimuli, suggesting that different populations encode value and salience (Bromberg-Martin et al., 2010). This finding revealed new functions of dopaminergic neurons in addition to their traditional role in coding valence, such that they may in fact code motivational salience (Taylor et al., 2016). However, it is important to consider that dopamine acts in many brain regions including the mPFC. Its specific effects likely differ between brain regions due to variations in the density of dopamine innervation, dopamine transporters, autoreceptors, postsynaptic receptors and their coupling efficacy.

The role of dopamine in pain perception is somewhat controversial and has not yet been

fully resolved. Chronic pain is a persisting aversive state (King et al., 2009). Pain relief under

chronic pain conditions can trigger dopamine release the reward circuits, and generate negative

reinforcement (Altier and Stewart, 1999; Navratilova et al., 2012), similar to reward learning in

the reward system. Interestingly, a clinical study using EEG (electroencephalogram) suggested

that actual painful stimuli can also generate predictive error, but through salience instead of

reward predictive error (Talmi et al., 2013). However, further studies are needed for better spatial

resolution and to understand whether dopamine plays a role in it.

1.4 The mPFC as a target of treatment for chronic pain

1.4.1 The mPFC and pain induced plasticity

The PFC is a heterogeneous area. It consists of the medial, orbital and lateral PFCs. Different

subregions of the PFC have different connections and play different roles (Arruda-Carvalho and

Clem, 2014; Peters et al., 2009; Suzuki et al., 2016; Vertes, 2004). Thus there is a need to focus on individual subregions and specific neuronal populations. The mPFC is known for its importance in decision making (Botvinick et al., 2004; Gehring and Willoughby, 2002) and

working memory (Corcoran and Quirk, 2007), and encodes expectations of positive and negative outcomes (Euston et al., 2012).

Injury-induced plasticity can alter the input to the mPFC, inducing plastic changes in the

mPFC (Kiritoshi et al., 2016; Zhang et al., 2015). Conversely, output from the mPFC can also be

modified to influence pain gating (Lee et al., 2015). Activation of the neuronal network

containing the lateral habenula, the VTA and the mPFC generates aversion in mice (Lammel et

al., 2012). Baliki et al. (2013) reported that the circuits involving the core regions of the NAc and

the mPFC are able to signal the reward value of pain relief. The same group also showed that the

8 connectivity between the mPFC and NAc plays a key role in the transition from acute to chronic pain (Baliki et al., 2012). Under pain conditions, decreased activity in mPFC neurons results from hyperactivity of the basolateral amygdala, which is associated with the fear of pain

(Neugebauer, 2015). The mPFC plays a key role in integrating pain signals instead of serving as a relay station. Plasticity in the mPFC cannot be independent from other circuits.

1.4.2 Descending modulation of pain from the mPFC

The mPFC plays a crucial role in gating the perception of pain via top-down control mechanisms. It calculates the valence of pain and decides whether a signal from a noxious stimulus should be magnified or ignored. One good example of the top-down gating of pain is the placebo effect, the analgesia that results psychologically from mock treatment. Clinical research has revealed that placebo analgesia can be mediated by the endogenous opioid system, or caused by alterations in coupling between different brain areas such as the mPFC and the PAG

(Bingel et al., 2006; Petrovic et al., 2010; Sevel et al., 2015). Basic research also provided evidence for mPFC-mediated pain gating. In the late 90s, it was shown that electrical stimulation in the mPFC led to analgesia (Hardy, 1985). More recently, Lee et al. (2015) demonstrated in a rat model that activation of the mPFC using a optogenetics tool caused pain relief. Huang and colleagues showed similar results, but also revealed a long-range circuit from amygdala to the mPFC to the ventrolateral PAG (vlPAG) that mediates this effect. Specifically, when mPFC- vlPAG glutamatergic output is enhanced using an optogenetic approach, there is an analgesic effect (Huang et al., 2019a). All those studies suggest that the mPFC might be a potential target for new strategies for treatment of chronic pain.

1.4.3 Subregions of mPFC and the prelimbic area

The mPFC contains the medial agranular, the anterior cingulate, the prelimbic, and the infralimbic subregions (Hoover and Vertes, 2007). Particularly, the prelimbic area and ACC play

important roles in the processing of pain signals, and this present study focuses on the prelimbic

area. The prelimbic area is located dorsally in the ventromedial PFC. Anatomically, the

prelimbic area has connections with the insular cortex, the NAc, the thalamus, the VTA, and the

basolateral amygdala (Lewis and O'Donnell, 2000; Vertes, 2004) (Figure 1-2). The prelimbic cortex consists of layer 1-3, 5, 6a, and 6b, forming local circuits and connecting to other brain areas (Figure 1-4). Within the prelimbic mPFC, the pyramidal cells are the major excitatory

neurons, and GABAergic neurons including parvalbumin, calbindin, and calretinin neurons are

the major inhibitory interneurons (Gabbott et al., 1997). Large pyramidal cells in layer 5 receive

inputs from layer 1 and 3 (Thomson et al., 2002), which may feed forward excitatory inputs from the amygdala (Bacon et al., 1996). Interestingly, the basolateral amygdala also projects to parvalbumin neurons, which further innervate pyramidal cells in the layer 5 (Gabbott et al.,

2006). Thus pyramidal cells in layer 5 in the cortex of the prelimbic area may provide an integration role for amygdala inputs. Previous studies have shown the importance of the prelimbic area in pain studies. Traub et al. (1996) observed increased c-fos expression in the prelimbic area in response to visceral pain. A change in mPFC neuronal activity under chronic neuropatic pain conditions was recently investigated by Zhang et al. (2015) focusing on the prelimbic area. It was demonstrated that the decreased pyramidal cell activities in the PFC prelimbic area in SNI mice resulted from feedforward input from the mPFC parvalbumin neurons. This finding is consistent with other studies (Ji and Neugebauer, 2011; Wang et al.,

2015). However, whether a similar mechanism also applies to other pain models remains to be investigated.

1.4.4 Plasticity in the mPFC prelimbic area and other subregions during chronic pain states

The mPFC, like other parts of the pain pathway, undergoes plasticity during chronic pain.

Clinical studies using volumetric analysis with magnetic resonance imaging (MRI) showed grey matter loss in the mPFC in patients with chronic pain (Apkarian et al., 2004; Kuchinad et al.,

2007). At the functional level, fMRI studies found both hyper- and hypoactivity in the mPFC in chronic pain patients (Baliki et al., 2006; Gundel et al., 2008). These apparently discrepant activity might be resulted from different pain-induced plasticity in different subregions of the mPFC. At the single neuron level in the rat infralimbic and prelimbic mPFC, there can be anatomical changes during chronic neuropathic pain, such that basal dendrites of pyramidal neurons show increased length and spine density in a neuropathic pain model (Metz et al., 2009).

In the prelimbic area of the mPFC, both neuropathic and inflammatory pain models caused a decrease in the neuronal activity of pyramidal neurons (Ji et al., 2010; Zhang et al., 2015). This decreased excitability was driven by increased activity of parvalbumin interneurons, and shown to be causal rather than being an epiphenomenon of chronic pain (Lee et al., 2015). In contrast with the prelimbic area, the ACC showed enhanced neuronal activity during chronic pain. The

Zhuo group demonstrated that the altered excitability is due to long-term potentiation and loss of long-term depression in glutamatergic synaptic transmission (Bliss et al., 2016; Wei et al., 2001;

Zhuo, 2002, 2006). The Séguéla group focused on a different aspect showing that changes in hyperpolarization-activated cyclic nucleotide-gated (HCN) channel function regulated by cAMP/PKA pathway contribute to hyperactivity in the ACC that lead to chronic pain (Cordeiro

Matos et al., 2015). Plasticity in the mPFC under chronic pain conditions can be different

depends on subregions. Thus it is crucial to keep consistent to one specific mPFC subregion

when study mechanisms related to chronic pain.

1.5 Neuronal plasticity in the mPFC can be modulated by dopamine signaling

The mPFC receives dopaminergic inputs from the VTA (Lewis and O'Donnell, 2000; Williams and Goldman-Rakic, 1998), and is an important target for dopamine modulation (Seamans and

Yang, 2004) (Figure 1-3). Both D1-like (D1R) and D2-like (D2R) G-protein coupled dopamine receptors are expressed in the mPFC. D1 receptor mRNA and radiolabeled D1Rs are localized to all cortical layers (Al-Tikriti et al., 1992; Gaspar et al., 1995) whereas, D2 receptor mRNA in mPFC is found primarily in deeper layers V and VI (Bouthenet et al., 1991; Santana et al., 2009).

Among layer V pyramidal and GABA neurons, 15%-20% express D1R or D2R (Gaspar et al.,

1995; Santana et al., 2009). D1R is Gs coupled and its activation activates adenylyl cyclase and increases the level of cAMP (Hyttel, 1984). D2R is Gi/o coupled and inhibit cAMP upon ligand binding (Enjalbert and Bockaert, 1983; Gurevich et al., 2016; Onali et al., 1985). D2R activation has been shown to modulate persistent sodium current (INaP), inwardly rectifying potassium

currents (Kir), and Ca2+ currents (Dong et al., 2004; Gorelova and Yang, 2000; Kisilevsky et al.,

2008; Seamans and Yang, 2004; Yang and Seamans, 1996; Young and Yang, 2004). Dopamine

modulation of INaP is mediated by D1R through the PKC (protein kinase C) pathway (Gorelova

and Yang, 2000). Dopamine acts on either D1 or D2Rs to enhance or inhibit protein kinase A to

modulate Kir channel function (Dong et al., 2004). Dopamine regulates Ca2+ current through second messenger pathways or via direct interaction between dopamine receptors and ion

2+ channels (Catterall, 2000; Kisilevsky et al., 2008). INaP, Kir and Ca channels play important roles in shaping membrane potential thus influencing cell excitability. Importantly, Ca2+

channels are also crucial for synaptic transmission. Dopamine also acts on ligand gated channels,

including NMDA and GABA receptors (Chen et al., 2004; Seamans et al., 2001a; Seamans et al.,

2001b), through PKA or IP3 signaling pathways (Flores-Hernandez et al., 2002; Snyder et al.,

1998; Trantham-Davidson et al., 2004). For example, dopamine activates D1R and enhances

NMDA current amplitude in layer V pyramidal neurons in the mPFC (Seamans et al., 2001a).

Dopamine modulation of GABA current have a bidirectional effect, depending on whether D1R

or D2R is activated (Seamans et al., 2001b). These effects of dopamine receptor activation on

ligand and voltage-gated ion channels provide the potential to modulate pain sensation both

through modulating neuronal excitability and via alteration of synaptic transmission.

1.6 Mesolimbic pathway as a target of treatment for chronic pain

1.6.1 Existing studies on mesocorticolimbic dopaminergic pathways related to pain

Elman and Borsook (2016) proposed that while acute pain activates dopamine transmission in

the mPFC, there are reduced dopamine levels during chronic pain. The decreased dopamine tone

is probably due to impaired neuronal activity in the VTA, the brain region that is the main

source of the dopaminergic input to the mPFC, during chronic pain (Ren et al., 2016).

Conversely, systemic restoration of dopamine concentration has analgesic effects. For example,

systemic administration of dopamine receptor agonists attenuated neuropathic pain in rats (Ren

et al., 2016; Sarkis et al., 2011). A similar effect was also observed with levodopa combined with

the non-steroidal anti-inflammatory drug (NSAID), naproxen (Ren et al., 2016). More specific to the mPFC, Sogabe et al. (2013) showed that VTA stimulation reduced nociceptive response in mPFC neurons induced by mechanical stimulation. The effect was abrogated upon depletion of dopaminergic neurons in the VTA, thus implicating dopamine. Dent and Neill (2012) further

demonstrated a role of dopamine in pain behavioural assays. Lopez-Avila et al. (2004)

demonstrated that dopamine application in the mPFC relieved hypersensitivity in a chronic pain

model. Collectively, these studies suggest that the dopamine system may be a potential target for

the treatment of chronic pain. However, it is not clear whether dopamine plays a direct analgesic

role in the mPFC via descending pathways, as opposed to working indirectly by altering complex

executive functions carried by the mPFC that can in turn interact with pain perception.

1.6.2 Potential mechanisms for dopamine modulation of pain signals in the mPFC

A number of studies have reported excitatory effects of dopamine on mPFC pyramidal neurons

whereby cortical networks can become more depolarized to form 'up’ states or cortical oscillations resulting from synchronous firing of pyramidal neurons (Sanchez-Vives and

McCormick, 2000; Sohal, 2012). Dopamine can bring mPFC neurons to ‘up states’, an effect that is dependent on mPFC D1R activation and thus facilitate postsynaptic depolarization to increase

NMDAR efficacy (Lewis and O'Donnell, 2000; Wang and O'donnell, 2001).‘Up-states’ of pyramidal cell networks occur in many areas of the cerebral cortex. Yet, the mPFC appears particularly well suited to this type of activity due to high levels of NR2B containing NMDARs.

The slow decay kinetics of NR2B-containing NMDAR currents, which can be up to two-fold

longer than other areas of the cortex, allows prefrontal cell networks to maintain stable firing

activity (Wang et al., 2008; Wang, 1999). Thus, dopamine action at D1Rs to increase NMDAR

function likely stabilizes ‘up-states’ in the mPFC. In contrast, activation of D2Rs in the mPFC

decreases cell firing (Gulledge and Jaffe, 1998, 2001; Mantz et al., 1988), making the network

require more stimuli to initiate transmission. Dopamine may function at D1Rs and D2Rs on

different cortical cellular populations to enhance the signal-to-noise ratio. In theory, dopamine

may enhance mPFC activity or signal-to-noise ratio to modulate chronic pain signals. However,

this theory is required to be tested in the present study.

1.6.3 Complexity in studying dopamine modulation of the mPFC

In the mPFC, the effect of dopamine has an inverted U-shape dose response curve (Cools and

D'Esposito, 2011) that arises in part from opposing effects of D1R and D2R. D1R mediates

excitation whereas D2R signaling is inhibitory. In addition, D1R has low affinity and requires

high dopamine concentrations to be activated, while D2R has high affinity and can be activated

by ambient dopamine (Marcellino et al., 2012). Consequently, dopamine concentration can be

critical for determining which type of dopamine receptor is activated and whether the response is excitatory or inhibitory. Moreover, a slow rise in dopamine concentration may result in different

early and late-phase responses. In addition, it has been reported that D1R and D2R can each show an inverted U-shaped response to dopamine (Monte-Silva et al., 2009; Vijayraghavan et al., 2007), such that if only one type of dopamine receptor is expressed, a very high level of dopamine can inhibit receptor function (perhaps via desensitization or activation of D2 autoreceptors). Thus, the net effect of dopamine on the mPFC neurons depends on dopamine concentration, cellular receptor expression and receptor density (Seamans and Yang, 2004).

The inverted U-shape response to dopamine results in dual effects on modulation of different ion channels and receptors. The effect of dopamine on INaP depends on the membrane

potential. When membrane potential is more negative than -40 mV, dopamine potentiates INaP.

Otherwise dopamine decreases INaP (Gorelova and Yang, 2000). The effect of dopamine on L-

type Ca2+ channels can also be bi-directional. L-type current was decreased by dopamine when

Ca2+ spikes were fully activated by strong injecting current (Yang and Seamans, 1996), and an opposite effect was observed when Ca2+ spikes were not fully triggered (Young and Yang,

2004). For GABA transmission, depending on whether D1R or D2R is activated, dopamine

application can lead to opposite effects on IPSCs (inhibitory postsynaptic currents) (Seamans et

al., 2001b) (see (Young and Yang, 2004) for detailed discussion). Thus, when examining the role

of dopamine in pain–induced plasticity, it is important to elucidate the physiological levels of

dopamine in the mPFC during chronic pain states compared to baseline, and apply the

corresponding concentrations when conducting experiments in vitro.

The complexity of studying how dopamine is involved in pain modulation in the mPFC

does not only arise from its unique response curve, but also due to the complexity of mPFC

neuronal network. In the mPFC, D1R and D2R are expressed in either or both pyramidal neurons

and GABA neurons (Santana et al., 2009). The GABA neurons are integrated in feed-forward

and feed-back inhibitory loops with pyramidal neurons. Thus the effects of a change in dopamine level competes between pyramidal and GABA networks, as discussed by Seamans and Yang

(2004). When NMDA current is low compared to GABA current, the overall outcome of D1R activation is to strengthen the GABA component. However if the NMDA current is dominant compared to GABA, the effect of D1R is to further enhance the NMDA component. Under chronic pain conditions where dopamine levels are reduced, the balance between the competing networks may be reset, leading to a change in the inhibitory loops and affecting the overall excitability in the mPFC.

Because D1R action can stabilize ‘up’-states, and D2 activation decreases firing, it is thought that dopamine can enhance the signal-to-noise ratio in the mPFC. For example, single or low frequency inputs tend to be filtered out by dopamine modulation, while the high frequency activities are enhanced Seamans and Yang (2004). At the functional level, O'Donnell (2003)

showed that by increasing the signal-to-noise ratio, dopamine is able to selectively enhance the activity of the neuronal ensemble that is activated during a specific task. Thereby dopaminergic inputs can strengthen the attention on a particular activity, and its associated neural

representation in the mPFC. Considering that neuronal activity is decreased in the mPFC

prelimbic area and dopamine is reduced during chronic pain, such excitatory effects of dopamine

would likely disrupt mPFC networks that respond to environmental or internal sensory

perceptions which underlie several cognitive processes, including decision-making and working

memory (Fuster et al., 2000; Wang, 2002). Therefore, to fully appreciate how pain signal is processed, it is important to understand how mPFC executive functions interact with the mesolimbic system that is involved in pain modulation.

1.7 Executive functions of the mPFC in pain signal processing and the role of dopamine

1.7.1 Roles of cognitive functions of mPFC in pain signaling

At the mPFC level, pain is no longer a simple sensory alert. It is an integrated signal with

salience, valence, emotion, and memory inputs (Figure 1-1). In addition, pain chronification is a long-term process, and often interacts with endogenous and exogenous factors. Thus, the neuronal activity in the mPFC is unlikely a pure reflection of pain. It is nearly impossible to separate pain modulation from other executive functions of the mPFC. The cognitive function of the mPFC can be disrupted by chronic pain (Alshelh et al., 2018; Mao et al., 2014; Simons et al.,

2014), which may further alter pain perception (Bushnell et al., 2013; Villemure and Bushnell,

2002). It is important to determine which cognitive functions are influenced by chronic pain,

how they in turn modulate pain perception, and how dopamine plays a role in this modulation.

The mPFC participates in pain signal processing in two stages (Botvinick, 2007). When

nociceptive information first reaches the brain, its salience needs to be evaluated quickly such

that the brain can make a decision as to whether a current goal-directed behaviour needs to be

interrupted (Peyron et al., 1999). At this stage, the mPFC exerts its role in persisting or

redirecting the goal. The second stage is an aversive learning process (Ploghaus et al., 2000;

Seymour et al., 2005). Information of the painful experience gathered during the first stage,

combined with participated and actual outcomes is further analysed, and stored to direct future

situations. The two stages are not isolated, as the second stage relies on the information passed

on from the first stage, which requires persistent activity of mPFC neurons (Miller and Cohen,

2001).

1.7.2 Mechanisms for dopamine modulation of attention to affect chronic pain

Determination of goals (decision making) and maintaining attention are key executive functions

of the mPFC. Rather than encoding information, the mPFC plays a role in controlling the flow of

information, by supporting specific non-automatic pathways in a biased manner (Miller and

Cohen, 2001). Pain signals interact with decision-making and attention in two ways. One is how

easily pain interrupts current goal and becomes the new focus. The other occurs during constant

pain, and how likely it is to distract the attention from pain (Buhle and Wager, 2010). Clinical

studies have shown that distraction tasks can decrease pain perception during nociceptive stimuli

(Bantick et al., 2002; Miron et al., 1989; Villemure and Bushnell, 2002). Distraction from pain is

associated with a change in mPFC activity (Gard et al., 2011). Clinical trials also showed that meditation could lead to relief of chronic back pain (Morone et al., 2008), indicating a possible therapeutic avenue for improving chronic pain by attention control through the mPFC.

Dopamine modulation in the mPFC was shown to be associated with attention. In a rat model, blocking D1R baseline activity impaired performance in an attentional set-shifting task, while enhancing D1R but not D2R improved performance (Fletcher et al., 2007; Granon et al.,

2000). However, how dopamine modulation of attention affects pain signal processing in the mPFC has not yet been investigated.

1.7.3 Mechanisms for dopamine modulation of working memory and learning to affect

chronic pain

Working memory and learning are crucial functions of the mPFC. Persistent activity of the active neural ensembles is able to link prediction and consequence, which occur in different time frames, thus promoting learning (Miller and Cohen, 2001). There are several hypotheses on how

learning processes are altered by chronic pain to result in further pain chronification. Apkarian

(2008) suggested that chronic pain leads to constant conditioning of negative emotional

experience within patients’ daily environment. By giving a negative coding of their normal

environment, it can trigger internal conflicts by having a negative experience within the normally

comforting surrounding, which leads to further emotional pain. Constant conditioning with pain

stimuli can trigger repeated dopamine release (Di Chiara et al., 1999; Elman and Borsook, 2016;

Mantz et al., 1989). Similar to aberrant learning in addiction, this repetitive dopamine release

may result in a change in function of dopamine receptors and lead to an allostatic load (Elman

and Borsook, 2016). The hypoactivity of dopamine signaling further impairs mPFC cognitive

functions (Brozoski et al., 1979), and leads to other chronic pain-associated symptoms such as

impaired cognition. Another hypothesis from Navratilova and Porreca (2014) is based on the

prediction error theory. Pain relief has been shown to generate negative reinforcement by

activating dopamine reward pathways (Navratilova et al., 2012). During chronic pain, sustained

craving for (but not receiving) pain relief may result in a prediction error, leading to a

hypodopaminergic status. This persistent pain relief-seeking burden can distort the dopaminergic

system, and cause plastic changes toward the vicious cycle of pain chronification (Navratilova

and Porreca, 2014). Understanding how changes in dopamine signalling affects learning during

pain progressing will provide better opportunities for patients to alleviate emotional suffering

caused by chronic pain.

1.8 Summary

The mPFC is important for integration and descending modulation of chronic pain signals. Pain and reward systems have shared brain circuits and features. Dopamine, an essential

neuromodulator that mediates motivation in the mesolimbocortical system, modulates plasticity

and fine tunes executive functions of the mPFC. Thus the mesocortical dopaminergic pathway

can be a potential target that modulates mPFC activity for treatment of chronic pain.

In this present study, I hypothesized that activation of the mesolimbic dopaminergic

pathway modulates the processing of chronic pain signals in the brain. There are two aims to

address in this study. First, determine whether VTA dopamine neurons undergo plasticity during

chronic pain states; Second, determine whether optogenetic activation of mesocortical pathway

alters pain associated behaviours, and if so, what are the mechanisms that mediate this process.

Figure 1-1. The shared neuronal circuit between pain and reward systems.

The nuclei encoding pain perception in the brain include, but are not limited to the PFC,

NAc, amygdala (Amy), and hippocampus (Hipp). These brain regions with different functions interact with each other, integrate nociceptive signals, and link pain signals to corresponding behaviours. The PFC, NAc, and Hipp receive afferent input from the VTA, and are involved in the reward system. Blue and red arrows indicate pain and reward circuits, respectively. The dashed line reflects ongoing studies and these linkages are yet to be confirmed. Yellow highlights are mesocortical and mesolimbic dopaminergic pathways which modulate the highlighted circuits.

A B Prelimbic PFC Infralimbic Th

NAc VTA Amg

Figure 1-2. Anatomy of the mPFC.

A. Location of the mPFC in a mouse brain, and its network related to pain. Th, thalamic nuclei.

B. Location of the prelimbic area of the mPFC on a coronal mouse brain section (one side).

Afferent inputs

Layer I

Layer II, III

Layer V

Figure 1-3. Neuronal circuits in the prelimbic area of the mPFC.

Layer I pyramidal neurons receive afferent inputs from the thalamus, other cortical areas, the amygdala and relay to neurons in layer II, III, and V. Layer V big pyramidal neurons receive afferent inputs from other brain areas directly, and also inputs from other layers. Parvalbumin

GABA neurons synapse mainly on the cell body on the layer V pyramidal cells, and excitatory inputs mainly synapse distally from the cell body. Orange, parvalbumin neurons. Blue and white, pyramidal neurons. Green, dopaminergic innervations.

Figure 1-4. Dopamine modulation of PFC neuronal activity.

Upper left, conditions that cause chronic pain induce decreased activity in the prelimbic (PL)

mPFC, while increasing activity in the ACC. In both the prelimbic mPFC and ACC, dopamine tone is decreased. Lower left, schematic representation of the prelimbic mPFC. Right, potential target of the dopamine modulation, including the Nav, Kir, Cav, GABAR, and NMDAR, which can affect membrane potential, neuronal activity, and synaptic transmission. Nav, voltage-gated

sodium channels. Cav, voltage-gated calcium channels. GABAR, GABA receptor.

Chapter Two: Material and Methods

2.1 Animals

All experiments involving animals were performed in accordance with the Canadian Council on

Animal Care Committee Guidelines. The use of animals and all procedures were approved by the

University of Calgary Animal Care Committee. Animals were maintained on a 12 h light-dark

cycle with free access to food and water. Behavioral experiments were conducted during the

light phase. Male adult DAT-Ires-Cre (DAT-Cre), DAT-Ires-cre x Ai9

( DATcreTdTomato), and DAT-Ires-Cre x Ai32 (DAT-ChR2) mice were used for experiments.

DATcreTdTomato mice were generated by crossing DAT-Cre with B6.Cg-

Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J (Ai9). DAT-ChR2 mice were generated by breeding

the B6.SJL-Slc6a3tm1.1(cre)Bkmn/J mouse line (DAT-Ires-Cre) with the B6.129S-

Gt(ROSA)26Sortm32(CAG-COP4*H134R/EYFP)Hze/J conditional allele mouse line (Ai32) to

express channelrhodopsin2 (ChR2) under the DAT promoter. DATcreTdTomato mice were used for validation of dopaminergic innervation in the mPFC. DAT-ChR2 mice were used in behavioral tests for mechanical withdrawal threshold and for validation of this mouse line. DAT-

Cre mice were used for all other experiments. Surgeries were performed on 6-8 week old mice.

Behavioral assessment, voltammetry, and fiber photometry were carried out between 8 to 12 weeks of age.

2.2 Spared nerve injury (SNI) neuropathic pain model

Adult mice (6-8 weeks) were placed under anesthesia. The sciatic nerve with three terminal branches (tibial, common peroneal, and sural nerves) of the left hind leg was identified. Tibial and common peroneal nerves were tightly tied using 6-0 silk suture, cut, and 1mm of nerve from the descending side was removed. The sural nerve was untouched. During SHAM surgery, the

sciatic nerve was identified and left intact. Incisions were covered with topical lidocaine cream

(EmlaTM) and animals were monitored after surgery for proper recovery. Mechanical withdrawal

threshold was tested from the SNI/SHAM operated mice at least 14 days post-surgery prior to

further experiments.

2.3 Mechanical withdrawal threshold

Mechanical withdrawal threshold tests were used to evaluate nerve injury-induced hypersensitivity (Huang et al., 2019a). Mice were placed in individual testing chambers on a metal grid platform. Mice were allowed to acclimate for at least 1 h before testing. During testing, a Dynamic Plantar Aesthesiometer (DPA) (Ugo Basile, Varese, Italy) was placed underneath the hind paw with the filament being applied to the paw two thirds of the distance from its distal end. The mechanical withdrawal threshold is the minimum force for mice to lift the testing paw.

2.4 Conditioned place preference (CPP)

Conditioned place preference tests (CPP) were conducted as previously described (Gambeta et al., 2017; King et al., 2009; Lammel et al., 2012). The CPP box consists of two conditioning chambers with distinct visual and tactile cues (i.e. different walls and floor patterns), as well as a connecting neutral chamber with an LED light strip on the top of the box. The CPP protocol was performed 4 weeks after virus injection and 2 weeks after the SNI/SHAM surgeries.

On the first day (pre), mice were individually placed into the neutral chamber with free access to both chambers for 15 minutes. The optic fiber was connected to the implant; however, no laser was applied during the test. The time spent in each conditioning chamber was recorded.

Mice that spent <30% or >70% of the total time in one of the conditioning chambers were

excluded of the test. A counterbalanced design (i.e., alternating the chamber in which mice were

conditioned) was performed to avoid experimental bias.

On the second day (conditioning), during the morning, mice were individually conditioned

in the designated chamber for 30 minutes without access to the other chamber. The optic fiber

was connected, and no-laser stimulation was applied. Four hours after the morning conditioning

(no laser), mice were individually conditioned in the opposite chamber for 30 minutes without

access to the other chamber. The optic fiber was connected, and blue laser stimulation (473 nm,

20 mW, 30 Hz phasic) was applied for 30 minutes.

On the last day (test), mice were individually positioned into the neutral chamber with free

access to both chambers for 15 minutes. The optic fiber was connected to the implant; however,

no laser was applied during the test. The time spent in each conditioning chamber was analyzed

to determine whether laser stimulation was able to produce CPP. An increase in time in the

chamber conditioned with laser stimulation compared to corresponding pre time was considered

CPP.

2.5 Fast-scan cyclic voltammetry (FSCV)

For FSCV tissue preparation, mice between 8 to 10 weeks old were used for FSCV recordings.

Mice were deeply anaesthetized with isofluorane, then perfused with ice-cold oxygenated

NMDG solution (159.2 mM NMDG, 74.6 mM KCl, 1.2 mM NaH2PO4, 30 mM NaHCO3, 20 mM HEPES, 25 mM D-glucose, 5 mM Na-ascorbate, 3 mM Na-pyruvate, 10 mM

MgSO4.7H2O, 2 mM Thiourea, and 0.5 mM CaCl2.2H2O, prepared in MilliQ H2O, pH=7.4).

The brain was removed from the skull and immediately placed into ice-cold, oxygenated NMDG solution (Ting et al., 2014). Coronal sections of 300 μm through the PL mPFC or NAc were cut with a vibratome (Leica VT 1200S) at room temperature. Slices were then incubated in 33˚C

NMDG solution for 12 min before transferred into 33˚C recording solution (126 mM NaCl, 1.6

mM KCl, 1.1 mM NaH2PO4, 1.4 mM MgCl2, 2.4 mM CaCl2, 26 mM NaHCO3, and 11 mM

glucose, PH=7.4) and incubated for 40 min prior to recording.

For FSCV data acquisition, following slicing, a brain section was placed into the recording

chamber superfused with aCSF (31.4-33ºC). A pellet reference electrode connected to the FSCV

head stage was placed into the aCSF, not touching the brain slice. The carbon fiber recording

microelectrode (constructed and calibrated using 0.8 mm glass capillary tubes according to

(Aragona et al., 2009)) was connected to the head stage and a triangular waveform of voltage

applied (oxidative scans, -0.4 to 1.3 V; reductive scans 1.3 to -0.4 V at 400 V/s). The recording

microelectrode was lowered into the desired location (e.g. NAcc, mPFC) and allowed to

equilibrate by cycling at 60Hz for 20 minutes, then at 10 Hz for 10 minutes. All recordings were

made at 10Hz waveform application. An optical fiber (5 mW, 200 μm, 0.39 NA) was targeted at

the nucleus accumbens core (NAcc) or mPFC to provide 473nm laser stimulation. Data were

first collected in the NAcc in response to laser stimulation (30 Hz, 40 pulses, 5 mW) to verify

viral transfection and efficacy, and then from PFC-containing sections (30 Hz, 40 pulses, 5 mW; no more than two PFC-containing sections per mouse).

Waveform generation and application, data collection and processing were conducted using

TarHeel CV software (ESA Biosciences Inc., Chelmsford, MA, USA) and High Definition

Cyclic Voltammetry Suite (University of North Carolina, Chapel Hill). The resultant output is a

background-subtracted current change over time reflecting concentration fluctuations of electro-

active substances at the recording carbon fibre electrode. Consequently, FSCV data are reported

as delta (∆; change) from background. Individual variability in electrode sensitivity was

accounted for by pre-calibration using aCSF, 250 nM, and 1000 nM [dopamine] flow cell

recordings (also recorded using TarHeel CV software).

FSCV data were collected and background subtracted at the lowest current value prior to

stimulation onset. Current was converted to [dopamine] changes using chemometric principle

components analysis (Keithley and Wightman, 2011). This produced a representative [dopamine]

trace per stimulation, at each stimulation parameter, for each slice. Three stimulations were

recorded at each stimulation parameter. These traces were averaged across trials to produce an

average [dopamine] trace per stimulation frequency. These traces were then averaged to produce

a single trace for each stimulation parameter for SHAM and SNI groups. [dopamine] changes at

each stimulation parameter were compared separately. Peak [dopamine] was also compared

between SHAM and SNI groups at each stimulation parameter.

2.6 Electrophysiology

Mice between 8 to 10 weeks old were used for electrophysiology recordings. Mice underwent

anesthesia with isoflurane. Mice were perfused with ice-cold oxygenated NMDG solution (159.2

mM NMDG, 74.6 mM KCl, 1.2 mM NaH2PO4, 30 mM NaHCO3, 20 mM HEPES, 25 mM D-

glucose, 5 mM Na-ascorbate, 3 mM Na-pyruvate, 10 mM MgSO4.7H2O, 2 mM Thiourea, and

0.5 mM CaCl2.2H2O, prepared in MilliQ H2O, pH=7.2). The brain was removed from the skull and immediately placed into ice-cold, oxygenated NMDG solution (Ting et al., 2014). Horizontal

sections of 260 μm through the VTA were cut with a vibratome (Leica VT 1200S) at room

temperature. Slices were then incubated in 33˚C NMDG solution for 12 min before transferred

into 33˚C recording solution (119 mM NaCl, 26 mM NaHCO3, 25 mM Glucose, 2.5 mM KCl,

1.25 mM NaH2PO4, 2.5 mM CaCl2, and1.3 mM MgSO4, prepared in double distilled H2O,

pH=7.2) (for post hoc validation of recording locations, 1.3 mM biocytin was added) and

incubated for 40 min prior to recording.

To validate ChR2 function in DAT-ChR2 mice, slice recordings were carried out on the YFP

positive neurons in the VTA slices using an amplifier (MultiClamp 700B, Molecular Devices)

and a digitizer (Digidata 1440A, Molecular Devices). Blue light evoked responses were

examined using voltage-clamp recordings, with glass pipettes (3-5 MΩ) filled with intracellular recording solution (130 Cs Methanesulphonate, 4 CsCl, 2 EGTA, 4 Mg-ATP, 0.3 Na-GTP, 10

HEPES, 5 QX-314). In voltage clamp, cells were held at -70 mV. Different frequencies of 10

mS, and a 1 s LED (λ=473 nm) stimuli was given to the cell. Current clamp was used to validate blue light-induced action potentials. A K-gluconate based internal solution was used (130 mM

K-gluconate, 10 mM HEPES, 0.2 mM EGTA, 10 mM Na2-phosphocreatine, 4 mM Mg-ATP,

and 0.3 mM Na-GTP). Cells were injected current to hold at -65 mV. 10 Hz 10 ms blue laser

stimulation was applied on VTA slices to trigger time-locked action potentials.

For measurement of cell excitability, spontaneous firing, frequency-current (F-I)

relationship, action potential threshold, input resistance, Ih measured from voltage sag, and

membrane potential were recorded in the td-tomato positive neurons. Membrane potential was read when holding current was 0 pA after cells were stabilized. Cells were then held at -80 mV for measurements of other excitability properties. The F-I relationship was determined by using a series of 1s 25 pA current steps starting from 0 pA, and data were plotted as number of spikes per second at different injecting current levels. To study cell input resistance, cells were injected using a series of 1s -25 pA current steps starting from 0 pA. Input resistance was determined by dividing the membrane potential of each trace by its injecting current. Ih was calculated by dividing voltage sag by input resistance. Voltage sag was calculated as subtracting peak response

by steady-state potential at -150 pA hyperpolarizing step (Lacey et al., 1989; Lammel et al.,

2008). Voltage threshold of action potential was measured using a 100 ms depolarizing current

ramp of 40-60 pA. All data were digitized at 20 kHz and filtered at 10 kHz. For blocker experiments, 20 µM Bicuculline , 50 µM D-AP5 , 10 µM DNQX , 500 µM Sulpiride , 200 nM

CGP55845 , 1 µM Strychnine were bath perfused after recording of excitability properties for 5 min. Excitability properties were then collected again with blockers. A junction potential of 15 mV (calculated using pClamp 10, Molecular Devices) was subtracted from all membrane potentials, including cell membrane potential, holding potential, and action potential threshold.

Electrophysiology data were analyzed using Clampfit (10.3) and OriginPro (9.1).

2.7 Fiber photometry

During fiber photometry recording, an optrode was connected to a Doric Mini Cube filter set

(FMC4_AE(405)_E(460-490)_F(500-550)_S) through a mono fiber optic patch cord (DORIC

MFP_400/460/900-0.48_2m_FC/MF2.5). The filter set separated yellow laser (λ=590 nm) light from LED light (λ=405/465 nm) and GCaMP signals. LEDs (465nm and 405nm from Doric) were controlled by a LED driver and console running Doric Studio software (Doric Lenses). The

LEDs (Doric) were modulated and the resulting signal demodulated using lock-in amplification.

The power of the LED light (λ=465) that excites GCaMP was calibrated to 30 µW, and the power of λ=405 light for movement control was adjusted to match the signal level of λ=465 light.

Mice expressing GCaMP in mPFC-vlPAG projecting neurons and ReaChR (C1V1) in dopaminergic neurons were habituated for three days before the testing day (see microinjection and optrode implantation sections for details). On the testing day, mice were connected to the photometry system and habituated for 40 min to 1 h in a small chamber (same as for mechanical

threshold test) placed on a metal mesh before recording. The recording protocol lasted for 30

min, and consisted of 10 min laser off (baseline), 10 min laser on (laser), and 10 min laser off

(postlaser) periods. During each 10 min period, 3 min GCaMP signals of spontaneous activity

was recorded. From 3 min to 6 min, GCaMP signals was recorded with von Frey stimulation (0.4

g filament) on the left hind paw applied every 30 s for 7 times in SNI/SHAM mice. From 6 to 10

min, GCaMP signals of spontaneous activity were recorded again. Brain tissue was harvested

after recordings for validation of virus expression and cannula locations.

Data collected were processed with MATLAB (R2018a) using custom script (available

on https://github.com/leomol/FPA) for data normalization, z-score calculation, and spontaneous

event peak number measurement. Spontaneous activity was reflected in the median of the z-

score. The peak amplitude of the foot stimulus-induced responses was measured by subtracting baseline (2-5s prior to stimuli) from each corresponding peak.

2.8 Optogenetics

Blue (λ=473 nm) and yellow (λ=590 nm) laser light was used to drive ChR2 and red- activatable channelrhodopsin (ReaChR) respectively. ReaChR (C1V1) was used for fiber photometry, and ChR2 was used for all other optogenetic experiments. For tonic stimulation (for mechanical threshold test), 10 ms laser flashes were delivered continuously at 1 Hz during the

“laser on” period. For phasic stimulation (for behavior and fiber photometry), 10 ms laser pulse trains of 20 flashes were delivered at 30 Hz, repetitively occurring every 2 s. For voltammetry, single pulse trains (30 Hz, 40 pulses) were delivered. For in vivo stimulation, blue and yellow laser power was calibrated to 20 mW and 10 mW respectively. For ex vivo stimulation, a blue laser was calibrated to 5 mW and delivered through a patch cable (M81L01, Thorlabs).

2.9 Microinjection

Retrobeads (green, Lumafluor Inc.) and virus were microinjected into the brain using a glass

micropipette (3-000-203-G/X, Drummond Scientific) connected with a microinjector (Nanojet II,

3-000-204). Mice had their heads fixed on a stereotaxic instrument (Stoelting). For retrolabeling

experiments, 150 nl retrobeads were injected into the right PL mPFC (from bregma: anterior-

posterior (AP): +1.90, medial-lateral (ML): -0.50, dorsal-ventral (DV): -2.10. Brain tissue

containing the VTA was harvested one week after injection. For assessment of mechanical

withdrawal threshold and CPP, c-fos experiments, and voltammetry, 65 nl AAV9-DIO-ChR2

virus (AAV9.EF1a.DIO.hChR2(H134R)-eYFP.WPRE.hGH, Addgene 20298), or AAV9-DIO-

YFP (AAV9.EF1a.DIO.eYFP.WPRE.hGH , Addgene 27056) for YFP control was injected in the right VTA (AP -3.20, ML -0.50, DV -4.60), and allowed 4 weeks for sufficient expression of

ChR2/YFP. For fiber photometry experiments, 500 nl retrovirus AAVrg-syn- jGCaMP7s(AAVrg-syn-jGCaMP7s-WPRE, Addgene 104487) was injected in the right vlPAG

(AP -4.61, ML -0.42, DV -3.35) two weeks prior to the injection of AAV9-DIO-C1V1 (AAV9-

Ef1a-DIO C1V1 (t/t)-TS-EYFP, Addgene 35497, diluted 1:3 in PBS, 130 nl) into the right VTA

(AP -3.20, ML -0.50, DV -4.60), and allowed another 4 weeks for sufficient expression of both

viruses.

2.10 Optrode implantation

Optrodes were implanted for behavioral, fiber photometry, and c-fos experiments. Anesthetized

mice had their heads immobilized on a stereotaxic instrument (Stoelting). Cannulas were glued

on the skull with Geristore syringeable resin (31457520, Geristore). For behavioral and c-fos

experiments, 2 mm optic cannulas (CFMC22L02, Thorlabs) with a 200 µm core were implanted

targeting the PLmPFC (AP +1.90, ML -0.50, DV -2.00) to transmit blue laser light. For fiber

photometry experiments, 2 mm optic cannulas (MFC_400/430-0.37_2mm_MF2.5_FLT, Doric

Lenses) with 400 µm cores were implanted targeting the prelimbic mPFC to deliver yellow laser

light as well as to bidirectionally transmit signals for fiber photometry.

2.11 Immunohistochemistry

SNI mice with laser stimulation (or patch cable only for no laser controls) for 20 min were

euthanized 1.5 h after stimulation. Animals were perfused with 4% paraformaldehyde under deep

anesthesia with isoflurane. Brains were removed, fixed 4 h for retrobeads and CamKII staining

(overnight for TH staining) at 4˚C with 4% paraformaldehyde, and cryoprotected with 30%

sucrose for three days. Brains were frozen with optimal cutting temperature compound (VWR) in

a dry ice-ethanol bath, and coronal sections were cut using a cryostat (Leica). Slides with 30 µm

brain sections were washed with 1XPBS before being blocked with vehicle (0.3% TritonX-100,

10% normal goat serum, 0.5% bovine serum albumin) for 1.5 h at room temperature, and incubated with primary antibody (rabbit anti-c-fos, 1:1,000, Abcam, ab190289; mouse anti-

CaMKIIα, 1:100, Thermo Fisher Scientific, MA1-048) overnight at 4˚C. Slides were then washed with vehicle, incubated with secondary antibody (Alexa Fluor 546 goat anti-rabbit,

1:400, Invitrogen, A11035; Alexa Fluor 633 goat anti-mouse, 1:400, Invitrogen, A21050) for 1.5 h at room temperature, and washed with vehicle, 1XPBS, and 0.5XPBS before being plated on coverslips with mounting media (Thermo). For immunohistochemistry of tyrosine hydroxylase

(TH), a Mouse Anti-TH antibody (1:1000 Sigma, T1299) was used as primary antibody, and a secondary goat anti mouse antibody (1:800, Alexa Fluor 546, Invitrogen, 11003) was applied.

Post hoc immunohistochemistry for recording locations was conducted on free-floating sections. VTA sections were fixed using 4% paraformaldehyde overnight to one week. Brain sections were washed with 1XPBS before being blocked with vehicle (0.3% TritonX-100, 10%

normal goat serum, 0.5% bovine serum albumin) for 1.5 h at room temperature, and incubated

with conjugated anti-biotin antibody (1:200, Jackson Immuno Research, 200-542-211) overnight at 4˚C. Slices were washed with vehicle, 1XPBS, and 0.5XPBS before air dried on s slide and being plated on coverslips with mounting media (Thermo).

Images were taken with confocal microscopes (LSM 510 Meta, Zeiss; LAS X, Leica) and

a slice scanner (VS120-5 Slide, Olympus).

2.12 Reverse transcription polymerase chain reaction (RT-PCR)

C57B6/L mice were used for RT-PCR experiments. Mice underwent SNI or SHAM surgery

were decapitated under anesthesia, and mPFC tissue was collected 10 days after surgery. Brain

tissue was immediately frozen with dry ice and stored at -80 °C. RNA was extracted with the

NucleoSpin RNA II extraction kit (Macherey-Nagel) and quantified with the NanoDrop. Single- stranded cDNA was synthesized with QuantiTect Reverse Transcription Kit (Qiagen). PCR amplification was repeated 3 times with each sample with the QuantStudio3 Real-Time PCR

System and TaqMan Universal PCR Master Mix No AmpErase UNG (Thermo Fisher

Scientific). Semi-quantitative determinations were made for the target genes D1R, D2R, and TH compared to reference genes GAPDH and ACTB. The polymerase reaction was at 95°C for 15 min, then 40 cycles of 15 s at 95°C plus 60 s at 60°C. The mRNA levels were calculated with the

2(-Delta C(T)) method. Data reported were relative mRNA units compared to reference genes.

2.13 Statistics

Two-way ANOVA was used to determine statistical significance when evaluating the effect of two factors between multiple groups. One-way ANOVA was used to compare the effect of a treatment between multiple groups. A Bonferroni or Tukey post hoc test was used for multiple comparisons. A Mann-Whitney test was used to compare a factor between two groups, when

35 normality could not be assumed (VTA neurons have different subpopulations thus non- parametric test was used) or could not be determined due to the sample size (n<10). A two sample t-test was used to compare between two groups when normality distribution was assumed. A Grubbs' test was used to detect outliers and performed with GraphPad. All other hypothesis tests were performed with OriginPro (9.1). P<0.05 is considered as statistically significant. * represents p < 0.05, ** represents p < 0.01, **** represents p < 0.0001

Table 2-1. Key Resources

REAGENT or RESOURCE SOURCE IDENTIFIER Antibodies Cat#ab190289, Rabbit polyclonal c-Fos antibody Abcam RRID:AB_2737414 CaMKII alpha Monoclonal Antibody Cat#MA1-048, Thermo Fisher Scientific (6G9) RRID:AB_325403 Monoclonal Anti-Tyrosine Hydroxylase Cat#T1299, Sigma antibody produced in mouse RRID:AB_477560 Goat Anti-Rabbit IgG (H+L) Highly Molecular Probes Cat#A-11035, Cross-adsorbed Antibody, Alexa Fluor (Invitrogen) RRID:AB_143051 546 Goat Anti-Mouse IgG (H+L) Antibody, Molecular Probes Cat#A-21050, Alexa Fluor 633 (Invitrogen) RRID:AB_141431 Goat Anti-Mouse IgG (H+L) Antibody, Molecular Probes Cat#A-11003, Alexa Fluor 546 (Invitrogen) RRID:AB_141370 Alexa Fluor 488-IgG Fraction Jackson ImmunoResearch Cat# 200-542-211, Monoclonal Mouse Anti-Biotin antibody Labs RRID:AB_2339040 Bacterial and Virus Strains Addgene (Karl Deisseroth AAV9.EF1a.DIO.hChR2(H134R)- Lab: Cre-activated AAV RRID:Addgene_20298 eYFP.WPRE.hGH expression plasmids Unpublished) Addgene (Karl Deisseroth Lab: Double Floxed AAV9.EF1a.DIO.eYFP.WPRE.hGH RRID:Addgene_27056 Inverted ORF Control Unpublished) Addgene (Dana et al RRID:Addgene_10448 AAVrg-syn-jGCaMP7s-WPRE bioRxiv 434589) 7 Addgene (Yizhar et al Nature. 2011 Jul AAV9-Ef1a-DIO C1V1 (t/t)-TS-EYFP RRID:Addgene_35497 27;477(7363):171-8. doi: 10.1038/nature10360.) Experimental Models: Organisms/Strains B6.SJL-Slc6a3tm1.1(cre)Bkmn/J Mus RRID:IMSR_JAX:006 Jax musculus 660 B6;129S-Gt(ROSA)26Sortm32(CAG- RRID:IMSR_JAX:012 Jax COP4*H134R/EYFP)Hze/J Mus musculus 569 B6.Cg-Gt(ROSA)26Sortm9(CAG- RRID:IMSR_JAX:007 Jax tdTomato)Hze/J Mus musculus 909 B6.SJL-Slc6a3tm1.1(cre)Bkmn/J x B6;129S- University of Calgary Gt(ROSA)26Sortm32(CAG- animal facility (Origin of N/A COP4*H134R/EYFP)Hze/J Mus musculus breeding pairs: Jax)

B6.SJL-Slc6a3tm1.1(cre)Bkmn/J x B6.Cg- University of Calgary Gt(ROSA)26Sortm9(CAG-tdTomato)Hze/J Mus animal facility (Origin of N/A musculus breeding pairs: Jax) Software and Algorithms OriginPro 9.1 Origin RRID:SCR_014212 MATLAB (R2018a) MATLAB RRID:SCR_001622 GraphPad GraphPad RRID:SCR_000306 pClamp Molecular Devices RRID:SCR_011323 TarHeel CV Software ESA Biosciences N/A High Definition Cyclic Voltammetry University of North N/A Suite Carolina Doric Neuroscience Studio Software Doric Lenses N/A

Chapter Three: Peripheral Nerve Injury-induced Alterations in VTA Neuron Firing

Properties

3.1 Abstract

The ventral tegmental area (VTA) is one of the main brain regions harboring dopaminergic neurons, and plays important roles in reinforcement and motivation. Recent studies have indicated that dopaminergic neurons not only respond to rewarding stimuli, but also to noxious stimuli. Furthermore, VTA dopaminergic neurons undergo plasticity during chronic pain. Lateral and medial VTA neurons project to different brain areas, and have been characterized via their distinct electrophysiological properties. In this study, we characterized electrophysiological properties of lateral and medial VTA dopaminergic neurons using DAT-cre reporter mice, and examined their plasticity during neuropathic pain states. We observed various dopaminergic subpopulations in both the lateral and medial VTA, as defined by action potential firing patterns, independently of synaptic inputs. Our results demonstrated that lateral and medial VTA dopaminergic neurons undergo differential plasticity during neuropathic pain. However, these changes only reside in specific dopaminergic subpopulations. This study suggests that lateral and medial VTA dopaminergic neurons are differentially affected during neuropathic pain conditions, and emphasizes the importance of subpopulation specificity when targeting VTA dopaminergic neurons for treatment of neuropathic pain.

Keywords: dopamine, ventral tegmental area, pain, prefrontal cortex, brain circuits.

3.2 Introduction

Dopaminergic neurons within the ventral tegmental area (VTA) play an important role in the

regulation of appetitive stimuli, anxiety, aversion and pain (Bromberg-Martin et al., 2010;

Budygin et al., 2012; Gunaydin et al., 2014; Lammel et al., 2012; Zweifel et al., 2011). The

precise function of VTA dopaminergic neurons in pain processing is incompletely understood. It

has been suggested that pain relief is signaled as reward via VTA dopaminergic neurons (King et

al., 2009). However, VTA dopaminergic neurons are also directly activated by noxious stimuli

(Brischoux et al., 2009). Recent studies revealed plasticity of VTA dopamine neurons during

neuropathic pain, expressed as decreased excitability (Ren et al., 2016; Watanabe et al., 2018).

Notably, noxious stimuli, such as footshocks, induce phasic firing in only specific subpopulations of VTA dopaminergic neurons (Brischoux et al., 2009; King et al., 2009).

However most, if not all existing studies reported chronic pain-induced plasticity in VTA dopaminergic neurons as a whole population. VTA dopaminergic neurons are heterogeneous.

They receive inputs from, and project to a number of brain regions, including the nucleus accumbens (NAc), the medial prefrontal cortex (mPFC), and the amygdala (Berger et al., 1976;

Carr and Sesack, 2000a, b), which have been implicated in the processing of both reward and pain (Dale et al., 2018; Lee et al., 2015; Zhang et al., 2015). While some subpopulations can be separated depending on their locations (i.e., NAc lateral and medial shell projecting neurons are mainly located in the lateral and medial VTA, respectively), others are intermingled within the same VTA subregion (mPFC, amygdala and NAc core projecting neurons are located mainly in the medial VTA) (Baimel et al., 2017; Lammel et al., 2011; Roeper, 2013).

Here, we examined biophysical properties and neuronal excitability of VTA dopaminergic neurons in SHAM operated mice, and in mice with a spared nerve injury (SNI) of the sciatic nerve which gives rise to chronic neuropathic pain (Decosterd and Woolf, 2000;

Zhang et al., 2015). In both the lateral and medial VTA, we used action potential firing pattern as an electrophysiological fingerprint to define different dopaminergic subpopulations. We find that only specific subtypes of medial and lateral dopaminergic VTA undergo injury-induced changes in their electrophysiological properties, thus suggesting that chronic pain states are associated with altered neuronal plasticity in specific dopaminergic neuron populations in the VTA.

3.3 Results

3.3.1 Nerve injury-induced changes in the total medial and lateral VTA neuron population

We performed recordings in slices from the lateral and medial VTA to ascertain putative

plasticity in dopaminergic neurons as a result of peripheral nerve injury (leading to neuropathic

pain). Prior to comparing neuronal excitability in SNI to that in SHAM operated mice,

electrophysiological properties were characterized in lateral and medial VTA dopaminergic

neurons that were identified by tdTomato fluorescence driven by the DAT promoter (Fig. 3-1).

Consistent with previous literature (Zhang et al., 2010), while some features were similar

between lateral and medial VTA (Fig. 3-2 a-c, f), we found that lateral VTA neurons are larger in size (49.06±2.32 pF, n=18/12) compared to medial VTA neurons (27.07±2.32 pF, n=15/11)(p=1.40E-5, mann whitney test), as reflected by cell capacitance (Neuhoff et al., 2002)

(Fig. 3-2d). This was associated with a bigger leak current (lateral -117.31±11.83 pA, n=13/10; medial 48.00±9.30 pA, n=5/5 ;p=0.0052, mann whitney test) as well as a smaller input resistance

(lateral 0.44±0.04 GΩ, n=14/13; medial 0.80±0.10 GΩ, n=12/12; p=0.0051, mann whitney test) in lateral VTA neurons (Fig. 3-2 c and e), which result in a lower excitability as reflected in the

F-I slope (lateral 0.039±0.003, n=17/12; medial 0.078±0.011, n=14/9; p=0.034, mann whitney test) (Fig. 3-2 i, k, l). Another well-documented electrophysiological fingerprint of lateral VTA versus medial is activation of the hyperpolarization-activated cation current (Ih) (Zhang et al.,

2010), which was also observed in our recordings as Vsag/Rinput (lateral 46.07±3.62 pA, n=16/10;

medial 11.80±2.10 pA, n=10/8; p=3.51E-5, mann whitney test) (Fig. 3-2 j and m).

After confirming the quality and location of our recordings, biophysical and excitability

properties were recorded in dopaminergic neurons from mice that had undergone SNI surgeries,

and compared with those in SHAM groups. SNI mice model was validated by behaviour test

(Fig. 3-3), using different mice than the ones for electrophysiology test, to avoid changes

induced by acute noxious stimuli. In the lateral VTA, SNI surgery induced a decrease in

spontaneous firing frequency (SHAM 1.81±0.27 Hz, n=27/18; SNI 1.01±0.19 Hz, n=21/14;

p=0.037, mann whitney test) (Fig. 3-4 a), but it did not affect other properties including F-I

slope, input resistance, first spike latency, action potential threshold and Vsag/Rinput reflecting Ih

(Fig. 3-4 b-f). In the medial VTA, no difference was observed between SNI and SHAM operated

mice in the properties that were examined (Fig. 3-4 g-l).

3.3.2 Injury induced changes in the firing behavior of lateral VTA neurons subtypes

Because we observed SNI-induced plasticity only in lateral VTA dopaminergic neurons, we

hypothesized that sub-populations within the lateral VTA may be more susceptible than others in

response to SNI, given the heterogeneity within the VTA (Baimel et al., 2017; Lammel et al.,

2014; Roeper, 2013). Based on action potential firing patterns observed during evoked action

potential recordings (see paradigm in Fig. 3-2 h, upper panel), lateral VTA dopaminergic

neurons were grouped into Types 1-3 (Fig. 3-5 a). Type 1 neurons have a delayed onset of first

spike and a regular (nonaccomodating) firing pattern. Type 2 neurons fire immediately at the

start of current stimulation. The firing frequency adapts and the excitability reflected by the F-I

slope is significantly higher compared to the other 2 types (Type 1, 0.041± 0.005, n=13/9; Type

2, 0.076± 0.019, n=4/4; Type 3, 0.036± 0.003, n=8/7; p=0.010, one-way ANOVA; p=0.021 Type

1 vs. 2, p=0.012 Type 2 vs. 3, p=1 Type 1 vs. 3, Bonferroni test) (Fig. 3-5 c). Type 3 neurons exhibit a similar firing pattern as Type 1 neurons, but exhibit a much larger and slower afterhyperpolarization potential (AHP). In addition, Type 2 and 3 neurons have a significantly bigger

Vsag/Rinput reflecting Ih compared to Type 1 neurons (Type 1, 33.5±3.3 pA, n=9/8; Type 2,

57.9±4.9 pA, n=3/3; Type 3, 57.3±3.5 pA, n=8/7; p=1.61E-4, one-way ANOVA; p=0.0044 Type

1 vs. 2, p=1 Type 2 vs. 3, p=2.82E-4 Type 1 vs. 3, Bonferroni test) (Fig. 3-5 d), but they differ in the kinetics of Vsag activation (Fig. 3-5 b). These characteristic firing patterns were maintained

in the presence of synaptic blockers (bicuculline 20 µM, D-AP5 50 µM, DNQX 10 µM,

sulpiride 500 µM, CGP55845 200 nM, strychnine 1 µM) that inhibit GABAA, NMDA, AMPA,

D2, GABAB, and glycine receptors, respectively, indicating that these firing patterns are intrinsic

(Fig. 3-6 a-c). For all three dopaminergic subpopulations in the lateral VTA, application of

antagonists increased action potential frequency at an identical stimulation current step, but did

not change action potential characteristics. This is consistent with previous findings that VTA

dopaminergic neurons are constrained by inhibitory tone (Johnson and North, 1992; Nugent et

al., 2007; Yim and Mogenson, 1980).

The majority of lateral dopaminergic neurons belong to Type 1 (28/58) and 3 (22/58)

(Fig. 3-5 e). We therefore compared electrophysiological properties between SHAM and SNI in

these two groups (Fig. 3-7). Our results demonstrated that the nerve injury-induced decreased

spontaneous firing frequency that was observed in the total lateral dopaminergic population (see

Fig. 3-4) is due to changes in the properties of Type 1 (SHAM 1.56± 0.32 Hz, n=15/13; SNI

0.59± 0.18 Hz, n=6/6; p=0.047, mann whitney test)(Fig. 3-7 a, b) but not Type 3 neurons (Fig.

3-7 h), while all other properties in both groups remain unchanged after SNI surgery.

Lateral VTA dopaminergic neurons were also compared at ventral-dorsal axis. Lateral central VTA dopaminergic neurons have a higher excitability compared to that in lateral ventral, reflected in F-I slope (central 0.054±0.008, n=20/17; ventral 0.034±0.003, n=17/13; dorsal

0.033±0.004, n=9/8; p=0.021, one-way ANOVA; p=0.0039, Bonferroni post-hoc test) (Fig. 3-8).

However, no difference between SHAM and SNI operated groups was observed in ventral,

central, nor dorsal medial VTA (data not shown).

3.3.3 Injury induced changes in the firing behavior of medial VTA neurons subtypes

Different dopaminergic subpopulations were also observed in the medial VTA (Fig. 3-9). dopaminergic neurons were grouped into subpopulations based on their firing patterns, with criteria in 1) accommodating, non-accommodating, bursting, and irregular spiking; 2) delayed and classic; 3) initial, transient, and repetitive, which are widely used for classification of cortical neurons and neurons in other brain areas (Jiang et al., 2014; Markram et al., 2004; Santos et al.,

2007; Stiefel et al., 2013). The observed firing patterns include, but are not limited to irregular

(8/42), delayed-accommodating (3/42), delayed-nonaccommodating (9/42), adapting (3/42),

initial burst (2/42), and high frequency (firing rate above 35 Hz at +200 pA current step)

subtypes types (2/42) (Fig. 3-9 a). Among these, the responses of irregular, delayed-non-

accommodating, and high frequency neurons to synaptic blockers were tested, revealing a drug-

induced increase in excitability, without a change in their overall firing patterns (Fig. 3-6 g-i).

This suggests that specific firing patterns in medial VTA dopaminergic neurons are an intrinsic

feature that is independent from synaptic input. The majority of the observed medial VTA

dopaminergic neuron subtypes were of the irregular and delayed-nonaccommodating

phenotypes, and we compared electrophysiological properties between SHAM and SNI of these

two subpopulations (whereas the others were observed too infrequently to permit a comparison

between SHAM and SNI states). In contrast with lateral VTA Type 1 neurons, the delayed-

nonaccommodating firing medial VTA neurons from SNI treated mice showed a higher degree

of excitability (as reflected by the F-I slope) compared to the SHAM group (SHAM 0.020±

0.004, n=4/4; SNI 0.065± 0.017, n=5/4; p=0.037, mann whitney test) (Fig. 3-9 b and d).

Consistently, we observed a more hyperpolarized action potential threshold (SHAM -44.68±1.80

mV, n=4/4; SNI -58.10±1.47 mV, n=4/4; p=0.030, mann whitney test) and larger Vsag/Rinput reflecting Ih amplitude (SHAM 10.28±1.60 pA, n=4/4; SNI 32.85±3.40 pA, n=4/4; p=0.030, mann whitney test) in the SNI group. There was no change in spontaneous firing frequency (Fig.

3-9 b) and other biophysical properties (data not shown) induced by SNI surgery. In contrast, the irregular firing medial VTA neurons did not exhibit a difference in cell excitability, spontaneous firing frequency (Fig. 3-9 c), as well as other biophysical properties (data not shown). Medial

VTA dopaminergic neurons were also compared at ventral-dorsal axis. However no electrophysiological fingerprint was identified only based on ventral-dorsal locations (Fig. 3-8), and no difference in electrophysiological properties between SHAM and SNI operated groups was observed in ventral, central, nor dorsal medial VTA (data not shown). Altogether, these data indicate that nerve injury alters the firing properties of specific subpopulations of VTA dopaminergic neurons.

3.4 Discussion

The role of VTA dopaminergic neurons in motivated behaviour and conditioned reinforcement has been well documented. However, their functions in pain or aversion signaling have remained controversial (Brischoux et al., 2009; King et al., 2009) and incompletely understood. Existing studies using electrophysiology (Ren et al., 2016; Watanabe et al., 2018) or indirect measurements by microdialysis (Ko et al., 2018) indicate that chronic pain states are associated with a hypodopaminergic tone in the VTA, while noxious stimuli is signaled as motivational salience and trigger dopamine release (Taylor et al., 2016). Chronic pain is different than acute noxious stimuli, and may involve different mechanisms and neuronal subpopulations in the

VTA. In the present study, we provide new evidence that the overall activity of dopaminergic neurons in the lateral, but not the medial VTA is decreased after peripheral nerve injury.

Furthermore, we dissected dopaminergic neurons into different subpopulations based on their firing patterns. Our results revealed that plasticity induced during neuropathic pain states only resides in a single specific dopaminergic subpopulation in both the lateral and medial VTA. The changes in delayed-nonaccommodating firing medial VTA neurons might be masked by other unchanged subtypes, due to the complexity of medial VTA dopaminergic populations as shown in this as well as previous studies (Lammel et al., 2014).

VTA dopaminergic neurons send projections to the NAc and the mPFC via mesolimbic and mesocortical pathways. These two limbic areas have been intensively studied in the context of pain perception and modulation, and dopamine has been suggested to play a role in these processes. Thus, changes in VTA dopaminergic neurons may be further reflected in their downstream targets (Coffeen et al., 2010; Ren et al., 2016; Sogabe et al., 2013; Zhang et al.,

2017). Zhang et. al. reported that neuropathic pain induced by CCI increased spontaneous firing frequency in VTA-NAc neurons. The change in neuronal activity in the VTA can further increase BDNF expression in the NAc, which has a causal relation to neuropathic pain (Zhang et al., 2017). The authors did not clearly state which subregion of NAc they focused on. Based on our results, giving the authors observed an increase in firing rate, the change that they observed in VTA neurons might project to the medial shell of the NAc. For another example, changed

VTA dopaminergic neuron activity can affect indirect pathway projections to spiny neurons in the NAc, which is known to have a causal relation to neuropathic pain (Ren et al., 2016).

However, compared to an overall decrease in medial VTA neurons reported in this study, our results showed an increase in the delayed-nonaccommodating firing subtype and no overall

change in the medial VTA. The different results might be due to different experimental

conditions. For example, we used DAT-reporter mice to specifically identify dopaminergic neurons, and we did not use TTX to block action potential firing during recordings. This difference might indicate that nerve injury-induced changes may affect VTA dopaminergic populations by both synaptic input and intrinsic properties. In addition, changes in VTA dopaminergic neuronal activity may modulate PFC activity via the mesocortical pathway, thus affecting pain perception through the PFC-PAG axis (Huang et al., 2019a; Huang et al., 2019b;

Sogabe et al., 2013).

Although our study reveals nerve injury-induced plasticity in the VTA, it is still unclear whether this change is causal to chronic neuropathic pain. Previous studies reported that lesion of dopaminergic neurons in VTA and terminals in the striatum using 6-OHDA increased hyperalgesia during by neuropathic pain conditions (Saade et al., 1997). In contrast, electrical stimulation in the VTA has analgesic effects (Sotres-Bayon et al., 2001; Wood, 2006). Moreover, stimulating NAc projecting VTA dopaminergic neurons reverses neuropathic pain (Watanabe et al., 2018). It is important to note that in the present study, the two groups of neurons that showed nerve injury-induced plasticity could only be identified based on their firing pattern. Further experiments using retrograde tracing techniques to investigate the input/output map will be needed to further dissect the circuitry.

In summary, our data reveal that peripheral nerve injury alters the activity of specific subpopulations of VTA neurons. Whereas, the molecular basis for the observed changes will require further study, our data suggest targeting the VTA as a possible locus for intervention into neuropathic pain.

Figure 3-1. Biocytin labeling in the internal recording solution allows post-hoc recovery of

recording locations. a VTA horizontal section showing recorded neurons from the medial (left arrow) and lateral

(right arrow) VTA. Red, DAT-positive neurons expressing td-Tomato. Green, biocytin label.

Scale bar, 100 μm. b and c are images of the same neurons with and without biocytin signal, showing morphological details of cells and the overlap between DAT and biocytin. Scale bar,

50 μm.

P=0.005

P=1.4E-5 P=0.005

P=0.04 P=3.5E-5

input /R sag V

Figure 3-2. Electrophysiological characterization of lateral and medial VTA dopaminergic neurons.

a Spontaneous firing frequencies in the lateral and medial VTA. b-f Biophysical properties including membrane potential (b), leak current (c), capacitance (d), input resistance (e), and action potential threshold (f) of medial and lateral VTA dopaminergic neurons. h Current clamp

stimulation protocols for measuring the F-I relation (upper) and Vsag (lower). Scale bar, 50 mV,

100 ms. i Representative current clamp traces showing action potential firing patterns at 100 pA

from lateral (upper) and medial (lower) VTA dopaminergic neurons. Scale bar, 20 mV,

100 ms. j Representative current clamp traces showing activation of Vsag in lateral (upper) and

medial (lower) VTA dopaminergic neurons. The red dashed lines indicate where peak response

and steady-state were measured for calculation of the voltage sag. Scale bar, 10 mV,

100 ms. k Average F-I relation curves for medial and lateral VTA dopaminergic neurons. l F-I slope for medial and lateral VTA dopaminergic neurons. m Vsag/Rinput reflecting Ih amplitude

recorded from medial and lateral VTA dopaminergic neurons. Statistics, Mann-Whitney test.

**, p < 0.01; ****, p < 0.0001. Numbers in parentheses reflect numbers of cells and animals, and are presented as (N = cells/N = animals).

Figure 3-3. Mechanical withdrawal threshold in SNI and SHAM mice. a Mechanical withdrawal threshold of ipsilateral (ipsi) and contralateral (contra) paws in 10 SNI operated mice. b Mechanical withdrawal threshold of ipsilateral (ipsi) and contralateral (contra) paws in 6 SHAM operated mice. Numbers in parentheses reflect numbers of animals.

P=0.04

input /R sag V

Figure 3-4. Electrophysiological properties of lateral and medial VTA dopaminergic neurons isolated from in SHAM versus SNI groups. a-f Comparison of spontaneous firing frequency (a), F-I slope (b), input resistance (c), first spike latency at 100 pA (d), AP (action potential) threshold (e), and Vsag/Rinput reflecting Ih amplitude

(f) between SHAM and SNI groups in lateral VTA dopaminergic neurons. g-l Comparison of spontaneous firing frequency (g), F-I slope (h), input resistance (i), first spike latency at 100 pA

(j), Action potential threshold (k), and Vsag/Rinput reflecting Ih amplitude (l) between SHAM and

SNI groups in medial VTA dopaminergic neurons. Statistics, Mann-Whitney test. *, p < 0.05.

Numbers in parentheses reflect numbers of cells and animals, and are presented as

(N = cells/N = animals)

Figure 3-5. Electrophysiological characterization of three subpopulations of lateral VTA dopaminergic neurons. a Examples of action potential firing patterns evoked by a 100 pA current step in Type 1–3 dopaminergic neurons. b Voltage sag activation evoked by a − 150 pA current step in Type 1–3 dopaminergic neurons. c Quantification of F-I slope in Type 1–3 dopaminergic neurons. d Quantification of Vsag/Rinput amplitude in Type 1–3 dopaminergic neurons. e) Proportion of Type 1–3 neuronal subtypes in the lateral VTA dopaminergic neurons. Scale bar, 20 mV, 100 ms. Statistics, One-way ANOVA and Bonferroni post-hoc test.

*, p < 0.05; **, p < 0.01; ***, p < 0.001. Numbers in parentheses reflect numbers of cells and animals, and are presented as (N = cells/N = animals). Characterization was done with SHAM operated mice.

Figure 3-6. Action potential firing patterns of different dopaminergic neuronal subpopulations in the lateral and medial VTA without and with synaptic blockers. a-c Examples of action potential firing patterns of lateral VTA Type 1–3 neurons evoked by a

100 pA current step, without blockers. d-f Action potential firing patterns of lateral VTA Type

1–3 neurons evoked by a 100 pA current step in the presence of synaptic blockers (Bicuculline

20 μM, D-AP5 50 μM, DNQX 10 μM, Sulpiride 500 μM, CGP55845 200 nM, Strychnine

1 μM). g-i Action potential firing patterns of medial VTA Irregular, Delayed- nonaccommodating, and High frequency dopaminergic neuron subpopulations at a 150 pA current step in the absence of synaptic blockers. j-l Action potential firing patterns of medial

VTA Irregular, Delayed-nonaccommodating, and High frequency dopaminergic neuron subpopulations at 150 pA current step, with the above synaptic blockers. Scale bar, 20 mV,

100 ms.

P=0.047

input /R sag V

Figure 3-7. Electrophysiological properties of Type 1 and 3 lateral VTA dopaminergic neuron subpopulations in SHAM and SNI groups. a-f (a) Representative traces of spontaneous firing of Type 1 neurons in SHAM (left) and SNI

(right) operated mice. Scale bar, 20 mV, 1 s. Comparison of spontaneous firing frequency (b), F-I slope (c), input resistance (d), first spike latency at 100 pA (e), action potential threshold (f), and

Vsag/Rinput reflecting Ih amplitude (g) between SHAM and SNI groups in Type 1 lateral VTA dopaminergic neurons. h-m Comparison of spontaneous firing frequency (h), F-I slope (i), input resistance (j), first spike latency at 100 pA (k), action potential threshold (l), and Vsag/Rinput reflecting Ih amplitude (m) between SHAM and SNI groups in Type 3 lateral VTA dopaminergic neurons. Statistics, Mann-Whitney test. *, p < 0.05. Numbers in parentheses reflect numbers of cells and animals, and are presented as (N = cells/N = animals).

Figure 3-8. Electrophysiological properties of ventral-dorsal VTA dopaminergic neurons.

a-d Spontaneous firing frequency (a), F-I slope (b), input resistance (c), and Vsag/Rinput reflecting

Ih (d) in central, ventral, and dorsal subregions of the lateral VTA. e-h Spontaneous firing frequency (e), F-I slope (f), input resistance (g), and Vsag/Rinput reflecting Ih (h) in central, ventral, and dorsal subregions of the medial VTA. Numbers in parentheses reflect numbers of cells. DATA were collected from 33 SHAM and 25 SNI mice.

P=0.04 P=0.03 P=0.03

input /R sag V

Figure 3-9. Electrophysiological properties of dopaminergic neuronal subpopulations in the medial VTA in SHAM and SNI groups. a Examples of action potential firing patterns evoked by a 200 pA current step of different dopaminergic neuronal subpopulations in the medial VTA. b Electrophysiological properties of Delayed-nonaccommodating firing type dopaminergic neurons in SHAM versus SNI groups. c Electrophysiological properties of Irregular firing type dopaminergic neurons in

SHAM versus SNI groups. d Representative current clamp traces showing the number of action potentials during a 150 pA current step in SHAM (upper) and SNI (lower) groups. Scale bar,

20 mV, 100 ms. Statistics, Mann-Whitney test. *, p < 0.05. Numbers in parentheses reflect numbers of cells and animals, and are presented as (N = cells/N = animals).

Chapter Four: Dopaminergic Inputs from the Ventral Tegmental Area into the Medial

Prefrontal Cortex Modulate Neuropathic Pain Associated Behaviors in Mice

4.1 Abstract

The medial prefrontal cortex (mPFC) is a brain region involved in affective components of pain, and undergoes plasticity during development of chronic pain. Dopamine is a key neuromodulator in the mesocortical circuit and modulates working memory and aversion. Although dopaminergic inputs into the mPFC have been shown to modulate plasticity, whether and how these inputs affect pain remains incompletely understood. By using optogenetic approaches, we found that phasic activation of dopaminergic inputs from the ventral tegmental area (VTA) into the mPFC reduced mechanical hypersensitivity during neuropathic pain states. Mice with neuropathic pain exhibited a preference for contexts paired with photostimulation of dopaminergic terminals in the mPFC. Fiber photometry based calcium imaging revealed that dopamine increases the activity of mPFC neurons projecting to the ventrolateral periaqueductal grey (vlPAG). Altogether, our findings indicate an important role of mPFC dopamine signaling in pain modulation.

Key words: neuropathic pain, medial prefrontal cortex, dopamine, optogenetics, VTA

4.2 Introduction

Nociceptive signals from the periphery are relayed and processed in the spinal cord and

transmitted to the brain, where they are perceived as an unpleasant sensory and emotional

experience. Multiple regions within the brain are associated with the processing of sensory and

affective components of pain signals (Auvray et al., 2010), such as the limbic system which

includes the PFC, nucleus accumbens (NAc), amygdala, and hippocampus (Apkarian et al.,

2011; Bingel et al., 2002). Among these brain areas, the PFC is an important top-down control

center that mediates executive functions such as working memory, learning, and attention

(D'Ardenne et al., 2012; Miller and Cohen, 2001; Rossi et al., 2009; Werchan et al., 2016). It is

now well established that the PFC, especially the mPFC, undergoes structural and functional

plasticity during chronic pain states (Apkarian et al., 2004; Metz et al., 2009). Several studies

have reported decreased activity of layer 5 pyramidal neurons in the prelimbic (PL) area of the

mPFC in rodents with chronic pain arising from peripheral nerve injury, whereas optogenetically

activating these neurons can reverse both sensory and affective aspects of chronic pain (Dale et

al., 2018; Zhang et al., 2015). This is thought to involve downstream targets such as the vlPAG

region, which is a key brain structure that regulates the descending modulation of pain signals

(Huang et al., 2019a).

The mPFC plays important roles in both the pain and reward systems (DosSantos et al.,

2017; Ong et al., 2019; Tzschentke, 2000). The VTA is one of the main reservoirs for

dopaminergic neurons that typically respond to reinforcing stimuli; however a subpopulation of

dopaminergic neurons also respond to noxious stimuli (Brischoux et al., 2009). VTA

dopaminergic neurons project to the mPFC (Lammel et al., 2008), including the PL mPFC

(Lammel et al., 2011; Popescu et al., 2016), as part of the mesocortical dopaminergic pathway

(Axelrod, 1977). They regulate neuronal excitability, synaptic transmission, as well as executive

functions in the mPFC (Buchta et al., 2017; Chen et al., 2004; Chudasama and Robbins, 2004;

Kroener et al., 2009; Lewis and O'Donnell, 2000; Ott and Nieder, 2019; Seamans and Yang,

2004). However, whether VTA-mPFC dopaminergic inputs can modulate mPFC neuronal

activity to affect the processing of chronic pain signals has remained unclear.

Based on the notion that mPFC activity is reduced with chronic pain (Dale et al., 2018;

Zhang et al., 2015), and the manner in which dopamine modulates mPFC neuronal activity, we

hypothesized that dopamine release may affect neuronal activity in the mPFC, thus tuning

population output to downstream targets that modulate neuropathic pain states. Because of the

complexity of the dopaminergic system (Huang et al., 2019b; Seamans and Yang, 2004), in vivo

tests are required to ascertain such a putative role of dopamine. Thus in the present study, we

combined optogenetics with behavioral assessments of sensory and affective aspects in mice

with chronic neuropathic pain to specifically investigate the role of dopamine released in the

mPFC by VTA originating projections.

4.3 Results

4.3.1 VTA dopaminergic neurons anatomically and functionally connect to the PL mPFC

To study the role of dopamine in the modulation of chronic pain signals in the mPFC, we first validated VTA dopaminergic neuron projections to the mPFC using retrobeads and imaging approaches (Figure 4-1A). Mice expressing the reporter tdTomato under the dopamine transporter promoter (DATcreTdTomato mice) were used to identify dopaminergic neuronal cell

bodies and terminals in mPFC slices from naïve animals. We observed sparse but clear

dopaminergic terminals in the mPFC (Figure 4-1B). We then injected green (Alexa Fluor 488)

retrobeads into the right mPFC (Figure 4-1C), and observed retrobead signals that overlapped with dopaminergic neurons in the right VTA one week after injection (Figure 4-1D and E).

These data indicate that dopaminergic projections from the VTA innervate the mPFC and are

consistent with previous literature (Lammel et al., 2011; Popescu et al., 2016).

To test whether peripheral neuropathy influences the ability to evoke dopamine in the

mPFC, we used fast-scan cyclic voltammetry (FSCV) in combination with optogenetics to

photostimulate VTA dopaminergic terminals in mPFC slices from mice subjected to a spared

nerve injury of the sciatic nerve (SNI), and from sham operated (SHAM) animals.

DATcretdTomato mice were injected with a cre-dependent virus to express channelrhodpsin2

(ChR2) in VTA dopaminergic neurons (AAV-DIO-ChR2) (Figure 4-1F). After 2 weeks, SNI or

SHAM surgeries were performed on the left (ipsilateral) hind limb to induce a neuropathic pain

state (Richner et al., 2011). Mechanical hypersensitivity was verified using a dynamic plantar

aesthesiometer (DPA) device, and only mice that exhibited a greater than 30% reduction in

ipsilateral mechanical thresholds were considered as neuropathic mice for further use. Two

weeks after the SNI or SHAM surgeries, FSCV was conducted on slices from the right

(contralateral) mPFC, as well as the NAc core as a positive control, as this area receives

significant dopaminergic projections and has robust dopamine release (Mateo et al., 2017).

Photostimulation of dopaminergic terminals (30 Hz 40 pulses, λ=473, 5 mW) in the NAc reliably increased dopamine concentration in slices from both SHAM and SNI mice (Figure 4-2 A-C). In the mPFC, photostimulation of dopaminergic terminals evoked a similar release in slices from both SHAM (23.5±1.57 nM) and SNI (28.1±2.27 nM) mice (Figure 4-1G-I) (see figure legends

for statistics). Altogether, these data show that VTA projections to the mPFC are capable of

releasing dopamine in an activity dependent manner, and serve as a validation for the in vivo

optogenetic approaches described below.

4.3.2 Phasic but not tonic activation of dopaminergic terminals in the mPFC reduces nerve

injury-induced mechanical hypersensitivity

To examine whether dopaminergic inputs into the mPFC can modulate mechanical withdrawal

threshold (Richner et al., 2011) in SNI and SHAM mice, we initially used DAT-cre mice crossed

with Ai32 mice to generate a transgenic line that expresses ChR2 in dopaminergic neurons

(Figure 4-3A). After validating ChR2 expression and function (Figure 4-4 A-F), we implanted an optic fiber in the right mPFC to deliver phasic (30 Hz, 20 pulses, every 2 s) and tonic (1 Hz continuously) laser stimulation to activate dopaminergic neuronal terminals (Figure 4-3B).

Mechanical threshold was measured without laser stimulation for baseline, then with phasic laser

stimulation (laser is on during the 5-10 minutes testing period), and with tonic laser stimulation.

The measurements were taken 45 minutes after establishing the baseline (Figure 4-3C). In

SHAM mice, neither phasic nor tonic photostimulation altered mechanical thresholds (Figure 4-

3D). In SNI mice we observed a significant difference in mechanical withdrawal thresholds

between ipsilateral and contralateral paws, which reflects the development of mechanical

hypersensitivity on the ipsilateral side. Interestingly, phasic, but not tonic laser stimulation

abolished the difference in withdrawal thresholds between the ipsilateral (ips) and contralateral

(con) paws. These data suggest that phasic activation of dopaminergic terminals in the mPFC

reverses nerve injury-induced hypersensitivity. In contrast, neither phasic nor tonic

photostimulation altered mechanical thresholds in SHAM mice (Figure 4-3E and 4-3F).

It is possible that the mPFC may have dopaminergic inputs from more than one region

(Yoshida et al., 1989). To test whether the observed reversal of mechanical hypersensitivity is

due to activation of VTA dopaminergic inputs to the mPFC, we then tested the effect of phasic

ChR2 activation using DAT-cre mice injected with a cre-dependent AAV9-DIO-ChR2 virus in

the VTA. A fiberoptic cannula was implanted in the mPFC to stimulate dopaminergic terminals

(Figure 4-3G). DAT-cre mice injected with virus exhibited higher ChR2 expression levels

(Figure 4-3H) compared to DAT-ChR2 mice (with the same experimental conditions and

settings). Consistent with findings with DAT-ChR2 mice (Figure 4-3E and 4-3F), phasic

stimulation of VTA dopaminergic terminals in the mPFC from SNI mice abolished the

difference in mechanical threshold between ipsilateral and contralateral paws (Figure 4-3I and

4-3J). These findings suggest that phasic stimulation of VTA dopamine release in the mPFC

reverses mechanical hypersensitivity induced by SNI.

It is important to ensure that the analgesic effect of optogenetic stimulation of VTA

terminals is mediated by dopamine release as opposed to other co-released neurotransmitters such as glutamate. We therefore locally infused dopamine receptor antagonists (SCH23390 for

D1 receptors, 3 mM, 0.2µl; combined with sulpiride for D2 receptors, 250 µM, 0.2 µl) 5 minutes before application of phasic laser stimulation through a dual cannula implanted in the mPFC

(Figure 4-3K). We found that application of this antagonist cocktail, but not vehicle abolished the effects of phasic laser stimulation in SNI mice (Figure 4-3L), thus confirming a critical role of dopamine and its receptors in the mPFC.

We also tested whether phasic laser stimulation of dopaminergic terminals modulates hypersensitivity in female mice. Female DAT-cre mice were injected with AAV-DIO-ChR2 in the VTA and an optic fiber implanted in the mPFC (see Figure 4-3G). As with male mice, phasic laser stimulation of dopaminergic terminals in the mPFC abolished the difference in

71 mechanical withdrawal threshold between ipsilateral and contralateral sides in female SNI mice, indicating that the effects of dopamine are sex independent (Figure 4-3M and 4-3N).

4.3.3 Phasic activation of dopaminergic terminals in the mPFC affects conditioned place preference

Phasic activation of dopaminergic terminals in the NAc core induces a conditioned place preference (CPP) (Tsai et al., 2009). While stimulation of habenular inputs to VTA projecting mPFC neurons induces conditioned place aversion (Lammel et al., 2012), tonic or phasic stimulation of dopaminergic inputs to the mPFC of naïve mice does not induce a preference or avoidance of the associated context (Ellwood et al., 2017). However, it is unknown whether phasic stimulation can shift preference for a context in mice with chronic pain. To evaluate the role of VTA-mPFC dopaminergic projections in place preference (Auvray et al., 2010), we used

DAT-cre mice with AAV9-DIO-ChR2 injected in the VTA, and activated the dopaminergic inputs into the mPFC using optogenetics. We applied a three-day pairing protocol (Lammel et al., 2012) (Figure 4-5A) to evaluate whether phasic activation of VTA-mPFC dopaminergic inputs induces preference to the laser-conditioned context in SNI and SHAM mice. While

SHAM mice did not have a preference for a context paired with laser stimulation of dopaminergic terminals in the mPFC, SNI mice showed a preference for contexts paired with such a stimulation in the mPFC (Figure 4-5B-E). These results suggest that phasic VTA dopamine release into the mPFC induces a CPP in mice with chronic neuropathic pain.

4.3.4 Nerve injury does not induce changes in dopamine signaling in the mPFC at mRNA levels

To investigate whether dopamine signaling is altered in the PFC during chronic pain, RT-PCR

for mRNA involved in dopamine signaling was examined using mPFC tissue from SHAM and

SNI C57BL/6 mice. These mRNAs include D1 and D2 receptors, and TH. Tissue of mPFC was

harvested 10 days post SHAM and SNI surgeries. The results did not show difference of any

mRNA examined between SHAM and SNI groups (Figure 4-6).

4.3.5 Nerve injury does not change D1R and NMDA interaction in the mPFC

Previous studies have shown a facilitating effect of D1 receptor agonist on NMDA current in the

PFC neurons (Chen et al., 2004; Seamans et al., 2001a). Thus the effect of D1 receptor agonist

on NMDA currents in PFC on pain signaling was studied using C57BL/6 mice.

Slice recordings were carried out 2-4 weeks after SNI/SHAM surgery on layer V pyramidal neurons of the contralateral mPFC. NMDA current was collected at +40 mV, in the presence of bicuculline and DNQX. Monosynaptic EPSCs were confirmed with a 20 Hz high

frequency stimulation at -70 mV prior to NMDA current recording. 10 µM D1 agonist

SKF81297 was applied with bath after 5 min recording of baseline NMDA current. Normal

extracellular recording solution was applied to wash off SKF 10 min after SKF application.

Average NMDA current amplitudes from 5 min baseline, from the last 5 min with SKF, and

from the last 5 min during wash off were compared for statistical significance. NMDA current

amplitude showed little change with 10 µM SKF (Figure 4-7, A-B). Thus the above experiments

were repeated with 30 µM SKF. Although the difference in NMDA amplitudes between

baseline, SKF, and wash off did not reach a statistical significance (Figure 4-7, C), the time course of normalized NMDA current amplitude showed an obvious response to 30 µM SKF administration (Figure 4-7, D). Thus 30 µM was used as SKF working concentration.

The effect of SKF on NMDA current in the mPFC was then compared between SHAM

and SNI groups. Same experimental protocols as described above were used. The time series

showed an enhancement of normalized NMDA current amplitude in response to bath application

of SKF in both SHAM and SNI mice (Figure 4-8A). SKF induced peak responses were

calculated using data points from 8-11 min. Statistics showed a significant difference between peak and baseline amplitude in SNI but not in SHAM group. However, the interaction between

SHAM and SNI groups is not significant (Figure 4-8B). Such that D1R interaction with NMDA current was not changed by nerve injury.

4.3.6 Phasic activation of VTA-mPFC inputs increases neuronal activity of mPFC-vlPAG projecting neurons

To ascertain how dopamine release in the mPFC modulates chronic pain states at the mechanistic level, we combined fiber photometry with optogenetics. Since the mPFC-vlPAG axis is crucial for descending modulation of pain signals (Huang et al., 2019a), we injected a retrogradely transported virus driven by the synapsin promoter in the vlPAG of DAT-cre mice (AAVrg-syn- jGCaMP7s) to selectively express GCaMP7 in mPFC pyramidal neurons that project to the vlPAG. We then injected AAV-DIO-C1V1 virus into the VTA to selectively express ReaChR in

VTA dopaminergic neurons. ReaChR can be activated by yellow light and thus does not interfere with GCaMP signals. An optic fiber was implanted in the mPFC to deliver yellow laser light

(λ=590) to activate ReaChR at dopaminergic terminals, and to transmit LED light (λ=405 and

465) to for excitation and detection of GCaMP7 signals within mPFC-vlPAG projecting neurons

(Figure 4-9A). Six weeks after injection, AAVrg was expressed in the vlPAG and mPFC, and

DIO virus was expressed in the VTA four weeks after injection (Figure 4-9 B-E).

During fiber photometry recordings, mice were confined in small individual chambers on a

metal mesh. We recorded GCaMP7 signals during spontaneous activity and during 0.4g von Frey

foot stimuli (every 30 s) to trigger mechanical responses in the injured paw. We measured

spontaneous activity and responses to foot stimuli with and without phasic laser activation of

dopaminergic terminals in the mPFC (Figure 4-9F). In SHAM mice, foot stimuli did not a trigger reliable time-locked calcium response (61.2±6.0% response rate). However, in SNI mice, we observed that foot stimuli could induce time-locked increases in GCaMP7 fluorescence regardless of stimulation of dopaminergic terminals, which occurred more reliably (93.9±4.2%

response rate) to each foot stimulation with an unchanged amplitude compared to the SHAM

group (Figure 4-9G, Figure 4-10 B-D). Phasic stimulation of mPFC dopaminergic terminals in

the absence of foot stimulation (30 Hz, 40 pulses, every 2 s) did not trigger time-locked calcium responses in SNI or SHAM mice (Figure 4-10A). However, the activation of dopaminergic terminals of SNI mice resulted in a significant increase in overall GCaMP7 signals that reflect spontaneous activity, suggesting increased output from the mPFC-vlPAG projecting neurons

(Figure 4-9H). The frequency of GCaMP7 fluorescence peaks during spontaneous activity remained unchanged (Figure 4-9I). By comparison, neither peak amplitude nor frequency were altered by laser stimulation of SHAM mice (Supplementary Figure 4-9E and 4-9F).

To confirm that photostimulation of dopamine terminals increases activity of mPFC output neurons observed in the fiber photometry experiment, we examined c-fos expression in layer 5 pyramidal neurons following phasic laser activation of dopaminergic terminals in the PL mPFC.

We expressed ChR2 in the VTA of DAT-cre mice subjected to SNI surgeries and photostimulated terminals in the mPFC with an implanted optic fiber. Following 20 min in vivo phasic stimulation, brain tissue was harvested and mPFC sections were double stained with c-fos

and CaMKII primary antibodies to label activated neurons and putative pyramidal neurons,

respectively. As a control, we used SNI mice connected to the patch cable for 20 min without

laser stimulation. We found that photostimulation of dopaminergic terminals significantly increased the percentage of putative pyramidal neurons that were activated (11.75±3.1% to

33.5±6.1%; Figure 4-9 K-M), consistent with our fiber photometry data. As a second negative

control, we ‘photostimulated’ dopaminergic terminals of mice injected with YFP expressing

virus to verify that the increased pyramidal neuronal activity was due to activation of

dopaminergic terminals instead of heat generated from the laser (Figure 4-9N). Overall, our data

show that activation of VTA dopaminergic inputs to the mPFC increases the activity of mPFC

output neurons in SNI mice, consistent with previous findings that boosting layer 5 pyramidal cell activity in the mPFC is analgesic (Dale et al., 2018; Zhang et al., 2015).

4.4 Discussion

In the present study, we combined optogenetics with behavioral assessment of sensory and affective aspects of chronic neuropathic pain to investigate the role of dopamine release from

VTA originating projections into the mPFC. We found that phasic but not tonic optogenetic activation of dopaminergic inputs into the mPFC lead to an analgesic effect. Furthermore, by using fiber photometry we are able to demonstrate that release of VTA dopamine in the mPFC increases the activity of neurons projecting from the mPFC to the vlPAG. Hence, the VTA can regulate pain-associated behaviors via a dopamine-dependent enhancement of mPFC output.

A number of studies have implicated the mPFC in the modulation of neuropathic chronic pain. Specifically, multiple laboratories reported a hypoactivity of PL mPFC pyramidal neurons in chronic pain states (Dale et al., 2018; Huang et al., 2019a; Zhang et al., 2015). Within the

mPFC, dopamine has been shown to modulate neuronal activity and to fine-tune executive

functions such as attention and working memory (Buchta et al., 2017; Chudasama and Robbins,

2004; Lewis and O'Donnell, 2000; Ott and Nieder, 2019; Seamans and Yang, 2004). There is

emerging evidence suggesting that dopaminergic innervation in the mPFC plays a role in

aversion (Lammel et al., 2012; Vander Weele et al., 2018). Here, we demonstrate that phasic activation of VTA dopaminergic terminals in the PL mPFC relieves sensory components of chronic neuropathic pain, and induces preference to the context conditioned with dopamine release. Moreover, we found the modulatory effect activates putative glutamatergic output neurons of the mPFC as demonstrated by increased c-fos expression. These observations are consistent with previous work showing that boosting mPFC output via optogenetic approaches mediates analgesia in mouse models of pain (Dale et al., 2018; Zhang et al., 2015). In our previous work (Huang et al., 2019a), we found that the mPFC anatomically and functionally projects to the vlPAG, and that increases in mPFC output culminate in altered activity of the vlPAG and its downstream projections to the spinal cord (Huang et al., 2019a). Here we show that photoactivation of VTA dopaminergic inputs in the mPFC also increased the activity of mPFC-vlPAG projecting neurons. This, and the associated dampening of nerve injury-induced hypersensitivity, demonstrates that dopamine modulation of mPFC activity alters pain-related behaviors via downstream projections consistent with our previous findings (Huang et al.,

2019a). A previous study showed that photoactivation of dopaminergic terminals in mPFC neurons that project to the dorsal PAG (dPAG) modulates calcium activity in response to aversive stimuli (Vander Weele et al., 2018). Specifically, the authors reported reliable foot shock-induced increases in calcium signals in dPAG projecting mPFC neurons and found that activation of dopaminergic terminals increased spontaneous event amplitude. The increased

spontaneous activity observed in both studies is consistent with previous findings that dopamine

release in the mPFC maintains the ‘up state’ of pyramidal neurons during spontaneous activity, which synchronizes synaptic events and leads to a change in population activity (Kroener et al.,

2009; Lewis and O'Donnell, 2000). We did not observe a significant change in the amplitude of von Frey filament induced events. Hence, it appears that there are specific differences in the way

VTA inputs modulate different subpopulations of mPFC neurons that project to different subregions of the PAG in response to noxious stimuli.

Our observations that dopaminergic input into the mPFC is analgesic are consistent with, but considerably extend previous findings reported in the literature. Sogabe and colleagues (Sogabe et al., 2013) reported that high frequency electrical stimulation of the VTA could inhibit in vivo electrophysiological responses in the PFC (including the prelimbic mPFC) to application of mechanical pressure to the tail of anesthetized rats. This inhibitory effect was mediated by dopamine as they mimicked and blocked the inhibitory effect by locally microinjecting D2R agonists and antagonists in the PFC, respectively. As the PFC receives both dopaminergic and non-dopaminergic projections from the VTA (Morales and Root, 2014) and dopaminergic inputs from other brain areas (Stratford and Wirtshafter, 1990; Yoshida et al., 1989), a more specific approach was warranted. Our use of DAT-cre mice combined with optogenetics to specifically activate VTA-mPFC dopaminergic inputs provided this added level of specificity and precision.

Moreover, unlike in previous work, we evaluated nociceptive responses by measuring mechanical withdrawal thresholds in awake mice with chronic neuropathic pain, and observed consistent inhibitory effects by optogenetically activating the mesocortical dopaminergic system.

The effects of dopaminergic stimulation appeared to manifest themselves only in mice that had been subjected to SNI. We did not observe a change of mechanical hypersensitivity, nor

CPP with photostimulation of dopaminergic terminals in the mPFC of SHAM mice. Consistent with this, phasic or tonic activation of dopaminergic terminals in the mPFC does not induce CPP or conditioned place aversion (CPA) in naïve mice (Ellwood et al., 2017). On the other hand, previous work from Lammel and colleagues suggested that activation of the LHb inputs to mPFC-projecting VTA dopamine neurons induces CPA in naïve mice (Lammel et al., 2012).

Notably, photostimulation occurred at lateral habenula terminals in the VTA, and not at dopaminergic terminals in the mPFC. Furthermore, VTA dopaminergic neurons that are innervated by lateral habenula mainly project to infralimbic area of the mPFC, a region that mediates different physiological responses than the prelimbic region, such as modulation of fear extinction (Laurent and Westbrook, 2009). Interestingly, while phasic stimulation of dopamine terminals in the mPFC had no effect in SHAM mice, our findings revealed a significant preference to the chamber that was paired with dopaminergic terminal stimulation in the mPFC of SNI mice, along with a reversal of mechanical hypersensitivity. This can be explained by the notion that peripheral nerve injury leads to changes in mPFC plasticity, such that there is reduction in layer 5 pyramidal cell activity and an associated weakening of descending pain modulation via the vlPAG-spinal cord neuraxis (Huang et al., 2019a; Zhang et al., 2015). As we show here, dopaminergic inputs boost this altered mPFC output, thus normalizing sensory responses and CPP in neuropathic, but not normal mice.

Altogether, we have demonstrated a modulatory role of VTA dopaminergic inputs in the mPFC during neuropathic pain, by increasing the activity of neurons projecting from the mPFC to the vlPAG. Further work will be required to identify whether these changes are due to alteration of synaptic properties, or in the activities of ion channels that regulate the excitability of the mPFC neurons, and which types of dopamine receptors may be involved. Some of these

79 issues are challenging to address given that the VTA dopaminergic inputs into the mPFC are very sparse. Irrespective of these details, our overall observations are consistent with a mechanism by which dopaminergic inputs into the mPFC may regulate descending pain modulation after nerve injury.

Figure 4-1. VTA dopaminergic neurons anatomically and functionally connect to the PL

mPFC

(A) Schematic representation of the mouse brain showing retrobead injection into the mPFC and

their transport to the dopaminergic neuron cell body in the VTA. (B) Dopaminergic terminal

innervation in layer 5 PL mPFC in a DATcreTdTomato mouse (with no retrobead injection, naïve

animal). Scale bar, 20 µm. (C) Retrobead injection site in the prelimbic mPFC. Scale bar, 500

µm. (D) Slice scanner picture showing the right VTA. Scale bar, 250 µm. (E) Confocal image

showing the enlarged dash-lined square from D. Scale bar, 20 µm. For A-E, Green, retrobeads;

Red, DAT; Blue, DAPI. (F) Schematic diagram showing AAV-DIO-ChR2 virus injected into the

VTA, and blue laser stimulation of the mPFC slice during voltammetry recording. (G) Averaged dopamine efflux curve in the mPFC from SHAM and SNI mice. The shaded areas above and below the averaged curves indicate SEM. The blue block indicates phasic laser stimulation.

Scale bars, vertical 10 nM, horizontal 2 s. (H) Representative dopamine color plots and voltammograms in mPFC slices from SHAM (upper graph) and SNI (lower graph) mice. (I)

Peak dopamine concentration in mPFC slices from SHAM and SNI mice (t(18) = -1.87, p=0.077, t-test; n=10 slices/5 mice). NS, not statistically significant. Numbers in parentheses indicate

(slice/animal) numbers. Data in the bar graph are presented as mean and SEM.

Figure 4-2. Photostimulation-triggered dopamine release in the NAc

(A) Schematic diagram illustrating that AAV-DIO-ChR2 virus was injected into the VTA, and blue laser stimulation of the NAc slice during voltammetry recording. (B) Averaged traces of photostimulation triggered dopamine release in the NAc (n=3). (C) Representative dopamine color plots and voltammograms in NAc slices from SHAM (upper graph) and SNI (lower graph) mice. The laser stimulation protocol was 30 Hz 40 pulses.

P=6.53E-5 P=0.0027

P=8.3E-4

P=0.0063

P=0.012

P=3.9E-4 P=0.006 P=0.01

P=0.006

Figure 4-3. Phasic but not tonic activation of dopaminergic terminals in the mPFC reduces

nerve injury-induced hypersensitivity

(A) Schematic diagram showing that DAT-Cre and Ai32 mouse lines were crossed to generate

DAT-ChR2 mice. (B) Schematic diagram showing the implantation of an optic fiber in the mPFC for blue (λ=473) laser stimulation. (C) Experimental design of behavioural tests. (D)

Mechanical withdrawal threshold in SHAM mice measured using a DPA in ipsilateral and

contralateral paws during baseline, phasic, and tonic laser stimulation (interaction: phasic

stimulation and ips/con): F(1,5)=0.096, p =0.77; tonic stimulation and ips/con interaction: F(1,5)

=0.56, p=0.49; two-way ANOVA repeated measures; n=8). (E) Mechanical withdrawal threshold

in SNI mice measured on ipsilateral (ipsi) and contralateral (con) paws during baseline (no

laser), phasic (30 Hz 20 pulses, every 2 s), and tonic (1 Hz) laser stimulation (phasic stimulation

and ips/con interaction: F(1,8) =9.62, p=0.015; tonic stimulation and ips/con interaction: F(1,8)

=1.57, p=0.25; two-way ANOVA repeated measures; baseline ips vs. con: p=6.53E-5; phasic ips vs. con: p=0.19; tonic ips vs. con: p=0.0027; Bonferroni post hoc test; n=9). (F) Paired comparison of mechanical withdrawal thresholds between baseline and laser stimulation on the ipsilateral side of SNI DAT-ChR2 mice (t(8)= -5.20, p=8.3E-4, paired t-test, n=9). The data

points are the same as those shown in panel E. (G) Schematic diagram showing AAV-DIO-ChR2

virus injection into the VTA of DAT-cre mouse, and an optic fiber implanted in the mPFC. (H)

ChR2 expression in the VTA in a DAT-cre x Ai32 mouse (left) and DAT-cre mouse with virus injection (right). Scale bar, 50 µm. (I) Mechanical withdrawal thresholds at ipsilateral and contralateral paws of SNI mice during baseline and phasic laser stimulation (ips/con and phasic photostimulation interaction: F(1,5) =34.30, p=0.002; two-way ANOVA repeated measures; baseline ips vs. con: p=0.0063; phasic ips vs. con: p=1; Bonferroni post hoc test; n=6). (J) Paired

comparison of mechanical withdrawal thresholds between baseline and laser stimulation on the

ipsilateral sides of AAV-injected SNI mice (t(5)= -3.88, p=0.012, paired t-test, n=6). The data

points are the same as those in panel I. (K) Experimental design for pharmacological inhibition

of optogenetic effects. Left, DIO-AAV-ChR2 injection in the VTA in DAT-cre mice; dual cannula implantation targeting the mPFC that allows both laser stimulation and intracranial injection of antagonists of D1 receptors (SCH23390, 3 mM, 0.2 µl) and D2 receptors (sulpiride,

250 µM, 0.2 µl). Right, behavioral experimental design. (L) Mechanical withdrawal thresholds

of SNI mice during baseline, and during phasic laser stimulation in the presence of either vehicle

or a combination of D1 and D2 receptor antagonists (ips/con and treatments interaction: F(2,4)

=53.30, p=0.001; two-way ANOVA repeated measures; baseline ips vs. con: p=3.9E-4; vehicle+phasic ips vs. con: p=1; antagonists+phasic ips vs. con: p=0.006; Bonferroni post hoc test; n=6). (M) Effects of phasic laser stimulation on mechanical withdrawal threshold in female

SNI mice, using the experimental procedures described in panel G. Ips/con and phasic photostimulation interaction: F(1,4) =36.35, p=0.004; two-way ANOVA repeated measures; baseline ips vs. con: p=0.010; phasic ips vs. con: p=0.60; Bonferroni post hoc test; n=5. (N)

Paired comparison of mechanical withdrawal thresholds between baseline and laser stimulation on the ipsilateral side of female AAV injected SNI mice (t(4)= -5.44, p=0.006, paired t-test, n=5). The data points are the same as those presented in panel M. * represents p < 0.05, ** represents p < 0.01, *** represents p < 0.001, **** represents p < 0.0001. NS, not statistically significant. Numbers in parentheses indicate animal numbers. Data in bar graphs are presented as mean and SEM.

Figure 4-4. Validation of ChR2 expression and function in DAT-ChR2 mice

(A) Left, ChR2 expression in the VTA. Right, ChR2 expression overlapping with TH staining.

Green: ChR2. Red: TH. Scar bars, 50 µm. (B-D) Representative traces of blue LED light (5 mW) induced inward current at different frequencies in YFP positive neurons in the VTA. Short blue lines represent blue light flashes. Each flash is 10 ms. (E) A prolonged inward current induced by a 1s blue light flash in YFP positive neurons in the VTA. B-E have the same scale bars. (F) Blue light induced action potentials in in YFP positive neurons in the VTA.

P=0.01 P=0.04

Figure 4-5. Phasic activation of dopaminergic terminals in the mPFC of mice with neuropathic pain induces preference to the photostimulation-conditioned context

(A) Schematic diagram illustrating the CPP protocol. (B) Difference scores (time spent on testing day minus that on the pre-testing day) of chambers conditioned with no laser and laser in SHAM mice. (t(7)=0.049, p=0.96, n=8, paired t-test). (C) Difference scores of chambers conditioned with no laser and laser in SNI mice (t(7)= -3.25, p=0.014, n=8, paired t-test). (D) Paired comparison of time spent in laser conditioned chamber on pretest and test day for SHAM mice

(t(7)= -0.26, p=0.80, paired t-test, n=8). (E) Paired comparison of time spent in the laser conditioned chamber on pretest and test day for SNI mice (t(7)= -2.57, p=0.037, paired t-test, n=8). * represents p < 0.05. NS, not statistically significant. Numbers in parentheses indicate animal numbers. Data in bar graphs are presented as mean and SEM.

Figure 4-6. Nerve injury did not induce change in dopamine signaling at mRNA level in the mPFC.

A. Experimental design of RT-PCR test. B. mRNA levels of TH, D1R, and D2R between

SHAM and SNI groups (p>0.05 for interaction and in all three subgroups, two-way ANOVA).

A 1.8 SKF81297 Wash off

1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2 Normalized NMDA current amplitude n=8 0.0 -200 0 200 400 600 800 1000 1200 1400 1600 Time (s)

B 1.8 SKF81297 Wash off 1.6

1.4

1.2

1.0

0.8

0.6

0.4

0.2 Normalized NMDA current amplitude n=3 0.0 -200 0 200 400 600 800 1000 1200 1400 1600 Time (s)

Figure 4-7. The effect of D1 agonist SKF82197 on NMDA current amplitude in mPFC pyramidal neurons.

A. Time course of normalized NMDA current amplitude did not show a response to 10 µM SKF administration. B. Time course of normalized NMDA current amplitude showed a response to 30

µM SKF administration.

P=0.04

Figure 4-8. Nerve injury did not affect D1R and NMDA interaction in the mPFC.

A. Representative trace of NMDA current (insert) and normalized NMDA current amplitude time series. B. Statistics on NMDA current peak response to SKF and vehicle control in SHAM and SNI groups (interaction p=0.41; effect of pain p=0.36; effect of drug p=0.009; two-way

ANOVA; vehicle vs. SKF p=0.037; other factors p>0.05; Tukey post-hoc test).

P=0.04 P=0.02

P=0.03

Figure 4-9. Phasic activation of VTA dopaminergic terminals increases spontaneous

activity of mPFC-vlPAG projecting neurons.

(A) Schematic diagram showing retrovirus AAVrg-syn-jGCaMP7s injection in the vlPAG, DIO

virus AAV-DIO-ChR2 in the VTA, and an optic fiber implanted in the mPFC. (B) Retrovirus

expression in the vlPAG. Scale bar, 100 µm. (C) Retrovirus expression in the PL mPFC. Scale

bar, 100 µm. (D) Enlarged image from the dash-lined area in C. Scale bar, 50 µm. (E) DIO virus expression in the VTA. Scale bar, 50 µm. (F) Schematic representation of the protocol for fiber photometry. Orange, laser off; Green, phasic laser stimulation. Foot stimuli were given every 30 s. (G) Representative fiber photometry traces from SNI (upper) and SHAM (lower) mice.

Orange, laser off; Green, phasic laser stimulation. Arrows, foot stimulation using a 0.4g von Frey filament. Scale bars, vertical 0.05 (ΔF), horizontal 30 s. (H) Amplitude (z-score median) of

spontaneous activity before (baseline), during (laser), and after (postlaser) phasic laser

stimulation of SNI mice (F(2, 18) =5.66, p=0.012, one-way ANOVA; baseline vs. laser:

p=0.039; laser vs. postlaser: p=0.021; baseline vs. postlaser: p=1; Bonferroni post hoc test; n=7).

(I) Peak frequency of spontaneous events before, during, and after phasic laser stimulation of

SNI mice (F(2, 18) =2.18, p=0.14, one-way ANOVA; n=7). (J) Peak z-score amplitude of foot stimuli induced response before, during, and after phasic laser stimulation (F(2, 18) =1.95, p=0.17, one-way ANOVA; n=7). For H, I, J, * represents p < 0.05. Numbers in parentheses indicate animal numbers. (K) C-fos (red) and CamKII (green) expression in the PL mPFC of SNI mice after optic fiber connecting to patch cable only (no laser-control treatment) for 20 min. (L)

C-fos and CamKII expression in the PL mPFC of SNI mice after phasic photostimulation (laser) of dopaminergic terminals in the mPFC for 20 min. (M) Statistical analysis of the percentage of

CamKII positive neurons activated in the mPFC of SNI operated mice with control treatment and

99 photostimulation (p=0.03, Mann-Whitney test, n=4 slices/2 mice). In K-M, DAT-cre mice were injected with AAV-DIO-ChR2 virus in the VTA. * represents p < 0.05. (N) Statistical analysis of the percentage of CamKII positive neurons activated in the mPFC of SNI operated DAT-cre mice that were injected with YFP DIO virus in the VTA, with control and photostimulation

(p=0.67, Mann-Whitney test, n=4 slices/3 mice). NS, not statistically significant. Numbers in parentheses indicate (slice/animal) numbers. Data in bar graphs are presented as mean and SEM.

100

P=8.1E-4

101

Figure 4-10. Supplementary traces and statistics for fiber photometry

(A) Spontaneous activities of GCaMP7 expressing neurons in the mPFC during laser off and on

in SNI (upper) and SHAM (lower) mice. Short yellow lines represent yellow laser flashes. Each

short line represents a 30 Hz 20 pulse laser stimulation. (B) Foot stimuli-induced responses

during baseline section in SHAM (upper) mice and SNI (lower) mice. Arrows, indicate time

points at which foot stimuli occurred. (C) Peak amplitude of foot stimuli induced responses in

SHAM and SNI mice (t(12)=1.34, p=0.20, t-test, n=7, 7). (D) Percentage of responses to foot stimuli in SHAM and SNI mice (t(12)= -4.44), p=8.1E-4, t-test, n=7, 7). (E) Spontaneous activity amplitude in the form of median of z-scores in SHAM mice (F(2,18) =3.24, p=0.062, one-way

ANOVA; n=7). (F) Frequency of spontaneous event peaks in SHAM mice (F(2,18) =1.35, p=0.28, one-way ANOVA, n=7). *** represents p < 0.001. NS, not statistically significant.

Numbers in parentheses indicate animal numbers. Data in bar graphs are presented as mean and

SEM.

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Chapter Five: Discussion

5.1 Summary of findings

This project examined peripheral nerve injury-induced plasticity in VTA dopaminergic neurons, and how mesocortical dopaminergic pathway modulates chronic pain-associated behaviour.

Chapter Three demonstrated plasticity in lateral and medial VTA dopaminergic neurons during neuropathic pain states, and showed that these changes reside in specific subpopulations in the lateral and medial VTA. In Chapter three, electrophysiological fingerprints of lateral and medial VTA dopaminergic neurons were compared and confirmed consistent with previous studies (Neuhoff et al., 2002; Zhang et al., 2010). Decreased spontaneous firing was observed in overall dopaminergic neurons in the lateral VTA in response to nerve injury, while no significant change was found in the medial VTA. In the lateral VTA, three dopaminergic subpopulations with different action potential firing patterns were reported. It was revealed that the overall decrease in spontaneous firing was led by one specific subpopulation, which is the major subpopulation (48%) in the lateral VTA. In the medial VTA, at least six dopaminergic subpopulations with distinct firing patterns were observed. Although there was no overall difference between SHAM and SNI groups in the medial VTA, a nerve injury-induced increase in neuronal excitability was observed in one particular subpopulation (delayed- nonaccommodating firing type), reflected in the F-I slope and action potential threshold. The delayed-nonaccommodating firing type dopaminergic neurons only encompass 21% of the total dopaminergic population in the medial VTA. Thus changes residing in such a small subpopulation may not lead to an overall change in the medial VTA.

Chapter Four demonstrated that activation of VTA-mPFC dopaminergic inputs can revert neuropathic pain-associated behaviour to SHAM levels, and this modulation is mediated by an

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enhanced glutamatergic output from the mPFC to the vlPAG. In Chapter Four, anatomical and

functional dopaminergic projections from the VTA to the mPFC were validated via retrograde

tracing and terminal release of dopamine detected by voltammetry. Phasic activation of VTA-

mPFC dopaminergic inputs using optogenetics reversed nerve injury-induced hypersensitivity, indicating an analgesic effect. This result is consistent with the observation of a conditioned place preference in the SNI mice to the context conditioned with dopamine release in the mPFC.

Furthermore, fiber photometry based calcium imaging revealed that dopamine release in the mPFC leads to increased spontaneous activities in the mPFC neurons that project to the vlPAG.

The present study highlighted the importance of targeting specific subpopulation when study pain related plasticity in VTA dopaminergic neurons. The study also showed an analgesic role of dopamine in neuropathic pain, indicating a potential target for treatment of neuropathic pain.

5.2 Heterogeneity in VTA dopaminergic population related to pain

5.2.1 Anatomic, functional, and electrophysiological characterization of lateral and medial

VTA dopaminergic neurons.

The VTA is a heterogeneous area within the midbrain. The diversity of VTA dopaminergic

neurons is reflected through the lateral-medial and the ventral-dorsal axis. In this study, nerve injury-induced plasticity differed between the lateral and medial VTA but not along the ventral-

dorsal axis. Multiple studies reported different properties between lateral and medial VTA

dopaminergic neurons. Anatomically, the lateral VTA projects to the lateral shell of the NAc, and the medial VTA projects to the NAc core, the NAc medial shell, the basolateral amygdala

(BLA), and the mPFC (Lammel et al., 2014). Functionally, the lateral VTA plays a role in goal

directed behavior and encoding salience value, while the medial VTA encoding reward

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prediction error and mediating Pavlovian learning (Cai et al., 2020; Heymann et al., 2019).

Electrophysiological characterization demonstrated a prominent Vsag/Rinput reflecting Ih in dopaminergic neurons in the lateral but not the medial VTA (Neuhoff et al., 2002; Zhang et al.,

2010), consistent with this present study. However, only using Vsag to differentiate lateral and

medial dopaminergic neurons can be inaccurate. Although the averaged Vsag/Rinput reflecting Ih is

significantly different between lateral and medial VTA neurons (lateral 46.07±3.62 pA; medial

11.80±2.10 pA), 4 out of 25 (16%) of lateral VTA neurons had an Ih reflected in Vsag/Rinput

between 20-30 pA, and the same range of Ih was also observed in 3 out of 17 (18%) medial VTA

dopaminergic neurons. Such overlap was also observed in Zhang et al. (2010). Other biophysical

properties were also characterized between lateral and medial VTA dopaminergic neurons,

which should be considered together as the electrophysiological fingerprint. These biophysical

properties include higher cell capacitance, bigger leak current, and higher action potential

threshold in the lateral VTA compared to medial, reported in this present study as well as by

Baimel et al. (2017). However, the present study did not show a higher spontaneous firing in the

medial VTA dopaminergic neurons observed by Baimel et al. (2017), but only a higher

excitability. This is probably due to differences in experimental settings such as intracellular or

extracellular solutions for electrophysiology.

5.2.2 Lateral VTA dopaminergic subpopulation

Though the lateral VTA dopaminergic neurons have more uniformed projections compared to the medial (Lammel et al., 2014), different dopaminergic subpopulations were observed in the lateral VTA based on their firing patterns. The type 1 neurons, identified by electrophysiological properties in Chapter 3, are the predominant dopaminergic subpopulation in the lateral VTA, and have typical firing pattern, shape of action potential, and Ih calculated from Vsag/Rinput consistent

105 with previous literature. The nerve injury-induced plasticity resides only in type 1 neurons in the lateral VTA. The observed plasticity is consistent with other pain-related studies, showing a decreased neuronal excitability or activity of dopamine neurons during chronic pain states (Ren et al., 2016; Watanabe et al., 2018). This is also consistent with the notion that past pain-related studies in the VTA mainly focused on the lateral VTA (Kami et al., 2018). Our results indicated that type 2 neurons are a less predominant dopaminergic subpopulation (14%) in the lateral

VTA. Similar firing patterns to type 2 lateral VTA dopamine neurons have been observed in rat

GABA and rat dopaminergic neurons of the VTA (Klink et al., 2001; Merrill et al., 2015).

Because VTA dopaminergic neurons barely (4.3%) overlap with GABA neurons (Zhang et al.,

2010), the type 2 neurons are probably a smaller subpopulation of lateral dopaminergic neurons that have not been well characterized. Type 3 neurons, which are located adjacent to the lateral

VTA, have a similar firing pattern to that observed in rat SNc dopaminergic neurons (Klink et al., 2001). The possibility that type 3 neurons are located on the border of lateral VTA and SNc could not be ruled out in the present study as the border between these two areas are not clearly defined by anatomical markers. Notably, type 2 and 3 neurons did not show plasticity in response to nerve injury. The present study showed cell type selectivity in neuronal responses to neuropathic pain and that type 1 neurons of the lateral VTA should be a potential target for future studies on neuropathic pain.

5.2.3 Medial VTA dopaminergic neurons heterogeneity

The present study revealed at least six different subgroups of dopaminergic neurons in the medial

VTA based on their firing patterns. Compared to the lateral VTA, medial VTA dopaminergic neurons are more heterogeneous perhaps due to their various descending targets. Lammel et al.

(2011) and Baimel et al. (2017) have characterized medial VTA dopaminergic subpopulations

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based on their projection targets. Medial VTA dopaminergic neurons that project to the medial

shell of the NAc and to the mPFC have been shown to exhibit very similar biophysical properties

including leak current and Ih. AMPA/NMDA ratios of the two subpopulation are also similar.

However, the AMPA/NMDA ratio of the NAc medial shell projecting neurons increases in response to cocaine, while that of the mPFC projecting neurons remains unchanged (Lammel et al., 2011). Baimel and colleagues demonstrated very similar electrophysiological properties between the NAc medial shell and the amygdala projecting neurons. Although some amygdala projecting neurons are also located in the lateral VTA, the overall amygdala projecting population lacks an Ih, fitting with medial VTA dopaminergic neuron phenotype. According to

Baimel and colleagues, the way that NAc medial shell projecting neurons differ from the amygdala projecting neurons is that the former show a response to orexin A. The two previous studies suggest that dopaminergic subpopulations in the medial VTA do not have a distinct electrophysiological fingerprint under normal physiological conditions. However, when the system is challenged by an abnormal environment, such as cocaine or orexin application, the roles of different subpopulations become evident. This is also true in the present study which demonstrated that other than firing properties, the various subpopulations showed differences only during neuropathic pain states—only the delayed-nonaccommodating subtype showed plasticity to nerve injury. However, whether the six dopaminergic subpopulations in the medial

VTA can be described based on their projecting targets still remains unknown.

5.3 Mesocortical modulation of pain signals

5.3.1 Nerve injury-induced plasticity in the mPFC

The VTA dopaminergic neurons, especially those from the medial VTA, are the source of the mesocortical pathway. This led to a question that whether this nerve injury-induced plasticity

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resides only in the VTA, or also in the dopamine signal transmission in the mPFC. The present

study tested mRNA expression levels of factors involved in dopamine signaling in the mPFC,

such as mRNAs of D1R, D2R, and TH. RT-PCR results did not show any change of dopamine signaling at the mRNA level in response to nerve injury. An electrophysiology approach was also used to examine potential changes in dopamine signaling in the mPFC. Several previous studies reported an increased NMDA current due to D1R activation in the mPFC (Chen et al.,

2004; Seamans et al., 2001a), while whether this modulation is altered by nerve injury has not

been examined. The present study demonstrated that application of D1R agonist SKF81297

increased NMDA current amplitude, consistent to previous studies. However, no difference was

observed between the SHAM and SNI groups. Although there might also be potential

mechanisms in dopamine signaling in the mPFC that have not yet been revealed, so far, plasticity

in mesocortical dopaminergic signaling induced by nerve-injury reported in the present study

resides primarily in medial VTA dopaminergic neurons.

5.3.2 Analgesic effect of mesocortical dopamine

The present study reported a reduced hypersensitivity in SNI mice by activating the mesocortical

dopaminergic pathway. This is consistent with previous studies which reported analgesic effects

from activation of the mesocortical pathway. For example, Sogabe et al. (2013) applied high

frequency stimulation in the VTA and showed a reduced response to tail pinch in

electrophysiology recordings in the prelimbic area and ACC of the mPFC. Dent and Neill (2012)

microinjected dopamine in the mPFC and observed a dose-dependent analgesic effect. The

authors demonstrated that low-dose (5 µg) dopamine increased thermal sensitivity reflected in

tail flick test, while a high dose (10-20 µg) reduced nociception. Lopez-Avila et al. (2004)

studied the effect of dopamine in a neuropathic pain model in the ACC. Although ACC exhibits

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increased neuronal activity during chronic pain states, compared to a decreased neuronal activity

in the prelimbic area, Lopez and colleagues showed a similar analgesic effect of dopamine

injection in the ACC, indicating that the mesocortical pathway projections that target the

prelimbic area and the ACC probably originate from the same subpopulation of dopaminergic

neurons in the VTA but modulate the prelimbic area and the ACC through different types of

dopamine receptors or different mechanisms. Lopez and colleagues also reported a dose- dependent effect of dopamine, showing that 100 nM is the most effective dose for analgesic effect. The present study also reported that only high concentrations of dopamine release result in an analgesic effect. Using a crossed mouse line DAT-ChR2, it was observed that phasic (30

Hz, 20 pulses, every 2 s) but not tonic (1 Hz) optostimulation reversed mechanical hypersensitivity in SNI mice. The dose of dopamine release in the mPFC during photostimulation is between 20-30 nM, which is less than the doses reported effective in previous in vivo studies (Lopez-Avila et al., 2004). However, the dose of dopamine detected in

the present study was based on ex vivo using slice voltammetry, and the stimulation protocol

used was 30 Hz 40 pulses instead of repetitive application of 20 pulses. In addition, the laser

power used on slices was 5 mW compared to 20 mW during in vivo tests. These different laser

powers were adjusted based on previous literature (Bass et al., 2013; Lammel et al., 2012).

Hence, that the dopamine concentration from photostimulation in vivo might actually much

higher than 30 nM.

5.3.3 Mechanisms underlying dopamine modulation of pain signalling in the mPFC

The present study reported that dopamine release in the mPFC enhances overall neuronal activity

of output neurons using fiber photometry and c-fos immunostaining. Previous studies from the

O’Donnell lab also showed increased neuronal activity in the mPFC in response to dopamine

109 application using electrophysiology (Lewis and O'Donnell, 2000; Wang and O'donnell, 2001).

The authors reported that the modulation effect is mediated by interactions between NMDA current and D1R, which was also observed in the present study. The increased neuronal activity may contribute to the maintenance of attention such that ongoing tasks are not easily switched by other sensory inputs (Wang, 1999). Together with the enhanced neuronal activity induced by dopamine release reported in the present study, it indicates that dopamine modulates neuronal activity. In addition, different physiological conditions between neuropathic and SHAM mice may lead to different modulation outcomes. These differences reflected in a decreased overall activity in pyramidal neurons in the mPFC in neuropathic but not SHAM mice (Huang et al.,

2019a). Thus dopamine may play a role in normalizing pathologic condition in SNI but not affecting SHAM animals which have normal mPFC neuronal activity. Fiber photometry results from the present study demonstrated reliable von-Frey foot stimuli-induced calcium events in

SNI but not in SHAM mice, although event amplitudes were similar between the two groups of animals. Together with previous literatures discussed above, it is speculated that dopamine release increases mPFC neuronal activity in SNI mice to maintain ongoing tasks not being disturbed by pain signals, leading to an increased mechanical threshold in DPA test. In SHAM mice, foot stimuli are not as noxious because there was no nerve injury, so the mechanical threshold in DPA test remains high. This explains different modulation outcomes in behavioral tests that in SHAM mice, where mechanical threshold and CPP was not affected by dopamine release in the mPFC.

5.4 Limitations and caveats

5.4.1 Whether VTA dopaminergic subpopulation that show plasticity to nerve injury project to the mPFC

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Because the mesocortical pathway originates from the medial VTA, attempts have been made

here to examine whether the delayed-nonaccommodating subpopulation belongs to the

mesocortical pathway. Retrobeads (Alexa Fluor 488) and cholera toxin subunit B (CTB Alexa

Fluor 647) were injected in the mPFC one-two weeks prior to recordings in the medial VTA of the DAT-tdTomato reporter mice. However, although theoretically applicable, under our experimental conditions, the retrograde labeling was sparse. Retrobeads have distinct distribution patterns in the cell and different fluorescent colour compared to the tdTomato. However, when a neuron does not have many beads or when retrobead signal is weak, the retrobead signal can be occluded by tdTomato fluorescence, because Alexa Fluor 488 (488 nM) shares overlapping wavelength with tdTomato (560 nM). To address this problem, CTB with Alexa Fluor 647 (647 nM) was used. However, none of the neurons with CTB signals (2-5 cells per mouse) express the reporter for dopamine neurons. Given the fact that overlap between retrobeads and the dopamine reporter have been observed in both slice recording and confocal imaging, it is possible that

CTB, particularly with Alexa Fluor 647, may not be well taken up by dopaminergic terminals.

Altogether, out of 18 mice that were injected with retrobeads or CTB, only two neurons were labeled with retrobeads showed overlap with the dopamine reporter. Thus in this present study, whether VTA dopamine neurons that are sensitive to nerve injury project to the mPFC still remains to be determined.

5.4.2 Whether nerve injury-induced plasticity in the medial VTA relates to behavioral observations

In the medial VTA, the nerve injury-induced increase in F-I slope, a lowered action potential threshold, and a greater Ih reflected in Vsag/Rinput, indicating increased overall excitability.

However, there was no change in spontaneous firing. If this subpopulation represented mPFC

111 projecting neurons, one may speculate that plasticity in the mPFC during neuropathic states may be revealed only with sufficient activation. Thus optogenetic approaches were used in the present study to artificially trigger dopamine release in the mPFC. In the present study, artificial activation of mesocortical dopaminergic inputs reversed nerve injury-induced hypersensitivity.

In SHAM animals, activation of the same inputs did not show an effect on mechanical threshold.

In the CPP test, SNI but not SHAM animals showed a preference to the context conditioned with triggered dopamine release in the mPFC. Results showing a selective effect on SNI mice could be explained by enhanced excitability from the medial VTA dopaminergic neurons. However, triggered dopamine release detected in voltammetry on mPFC slices did not show a difference between SHAM and SNI mice. Yet it remains to be tested whether this is also true in in vivo condition. Thus it requires in vivo test of dopamine release to determine whether the biased mesocortical dopaminergic modulation of behaviour in SNI but not SHAM animals is related to nerve injury-induced plasticity in the VTA.

5.4.3 Whether an enhanced activity of mPFC output neurons is required of dopamine modulation of neuropathic pain related behaviour

The present study demonstrated that dopamine release in the mPFC enhances the spontaneous activity but not noxious event-triggered signals in the projecting neurons to the vlPAG. This modulation reduced the “signal to noise ratio” of noxious inputs and may maintain the ongoing behaviour not being disturbed by noxious events. However, this is not a direct evidence showing the necessity of this enhanced neuronal activity in reversing nerve injury-induced hypersensitivity or inducing CPP in SNI mice by dopamine release in the mPFC. Future studies will be needed to address this question which will be discussed below in the “future directions” section.

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5.4.4 Whether dopamine modulation of neuropathic pain-associated behaviour also exist in

female mice

Sex differences have been indicated in many studies across various areas, including pain (Mogil

and Bailey, 2010). For example, clinical studies revealed that kappa-opioid induced analgesia is

more effective in females (Gear et al., 1996, 1999). Interestingly, in animal study, this

antinociceptive effect is more potent in male rat (Barrett et al., 2002). In addition, animal studies

also reported sex differences in antinociceptive effects of cholinergic agents and cannabinoids

(Lavand'homme and Eisenach, 1999; Tseng and Craft, 2001). More relevant to the present study,

Shiers et al. (2018) demonstrated that SNI surgery induced pain-related deficits in executive functions in the infralimbic mPFC exist in male instead of female mice.

Sex differences also exist in midbrain dopaminergic neurons (Virdee et al., 2014), and more specifically, in the mesocortical pathway (Kritzer and Creutz, 2008). For example, Kritzer and colleagues reported that within the mesocortical projecting neurons, the proportion of dopaminergic neurons is higher in the females compared to that in the males. Thus if the modulatory effect of dopamine on neuropathic pain-associated behaviour is true in the females remains to be tested in future studies.

5.5 Future directions

The present study demonstrated nerve injury-induced plasticity in the delayed- nonaccommodating subtype of medial VTA dopaminergic neurons. However, whether this subpopulation expresses specific protein markers, or projects to a specific targets in the mPFC remains unclear. To do this investigation, single-cell RT-PCR and retrograde tracing will be required. Considering that Alexa Fluor 647 CTB may not label dopaminergic neurons efficiently,

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alternative methods could perhaps be used. For example, a mouse line with a GFP reporter for

dopamine can be used to identify dopaminergic neurons, combined with Alexa Fluor 555 CTB;

or one may use a reporter retrovirus to label projection specific neurons.

Although we demonstrated a modulatory role of VTA-mPFC dopaminergic inputs in

sensory and affective aspects of chronic pain, there are remaining questions that require future

studies. Our fiber photometry results explained how dopaminergic inputs modulate noxious

signals encoded in the mPFC at neuronal population and circuit levels. However, this did not

provide details about which dopamine receptors were involved in the mPFC, and how dopamine

receptors affect downstream targets to modulate neuronal excitability or synaptic transmission

properties that leads to a population change. These further investigations may face some

challenges, especially in an ex vivo system when neuronal circuits are not intact. Dopaminergic

innervations are also sparse in the mPFC, thus synaptically released dopamine may diffuse

quickly in a bath system such as that used in electrophysiology experiments.

We reported that activation of VTA-mPFC dopaminergic terminals induced CPP in animals with chronic pain. The PFC mediates many important executive functions including working memory and attention (Chudasama and Robbins, 2004). For example, Popescu et al.

(2016) described that dopamine release in the mPFC enhanced stimulus discrimination. Thus it would be useful to understand which executive functions are modulated by dopamine, and how noxious signals in the chronic pain status are encoded, presented, and gated in the mPFC.

An enhanced neuronal activity of mPFC output neurons was demonstrated suggesting that

dopamine mediates analgesia by reducing “signal to noise ratio” of noxious inputs. More

experiments can be done to show this modulation is required for reversing nerve injury-induced

hypersensitivity. For example, inhibitory DREADDs (designer receptors exclusively activated

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by designer drugs) could be used to reduce mPFC output neuron activity. Dopamine modulation

of nerve injury-induced hypersensitivity can then be test without and with activation of

inhibitory DREADDs.

It would also be interesting to see the relationship between dopamine levels and output

neuron activity in the mPFC in vivo. To test this relationship, excitatory retrograde cre-dependent

DREADDs can be injected in the mPFC in DAT-cre mice to activate mesocortical dopaminergic inputs. Two retrograde virus driven by synapsin can be injected in the vlPAG to so that RCaMP

(red-fluorescent genetically encoded calcium indicators) and dLight (fluorescent dopamine sensor) can be retrogradely expressed in the mPFC for calcium imaging. These tools will enable researchers to observe real time dopamine modulation of mPFC output neurons during different behavioural tasks, including test of hypersensitivity, CPP, and tests of mPFC executive functions that may be involved in the CPP test.

5.6 Significance

The present study reported subpopulation-specific changes induced by nerve injury in VTA dopaminergic neurons. The results suggest that the reduced dopaminergic neuronal activity in the

VTA during chronic pain states reported by previous literature mainly resides in the major subpopulation of the lateral VTA. We also further revealed the complexity of medial VTA dopaminergic neurons based on their firing patterns, and reported a subpopulation specific increase in neuronal excitability, which has never been documented by previous studies.

Dopamine is not the only candidate that induces differential responses in different subpopulations. In addition to Lammel et al. (2011) and Baimel et al. (2017), Mejias-Aponte et al. (2015) and Eddine et al. (2015) reported differential responses from different dopaminergic subpopulation in the VTA to cocaine and nicotine application, respectively. In both cases, while

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one subpopulation was excited, there is also a subpopulation that was inhibited by the drug

application, which is similar to what was seen in the present study. Another study demonstrated

that acute ethanol-induced responses (concentration dependent increase in spontaneous firing) only reside in one subpopulation of VTA dopaminergic neurons. Though these previous studies used different stimulants other than nerve injury, altogether with the present study, it emphasizes the notion of being specific when investigating plasticity in VTA dopaminergic neurons, by tracing projecting targets, identification of protein markers, or using electrophysiological fingerprints.

Furthermore, the present study demonstrated that activation projections from VTA dopaminergic neurons to the prelimbic mPFC led to an analgesic effect, which is mediated by enhanced mPFC output to the vlPAG. Consistent with previous studies, this study showed again that the hypoactivity of mPFC pyramidal neurons has a causal relationship with chronic pain.

Thompson and Neugebauer (2017) and Huang et al. (2019a) both demonstrated independently that the hypoactivity in mPFC principal neurons results from enhanced feed-forward inhibition from hyperactivity in amygdala neurons. Huang and colleagues also showed that optogenetic inhibition of amygdala neurons leads to activation of mPFC output neurons to the vlPAG which causes analgesia. The present study added a new aspect of how pain signals are integrated and processed, showing that anatomically specific circuits are not independent in their roles in modulation of pain signals. While specific projections target a small subpopulation of mPFC neurons, neuromodulators such as dopamine add a layer of overall modulation that fine tune the input-output of that specific modulation on neurons that express dopamine receptors. Although previous studies suggested that activation of mPFC neurons is analgesic, clinical application of this method can be invasive. In comparison, giving neuromodulators such as dopamine may be

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more readily applicable, considering medicines that are available on the market including L-dopa for treatment of Parkinson or dopamine reuptake inhibitors for treatment of depression. The downside of using dopamine for treatment of chronic pain resides in the diverse targets of dopamine such that there are potential side effects. However, a significant number of chronic

pain patients also suffer from depression. Thus using antidepressants to increase overall

dopamine levels in the brain to target this specific population of patients can be helpful for both

indications.

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References

Abercrombie, E.D., Keefe, K.A., DiFrischia, D.S., and Zigmond, M.J. (1989). Differential effect of stress on in vivo dopamine release in striatum, nucleus accumbens, and medial frontal cortex. Journal of neurochemistry 52, 1655-1658. Aggleton, J.P., Burton, M.J., and Passingham, R.E. (1980). Cortical and subcortical afferents to the amygdala of the rhesus monkey (Macaca mulatta). Brain research 190, 347-368. Al-Tikriti, M.S., Roth, R.H., Kessler, R.M., and Innis, R.B. (1992). Autoradiographic localization of dopamine D1 and D2 receptors in rat cerebral cortex following unilateral neurotoxic lesions. Brain research 575, 39-46. Allouche, S., Noble, F., and Marie, N. (2014). Opioid receptor desensitization: mechanisms and its link to tolerance. Frontiers in pharmacology 5, 280. Alshelh, Z., Marciszewski, K.K., Akhter, R., Di Pietro, F., Mills, E.P., Vickers, E.R., Peck, C.C., Murray, G.M., and Henderson, L.A. (2018). Disruption of default mode network dynamics in acute and chronic pain states. NeuroImage Clinical 17, 222-231. Altier, N., and Stewart, J. (1999). The role of dopamine in the nucleus accumbens in analgesia. Life sciences 65, 2269-2287. Apkarian, A.V. (2008). Pain perception in relation to emotional learning. Current opinion in neurobiology 18, 464-468. Apkarian, A.V., Bushnell, M.C., Treede, R.D., and Zubieta, J.K. (2005). Human brain mechanisms of pain perception and regulation in health and disease. Eur J Pain 9, 463- 484. Apkarian, A.V., Hashmi, J.A., and Baliki, M.N. (2011). Pain and the brain: specificity and plasticity of the brain in clinical chronic pain. Pain 152, S49-64. Apkarian, A.V., Sosa, Y., Sonty, S., Levy, R.M., Harden, R.N., Parrish, T.B., and Gitelman, D.R. (2004). Chronic back pain is associated with decreased prefrontal and thalamic gray matter density. The Journal of neuroscience : the official journal of the Society for Neuroscience 24, 10410-10415. Aragona, B.J., Day, J.J., Roitman, M.F., Cleaveland, N.A., Wightman, R.M., and Carelli, R.M. (2009). Regional specificity in the real-time development of phasic dopamine transmission patterns during acquisition of a cue-cocaine association in rats. The European journal of neuroscience 30, 1889-1899. Arruda-Carvalho, M., and Clem, R.L. (2014). Pathway-selective adjustment of prefrontal- amygdala transmission during fear encoding. The Journal of neuroscience : the official journal of the Society for Neuroscience 34, 15601-15609. Auvray, M., Myin, E., and Spence, C. (2010). The sensory-discriminative and affective- motivational aspects of pain. Neuroscience and biobehavioral reviews 34, 214-223. Axelrod, J. (1977). Nonstriatal dopaminergic neurons: Section I. Mesocortical dopaminergic neurons: Introduction: mesocortical dopaminergic neurons. Advances in biochemical psychopharmacology 16, 1-3. Bacon, S.J., Headlam, A.J., Gabbott, P.L., and Smith, A.D. (1996). Amygdala input to medial prefrontal cortex (mPFC) in the rat: a light and electron microscope study. Brain research 720, 211-219. Baimel, C., Lau, B.K., Qiao, M., and Borgland, S.L. (2017). Projection-Target-Defined Effects of Orexin and Dynorphin on VTA Dopamine Neurons. Cell reports 18, 1346-1355.

118

Baliki, M.N., Chialvo, D.R., Geha, P.Y., Levy, R.M., Harden, R.N., Parrish, T.B., and Apkarian, A.V. (2006). Chronic pain and the emotional brain: specific brain activity associated with spontaneous fluctuations of intensity of chronic back pain. The Journal of neuroscience : the official journal of the Society for Neuroscience 26, 12165-12173. Baliki, M.N., Mansour, A., Baria, A.T., Huang, L., Berger, S.E., Fields, H.L., and Apkarian, A.V. (2013). Parceling human accumbens into putative core and shell dissociates encoding of values for reward and pain. The Journal of neuroscience : the official journal of the Society for Neuroscience 33, 16383-16393. Baliki, M.N., Petre, B., Torbey, S., Herrmann, K.M., Huang, L., Schnitzer, T.J., Fields, H.L., and Apkarian, A.V. (2012). Corticostriatal functional connectivity predicts transition to chronic back pain. Nature neuroscience 15, 1117-1119. Bantick, S.J., Wise, R.G., Ploghaus, A., Clare, S., Smith, S.M., and Tracey, I. (2002). Imaging how attention modulates pain in humans using functional MRI. Brain 125, 310-319. Barrett, A.C., Cook, C.D., Terner, J.M., Roach, E.L., Syvanthong, C., and Picker, M.J. (2002). Sex and rat strain determine sensitivity to kappa opioid-induced antinociception. Psychopharmacology 160, 170-181. Bass, C.E., Grinevich, V.P., Kulikova, A.D., Bonin, K.D., and Budygin, E.A. (2013). Terminal effects of optogenetic stimulation on dopamine dynamics in rat striatum. Journal of neuroscience methods 214, 149-155. Beier, K.T., Steinberg, E.E., DeLoach, K.E., Xie, S., Miyamichi, K., Schwarz, L., Gao, X.J., Kremer, E.J., Malenka, R.C., and Luo, L. (2015). Circuit Architecture of VTA Dopamine Neurons Revealed by Systematic Input-Output Mapping. Cell 162, 622-634. Berger, B., Thierry, A.M., Tassin, J.P., and Moyne, M.A. (1976). Dopaminergic innervation of the rat prefrontal cortex: a fluorescence histochemical study. Brain research 106, 133- 145. Bingel, U., Lorenz, J., Schoell, E., Weiller, C., and Buchel, C. (2006). Mechanisms of placebo analgesia: rACC recruitment of a subcortical antinociceptive network. Pain 120, 8-15. Bingel, U., Quante, M., Knab, R., Bromm, B., Weiller, C., and Buchel, C. (2002). Subcortical structures involved in pain processing: evidence from single-trial fMRI. Pain 99, 313- 321. Bliss, T.V., Collingridge, G.L., Kaang, B.K., and Zhuo, M. (2016). Synaptic plasticity in the anterior cingulate cortex in acute and chronic pain. Nature reviews Neuroscience 17, 485- 496. Bonica, J.J. (1979). The need of a taxonomy. Pain 6, 247-248. Botvinick, M.M. (2007). Conflict monitoring and decision making: reconciling two perspectives on anterior cingulate function. Cognitive, affective & behavioral neuroscience 7, 356- 366. Botvinick, M.M., Cohen, J.D., and Carter, C.S. (2004). Conflict monitoring and anterior cingulate cortex: an update. Trends in cognitive sciences 8, 539-546. Bouthenet, M.-L., Souil, E., Martres, M.-P., Sokoloff, P., Giros, B., and Schwartz, J.-C. (1991). Localization of dopamine D3 receptor mRNA in the rat brain using in situ hybridization histochemistry: comparison with dopamine D2 receptor mRNA. Brain research 564, 203- 219. Braz, J., Solorzano, C., Wang, X., and Basbaum, A.I. (2014). Transmitting pain and itch messages: a contemporary view of the spinal cord circuits that generate gate control. Neuron 82, 522-536.

119

Brischoux, F., Chakraborty, S., Brierley, D.I., and Ungless, M.A. (2009). Phasic excitation of dopamine neurons in ventral VTA by noxious stimuli. Proceedings of the National Academy of Sciences of the United States of America 106, 4894-4899. Bromberg-Martin, E.S., Matsumoto, M., and Hikosaka, O. (2010). Dopamine in motivational control: rewarding, aversive, and alerting. Neuron 68, 815-834. Brown, A., and Weaver, L.C. (2012). The dark side of neuroplasticity. Experimental neurology 235, 133-141. Brozoski, T.J., Brown, R.M., Rosvold, H., and Goldman, P.S. (1979). Cognitive deficit caused by regional depletion of dopamine in prefrontal cortex of rhesus monkey. Science 205, 929-932. Buchta, W.C., Mahler, S.V., Harlan, B., Aston-Jones, G.S., and Riegel, A.C. (2017). Dopamine terminals from the ventral tegmental area gate intrinsic inhibition in the prefrontal cortex. Physiological reports 5. Budygin, E.A., Park, J., Bass, C.E., Grinevich, V.P., Bonin, K.D., and Wightman, R.M. (2012). Aversive stimulus differentially triggers subsecond dopamine release in reward regions. Neuroscience 201, 331-337. Buhle, J., and Wager, T.D. (2010). Performance-dependent inhibition of pain by an executive working memory task. Pain 149, 19-26. Burma, N.E., Leduc‐Pessah, H., Fan, C.Y., and Trang, T. (2016). Animal models of chronic pain: advances and challenges for clinical translation. Journal of neuroscience research. Bushnell, M.C., Ceko, M., and Low, L.A. (2013). Cognitive and emotional control of pain and its disruption in chronic pain. Nature reviews Neuroscience 14, 502-511. Cai, L., Pizano, K., Gundersen, G., Hayes, C., Fleming, W., and Witten, I.B. (2020). Distinct signals in medial and lateral VTA dopamine neurons modulate fear extinction at different times. bioRxiv. Carr, D.B., and Sesack, S.R. (2000a). GABA-containing neurons in the rat ventral tegmental area project to the prefrontal cortex. Synapse 38, 114-123. Carr, D.B., and Sesack, S.R. (2000b). Projections from the rat prefrontal cortex to the ventral tegmental area: target specificity in the synaptic associations with mesoaccumbens and mesocortical neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience 20, 3864-3873. Carrasquillo, Y., and Gereau, R.W.t. (2008). Hemispheric lateralization of a molecular signal for pain modulation in the amygdala. Molecular pain 4, 24. Catterall, W.A. (2000). Structure and regulation of voltage-gated Ca2+ channels. Annual review of cell and developmental biology 16, 521-555. Chen, G., Greengard, P., and Yan, Z. (2004). Potentiation of NMDA receptor currents by dopamine D1 receptors in prefrontal cortex. Proceedings of the National Academy of Sciences of the United States of America 101, 2596-2600. Chessell, I.P., Hatcher, J.P., Bountra, C., Michel, A.D., Hughes, J.P., Green, P., Egerton, J., Murfin, M., Richardson, J., and Peck, W.L. (2005). Disruption of the P2X 7 purinoceptor gene abolishes chronic inflammatory and neuropathic pain. Pain 114, 386-396. Chudasama, Y., and Robbins, T.W. (2004). Dopaminergic modulation of visual attention and working memory in the rodent prefrontal cortex. Neuropsychopharmacology 29, 1628- 1636.

120

Coffeen, U., Ortega-Legaspi, J.M., de Gortari, P., Simon-Arceo, K., Jaimes, O., Amaya, M.I., and Pellicer, F. (2010). Inflammatory nociception diminishes dopamine release and increases dopamine D2 receptor mRNA in the rat's insular cortex. Molecular pain 6, 75. Cools, R., and D'Esposito, M. (2011). Inverted-U-shaped dopamine actions on human working memory and cognitive control. Biological psychiatry 69, e113-125. Corcoran, K.A., and Quirk, G.J. (2007). Activity in prelimbic cortex is necessary for the expression of learned, but not innate, fears. The Journal of neuroscience : the official journal of the Society for Neuroscience 27, 840-844. Cordeiro Matos, S., Zhang, Z., and Seguela, P. (2015). Peripheral Neuropathy Induces HCN Channel Dysfunction in Pyramidal Neurons of the Medial Prefrontal Cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience 35, 13244-13256. Cox, J.J., Reimann, F., Nicholas, A.K., Thornton, G., Roberts, E., Springell, K., Karbani, G., Jafri, H., Mannan, J., Raashid, Y., et al. (2006). An SCN9A channelopathy causes congenital inability to experience pain. Nature 444, 894-898. Creac'h, C., Henry, P., Caille, J.M., and Allard, M. (2000). Functional MR imaging analysis of pain-related brain activation after acute mechanical stimulation. AJNR American journal of neuroradiology 21, 1402-1406. D'Ardenne, K., Eshel, N., Luka, J., Lenartowicz, A., Nystrom, L.E., and Cohen, J.D. (2012). Role of prefrontal cortex and the midbrain dopamine system in working memory updating. Proceedings of the National Academy of Sciences of the United States of America 109, 19900-19909. Dale, J., Zhou, H., Zhang, Q., Martinez, E., Hu, S., Liu, K., Urien, L., Chen, Z., and Wang, J. (2018). Scaling Up Cortical Control Inhibits Pain. Cell reports 23, 1301-1313. Davis, K.D., Wood, M.L., Crawley, A.P., and Mikulis, D.J. (1995). fMRI of human somatosensory and cingulate cortex during painful electrical nerve stimulation. Neuroreport 7, 321-325. de Jong, J.W., Afjei, S.A., Pollak Dorocic, I., Peck, J.R., Liu, C., Kim, C.K., Tian, L., Deisseroth, K., and Lammel, S. (2019). A Neural Circuit Mechanism for Encoding Aversive Stimuli in the Mesolimbic Dopamine System. Neuron 101, 133-151 e137. Decosterd, I., and Woolf, C.J. (2000). Spared nerve injury: an animal model of persistent peripheral neuropathic pain. Pain 87, 149-158. Dent, M.F., and Neill, D.B. (2012). Dose-dependent effects of prefrontal dopamine on behavioral state in rats. Behavioral neuroscience 126, 620-639. Di Chiara, G., Loddo, P., and Tanda, G. (1999). Reciprocal changes in prefrontal and limbic dopamine responsiveness to aversive and rewarding stimuli after chronic mild stress: implications for the psychobiology of depression. Biological psychiatry 46, 1624-1633. Dong, Y., Cooper, D., Nasif, F., Hu, X.T., and White, F.J. (2004). Dopamine modulates inwardly rectifying potassium currents in medial prefrontal cortex pyramidal neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience 24, 3077-3085. DosSantos, M.F., Moura, B.S., and DaSilva, A.F. (2017). Reward Circuitry Plasticity in Pain Perception and Modulation. Frontiers in pharmacology 8, 790. Durstewitz, D., Seamans, J.K., and Sejnowski, T.J. (2000). Dopamine-mediated stabilization of delay-period activity in a network model of prefrontal cortex. Journal of neurophysiology 83, 1733-1750.

121

Eddine, R., Valverde, S., Tolu, S., Dautan, D., Hay, A., Morel, C., Cui, Y., Lambolez, B., Venance, L., Marti, F., et al. (2015). A concurrent excitation and inhibition of dopaminergic subpopulations in response to nicotine. Scientific reports 5, 8184. Ellwood, I.T., Patel, T., Wadia, V., Lee, A.T., Liptak, A.T., Bender, K.J., and Sohal, V.S. (2017). Tonic or Phasic Stimulation of Dopaminergic Projections to Prefrontal Cortex Causes Mice to Maintain or Deviate from Previously Learned Behavioral Strategies. The Journal of neuroscience : the official journal of the Society for Neuroscience 37, 8315-8329. Elman, I., and Borsook, D. (2016). Common Brain Mechanisms of Chronic Pain and Addiction. Neuron 89, 11-36. Enjalbert, A., and Bockaert, J. (1983). Pharmacological characterization of the D2 dopamine receptor negatively coupled with adenylate cyclase in rat anterior pituitary. Molecular pharmacology 23, 576-584. Euston, D.R., Gruber, A.J., and McNaughton, B.L. (2012). The role of medial prefrontal cortex in memory and decision making. Neuron 76, 1057-1070. Ferenczi, E.A., Zalocusky, K.A., Liston, C., Grosenick, L., Warden, M.R., Amatya, D., Katovich, K., Mehta, H., Patenaude, B., Ramakrishnan, C., et al. (2016). Prefrontal cortical regulation of brainwide circuit dynamics and reward-related behavior. Science 351, aac9698. Fletcher, P.J., Tenn, C.C., Sinyard, J., Rizos, Z., and Kapur, S. (2007). A sensitizing regimen of amphetamine impairs visual attention in the 5-choice serial reaction time test: reversal by a D1 receptor agonist injected into the medial prefrontal cortex. Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology 32, 1122-1132. Flores-Hernandez, J., Cepeda, C., Hernandez-Echeagaray, E., Calvert, C.R., Jokel, E.S., Fienberg, A.A., Greengard, P., and Levine, M.S. (2002). Dopamine enhancement of NMDA currents in dissociated medium-sized striatal neurons: role of D1 receptors and DARPP-32. Journal of neurophysiology 88, 3010-3020. Fossat, P., Dobremez, E., Bouali-Benazzouz, R., Favereaux, A., Bertrand, S.S., Kilk, K., Leger, C., Cazalets, J.R., Langel, U., Landry, M., et al. (2010). Knockdown of L calcium channel subtypes: differential effects in neuropathic pain. The Journal of neuroscience : the official journal of the Society for Neuroscience 30, 1073-1085. Fuster, J.M., Bodner, M., and Kroger, J.K. (2000). Cross-modal and cross-temporal association in neurons of frontal cortex. Nature 405, 347. Gabbott, P.L., Dickie, B.G., Vaid, R.R., Headlam, A.J., and Bacon, S.J. (1997). Local-circuit neurones in the medial prefrontal cortex (areas 25, 32 and 24b) in the rat: morphology and quantitative distribution. The Journal of comparative neurology 377, 465-499. Gabbott, P.L., Warner, T.A., and Busby, S.J. (2006). Amygdala input monosynaptically innervates parvalbumin immunoreactive local circuit neurons in rat medial prefrontal cortex. Neuroscience 139, 1039-1048. Gabbott, P.L., Warner, T.A., Jays, P.R., Salway, P., and Busby, S.J. (2005). Prefrontal cortex in the rat: projections to subcortical autonomic, motor, and limbic centers. The Journal of comparative neurology 492, 145-177. Gambeta, E., Kopruszinski, C.M., Dos Reis, R.C., Zanoveli, J.M., and Chichorro, J.G. (2017). Facial pain and anxiety-like behavior are reduced by pregabalin in a model of facial carcinoma in rats. Neuropharmacology 125, 263-271.

122

Garcia-Caballero, A., Gadotti, V.M., Stemkowski, P., Weiss, N., Souza, I.A., Hodgkinson, V., Bladen, C., Chen, L., Hamid, J., Pizzoccaro, A., et al. (2014). The deubiquitinating enzyme USP5 modulates neuropathic and inflammatory pain by enhancing Cav3.2 channel activity. Neuron 83, 1144-1158. Gard, T., Hölzel, B.K., Sack, A.T., Hempel, H., Lazar, S.W., Vaitl, D., and Ott, U. (2011). Pain attenuation through mindfulness is associated with decreased cognitive control and increased sensory processing in the brain. Cerebral Cortex 22, 2692-2702. Gaspar, P., Bloch, B., and Le Moine, C. (1995). D1 and D2 receptor gene expression in the rat frontal cortex: cellular localization in different classes of efferent neurons. The European journal of neuroscience 7, 1050-1063. Gear, R.W., Miaskowski, C., Gordon, N.C., Paul, S.M., Heller, P.H., and Levine, J.D. (1996). Kappa-opioids produce significantly greater analgesia in women than in men. Nature medicine 2, 1248-1250. Gear, R.W., Miaskowski, C., Gordon, N.C., Paul, S.M., Heller, P.H., and Levine, J.D. (1999). The kappa opioid nalbuphine produces gender- and dose-dependent analgesia and antianalgesia in patients with postoperative pain. Pain 83, 339-345. Gehring, W.J., and Willoughby, A.R. (2002). The medial frontal cortex and the rapid processing of monetary gains and losses. Science 295, 2279-2282. Gorelova, N.A., and Yang, C.R. (2000). Dopamine D1/D5 receptor activation modulates a persistent sodium current in rat prefrontal cortical neurons in vitro. Journal of neurophysiology 84, 75-87. Granon, S., Passetti, F., Thomas, K.L., Dalley, J.W., Everitt, B.J., and Robbins, T.W. (2000). Enhanced and impaired attentional performance after infusion of D1 dopaminergic receptor agents into rat prefrontal cortex. Journal of Neuroscience 20, 1208-1215. Gregory, N.S., Harris, A.L., Robinson, C.R., Dougherty, P.M., Fuchs, P.N., and Sluka, K.A. (2013). An overview of animal models of pain: disease models and outcome measures. The journal of pain : official journal of the American Pain Society 14, 1255-1269. Gulledge, A.T., and Jaffe, D.B. (1998). Dopamine decreases the excitability of layer V pyramidal cells in the rat prefrontal cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience 18, 9139-9151. Gulledge, A.T., and Jaffe, D.B. (2001). Multiple effects of dopamine on layer V pyramidal cell excitability in rat prefrontal cortex. Journal of neurophysiology 86, 586-595. Gunaydin, L.A., Grosenick, L., Finkelstein, J.C., Kauvar, I.V., Fenno, L.E., Adhikari, A., Lammel, S., Mirzabekov, J.J., Airan, R.D., Zalocusky, K.A., et al. (2014). Natural neural projection dynamics underlying social behavior. Cell 157, 1535-1551. Gundel, H., Valet, M., Sorg, C., Huber, D., Zimmer, C., Sprenger, T., and Tolle, T.R. (2008). Altered cerebral response to noxious heat stimulation in patients with somatoform pain disorder. Pain 137, 413-421. Gurevich, E.V., Gainetdinov, R.R., and Gurevich, V.V. (2016). G protein-coupled receptor kinases as regulators of dopamine receptor functions. Pharmacological research 111, 1- 16. Halbout, B., Marshall, A.T., Azimi, A., Liljeholm, M., Mahler, S.V., Wassum, K.M., and Ostlund, S.B. (2019). Mesolimbic dopamine projections mediate cue-motivated reward seeking but not reward retrieval in rats. eLife 8. Hardy, S.G. (1985). Analgesia elicited by prefrontal stimulation. Brain research 339, 281-284.

123

Heymann, G., Jo, Y.S., Reichard, K.L., McFarland, N., Chavkin, C., Palmiter, R.D., Soden, M.E., and Zweifel, L.S. (2019). Synergy of Distinct Dopamine Projection Populations in Behavioral Reinforcement. Neuron. Hoover, W.B., and Vertes, R.P. (2007). Anatomical analysis of afferent projections to the medial prefrontal cortex in the rat. Brain structure & function 212, 149-179. Huang, J., Gadotti, V.M., Chen, L., Souza, I.A., Huang, S., Wang, D., Ramakrishnan, C., Deisseroth, K., Zhang, Z., and Zamponi, G.W. (2019a). A neuronal circuit for activating descending modulation of neuropathic pain. Nature neuroscience 22, 1659-1668. Huang, S., Borgland, S.L., and Zamponi, G.W. (2019b). Dopaminergic modulation of pain signals in the medial prefrontal cortex: Challenges and perspectives. Neuroscience letters 702, 71-76. Hyttel, J. (1984). Functional evidence for selective dopamine D-1 receptor blockade by SCH 23390. Neuropharmacology 23, 1395-1401. Inoue, K., Tsuda, M., and Koizumi, S. (2005). ATP receptors in pain sensation: Involvement of spinal microglia and P2X(4) receptors. Purinergic signalling 1, 95-100. Jarvis, M.F., Honore, P., Shieh, C.-C., Chapman, M., Joshi, S., Zhang, X.-F., Kort, M., Carroll, W., Marron, B., and Atkinson, R. (2007). A-803467, a potent and selective Nav1. 8 sodium channel blocker, attenuates neuropathic and inflammatory pain in the rat. Proceedings of the National Academy of Sciences 104, 8520-8525. Jay, T.M., and Witter, M.P. (1991). Distribution of hippocampal CA1 and subicular efferents in the prefrontal cortex of the rat studied by means of anterograde transport of Phaseolus vulgaris-leucoagglutinin. The Journal of comparative neurology 313, 574-586. Ji, G., and Neugebauer, V. (2009). Hemispheric lateralization of pain processing by amygdala neurons. Journal of neurophysiology 102, 2253-2264. Ji, G., and Neugebauer, V. (2011). Pain-related deactivation of medial prefrontal cortical neurons involves mGluR1 and GABA(A) receptors. Journal of neurophysiology 106, 2642-2652. Ji, G., Sun, H., Fu, Y., Li, Z., Pais-Vieira, M., Galhardo, V., and Neugebauer, V. (2010). Cognitive impairment in pain through amygdala-driven prefrontal cortical deactivation. Journal of Neuroscience 30, 5451-5464. Jiang, H., Fang, D., Kong, L.Y., Jin, Z.R., Cai, J., Kang, X.J., Wan, Y., and Xing, G.G. (2014). Sensitization of neurons in the central nucleus of the amygdala via the decreased GABAergic inhibition contributes to the development of neuropathic pain-related anxiety-like behaviors in rats. Molecular brain 7, 72. Johnson, S.W., and North, R.A. (1992). Opioids excite dopamine neurons by hyperpolarization of local interneurons. The Journal of neuroscience : the official journal of the Society for Neuroscience 12, 483-488. Kami, K., Tajima, F., and Senba, E. (2018). Activation of mesolimbic reward system via laterodorsal tegmental nucleus and hypothalamus in exercise-induced hypoalgesia. Scientific reports 8, 11540. Kandel, E.R., Schwartz, J.H., Jessell, T.M., Biochemistry, D.o., Jessell, M.B.T., Siegelbaum, S., and Hudspeth, A. (2000). Principles of neural science, Vol 4 (McGraw-hill New York). Keithley, R.B., and Wightman, R.M. (2011). Assessing principal component regression prediction of neurochemicals detected with fast-scan cyclic voltammetry. ACS chemical neuroscience 2, 514-525. Khakh, B.S., and North, R.A. (2012). Neuromodulation by extracellular ATP and P2X receptors in the CNS. Neuron 76, 51-69.

124

King, T., Vera-Portocarrero, L., Gutierrez, T., Vanderah, T.W., Dussor, G., Lai, J., Fields, H.L., and Porreca, F. (2009). Unmasking the tonic-aversive state in neuropathic pain. Nature neuroscience 12, 1364-1366. Kiritoshi, T., Ji, G., and Neugebauer, V. (2016). Rescue of Impaired mGluR5-Driven Endocannabinoid Signaling Restores Prefrontal Cortical Output to Inhibit Pain in Arthritic Rats. The Journal of neuroscience : the official journal of the Society for Neuroscience 36, 837-850. Kisilevsky, A.E., Mulligan, S.J., Altier, C., Iftinca, M.C., Varela, D., Tai, C., Chen, L., Hameed, S., Hamid, J., Macvicar, B.A., et al. (2008). D1 receptors physically interact with N-type calcium channels to regulate channel distribution and dendritic calcium entry. Neuron 58, 557-570. Klink, R., de Kerchove d'Exaerde, A., Zoli, M., and Changeux, J.P. (2001). Molecular and physiological diversity of nicotinic acetylcholine receptors in the midbrain dopaminergic nuclei. The Journal of neuroscience : the official journal of the Society for Neuroscience 21, 1452-1463. Ko, M.Y., Jang, E.Y., Lee, J.Y., Kim, S.P., Whang, S.H., Lee, B.H., Kim, H.Y., Yang, C.H., Cho, H.J., and Gwak, Y.S. (2018). The Role of Ventral Tegmental Area Gamma- Aminobutyric Acid in Chronic Neuropathic Pain after Spinal Cord Injury in Rats. Journal of neurotrauma 35, 1755-1764. Koob, G.F., and Le Moal, M. (2005). Plasticity of reward neurocircuitry and the 'dark side' of drug addiction. Nature neuroscience 8, 1442-1444. Kritzer, M.F., and Creutz, L.M. (2008). Region and sex differences in constituent dopamine neurons and immunoreactivity for intracellular estrogen and androgen receptors in mesocortical projections in rats. The Journal of neuroscience : the official journal of the Society for Neuroscience 28, 9525-9535. Kroener, S., Chandler, L.J., Phillips, P.E., and Seamans, J.K. (2009). Dopamine modulates persistent synaptic activity and enhances the signal-to-noise ratio in the prefrontal cortex. PloS one 4, e6507. Kuchinad, A., Schweinhardt, P., Seminowicz, D.A., Wood, P.B., Chizh, B.A., and Bushnell, M.C. (2007). Accelerated brain gray matter loss in fibromyalgia patients: premature aging of the brain? The Journal of neuroscience : the official journal of the Society for Neuroscience 27, 4004-4007. Kuner, R., and Flor, H. (2016). Structural plasticity and reorganisation in chronic pain. Nature reviews Neuroscience 18, 20-30. Lacey, M.G., Mercuri, N.B., and North, R.A. (1989). Two cell types in rat substantia nigra zona compacta distinguished by membrane properties and the actions of dopamine and opioids. The Journal of neuroscience : the official journal of the Society for Neuroscience 9, 1233-1241. Lammel, S., Hetzel, A., Hackel, O., Jones, I., Liss, B., and Roeper, J. (2008). Unique properties of mesoprefrontal neurons within a dual mesocorticolimbic dopamine system. Neuron 57, 760-773. Lammel, S., Ion, D.I., Roeper, J., and Malenka, R.C. (2011). Projection-specific modulation of dopamine neuron synapses by aversive and rewarding stimuli. Neuron 70, 855-862. Lammel, S., Lim, B.K., and Malenka, R.C. (2014). Reward and aversion in a heterogeneous midbrain dopamine system. Neuropharmacology 76 Pt B, 351-359.

125

Lammel, S., Lim, B.K., Ran, C., Huang, K.W., Betley, M.J., Tye, K.M., Deisseroth, K., and Malenka, R.C. (2012). Input-specific control of reward and aversion in the ventral tegmental area. Nature 491, 212-217. Laurent, V., and Westbrook, R.F. (2009). Inactivation of the infralimbic but not the prelimbic cortex impairs consolidation and retrieval of fear extinction. Learn Mem 16, 520-529. Lavand'homme, P.M., and Eisenach, J.C. (1999). Sex differences in cholinergic analgesia II: differing mechanisms in two models of allodynia. Anesthesiology 91, 1455-1461. Lazniewska, J., and Weiss, N. (2014). The "sweet" side of ion channels. Reviews of physiology, biochemistry and pharmacology 167, 67-114. Le Magnen, J., Marfaing-Jallat, P., Miceli, D., and Devos, M. (1980). Pain modulating and reward systems: a single brain mechanism? Pharmacology, biochemistry, and behavior 12, 729-733. Lee, M., Manders, T.R., Eberle, S.E., Su, C., D'Amour, J., Yang, R., Lin, H.Y., Deisseroth, K., Froemke, R.C., and Wang, J. (2015). Activation of corticostriatal circuitry relieves chronic neuropathic pain. The Journal of neuroscience : the official journal of the Society for Neuroscience 35, 5247-5259. Leknes, S., and Tracey, I. (2008). A common neurobiology for pain and pleasure. Nature reviews Neuroscience 9, 314-320. Lewis, B.L., and O'Donnell, P. (2000). Ventral tegmental area afferents to the prefrontal cortex maintain membrane potential 'up' states in pyramidal neurons via D(1) dopamine receptors. Cereb Cortex 10, 1168-1175. Loggia, M.L., Berna, C., Kim, J., Cahalan, C.M., Gollub, R.L., Wasan, A.D., Harris, R.E., Edwards, R.R., and Napadow, V. (2014). Disrupted brain circuitry for pain-related reward/punishment in fibromyalgia. Arthritis Rheumatol 66, 203-212. Lopez-Avila, A., Coffeen, U., Ortega-Legaspi, J.M., del Angel, R., and Pellicer, F. (2004). Dopamine and NMDA systems modulate long-term nociception in the rat anterior cingulate cortex. Pain 111, 136-143. Mantz, J., Milla, C., Glowinski, J., and Thierry, A.M. (1988). Differential effects of ascending neurons containing dopamine and noradrenaline in the control of spontaneous activity and of evoked responses in the rat prefrontal cortex. Neuroscience 27, 517-526. Mantz, J., Thierry, A.M., and Glowinski, J. (1989). Effect of noxious tail pinch on the discharge rate of mesocortical and mesolimbic dopamine neurons: selective activation of the mesocortical system. Brain research 476, 377-381. Mao, C.P., Zhang, Q.L., Bao, F.X., Liao, X., Yang, X.L., and Zhang, M. (2014). Decreased activation of cingulo-frontal-parietal cognitive/attention network during an attention- demanding task in patients with chronic low back pain. Neuroradiology 56, 903-912. Marcellino, D., Kehr, J., Agnati, L.F., and Fuxe, K. (2012). Increased affinity of dopamine for D(2) -like versus D(1) -like receptors. Relevance for volume transmission in interpreting PET findings. Synapse 66, 196-203. Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., and Wu, C. (2004). Interneurons of the neocortical inhibitory system. Nature reviews Neuroscience 5, 793- 807. Masuda, T., Ozono, Y., Mikuriya, S., Kohro, Y., Tozaki-Saitoh, H., Iwatsuki, K., Uneyama, H., Ichikawa, R., Salter, M.W., Tsuda, M., et al. (2016). Dorsal horn neurons release extracellular ATP in a VNUT-dependent manner that underlies neuropathic pain. Nature communications 7, 12529.

126

Mateo, Y., Johnson, K.A., Covey, D.P., Atwood, B.K., Wang, H.L., Zhang, S., Gildish, I., Cachope, R., Bellocchio, L., Guzman, M., et al. (2017). Endocannabinoid Actions on Cortical Terminals Orchestrate Local Modulation of Dopamine Release in the Nucleus Accumbens. Neuron 96, 1112-1126 e1115. Mejias-Aponte, C.A., Ye, C., Bonci, A., Kiyatkin, E.A., and Morales, M. (2015). A subpopulation of neurochemically-identified ventral tegmental area dopamine neurons is excited by intravenous cocaine. The Journal of neuroscience : the official journal of the Society for Neuroscience 35, 1965-1978. Merrill, C.B., Friend, L.N., Newton, S.T., Hopkins, Z.H., and Edwards, J.G. (2015). Ventral tegmental area dopamine and GABA neurons: Physiological properties and expression of mRNA for endocannabinoid biosynthetic elements. Scientific reports 5, 16176. Metz, A.E., Yau, H.J., Centeno, M.V., Apkarian, A.V., and Martina, M. (2009). Morphological and functional reorganization of rat medial prefrontal cortex in neuropathic pain. Proceedings of the National Academy of Sciences of the United States of America 106, 2423-2428. Miller, E.K., and Cohen, J.D. (2001). An integrative theory of prefrontal cortex function. Annual review of neuroscience 24, 167-202. Mirenowicz, J., and Schultz, W. (1996). Preferential activation of midbrain dopamine neurons by appetitive rather than aversive stimuli. Nature 379, 449-451. Miron, D., Duncan, G.H., and Bushnell, M.C. (1989). Effects of attention on the intensity and unpleasantness of thermal pain. Pain 39, 345-352. Moghaddam, B., Roth, R.H., and Bunney, B.S. (1990). Characterization of dopamine release in the rat medial prefrontal cortex as assessed by in vivo microdialysis: comparison to the striatum. Neuroscience 36, 669-676. Mogil, J.S., and Bailey, A.L. (2010). Sex and gender differences in pain and analgesia. Progress in brain research 186, 141-157. Monte-Silva, K., Kuo, M.F., Thirugnanasambandam, N., Liebetanz, D., Paulus, W., and Nitsche, M.A. (2009). Dose-dependent inverted U-shaped effect of dopamine (D2-like) receptor activation on focal and nonfocal plasticity in humans. The Journal of neuroscience : the official journal of the Society for Neuroscience 29, 6124-6131. Morales, M., and Root, D.H. (2014). Glutamate neurons within the midbrain dopamine regions. Neuroscience 282, 60-68. Morone, N.E., Greco, C.M., and Weiner, D.K. (2008). Mindfulness meditation for the treatment of chronic low back pain in older adults: a randomized controlled pilot study. Pain 134, 310-319. Narita, M., Ozaki, S., Ise, Y., Yajima, Y., and Suzuki, T. (2003). Change in the expression of c- fos in the rat brain following sciatic nerve ligation. Neuroscience letters 352, 231-233. Navratilova, E., and Porreca, F. (2014). Reward and motivation in pain and pain relief. Nature neuroscience 17, 1304-1312. Navratilova, E., Xie, J.Y., Okun, A., Qu, C., Eyde, N., Ci, S., Ossipov, M.H., King, T., Fields, H.L., and Porreca, F. (2012). Pain relief produces negative reinforcement through activation of mesolimbic reward-valuation circuitry. Proceedings of the National Academy of Sciences of the United States of America 109, 20709-20713. Neugebauer, V. (2015). Amygdala pain mechanisms. Handbook of experimental pharmacology 227, 261-284.

127

Neuhoff, H., Neu, A., Liss, B., and Roeper, J. (2002). I(h) channels contribute to the different functional properties of identified dopaminergic subpopulations in the midbrain. The Journal of neuroscience : the official journal of the Society for Neuroscience 22, 1290- 1302. Nicholl, B.I., Macfarlane, G.J., Davies, K.A., Morriss, R., Dickens, C., and McBeth, J. (2009). Premorbid psychosocial factors are associated with poor health-related quality of life in subjects with new onset of chronic widespread pain - results from the EPIFUND study. Pain 141, 119-126. Nishino, H., Ono, T., Muramoto, K., Fukuda, M., and Sasaki, K. (1987). Neuronal activity in the ventral tegmental area (VTA) during motivated bar press feeding in the monkey. Brain research 413, 302-313. Nugent, F.S., Penick, E.C., and Kauer, J.A. (2007). Opioids block long-term potentiation of inhibitory synapses. Nature 446, 1086-1090. O'Donnell, P. (2003). Dopamine gating of forebrain neural ensembles. The European journal of neuroscience 17, 429-435. Onali, P., Olianas, M.C., and Gessa, G.L. (1985). Characterization of dopamine receptors mediating inhibition of adenylate cyclase activity in rat striatum. Molecular pharmacology 28, 138-145. Ong, W.Y., Stohler, C.S., and Herr, D.R. (2019). Role of the Prefrontal Cortex in Pain Processing. Molecular neurobiology 56, 1137-1166. Ongur, D., and Price, J.L. (2000). The organization of networks within the orbital and medial prefrontal cortex of rats, monkeys and humans. Cereb Cortex 10, 206-219. Ott, T., and Nieder, A. (2019). Dopamine and Cognitive Control in Prefrontal Cortex. Trends in cognitive sciences 23, 213-234. Park, J., and Luo, Z.D. (2010). Calcium channel functions in pain processing. Channels (Austin) 4, 510-517. Peddareddygari, L.R., Oberoi, K., and Grewal, R.P. (2014). Congenital insensitivity to pain: a case report and review of the literature. Case reports in neurological medicine 2014, 141953. Peters, J., Kalivas, P.W., and Quirk, G.J. (2009). Extinction circuits for fear and addiction overlap in prefrontal cortex. Learn Mem 16, 279-288. Petrovic, P., Kalso, E., Petersson, K.M., Andersson, J., Fransson, P., and Ingvar, M. (2010). A prefrontal non-opioid mechanism in placebo analgesia. Pain 150, 59-65. Peyron, R., Garcia-Larrea, L., Gregoire, M.C., Costes, N., Convers, P., Lavenne, F., Mauguiere, F., Michel, D., and Laurent, B. (1999). Haemodynamic brain responses to acute pain in humans: sensory and attentional networks. Brain 122 ( Pt 9), 1765-1780. Ploghaus, A., Tracey, I., Clare, S., Gati, J.S., Rawlins, J.N., and Matthews, P.M. (2000). Learning about pain: the neural substrate of the prediction error for aversive events. Proceedings of the National Academy of Sciences of the United States of America 97, 9281-9286. Popescu, A.T., Zhou, M.R., and Poo, M.M. (2016). Phasic dopamine release in the medial prefrontal cortex enhances stimulus discrimination. Proceedings of the National Academy of Sciences of the United States of America 113, E3169-3176. Ren, W., Centeno, M.V., Berger, S., Wu, Y., Na, X., Liu, X., Kondapalli, J., Apkarian, A.V., Martina, M., and Surmeier, D.J. (2016). The indirect pathway of the nucleus accumbens shell amplifies neuropathic pain. Nature neuroscience 19, 220-222.

128

Richner, M., Bjerrum, O.J., Nykjaer, A., and Vaegter, C.B. (2011). The spared nerve injury (SNI) model of induced mechanical allodynia in mice. Journal of visualized experiments : JoVE. Robinson, T.E., and Kolb, B. (1997). Persistent structural modifications in nucleus accumbens and prefrontal cortex neurons produced by previous experience with amphetamine. The Journal of neuroscience : the official journal of the Society for Neuroscience 17, 8491- 8497. Roeper, J. (2013). Dissecting the diversity of midbrain dopamine neurons. Trends in neurosciences 36, 336-342. Rossi, A.F., Pessoa, L., Desimone, R., and Ungerleider, L.G. (2009). The prefrontal cortex and the executive control of attention. Experimental brain research 192, 489-497. Saade, N.E., Atweh, S.F., Bahuth, N.B., and Jabbur, S.J. (1997). Augmentation of nociceptive reflexes and chronic deafferentation pain by chemical lesions of either dopaminergic terminals or midbrain dopaminergic neurons. Brain research 751, 1-12. Sanchez-Vives, M.V., and McCormick, D.A. (2000). Cellular and network mechanisms of rhythmic recurrent activity in neocortex. Nature neuroscience 3, 1027. Santana, N., Mengod, G., and Artigas, F. (2009). Quantitative analysis of the expression of dopamine D1 and D2 receptors in pyramidal and GABAergic neurons of the rat prefrontal cortex. Cereb Cortex 19, 849-860. Santos, S.F., Rebelo, S., Derkach, V.A., and Safronov, B.V. (2007). Excitatory interneurons dominate sensory processing in the spinal substantia gelatinosa of rat. The Journal of physiology 581, 241-254. Sarkis, R., Saade, N., Atweh, S., Jabbur, S., and Al-Amin, H. (2011). Chronic dizocilpine or apomorphine and development of neuropathy in two rat models I: behavioral effects and role of nucleus accumbens. Experimental neurology 228, 19-29. Schultz, W. (1998). Predictive reward signal of dopamine neurons. Journal of neurophysiology 80, 1-27. Seamans, J.K., Durstewitz, D., Christie, B.R., Stevens, C.F., and Sejnowski, T.J. (2001a). Dopamine D1/D5 receptor modulation of excitatory synaptic inputs to layer V prefrontal cortex neurons. Proceedings of the National Academy of Sciences of the United States of America 98, 301-306. Seamans, J.K., Gorelova, N., Durstewitz, D., and Yang, C.R. (2001b). Bidirectional dopamine modulation of GABAergic inhibition in prefrontal cortical pyramidal neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience 21, 3628- 3638. Seamans, J.K., and Yang, C.R. (2004). The principal features and mechanisms of dopamine modulation in the prefrontal cortex. Progress in neurobiology 74, 1-58. Seminowicz, D.A., and Moayedi, M. (2017). The Dorsolateral Prefrontal Cortex in Acute and Chronic Pain. The journal of pain : official journal of the American Pain Society 18, 1027-1035. Sesack, S.R., Deutch, A.Y., Roth, R.H., and Bunney, B.S. (1989). Topographical organization of the efferent projections of the medial prefrontal cortex in the rat: an anterograde tract- tracing study with Phaseolus vulgaris leucoagglutinin. The Journal of comparative neurology 290, 213-242. Sevel, L.S., Craggs, J.G., Price, D.D., Staud, R., and Robinson, M.E. (2015). Placebo analgesia enhances descending pain-related effective connectivity: a dynamic causal modeling

129

study of endogenous pain modulation. The journal of pain : official journal of the American Pain Society 16, 760-768. Seymour, B., O'Doherty, J.P., Koltzenburg, M., Wiech, K., Frackowiak, R., Friston, K., and Dolan, R. (2005). Opponent appetitive-aversive neural processes underlie predictive learning of pain relief. Nature neuroscience 8, 1234-1240. Shiers, S., Pradhan, G., Mwirigi, J., Mejia, G., Ahmad, A., Kroener, S., and Price, T. (2018). Neuropathic Pain Creates an Enduring Prefrontal Cortex Dysfunction Corrected by the Type II Diabetic Drug Metformin But Not by Gabapentin. The Journal of neuroscience : the official journal of the Society for Neuroscience 38, 7337-7350. Simons, L.E., Elman, I., and Borsook, D. (2014). Psychological processing in chronic pain: a neural systems approach. Neuroscience and biobehavioral reviews 39, 61-78. Smith, W.J., Stewart, J., and Pfaus, J.G. (1997). Tail pinch induces fos immunoreactivity within several regions of the male rat brain: effects of age. Physiology & behavior 61, 717-723. Snyder, G.L., Fienberg, A.A., Huganir, R.L., and Greengard, P. (1998). A dopamine/D1 receptor/protein kinase A/dopamine- and cAMP-regulated phosphoprotein (Mr 32 kDa)/protein phosphatase-1 pathway regulates dephosphorylation of the NMDA receptor. The Journal of neuroscience : the official journal of the Society for Neuroscience 18, 10297-10303. Sogabe, S., Yagasaki, Y., Onozawa, K., and Kawakami, Y. (2013). Mesocortical dopamine system modulates mechanical nociceptive responses recorded in the rat prefrontal cortex. BMC neuroscience 14, 65. Sohal, V.S. (2012). Insights into cortical oscillations arising from optogenetic studies. Biological psychiatry 71, 1039-1045. Sotres-Bayon, F., Torres-Lopez, E., Lopez-Avila, A., del Angel, R., and Pellicer, F. (2001). Lesion and electrical stimulation of the ventral tegmental area modify persistent nociceptive behavior in the rat. Brain research 898, 342-349. Stemkowski, P., Garcia-Caballero, A., Gadotti, V.M., M'Dahoma, S., Huang, S., Black, S.A., Chen, L., Souza, I.A., Zhang, Z., and Zamponi, G.W. (2016a). TRPV1 Nociceptor Activity Initiates USP5/T-type Channel-Mediated Plasticity. Cell reports 17, 2901-2912. Stemkowski, P., Garcia-Caballero, A., Gadotti, V.M., M'Dahoma, S., Huang, S., Black, S.A.G., Chen, L., Souza, I.A., Zhang, Z., and Zamponi, G.W. (2016b). TRPV1 Nociceptor Activity Initiates USP5/T-type Channel-Mediated Plasticity. Cell reports 17, 2901-2912. Stiefel, K.M., Englitz, B., and Sejnowski, T.J. (2013). Origin of intrinsic irregular firing in cortical interneurons. Proceedings of the National Academy of Sciences of the United States of America 110, 7886-7891. Stratford, T.R., and Wirtshafter, D. (1990). Ascending dopaminergic projections from the dorsal raphe nucleus in the rat. Brain research 511, 173-176. Suzuki, S., Saitoh, A., Ohashi, M., Yamada, M., Oka, J.-I., and Yamada, M. (2016). The infralimbic and prelimbic medial prefrontal cortices have differential functions in the expression of anxiety-like behaviors in mice. Behavioural Brain Research. Talmi, D., Atkinson, R., and El-Deredy, W. (2013). The feedback-related negativity signals salience prediction errors, not reward prediction errors. The Journal of neuroscience : the official journal of the Society for Neuroscience 33, 8264-8269. Taylor, A.M., Becker, S., Schweinhardt, P., and Cahill, C. (2016). Mesolimbic dopamine signaling in acute and chronic pain: implications for motivation, analgesia, and addiction. Pain 157, 1194-1198.

130

Taylor, A.M., Castonguay, A., Taylor, A.J., Murphy, N.P., Ghogha, A., Cook, C., Xue, L., Olmstead, M.C., De Koninck, Y., Evans, C.J., et al. (2015). Microglia disrupt mesolimbic reward circuitry in chronic pain. The Journal of neuroscience : the official journal of the Society for Neuroscience 35, 8442-8450. Thierry, A.M., Tassin, J.P., Blanc, G., and Glowinski, J. (1976). Selective activation of mesocortical DA system by stress. Nature 263, 242-244. Thompson, J.M., and Neugebauer, V. (2017). Amygdala Plasticity and Pain. Pain research & management 2017, 8296501. Thomson, A.M., Bannister, A.P., Mercer, A., and Morris, O.T. (2002). Target and temporal pattern selection at neocortical synapses. Philosophical Transactions of the Royal Society B: Biological Sciences 357, 1781-1791. Ting, J.T., Daigle, T.L., Chen, Q., and Feng, G. (2014). Acute brain slice methods for adult and aging animals: application of targeted patch clamp analysis and optogenetics. Methods Mol Biol 1183, 221-242. Trang, T., Al-Hasani, R., Salvemini, D., Salter, M.W., Gutstein, H., and Cahill, C.M. (2015). Pain and Poppies: The Good, the Bad, and the Ugly of Opioid Analgesics. The Journal of neuroscience : the official journal of the Society for Neuroscience 35, 13879-13888. Trantham-Davidson, H., Neely, L.C., Lavin, A., and Seamans, J.K. (2004). Mechanisms underlying differential D1 versus D2 dopamine receptor regulation of inhibition in prefrontal cortex. The Journal of neuroscience : the official journal of the Society for Neuroscience 24, 10652-10659. Traub, R.J., Silva, E., Gebhart, G.F., and Solodkin, A. (1996). Noxious colorectal distention induced-c-Fos protein in limbic brain structures in the rat. Neuroscience letters 215, 165- 168. Treede, R.D., Kenshalo, D.R., Gracely, R.H., and Jones, A.K. (1999). The cortical representation of pain. Pain 79, 105-111. Tsai, H.C., Zhang, F., Adamantidis, A., Stuber, G.D., Bonci, A., de Lecea, L., and Deisseroth, K. (2009). Phasic firing in dopaminergic neurons is sufficient for behavioral conditioning. Science 324, 1080-1084. Tseng, A.H., and Craft, R.M. (2001). Sex differences in antinociceptive and motoric effects of cannabinoids. European journal of pharmacology 430, 41-47. Tzschentke, T.M. (2000). The medial prefrontal cortex as a part of the brain reward system. Amino acids 19, 211-219. Vander Weele, C.M., Siciliano, C.A., Matthews, G.A., Namburi, P., Izadmehr, E.M., Espinel, I.C., Nieh, E.H., Schut, E.H.S., Padilla-Coreano, N., Burgos-Robles, A., et al. (2018). Dopamine enhances signal-to-noise ratio in cortical-brainstem encoding of aversive stimuli. Nature 563, 397-401. Vertes, R.P. (2004). Differential projections of the infralimbic and prelimbic cortex in the rat. Synapse 51, 32-58. Vijayraghavan, S., Wang, M., Birnbaum, S.G., Williams, G.V., and Arnsten, A.F. (2007). Inverted-U dopamine D1 receptor actions on prefrontal neurons engaged in working memory. Nature neuroscience 10, 376-384. Villemure, C., and Bushnell, C.M. (2002). Cognitive modulation of pain: how do attention and emotion influence pain processing? Pain 95, 195-199. Virdee, K., McArthur, S., Brischoux, F., Caprioli, D., Ungless, M.A., Robbins, T.W., Dalley, J.W., and Gillies, G.E. (2014). Antenatal glucocorticoid treatment induces adaptations in

131

adult midbrain dopamine neurons, which underpin sexually dimorphic behavioral resilience. Neuropsychopharmacology 39, 339-350. Wang, G.Q., Cen, C., Li, C., Cao, S., Wang, N., Zhou, Z., Liu, X.M., Xu, Y., Tian, N.X., Zhang, Y., et al. (2015). Deactivation of excitatory neurons in the prelimbic cortex via Cdk5 promotes pain sensation and anxiety. Nature communications 6, 7660. Wang, H., Stradtman, G.G., Wang, X.-J., and Gao, W.-J. (2008). A specialized NMDA receptor function in layer 5 recurrent microcircuitry of the adult rat prefrontal cortex. Proceedings of the National Academy of Sciences. Wang, J., and O'donnell, P. (2001). D1 dopamine receptors potentiate NMDA-mediated excitability increase in layer V prefrontal cortical pyramidal neurons. Cerebral Cortex 11, 452-462. Wang, X.-J. (1999). Synaptic basis of cortical persistent activity: the importance of NMDA receptors to working memory. Journal of Neuroscience 19, 9587-9603. Wang, X.-J. (2002). Probabilistic decision making by slow reverberation in cortical circuits. Neuron 36, 955-968. Watanabe, M., Narita, M., Hamada, Y., Yamashita, A., Tamura, H., Ikegami, D., Kondo, T., Shinzato, T., Shimizu, T., Fukuchi, Y., et al. (2018). Activation of ventral tegmental area dopaminergic neurons reverses pathological allodynia resulting from nerve injury or bone cancer. Molecular pain 14, 1744806918756406. Wei, F., Wang, G.D., Kerchner, G.A., Kim, S.J., Xu, H.M., Chen, Z.F., and Zhuo, M. (2001). Genetic enhancement of inflammatory pain by forebrain NR2B overexpression. Nature neuroscience 4, 164-169. Werchan, D.M., Collins, A.G., Frank, M.J., and Amso, D. (2016). Role of Prefrontal Cortex in Learning and Generalizing Hierarchical Rules in 8-Month-Old Infants. The Journal of neuroscience : the official journal of the Society for Neuroscience 36, 10314-10322. Wiech, K., Ploner, M., and Tracey, I. (2008). Neurocognitive aspects of pain perception. Trends in cognitive sciences 12, 306-313. Williams, S.M., and Goldman-Rakic, P.S. (1998). Widespread origin of the primate mesofrontal dopamine system. Cereb Cortex 8, 321-345. Willis Jr, W. (1985). The pain system: the neural basis of nociceptive transmission in the mammalian nervous system. Pain and headache 8. Wise, S.P. (2008). Forward frontal fields: phylogeny and fundamental function. Trends in neurosciences 31, 599-608. Wood, P.B. (2006). Mesolimbic dopaminergic mechanisms and pain control. Pain 120, 230-234. Yang, C.R., and Seamans, J.K. (1996). Dopamine D1 receptor actions in layers V-VI rat prefrontal cortex neurons in vitro: modulation of dendritic-somatic signal integration. The Journal of neuroscience : the official journal of the Society for Neuroscience 16, 1922- 1935. Yim, C.Y., and Mogenson, G.J. (1980). Electrophysiological studies of neurons in the ventral tegmental area of Tsai. Brain research 181, 301-313. Yoshida, M., Shirouzu, M., Tanaka, M., Semba, K., and Fibiger, H.C. (1989). Dopaminergic neurons in the nucleus raphe dorsalis innervate the prefrontal cortex in the rat: a combined retrograde tracing and immunohistochemical study using anti-dopamine serum. Brain research 496, 373-376. Young, C.E., and Yang, C.R. (2004). Dopamine D1/D5 receptor modulates state-dependent switching of soma-dendritic Ca2+ potentials via differential protein kinase A and C

132

activation in rat prefrontal cortical neurons. The Journal of neuroscience : the official journal of the Society for Neuroscience 24, 8-23. Zamponi, G.W., Lewis, R.J., Todorovic, S.M., Arneric, S.P., and Snutch, T.P. (2009). Role of voltage-gated calcium channels in ascending pain pathways. Brain research reviews 60, 84-89. Zamponi, G.W., Striessnig, J., Koschak, A., and Dolphin, A.C. (2015). The Physiology, Pathology, and Pharmacology of Voltage-Gated Calcium Channels and Their Future Therapeutic Potential. Pharmacological reviews 67, 821-870. Zhang, H., Qian, Y.L., Li, C., Liu, D., Wang, L., Wang, X.Y., Liu, M.J., Liu, H., Zhang, S., Guo, X.Y., et al. (2017). Brain-Derived Neurotrophic Factor in the Mesolimbic Reward Circuitry Mediates Nociception in Chronic Neuropathic Pain. Biological psychiatry 82, 608-618. Zhang, J., Hoffert, C., Vu, H.K., Groblewski, T., Ahmad, S., and O'Donnell, D. (2003). Induction of CB2 receptor expression in the rat spinal cord of neuropathic but not inflammatory chronic pain models. European Journal of Neuroscience 17, 2750-2754. Zhang, T.A., Placzek, A.N., and Dani, J.A. (2010). In vitro identification and electrophysiological characterization of dopamine neurons in the ventral tegmental area. Neuropharmacology 59, 431-436. Zhang, Z., Gadotti, V.M., Chen, L., Souza, I.A., Stemkowski, P.L., and Zamponi, G.W. (2015). Role of Prelimbic GABAergic Circuits in Sensory and Emotional Aspects of Neuropathic Pain. Cell reports 12, 752-759. Zhao, J., Seereeram, A., Nassar, M.A., Levato, A., Pezet, S., Hathaway, G., Morenilla-Palao, C., Stirling, C., Fitzgerald, M., McMahon, S.B., et al. (2006). Nociceptor-derived brain- derived neurotrophic factor regulates acute and inflammatory but not neuropathic pain. Molecular and cellular neurosciences 31, 539-548. Zhuo, M. (2002). Glutamate receptors and persistent pain: targeting forebrain NR2B subunits. Drug discovery today 7, 259-267. Zhuo, M. (2006). Molecular mechanisms of pain in the anterior cingulate cortex. Journal of neuroscience research 84, 927-933. Zweifel, L.S., Fadok, J.P., Argilli, E., Garelick, M.G., Jones, G.L., Dickerson, T.M., Allen, J.M., Mizumori, S.J., Bonci, A., and Palmiter, R.D. (2011). Activation of dopamine neurons is critical for aversive conditioning and prevention of generalized anxiety. Nature neuroscience 14, 620.

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Chapter Six: Appendices

6.1 Neuronal plasticity during acute and chronic inflammatory pain states

6.1.1 Introduction of experimental design

An optogenetic acute pain model and the Complete Freund's adjuvant (CFA) inflammatory pain model were used to compare data from previous studies using the SNI neuropathic pain model.

The optogenetic model is a novel acute pain model developed by Dr. Patrick Stemkowski from the Zamponi lab (Stemkowski et al., 2016a). In this model, blue laser light was applied to the right paw of the transgenic mice expressing ChR2 under the promotor for TRPV1 to non- invasively activate pain sensing neurons. For control group, mice were put under anesthesia but no light was applied. Slice recordings were carried out 1.5 h after either stimulation or control treatment.

In the CFA model, 7 days after CFA injection on one (left or right) paw, mice were tested for thermal or mechanical threshold before being sacrificed for slice recording. For the control group, mice were injected with PBS instead of CFA.

Cell excitability and synaptic transmission properties in mPFC layer V pyramidal neurons were compared to examine if there is a similar change in the optogenetic and CFA models compared to the SNI model.

6.1.2 Photostimulation induced temporal c-fos expression in the mPFC

Immunohistochemistry was carried out to examine c-fos expression at different time points after photostimulation. It was found that 10 min photostimulation was able to induce c-fos expression in the contralateral side of the mPFC 1-3h after stimulation (Figure 6-1, 6-2). However, c-fos expression was not observed 24 h after stimulation (Figure 6-3), suggesting an acute instead of

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chronic alteration. C-fos expression was also observed in the ipsilateral side of the mPFC, but at

a much lower level compared to the contralateral side. In order to investigate which type of

neurons were activated, double staining of c-fos and parvalbumin was carried out. C-fos signal

only partially overlapped with parvalbumin (Figure 6-4). C-fos expression was also observed in the thalamus 1-3 h after stimulation, and returned to a baseline level 24 h after stimulation

(Figure 6-5). Compared to the mPFC, the baseline neuronal activity in the thalamus was much higher.

6.1.3 Photostimulation altered inhibitory/excitatory input ratio to mPFC pyramidal neurons

Mice were sacrificed 1.5 h after stimulation, and slice recording was carried out on the layer V pyramidal neurons on the contralateral side of the mPFC. Spontaneous EPSCs and IPSCs were recorded using whole-cell voltage-clamp at the reverse potential of -70 mV and 0 mV respectively (Figure 6-6, A-B). The reversal potential of EPSC was verified using bicuculline, and the reversal potential of IPSCs was verified using AP-5 and DNQX. There was no change of

EPSC or IPSC interspike intervals before and after the application of blockers (Figure 6-6, C-F).

Spontaneous EPSC and IPSC interspike intervals were compared between control and

stimulated groups, and no significant difference was observed (Figure 6-7, A-B). However, the

EPSC/IPSC interspike interval ratio was significantly increased in the stimulated group (Figure

6-7, C-D). Since interspike interval is the reciprocal of frequency, this change indicated an increased inhibitory input onto the PFC layer V pyramidal neurons by photostimulation. The observation that there was no change in EPSC or IPSC interspike interval was probably due to the heterogeneous nature of the mPFC pyramidal neurons, and calculating the ratio was able to normalize the variation brought by the heterogeneity. Spontaneous EPSC and IPSC amplitudes

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were compared between the control and stimulated groups, and no difference was found (Figure

6-8, A-B). There was also no change in the EPSC/IPSC amplitude ratio after stimulation (Figure

6-8, C).

6.1.4 Photostimulation did not alter the cell excitability of mPFC pyramidal neurons

Cell excitability properties were recorded using whole-cell current-clamp. Frequency-current

relation, current threshold of action potential (rheobase current), and cell membrane input

resistance were compared between control and stimulated groups. It was found that

photostimulation did not alter cell excitability (Figure 6-9). Although there was an increase in

inhibitory input, the average interspike intervals of IPSC and EPSC were 200-300 ms and 900-

1300 ms respectively. The relatively low frequency of spontaneous EPSC and IPSC may lead to

an overall hyperpolarization of the neuron, however it could be hard to detect in the cell

excitability properties since all of them were tested over a period less than 1000 ms.

In sum, photostimulation using the optogenetic acute pain activated neurons in the

contralateral mPFC. The stimulation also led to an increase of inhibitory input onto the mPFC

layer V pyramidal neurons, however the change was minor and did not further alter cell excitability.

6.1.5 Experimental design for studying CFA-induced plasticity in the mPFC

The Complete Freund's adjuvant (CFA) inflammatory pain model were used to compare with the

SNI neuropathic pain model and the optogenetic acute pain model.

In the CFA model, 7 days after CFA injection into the right paw, mice were tested for thermal or mechanical threshold before being sacrificed for slice recording. For the control group, mice were be injected with PBS instead of CFA.

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Cell excitability and synaptic transmission properties in mPFC layer V pyramidal

neurons were compared to investigate differences between CFA model, optogenetic model, and

SNI model.

6.1.6 Behavioural evaluation of CFA-induced thermal and mechanical hypersensitivity

Behavioural tests showed that CFA treatment significantly decreased the thermal and mechanical

threshold on the injected (ipsilateral) paw 7 days after injection (Figure 6-10, A-C), indicating increased thermal and mechanical sensitivity.

6.1.7 CFA treatment altered action potential threshold but not other cell excitability properties in mPFC pyramidal neurons

Slice recordings were carried out on layer V pyramidal neurons in the left (contralateral) side of the mPFC. Cell excitability were compared between CFA and PBS treated groups. The electrophysiology results showed no difference in the frequency-current (F-I) relation and input resistance between CFA and PBS treated groups (Figure 6-11, A, C). However there was a significant hyperpolarization of the action potential voltage threshold in the CFA treated group

(Figure 6-11, B), suggesting increased cell excitability. The hyperpolarization in action potential threshold did not alter the F-I relation. This might due to the fact that the change in the action potential threshold was very small (-42.4 mV versus -44.3 mV). In the F-I relation protocol, the current step increased by 25 pA per step, which caused a change in the membrane potential of around 10 mV. Thus a change of action potential threshold of 1.9 mV was not big enough to change the F-I relation.

Previous studies have suggested hemispheric lateralization in pain signal processing in the amygdala, and showed that the right amygdala plays a dominant role (Carrasquillo and

Gereau, 2008; Ji and Neugebauer, 2009). In order to rule out the possibility that changes caused

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by CFA treatment were not observed because that the left mPFC remains unchanged in pain, it is

necessary to investigate if this hemispheric lateralization also applies to pain signaling in the

mPFC.

6.1.8 Investigation of hemispheric lateralization in pain signal processing in the mPFC

(CFA model).

Mice were injected with CFA or PBS on their left paw. Pain threshold were tested before slice

recordings. Right (contralateral) side of the mPFC were be used for electrophysiology study. Cell

excitability properties in the contralateral mPFC layer V pyramidal neurons were compared

between mice receiving CFA injections on the left paws versus mice with right paw injections.

Mechanical threshold was compared between groups with CFA injections on the left

versus the right paw, and the results showed CFA injection on either side of paw could

significantly lower the withdraw threshold on the injected paw (Figure 6-10, B-C). The F-I

relation was compared and the result showed no difference between left paw and right paw

injected mice in the CFA or PBS treated group (Figure 6-12, A, B). The F-I relation results were

then separated into left and right paw injected groups, and in each group CFA treated mice were

compared with PBS treated ones. However no significant difference was found in either group

(Figure 6-12, C-D). Action potential voltage threshold and membrane input resistance were also

tested from left versus right paw injected mice, respectively. In neither group CFA treatment

altered action potential threshold or input resistance (Table 6-1).

In summary, in the CFA inflammatory pain model, cell excitability was slightly increased and action potential voltage threshold was hyperpolarized with CFA treatment. However the change was minor and did not alter the firing pattern in the F-I relation. Since the F-I relation remains unchanged, synaptic transmission properties were not further investigated. Hemispheric

138 lateralization in pain signaling in the mPFC was also investigated using the CFA model, and no difference in neuronal excitability was found between the right paw injected or left paw injected mice.

6.1.9 Conclusion

The optogenetic acute pain model, the CFA inflammatory pain model, and the SNI neuropathic pain model involve different mechanisms related to pain signaling. Accordingly, the mPFC responded differently to different pain models. The SNI model induced an increased excitatory input onto GABA neurons, which further decreased action potential frequency in F-I relation in the PFC pyramidal neurons (Zhang et al., 2015). In comparison, action potential frequency in the

F-I relation remains unchanged in the optogenetic and CFA model, although slight changes were observed in other synaptic transmission or cell excitability properties. Thus as a more reliable pain model, SNI model is more suitable for further study in the mPFC related to pain signaling.

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A B

C D

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Figure 6-1. Photostimulation on the right paw induced c-fos expression in the left mPFC 3 h after stimulation.

A. C-fos expression in the left (contralateral) mPFC 3 h after stimulation. B. C-fos expression in the right (ipsilateral) mPFC 3h after stimulation. C. C-fos expression in the left mPFC in the control group. D. C-fos expression in the right mPFC in the control group. Scale bar, 20 µm.

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A. 1h stimulation B. Control C. Secondary only

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Figure 6-2. Photostimulation induced c-fos expression in the mPFC 1 h after stimulation.

A. C-fos expression in the mPFC 1 h after photostimulation on both sides of paws. B. C-fos expression in the control group which underwent anesthesia only. C. Negative control without primary antibody. Scale bar, 20 µm.

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A B

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Figure 6-3. C-fos expression was not observed in the mPFC 24 h after photostimulation on the right paw.

A. C-fos expression in the left (contralateral) mPFC 24 h after photostimulation on the right paw.

B. C-fos expression in the right mPFC 24 h after photostimulation on the right paw. Scale bar, 20

µm.

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A B C

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Figure 6-4. C-fos signal from photostimulation partially overlapped with parvalbumin

neurons in the mPFC.

A, B. Double immunostaining of c-fos and parvalbumin in the mPFC. Green, parvalbumin; Red,

c-fos. Arrows, parvalbumin signal overlapped with c-fos signal. Scale bar, 20 µm. C. C-fos and parvalbumin double staining on mPFC from the control (anesthesia only) group.

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A B C D E

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Figure 6-5. C-fos expression in the thalamus induced by photostimulation.

A. C-fos expression in the thalamus 1 h after stimulation. B. C-fos expression in the thalamus 1 h after anesthesia in the control group. C. C-fos expression in the thalamus 3 h after stimulation. D.

C-fos expression in the thalamus 3 h after anesthesia in the control group. E. C-fos expression in the thalamus 24 h after stimulation. Scale bar, 20 µm.

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A B

C D

300 1000

250 800

200 600

150

400 100

200 IPSC interspike interval (ms) 50 EPSC interspike interval (ms)

0 0 "IntervalERS Ave" "IntervalERS+AP-5+DNQX Ave with blockers" "EPSP ERSinterval ave" "EPSP internalERS+Bic ave with blockers" n=3 n=3 n=6 n=6

E F 500 1800 ERS ERS 450 ERS+AP-5+DNQX 1600 ERS+Bic

400 1400 350 1200 300 1000 250 800 200 600 150 400 100 IPSC interspike interval (ms) EPSC interspike interval (ms) 50 200

0 0 Cell1 Cell2 Cell3 Cell1 Cell2 Cell3 Cell4 Cell5 Cell6

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Figure 6-6. Validation of spontaneous IPSC and EPSC at 0 mV and -70 mV respectively.

A. Representative trace of spontaneous EPSC at 0 mV. B. Representative trace of spontaneous

IPSC at -70 mV. Scale, 50 pA, 100 ms. C. Spontaneous IPSC interspike interval did not change

after the application of AMPA and NMDA receptor blockers DNQX and AP-5 (p= 0.65, paired t-test). ERS, extracellular recording solution. D. Spontaneous EPSC interspike interval did not change after the application of GABA receptor blocker bicuculline (p= 0.91, paired t-test). E, F.

Scatter plots of spontaneous IPSC (E, p= 1.00, Mann-Whitney test) and EPSC (F, p= 0.94,

Mann-Whitney test) interspike interval before and after the application of blockers.

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152

Figure 6-7. Photostimulation altered the ratio of spontaneous inhibitory/excitatory input onto mPFC layer V pyramidal neurons.

A, B. Photostimulation did not change interspike intervals of spontaneous EPSC (A, p= 0.22, t- test) or IPSC (B, p= 0.46, t-test). C, D. Photostimulation significantly increased EPSC/IPSC interspike interval ratio from 3.50±0.51 to 5.64±0.51, suggesting increased inhibitory input (C. p< 0.01, t-test; D. p< 0.05, Mann-Whitney test).

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A 40

EPSC amplitude (pA) 10

0 Control Stimulated B n=11 n=12 35

IPSC amplitude (pA) 10

0 Control Stimulated n=14 n=13 C 1.2

1.0

0.8

0.6

0.4 EPSC/IPSC amplitude EPSC/IPSC

0.2

0.0 Control Stimulated n=11 n=12

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Figure 6-8. Photostimulation did not alter spontaneous EPSC or IPSC amplitudes in mPFC layer V pyramidal neurons.

A. Statistics on EPSC amplitude in control and stimulated groups (p= 0.23, t-test). B. Statistics

on IPSC amplitude in control and stimulated groups (p= 0.76, t-test). C. Photostimulation did not alter the ratio of EPSC/IPSC amplitude (p= 0.56, t-test).

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A 25 Stimulated (7) Control (8) 20

10 Frequency (Hz) Frequency

0 0 50 100 150 200 B Injecting current (nA) 1.2

1.0

0.8

0.6

0.4

0.2 AP Current Threshold (nA) Threshold Current AP

0.0 Control"Ctrl" Stimulated"Stim" n=9 n=7 C 2.5

) 2.0 Ω

1.5

1.0

Input Resistance (G Resistance Input 0.5

0.0 "controlControl input r" "stimStimulated input R" n=9 n=6

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Figure 6-9. Photostimulation did not alter cell excitability of mPFC layer V pyramidal

neurons.

A. Photostimulation did not change the F-I relation. B. Photostimulation did not change the current threshold of action potential (p= 0.10, t-test). C. Photostimulation did not change the cell membrane input resistance (p= 0.40, t-test).

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20 * ** ** 18

6 Thermal threshold (s) 4

2 n=7 n=7 n=7 n=7 0 PBS Ipsi PBS Contra CFA Ipsi CFA Contra

12 *** ** 10 *

4 Mechanical threshold (g) 2

n=13 n=13 n=3 n=3 0 PBS Ipsi PBS Contra CFA Ipsi CFA Contra

12 **

4 Mechanical threshold (g) 2

n=5 n=5 n=5 n=5 0 PBS Ipsi PBS Contra CFA Ipsi CFA Contra

158

Figure 6-10. CFA treatment significantly dropped the thermal and mechanical threshold of the ipsilateral paws 7 days after injection.

A. Thermal threshold 7 days after CFA/PBS injection on the right paw (P<0.01, one-way

ANOVA). B. Mechanical threshold 7 days after CFA/PBS injection on the right paw (P<0.001, one-way ANOVA). C. Mechanical threshold 7 days after CFA/PBS injection on the left paw

(P<0.05, one-way ANOVA). Tukey test was used as post hoc test for A, B, and C.

159

35 CFA (25) 30 PBS (18)

10 AP frequency (Hz) 5

-5 -50 0 50 100 150 200 250 300 350 400 Injecting current (pA)

B 0

-10

-20

-30 AP threshold (mV)

-40

* -50 PBS CFA n=19 n=23 C

200

) 150

100

Input Resistance (M Ω 50

0 PBS CFA n=16 160 n=22

Figure 6-11. CFA treatment slightly increased the cell excitability of mPFC layer V pyramidal neurons.

A. CFA treatment did not alter the F-I relation. B. CFA treatment hyperpolarized action potential voltage threshold from -42.4±2.8 to -44.3±3.2 mV (p<0.05, t-test). C. CFA treatment caused no change in the cell input resistance (p= 0.69, t-test).

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A B 35 35 CFA right PFC (7) PBS right PFC (6) CFA left PFC (16) 30 30 PBS left PFC (12)

25 25

20 20

15 15

10 10 AP frequency (Hz) AP frequency (Hz) 5 5

0 0

-5 -5 -50 0 50 100 150 200 250 300 350 400 -50 0 50 100 150 200 250 300 350 400 Injecting current (pA) Injecting current (pA)

C D 35 35 CFA right PFC (7) CFA left PFC (16) PBS right PFC (6) 30 30 PBS left PFC (12)

25 25

20 20

15 15

10 10 AP frequency (Hz) AP frequency (Hz) 5 5

0 0

-5 -5 -50 0 50 100 150 200 250 300 350 400 -50 0 50 100 150 200 250 300 350 400 Injecting current (pA) Injecting current (pA)

162

Figure 6-12. Hemispheric lateralization was not observed in the mPFC pyramidal neurons

when comparing right versus left paw injected mice.

In neither CFA treated group (A) nor PBS treated group (B), F-I relation showed a difference

between the left versus right paw injected mice. C, D. No difference was observed in F-I relation between CFA and PBS treated group with left paw injected (C) or right paw injected (D) mice.

P> 0.05, one-way ANOVA.

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Group n AP Threshold (mV) SEM P PBS (R) 6 -42.41 1.29 0.26 CFA (R) 5 -44.38 0.92

PBS (L) 13 -42.33 0.78 0.09 CFA (L) 18 -44.31 0.76

Group n Input Resistance (GΩ) SEM P PBS (R) 6 1.47 0.02 0.89 CFA (R) 6 1.43 0.02

PBS (L) 10 1.76 0.01 0.58 CFA (L) 16 1.64 0.01

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Table 6-1. Action potential voltage threshold and cell membrane input resistance in PFC

pyramidal neurons from left paw versus right paw injected mice.

CFA treatment did not alter the action potential threshold or input resistance in either group. R, right PFC from left paw injected mice; L, left PFC from right paw injected mice.

165