The Effects of Nicotine on the Neurobiology of Selective Attention In

Home , Systems neuroscience

THE EFFECTS OF NICOTINE ON THE NEUROBIOLOGY OF SELECTIVE

ATTENTION IN SCHIZOPHRENIA

JASON SMUCNY

M.S., Yale University, New Haven, 2007

B.A., Amherst College, Amherst, 2004

A thesis submitted to the

Faculty of the Graduate School of the

University of Colorado in partial fulfillment

of the requirements for the degree of

Doctor of Philosophy

Neuroscience Program

2016

This thesis for the Doctor of Philosophy degree by

Jason Smucny

has been approved for the

Neuroscience Program

Benzi Kluger, Chair

Jason R. Tregellas, Advisor

Jody Tanabe

Brian D. Berman

Diego Restrepo

Date: December 16, 2016

Smucny, Jason (Ph.D., Neuroscience)

The Effects of Nicotine on the Neurobiology of Selective Attention in

Schizophrenia

Thesis directed by Associate Professor Jason Tregellas

ABSTRACT

Functional magnetic resonance imaging (fMRI) was used to examine the neuronal effects of nicotine during a selective attention task in patients with schizophrenia and healthy control subjects. Two analyses were performed. The first analysis examined the effects of acute nicotine (vs. placebo) administration on hippocampal and ventral parietal cortex (VPC) response during a go/no-go

task in combination with environmental noise distractors. Significant drug X

diagnosis interactions were observed in the hippocampus and VPC, driven by

relatively increased response in patients under placebo conditions and reversal

of this difference under nicotine.

The second analysis examined the effect of nicotine on connectivity of the

ventral attention network (VAN) during the attention task as well as during the

resting state. A significant drug X diagnosis interaction was observed on VAN

connectivity, driven by reduced connectivity in patients under placebo conditions

and increased connectivity in patients after nicotine. Under resting state

conditions, a significant main effect of diagnosis was observed in which patients showed increased VAN connectivity under placebo. No main effect of drug or

iii

drug X diagnosis interaction was observed, however, suggesting that nicotine did not affect resting state connectivity of the network.

Overall, these results suggest that nicotine affects neuronal response and connectivity in schizophrenia in an attention-dependent manner. The observed reduction in hippocampal response is consistent with previous studies demonstrating reduced response of the area during smooth pursuit eye movements. This study also suggests a mechanism by which nicotine may enhance the function of attentional systems in schizophrenia.

The form and content of this abstract are approved. I recommend its publication.

Approved: Jason Tregellas

ACKNOWLEDGEMENTS

I would like to express my sincere appreciation to Dr. Jason Tregellas, my advisor, for his continuing guidance, support, and encouragement.

I would also like to thank Debra Singel, Robert Freedman, and the

Schizophrenia Research Center, without whom this work would not be possible.

Lastly, my thanks to my family, especially my wife Vanessa, for their love and support.

COMIRB Policy ID: 03-569

TABLE OF CONTENTS

CHAPTER

I. INTRODUCTION ...... 1

Thesis Outline ...... 4

II. NICOTINE, SCHIZOPHRENIA, AND THE “SELF-MEDICATION” HYPOTHESIS: EVIDENCE FROM NEUROPHARMACOLOGY TO NEUROIMAGING ...... 6

Neuropharmacology of Nicotinic Receptors ...... 7 Nicotinic Receptors in Schizophrenia ...... 8 Nicotine Targets P50 Gating Deficits in Schizophrenia ...... 10 Nicotinic Agonists Modulate Brain Networks in Schizophrenia ...... 12 fMRI: Basis and Applications ...... 13 Using fMRI to Examine Oculomotor Networks in Schizophrenia ...... 14 The Default Network ...... 16 The Salience Network ...... 19 Neuroimaging the Cognitive Effects of Nicotine in Schizophrenia ...... 22 Working Memory ...... 22 Attention ...... 23 Perspectives and Future Directions ...... 25

III. HIPPOCAMPAL HYPERACTIVITY IN SCHIZOPHRENIA AND ITS RELATIONSHIP TO ATTENTION DEFICITS ...... 31

Morphology of the Hippocampus ...... 32 Hippocampal Pathology in Schizophrenia: Structural Studies ...... 32 Hippocampal Pathology in Schizophrenia: Functional Studies ...... 33 Relevance to Attention Deficits in Schizophrenia ...... 35

IV. ATTENTION NETWORKS IN SCHIZOPHRENIA ...... 40

Attention Networks of the Brain ...... 40 Network Dysfunction in Schizophrenia ...... 41

V. ANALYSIS 1: EFFECTS OF NICOTINE ON HIPPOCAMPAL AND VENTRAL PARIETAL CORTEX ACTIVITY DURING AUDITORY SELECTIVE ATTENTION IN SCHIZOPHRENIA ...... 45

Materials and Methods ...... 45 Subjects ...... 45 Study Design ...... 47 Auditory Stimuli ...... 48 Task Description ...... 49 fMRI Scanning Parameters ...... 51 fMRI Preprocessing ...... 52 Region-of-Interest (ROI) Analysis of BOLD Response ...... 52 Whole-Brain Analysis of BOLD Response ...... 54 Results ...... 54 Physiological Effects of Nicotine ...... 54 Behavioral Data ...... 54 ROI Analysis of BOLD Response ...... 55 Whole-Brain Analysis ...... 56 Behavioral Correlates ...... 57

VI. ANALYSIS 2: EFFECTS OF NICOTINE ON VENTRAL ATTENTION NETWORK CONNECTIVITY IN SCHIZOPHRENIA ...... 65

Materials and Methods ...... 65 fMRI Scanning Parameters: Resting State ...... 65 fMRI Preprocessing (SART and Resting State) ...... 66 Connectivity Analysis: Seed and Target ROI Definitions ...... 66 Connectivity Analysis: Implementation ...... 67 Task-Independent Effects of Nicotine on VAN Connectivity ...... 68 Correlation Analyses ...... 69 Results ...... 69 gPPI Analysis ...... 69 Resting State Connectivity Analysis ...... 70 Correlation Analysis ...... 70

vii

VII. DISCUSSION ...... 75

Analysis 1 Results Summary ...... 75 Analysis 2 Results Summary ...... 75 Discussion: Hippocampal Findings ...... 76 Discussion: VAN Findings ...... 77 Effects of Nicotine: Patients vs. Controls ...... 81 Limitations ...... 82 Conclusion ...... 84

REFERENCES ...... 86

APPENDIX A: EFFECTS OF SMOKING STATUS ON P50 GATING AND COGNITIVE PERFORMANCE (In Fulfillment of CCTSI Requirements) ...... 108

Statement ...... 108 Rationale ...... 108 Materials and Methods ...... 109 Subjects ...... 109 RBANS ...... 110 P50 Gating ...... 112 Data Analysis ...... 114 Results ...... 114 RBANS ...... 114 P50 Gating ...... 115 RBANS and P50: Correlation Analyses ...... 115 Comment ...... 115

APPENDIX B: NICOTINIC MODULATION OF SALIENCE NETWORK CONNECTIVITY AND CENTRALITY IN SCHIZOPHRENIA ...... 120

Introduction ...... 120 Materials and Methods ...... 124 Subjects ...... 124 Study Design and Drug Administration ...... 125 fMRI Acquisition ...... 126

viii

fMRI Preprocessing – Realignment, Coregistration and Smoothing ...... 127 Functional Connectivity Analysis ...... 127 Topological Analysis ...... 130 Correlation Analyses ...... 135 Results ...... 136 Physiological Effects of Nicotine ...... 136 Whole-Brain Seed to Voxel Connectivity Analysis: Overview ...... 136 Seed-to-Voxel Connectivity Results: ACC Seed ...... 137 Seed-to-Voxel Connectivity Results: Left Insula Seed ...... 138 Seed-to-Voxel Connectivity Results: Right Insula Seed ...... 138 Graph Analysis – Binary Graphs ...... 138 Graph Analysis – Weighted Graphs ...... 139 Effects of Movement-Based Volume Censoring ...... 140 Clinical Correlates ...... 140 Discussion ...... 141

ix CHAPTER I

INTRODUCTION

Although pervasive positive symptoms are necessary and sufficient to meet DSM-5 criteria for schizophrenia (American Psychiatric Association 2013)

cognitive symptoms of the illness confer the heaviest burden on quality of life

(Green 1996). Due in large part to significant cognitive impairment, patients suffer

high rates of unemployment, homelessness, and poor everyday functioning, and

high risk for suicide (Torrey 2006). Unfortunately, no treatment has yet earned a

federal indication for cognitive symptoms in schizophrenia.

One of the more striking cognitive deficits in schizophrenia is poor

attention, particularly in the presence of distraction. As documented by McGhie

and Chapman (1961) and later Venables (1964), patients commonly report being

unable to ignore distracting noises in the environment, such as a fan whirring,

clocking ticking, or traffic on a busy street. Although the neurobiological

mechanisms are unclear, one hypothesis postulates that a reduction in inhibitory

neuronal tone in these patients impairs the ability of the brain to attenuate (or

“gate”) response to repeated stimuli, thereby increasing distractibility (Miwa et al.

2011). Loss of inhibitory tone may be reflected as relative neuronal hyperactivity

in the brain’s gating “generators,” such as the hippocampus (Grunwald et al.

2003; Williams et al. 2011). Indeed, hyperactivity of the region in schizophrenia

has been observed during a variety of tasks. These include tracking a moving dot

(i.e. smooth pursuit eye movement (SPEM)) (Tregellas et al. 2004a), fixating on a point (Malaspina et al. 1999b), watching faces (Holt et al. 2005), listening to

repeated clicks (Tregellas et al. 2007a) and listening to environmental noise

(Tregellas et al. 2009). Resting hippocampal hyperactivity also predicts cognitive deficits in schizophrenia, including attention deficits (Tregellas et al. 2014).

Interestingly, hippocampal hyperactivity may also help explain why schizophrenia

patients find environmental distracting noises (e.g. a ticking clock) particularly

bothersome. Specifically, dysfunction in hippocampal inhibitory circuitry (i.e.

hyperactivity) may result in distracting auditory stimuli being interpreted as

“novel” regardless of presentation frequency, allocating inappropriate and

persistently high salience to the stimuli (Vinogradova 2001).

A second plausible neurobiological mechanism by which attention deficits may occur in schizophrenia is dysfunction in the neuronal circuitry underlying attention. Two primary attention networks are known to exist in the human brain

(Corbetta and Shulman 2002; Vossel et al. 2014). “Top-down,” goal-directed attention is the primary function of a dorsal attention network consisting of the intraparietal sulcus of the dorsal parietal cortex and frontal eye fields. “Bottom-

up,” stimulus-driven attention (e.g. reorienting to stimuli when they appear in

unexpected locations) is the primary function of a ventral attention network (VAN)

consisting of the ventral parietal cortex (VPC) and inferior frontal gyrus (IFG).

Although dysfunction of both networks has been reported in schizophrenia,

abnormal activity of the VAN is most frequently reported during selective

attention tasks involving distracting stimuli (Keedy et al. 2015; Kiehl and Liddle

2001; Laurens et al. 2005; Roiser et al. 2013; Smucny et al. 2013b; Tregellas et al. 2012)

If the hippocampus and VAN are functionally abnormal during selective attention in schizophrenia, pharmacologically targeting their dysfunction may have clinical utility. One promising class of drugs that may target these areas to improve attention in the illness is nicotinic agonists. As discussed in Chapter 2, the effects of nicotine in schizophrenia have been investigated for decades, due in large part to the observation that the prevalence of cigarette smoking in schizophrenia is higher than any other psychiatric disease (70% or more) (de

Leon et al. 1995; de Leon and Diaz 2005; George and Krystal 2000; Goff et al.

1992; Mandavilli 2004; Winterer 2010). Schizophrenia patients also show reduced expression of nicotinic receptors in several brain areas, including the hippocampus (Court et al. 1999; D'Souza et al. 2012; Freedman et al. 1995;

Leonard et al. 2000). Cigarette smoking has therefore been hypothesized to be a form of “self-medication” in the illness in order to correct a fundamental deficit in nicotinic signaling (Winterer 2010). Nicotine has demonstrated pro-cognitive

(including pro-attentional) effects in schizophrenia patients (D'Souza and Markou

2012; Freedman 2014) and nicotine withdrawal may worsen symptoms (Dalack and Meador-Woodruff 1996).

Although nicotine has demonstrated efficacy as cognitive/attention enhancer in schizophrenia, the underlying neurobiological mechanisms remain

poorly understood. In one previous study, Hong et al. (2011) observed no significant task-specific functional effects after acute administration of 21-35 mg

of nicotine in smoking patients during a sustained attention task. No study,

however, has yet examined the neuronal effects of nicotine during an attention

task that uses distracting stimuli in schizophrenia. Filling in this knowledge gap is

particularly important given that distractibility is a characteristic feature of

attentional dysfunction in schizophrenia.

The goal of the proposed experiments was to use functional neuroimaging

(specifically functional magnetic resonance imaging (fMRI)) to better understand

the neurobiological basis of attention-related effects of nicotine in schizophrenia.

Based on their understood roles in sensory filtering and stimulus-driven attention,

I hypothesized that the hippocampus and ventral attention network may be

modulated in a task-dependent manner after nicotine administration. To test this

hypothesis, I developed an fMRI-compatible auditory selective attention task that

combined target stimuli with auditory environmental noise distractors. I then

examined the neuronal effects of nicotine (vs. placebo) on that task in patients

with schizophrenia and healthy control subjects using two analytic approaches.

Thesis Outline

The outline of the present dissertation is as follows:

Chapters 2-4 are introductory chapters provided in support of the

hypothesis and methodology. Specifically, Chapter 2 reviews pharmacological

and functional evidence in support of the hypothesis that high rates of nicotine abuse in the illness may be a form of “self-medication” to correct an intrinsic deficit in nicotinic signaling. Chapter 3 links the deficits in nicotinic signaling discussed in Chapter 2 to an emerging biomarker of schizophrenia, hippocampal hyperactivity, and why this phenotype might contribute to deficits in selective attention. Chapter 4 reviews previous work suggesting that attention networks are also dysfunctional in schizophrenia.

Chapters 5-7 present the methods, results, and discussion of findings.

Specifically, Chapter 5 discusses the first analytic approach used in the present experiments, in which task-associated neuronal response was examined in the hippocampus and ventral parietal cortex. Chapter 6 discusses the second approach, in which task-associated connectivity was examined in the ventral attention network. Chapter 7 provides a discussion of the implications of the findings and directions for future study.

Appendices A and B describe additional studies I performed related to schizophrenia and nicotine.

CHAPTER II

NICOTINE, SCHIZOPHRENIA, AND THE “SELF-MEDICATION”

HYPOTHESIS: EVIDENCE FROM NEUROPHARMACOLOGY TO

NEUROIMAGING

According to a 2005 worldwide meta-analysis, patients with schizophrenia are over five times as likely to be cigarette smokers than the general population, with over 70% of male patients and 40% of female patients being smokers (de

Leon and Diaz 2005). Schizophrenia patients are more likely to smoke than

patients of any other psychiatric disease (George and Krystal 2000). Patients

also ingest more nicotine per cigarette (Olincy et al. 1997) and smoke more

cigarettes per day (Olincy et al. 1997) than otherwise healthy smokers.

Researchers have yet to reach a consensus as to why schizophrenia patients smoke. Among the hypotheses commonly put forth are that patients

“have nothing better to do” (particularly if unemployed) or for the same reasons that otherwise healthy people smoke, e.g. to improve cognition or relieve anxiety

or withdrawal (Winterer 2010). Although these viewpoints are supported by

patient questionnaires (Barr et al. 2008a; Herran et al. 2000), they are unlikely to

fully explain the disproportionate percentage of schizophrenia patients who

smoke relative to other populations. An alternative hypothesis that has gained

substantial traction in the previous two decades is that nicotinic receptor signaling

is fundamentally decreased in the illness, and by self-administering nicotine,

patients are using the most readily available method for restoring normal levels of signaling and thereby rectifying the associated dysfunctional neuronal circuitry.

Smoking therefore becomes a way for patients to “self-medicate.” Evidence for

this hypothesis is discussed in the proceeding sections following an introduction

to the pharmacology of nicotinic receptors.

Neuropharmacology of Nicotinic Receptors

Nicotinic receptors are ionotropic, ligand-gated channels that are

composed of various combinations of five α (α2-α10) and/or β (β2-β4) subunits

(Iversen 2009). Each subunit is coded by a separate gene. Nicotinic receptors

can either be heteromeric (e.g. α4β2) or homomeric (e.g. α7), although the most prevalent combinations in the central nervous system are the α4β2 and α7

subtypes (Iversen 2009). Upon activation, the primary function of nicotinic

receptors is to depolarize the cell. This depolarizing current is carried by an influx

of sodium through α4β2 receptors, and an influx of calcium through α7 receptors

(Role 1992). The influx of calcium through presynaptic α7 receptors also activates second-messenger systems that can induce the release of neurotransmitters into the synaptic cleft (Wonnacott et al. 2006). Nicotinic receptors are endogenously stimulated by release of the neurotransmitter acetylcholine from cholinergic neurons that originate from the basal forebrain

(Iversen 2009). Depending on the subunit composition, nicotinic receptors show different affinities for various exogenous ligands; e.g. nicotine, the direct agonist

found in tobacco products, binds with high (nM) affinity to the α4β2 receptor and

low (μM) affinity to the α7 receptor (Le Houezec 1998). This agonist specificity

suggests that receptor subtypes may be differentially targeted by pharmacologic

compounds.

Nicotinic receptor subtypes display a variety of regional, cell-specific, and

subcellular localizations that convey functional specialization. Regional areas of

expression include the anterior and posterior cingulate, thalamus, basal ganglia,

hippocampus, frontal and parietal cortex. The level of expression varies

depending on subtype (reviewed by (Paterson and Nordberg 2000)). Nicotinic

receptors are not only expressed in excitatory cells but also on inhibitory

interneurons, where they exert localized control over activity and influence

oscillatory firing patterns. Depending on the receptor subtype and the brain

region and cell type in which they are expressed, receptors may be located on

dendritic arbors, cell bodies, and/or pre/postsynaptic terminals. Nicotinic

receptors are thus positioned to affect brain response on both a global and local

level, and influence neuronal input and output (Picciotto et al. 2012).

Nicotinic Receptors in Schizophrenia

Multiple lines of evidence suggest that nicotinic cholinergic signaling is

fundamentally altered in schizophrenia. Single nucleotide polymorphisms (SNPs)

in the 5’ upstream regulatory region of the α7 nicotinic receptor gene are associated with reduced expression of α7 receptors and increased risk for

schizophrenia (Freedman et al. 2001; Gault et al. 2003; Leonard and Freedman

2006; Sinkus et al. 2009; Stephens et al. 2009). Decreased postmortem α7

receptor expression has been observed in schizophrenia in the hippocampus,

thalamus and cingulate cortex, among other areas (Freedman et al. 1995). As the α7 receptor is primarily expressed on interneurons but not pyramidal cells in

the adult hippocampus, loss of these receptors is hypothesized to primarily affect

inhibitory neuronal function (Frazier et al. 1998).

Although genetic factors account for a substantial (> 50%) proportion of

the risk for schizophrenia (Gottesman and Shields 1967) and significantly

influence receptor expression, environmental factors play an important role as

well. Prenatal stress, whether brought about by maternal physical factors (e.g.

infection, famine) or mental illness (e.g. depression) is associated with high risk

for schizophrenia in offspring (Brown and Derkits 2010; Fine et al. 2014; Susser

et al. 1996), potentially due to the sequestration of choline in the stressed mother

preventing adequate levels of the acetylcholine precursor from reaching the fetal

brain during development (Freedman and Ross 2015). Although the effects of

stress on nicotinic receptor levels in humans are unknown, restraint stress in

pregnant mothers has been shown to reduce α7 receptor expression in the

cortex and hippocampus in their newborn pups (Baier et al. 2015). Exercise may

also increase hippocampal nicotinic receptor expression through upregulation of

brain derived neurotrophic factor (Kimhy et al. 2015; Massey et al. 2006; Phillips

et al. 2014). Finally, smokers with schizophrenia show higher levels of nicotinic

receptor expression than nonsmokers, largely due to chronic nicotine-induced receptor upregulation (Esterlis et al. 2014; Mexal et al. 2010). The potential applications of dietary, exercise, and nicotinic agonist-based interventions in the treatment and/or prevention of schizophrenia are currently being investigated or considered (Freedman 2014; Freedman and Ross 2015; Kimhy et al. 2015).

Nicotine Targets P50 Gating Deficits in Schizophrenia

Schizophrenia, with its tripartite symptomatology (positive, negative, and cognitive symptoms), is an extraordinarily complex disease. It is not surprising, therefore, that no single gene has been discovered that can account for all of its symptoms on a pharmacological, cellular, circuit, or neuronal systems level. In

the search for genetic influences, therefore, researchers have turned towards

discovering endophenotypes, or stable phenotypes with clear genetic influences.

A primary aim of this research is to identify associations between genes that may

influence expression of proteins that can in turn disrupt simple neuronal circuits

associated with electrophysiological abnormalities in the illness.

Perhaps the most successful example of this approach is the development

of P50 gating as a nicotinic receptor-associated endophenotype for

schizophrenia, an effort primarily lead by Dr. Robert Freedman and others at the

University of Colorado. The study of P50 gating impairment in schizophrenia has

its origins from work in the 1960s by McGhie and Chapman (1961) as well as

Venables (1964), who published extensive patient case reports of perceptual

abnormalities. Many of these reports described a “hypervigilant” state in which patients were unable to ignore persistent distracting noises in the environment.

As a result, patients found it hard to concentrate on any one stimulus in a noisy environment, such as the voice of a single person in a bustling crowd.

Hypervigilance was later hypothesized to play a role in the emergence of positive symptoms. For example, increased salience of the sounds of squealing tires may cause the noises to be reinterpreted as a screaming baby (Freedman et al.

1991).

The hypervigilant state found in schizophrenia led Adler et al. (1982) to postulate that patients may show a deficit in the ability of the brain to physiologically decrease, or “gate,” its response to repeated stimuli. This brain response is postulated to play a major role in the ability of healthy subjects to subconsciously ignore irrelevant, incessant stimuli in the environment such as a clock ticking (Freedman et al. 1991). Based on electroencephalographic (EEG) methods developed in the 1960s for studying repetitive auditory stimuli (Davis et al. 1966), Adler et al. (1982) observed reduced capacity in schizophrenia to

diminish early (50 ms post-stimulus, or P50) responses to the second of a pair

closely-spaced identical (~0.5 s) clicks (Figure 1). This phenomenon has since

been replicated in many laboratories, is predictive of cognitive function in several

domains including attention (Cullum et al. 1993; Potter et al. 2006; Smith et al.

2010; Smucny et al. 2013a) (Figure 2), and is one of the most frequently

investigated electrophysiological endophenotypes in schizophrenia.

After the initial discovery in schizophrenia, Adler et al. (1992) postulated

that gating deficits had a cholinergic basis based on findings in rats in which

lesions to the septal nucleus (a cholinergic input to the hippocampus) disrupted auditory gating (Vinogravoda 1975). In support of this hypothesis, Adler et al. found that cigarette smoking improves P50 gating in patients (Adler et al. 1993).

Interestingly, in another study Adler et al. also found that nicotine improved gating in first-degree relatives, suggesting that normalization could occur independent of diagnosis and possibly have a genetic component (Adler et al.

1992). A genetic linkage between nicotinic receptors and P50 gating in schizophrenia was later found in the α7 receptor gene (Freedman et al. 1997), and α7 promoter variants predict P50 gating deficits in healthy subjects (Leonard et al. 2002). Smoking, therefore, may be a method of “self-medicating” an electrophysiological abnormality associated with an intrinsic signaling deficit caused by the loss of nicotinic receptors associated with polymorphisms in the

α7 gene. A direct link between these SNPs and receptor expression, however, has yet to be demonstrated.

Nicotinic Agonists Modulate Brain Networks in Schizophrenia

After an introduction to fMRI, the following sections briefly review evidence from fMRI studies that suggest that nicotine and nicotinic agonists may target brain dysfunction in schizophrenia.

fMRI: Basis and Applications

The origins of fMRI stem from the hypotheses and observations of Roy and Sherrington (1890) as well as Mosso (1881) who proposed that local brain activity was associated with a regional increase in blood flow. A century later,

Ogawa et al. (1990) found that local cerebral changes in blood flow (called the

Blood Oxygenation Level Dependent, or BOLD response) could be detected using magnetic resonance imaging, giving birth to the modern field of fMRI.

The basis of the BOLD response lies in the magnetic properties of oxygenated vs. deoxygenated blood. Due to neurovascular coupling, regional brain activity results in an increase of oxygenated blood flow to that area. Due to the binding of oxygen to iron in hemoglobin molecules in red blood cells, oxygenated blood is diamagnetic. Deoxygenated blood, on the other hand, is paramagnetic and consequently disrupts magnetic fields. Concordantly, the regional ratio of deoxygenated/oxygenated blood is correlated with the disruption of the magnetic field in that area, producing regionally specific differences in the

BOLD signal that can be detected by MRI. fMRI thus measures changes in regional blood flow associated with changes in neuronal activity.

Advantages of fMRI are its noninvasive nature (nothing radioactive is injected as in positron emission tomography (PET), and nothing is implanted as in intercranial electrocorticography), spatial resolution (on the order of a few millimeters), whole brain coverage (electroencephalography, another noninvasive technique, cannot directly measure subcortical activity), and relatively low cost

($800-$1000 per scan, as opposed to thousands of dollars for a PET scan)

(Hales et al. 2014). Unsurprisingly, fMRI is most widely used research method today for examining brain activity in patient populations, including schizophrenia.

Although measurement of the BOLD response was initially developed to examine regional brain activity, more recent developments have enabled researchers to study neuronal function from additional perspectives. For example, studies that examine functional connectivity have become more prevalent than studies that examine activity. In most imaging research contexts,

“activity” refers to the average amplitude of oscillatory signal fluctuations in a region or network, and in fMRI studies is quantified by the relative percent BOLD signal. “Hyperactivity” thus implies greater BOLD signal compared to another group or neuronal state. Connectivity, on the other hand, refers to the degree to which two or more regions are synchronously active or inactive.

“Hyperconnectivity” between two regions thus implies that the BOLD signal for one region modulates in time with the signal for the second region. Connectivity within an entire network can also be estimated by averaging connectivity between all nodes of that network. Hyperconnectivity does not imply that the amplitude of this change is similar for both regions; neither does it imply that the overall level of network activity is increased.

Using fMRI to Examine Oculomotor Networks in Schizophrenia

In the early 2000s, Tregellas and colleagues examined BOLD response

during smooth pursuit eye movements (SPEM) in an elegant series of studies that were one of the first to use fMRI to characterize a functional neuronal abnormality in schizophrenia and its modulation by nicotine.

In order to follow important moving objects (such as prey in the wild), mammalian carnivores have developed two oculomotor processes: one to rapidly saccade to stimuli of interest, and another to slowly track stimuli as they move across the visual field. The latter process, known as SPEM, was first shown to be abnormal in schizophrenia by Diefendorf and Dodge (1908) and is now one of the most consistently reported abnormalities of the disease (Levy et al. 1993; Levy et al. 2010). SPEM performance is quantified by the relative ability of the retina to follow a moving target without shifting its focus to another location, and may be a measure of inhibitory dysfunction. Interestingly, a meta-analysis by O’Driscoll and

Callahan (2008) found that increased intrusive anticipatory saccade rate is the specific feature of SPEM with the largest effect size in schizophrenia (d = 1.31).

Although eye-tracking deficits had been reported in schizophrenia for almost a century, the neuronal circuitry involved was not examined using fMRI until Tregellas et al. developed an MRI-compatible method for tracking eye movements (Tregellas et al. 2002). Using this technique, in a series of studies

Tregellas et al. (2004a) found that schizophrenia patients showed hyperactivity of the hippocampus, fusiform gyrus, thalamus, and parietal eye fields. Decreased activity was observed in the frontal eye fields, cingulate gyrus, and occipital gyrus.

Based on previous findings demonstrating that abnormal intrusive saccades are decreased in schizophrenia patients after cigarette smoking (Olincy et al. 1998), in a subsequent fMRI study Tregellas et al. (2005) examined the neuronal effects of acute nicotine administration (4-6 mg gum) in schizophrenia during SPEM. Increased activity after nicotine (vs. placebo) was observed in the cingulate gyrus, occipital gyrus, and precuneus. Decreased activity was observed in the hippocampus and parietal eye fields. Collectively, these results suggest that nicotine effectively reversed many of the abnormalities observed in schizophrenia in the initial study comparing patients to control subjects. A later study found that the α7 nicotinic receptor partial agonist 3-2,4- dimethoxybenzylidene anabaseine (DMXB) also reduced hippocampal hyperactivity during the SPEM task (Tregellas et al. 2010).

The Default Network

fMRI studies have traditionally focused on “task related” activity, i.e. how local brain areas are recruited during sensory stimulation and cognitive functions.

These studies observed that a network of brain areas were consistently deactivated, reflecting suppression during the more cognitively demanding “task” condition compared to the baseline or low-load condition (Greicius et al. 2003).

Due to its tendency to be down-modulated during many tasks, and therefore be

active as a “default”, the network was coined the Default Mode Network (DMN).

The primary hubs of the DMN are 1) the precuneus/posterior cingulate cortex (PCC), 2) the medial prefrontal cortex (mPFC), and 3) the bilateral inferior parietal cortex (IPC) (Figure 3). The bilateral hippocampus/medial temporal lobe is considered an accessory hub of the network due its being less functionally connected to the other three DMN hubs, on average, relative to the primary hubs

(Buckner et al. 2008). The DMN is readily and reproducibly detectable regardless of the analytic technique used, and irrespective of the cognitive state of the individual, be it during an effortful task, rest, or even during sleep (Whitfield-

Gabrieli and Ford 2012). The functions of the DMN are not completely understood, although the network is particularly active during actions that are self-referential: e.g. reflecting on the past, planning for the future, or monitoring internal state (Buckner et al. 2008). Abnormally high DMN activity during cognitively challenging tasks is associated with poor performance, likely due to competing resource allocation towards task-irrelevant thoughts (Gordon et al.

2012).

Soon after the discovery of the DMN, abnormalities in its function were observed in schizophrenia. Patients inappropriately recruit the DMN, as evidenced by hyperactivity of the network during an auditory oddball task (Garrity et al. 2007), working memory tasks (Meyer-Lindenberg et al. 2005; Pomarol-

Clotet et al. 2008; Whitfield-Gabrieli et al. 2009), and language (semantic priming) tasks (Jeong and Kubicki 2010). Patients are similarly impaired in their ability to deactivate the DMN as task difficulty is increased (Meyer-Lindenberg et

al. 2005; Pomarol-Clotet et al. 2008; Whitfield-Gabrieli et al. 2009). DMN abnormalities extend to task-free (resting) states, during which DMN hyperactivity and hyperconnectivity have been frequently observed (Jafri et al. 2008; Liu et al.

2012; Skudlarski et al. 2010). DMN dysfunction is correlated with the severity of positive symptoms (Garrity et al. 2007), negative symptoms (Bluhm et al. 2007), and impaired social cognition (Holt et al. 2011). DMN hyperactivity and hyperconnectivity have been reported in unaffected first-degree relatives of schizophrenia patients, albeit to a lesser extent (Liu et al. 2012; Whitfield-Gabrieli et al. 2009). These findings suggest that DMN pathology has both state- dependent and independent characteristics.

Given that DMN pathology (e.g. hyperactivity) may predict symptoms of schizophrenia as well as cognitive function, nicotinic targeting of the DMN may represent a neurobiological form of “self-medication.” To test the hypothesis that a nicotinic agonist could target DMN pathology, Tregellas and colleagues examined DMN activity in 16 patients after chronic (1 month) treatment with the nicotinic agonist DMXB (ref). The investigators found that relative to placebo,

DMXB reduced DMN activity in its PCC, bilateral IPC, and mPFC hubs.

Decreased PCC activity was correlated with improved symptomatology. These findings suggest that α7 receptor activation may normalize DMN hyperactivity in schizophrenia and have clinical benefit.

The Salience Network

The brain is constantly bombarded by information. In order to process what is important, ignore what is irrelevant, and update its internal model of the external world – in other words, process saliency – it must have a way of shifting focus from self-monitoring to monitoring external information (and vice-versa) as needed. In this manner, a stimulus that is pleasurable, threatening, or important to a task is given precedence. This process is the primary task of a functionally and structurally interconnected set of cortical and subcortical areas called the

Salience Network (SN).

The SN is anchored by the anterior cingulate cortex (ACC) and bilateral insula (Figure 4). These hubs in turn are connected to limbic structures as well as to the PCC and dorsolateral prefrontal cortex (DLPFC). Inputs into the insula provide sensory and emotional information (Wylie and Tregellas 2010). Inputs into the anterior cingulate from the DLPFC provide information regarding goals, expectations, and internal representations (Menon 2011). As a result, the SN effectively integrates external and internal information so that a course of action

(or inaction) can be chosen. The integrative nature of SN processing may be why the network is hypothesized to be important for distinguishing “self” from “non- self“ as well as establishing a strong sense of identity, purpose, and self-worth

(Palaniyappan and Liddle 2012; Wylie and Tregellas 2010).

The functions of the SN suggest that pathology of the network could be important for symptom etiology in schizophrenia. Hallucinations, for example,

occur when patients have difficulty distinguishing external stimuli from internal thoughts. Given that separating self from non-self is an important function of the

SN, dysfunction of the network may thus contribute to this positive symptom.

Loss of affect and motivation may arise in part due to pathology of the network; accordingly, loss of GM volume in the insula is associated with severity of negative symptoms (Koutsouleris et al. 2008). Cognitive symptoms may be partially explained by a relative inability of the SN to modulate activity in sensory areas according to perceptual expectations (Chambon et al. 2011). Patients may thus become overly reliant on sensory evidence to interpret their surroundings.

As a result, the salience of external stimuli is inappropriately enhanced, leading to perceptual abnormalities (Palaniyappan et al. 2012).

Structural and functional neuroimaging evidence suggests that the SN is indeed dysfunctional in schizophrenia. Loss of gray matter volume in the insula and ACC are among the most striking and consistently replicated structural brain abnormalities in the illness (reviewed by (Wylie and Tregellas 2010)). Gray matter loss of the insula and ACC is present at the first episode of psychosis, but also may be progressive in chronic schizophrenia (Chan et al. 2011; Ellison-

Wright et al. 2008). Insula and ACC structural deficits have been linked to reality distortion, suggesting that SN dysfunction is associated with abnormal stimulus processing and positive symptoms (Palaniyappan et al. 2011). Functionally, abnormalities in SN activation across a variety of paradigms have been observed in schizophrenia, including working memory (Repovs and Barch 2012), social

and affective processing (Mitchell et al. 2004; Phillips et al. 1999; Seiferth et al.

2009), and error processing (Laurens et al. 2003; Polli et al. 2008). Network analyses have found a loss of connectivity between nodes of the SN and other brain areas both at rest (Moran et al. 2012; Tu et al. 2012) and during tasks

(Gradin et al. 2012; Tu et al. 2010; White et al. 2010) in patients. This loss of connectivity has been proposed to underlie the relative inability of patients to adjust DMN activity according to task demands, resulting in performance deficits

(Menon 2011).

Like the DMN, immunohistochemical evidence suggests that the SN would be responsive to nicotinic modulation. The insula and ACC receive cholinergic input from the basal forebrain (Selden et al. 1998), and show enriched nicotinic receptor expression (Breese et al. 1997; Paterson and Nordberg 2000). Reduced

α7 receptor expression and increased α4β2 receptor expression has been observed in the ACC in patient postmortem brain (Marutle et al. 2001). In addition, nicotine administration increases basal CBF in the ACC and insula

(Stein et al. 1998).

Accordingly, Moran et al. (Moran et al. 2012) have investigated the effects of nicotine administration on resting state brain activity in smoking patients schizophrenia and normal smokers. Overall, Moran et al. found 1) decreased connectivity within the SN in patients, and 2) increased connectivity between the SN and parietal cortex and SN and occipital cortex after nicotine administration. No effects of nicotine on connectivity within the SN were reported,

however, suggesting that primary SN circuit abnormalities in schizophrenia were unaffected by nicotine.

Neuroimaging the Cognitive Effects of Nicotine in Schizophrenia

The previous section focused on fMRI studies that either examined the effects of nicotine on neuronal function in schizophrenia during a simple, low- level task (smooth pursuit) or at rest. Previous fMRI studies have also found effects of nicotine during cognitive tasks in schizophrenia, although the field remains poorly developed and findings are limited.

Working Memory

Working memory (WM), the process by which information is held in short- term storage for immediate use and manipulation, is one of the most frequently examined cognitive deficits in schizophrenia. Indeed, several WM tasks were recently nominated by the Cognitive Neuroscience Treatment Research to

Improve Cognition in Schizophrenia (CNTRICS) consortium as potential neuroimaging biomarkers (biological indicators of disease state) due to their ability to probe the most deleterious aspects of WM in schizophrenia (goal maintenance and interference control) (Barch et al. 2012). Given that WM deficits are predictive of everyday functioning in schizophrenia (Evans et al. 2003;

Shamsi et al. 2011) there is great interest in developing interventions that can target this core cognitive deficit.

Previous work suggests that nicotinic agonists improve WM in schizophrenia patients (D'Souza and Markou 2012; Mackowick et al. 2014;

Radek et al. 2010; Sacco et al. 2005) and animal models of schizophrenia

(Mackowick et al. 2014; Rushforth et al. 2011). Conversely, nicotine deprivation in patient smokers impairs WM performance (George et al. 2002; Ghiasi et al.

2013; Sacco et al. 2005), and nicotinic receptor blockade impairs WM in rats

(Pocivavsek et al. 2006). Nonetheless, surprisingly little work has been

conducted using fMRI to examine the neuronal effects of nicotinic agents in

working memory tasks in schizophrenia. An early study by Jacobsen et al. (2004)

reported enhanced performance in abstinent smoking patients given nicotine

relative to placebo as well as increased recruitment of the anterior cingulate and

thalamus during an n-back task. Nicotine also increased thalamic connectivity in

patients to a greater extent than healthy smokers. The neuronal effects of α7-

selective agonists during WM tasks in schizophrenia are unknown.

Attention

Deficits in attention in schizophrenia have been reported and replicated for

many years across a variety of tasks (Cornblatt and Keilp 1994; Hahn et al. 2012;

Keefe and Harvey 2012; Laurent et al. 1999; Mayer et al. 2015; Smith and

Cornblatt 2005; Suwa et al. 2004; Verleger et al. 2013). Both sustained attention

(the ability to maintain focus on a task over time) and selective attention (the

ability to focus on task-relevant stimuli amongst competing stimuli) are affected.

Due to its ability to predict functional outcomes in schizophrenia, attention has been recently recognized by CNTRICS as an important area for future imaging biomarker development (Luck et al. 2012).

Nicotinic agonists have also been shown to improved performance on attention tasks in schizophrenia. A study by Harris et al. (2004) found that

nicotine improved scores on the attention index of the MCCB in nonsmoking

patients. Performance enhancement after nicotine was also observed in

nonsmoking patients during the continuous performance task (CPT) in a later

study (Barr et al. 2008b). Acute nicotine has also been shown to improve

performance during the CPT in smoking patients that abstain from smoking for

10-24 hours (Sacco et al. 2005; Smith et al. 2006). Nicotinic effects on attention

in schizophrenia may be α7 receptor-dependent, as the α7 receptor partial agonist and α4/β2 antagonist DMXB-A improved attention scores on the MCCB during the first arm of a double crossover phase 2 trial in nonsmoking patients

(Freedman et al. 2008).

The neuronal mechanisms that underlie nicotine’s effects on attention in schizophrenia are poorly understood. An fMRI study by Hong et al. (2011) showed that in deprived patient smokers, nicotine improved accuracy and reaction time during a visual sustained attention task as well as increased activity in the thalamus, anterior cingulate, frontal cortex, parietal cortex, and precuneus.

No task condition (i.e. task difficulty) X drug (nicotine vs. placebo), diagnosis

(control vs. patient) X drug, or condition X drug X diagnosis interactions were

observed, however, suggesting drug effects were non-specific to the task or diagnostic group.

Perspectives and Future Directions

The overall pattern of results presented in this chapter suggest that high rates of smoking in schizophrenia may be explained by a combination of factors, including intrinsic deficits in nicotinic signaling, targeting by nicotine of functionally abnormal circuits, and improved cognition. It is not surprising, therefore, that a number of clinical trials using investigational compounds that target the nicotinic receptor have been completed or are currently being conducted in schizophrenia (Freedman et al. 2008; Lieberman et al. 2013;

Preskorn et al. 2014; Shim et al. 2012; Umbricht et al. 2014; Winterer et al.

2013). The results of these trials on cognitive endpoints thus far have been mixed, perhaps because the neuronal effects of nicotinic agents in schizophrenia

(particularly during cognitive tasks) remain poorly understood. No studies, for example, have used fMRI to examine the neuronal effects of nicotine during selective attention in schizophrenia.

An important, often overlooked aspect of understanding nicotine’s effects in schizophrenia is how it affects neural response in nonsmoking schizophrenia patients. Indeed, a significant proportion (20-40%) of patients are nonsmokers, making it essential to understand how nicotinic agents affect this subpopulation.

Furthermore, studies involving nonsmoking subjects avoid the potentially

confounding effects of nicotine withdrawal or receptor desensitization from smoking. Nonetheless, to my knowledge at the time of writing this dissertation no

studies have yet used fMRI to examine the neuronal effects of nicotine on any

cognitive process in nonsmoking schizophrenia patients, including attention.

Figure 1. P50 gating deficits in schizophrenia. Illustrative event related potentials (ERPs) demonstrating reduced response to the second of a pair of clicks in control subjects, and no difference between responses in patients. S1 = stimulus 1, S2 = stimulus 2. P50 ratio is defined as ratio of the amplitude of the post S2 ERP / the amplitude of the post S1 ERP (S2/S1). Nicotinic agonists are known to normalize P50 gating ratios in patients.

Figure 2. P50 gating deficits predict effects of noise on reaction time during an attention task in schizophrenia. Across all subjects, R = 0.45, p = 0.005. Patient group alone, R = 0.48, p = 0.05. Figure taken from Smucny et al. (2013a).

Figure 3. Primary hubs of the Default Mode Network. 1) precuneus / posterior cingulate cortex (PCC), 2) medial prefrontal cortex (mPFC), 3) bilateral inferior parietal cortex (IPC). Figure taken from Smucny and Tregellas (2013).

Figure 4. Primary hubs of the Salience Network. 1) anterior cingulate cortex (ACC), 2) bilateral insula. Figure taken from Smucny and Tregellas (2013).

CHAPTER III

HIPPOCAMPAL HYPERACTIVITY IN SCHIZOPHRENIA AND ITS

RELATIONSHIP TO ATTENTION DEFICITS

As discussed in Chapter 2, schizophrenia is associated with the loss of

nicotinic receptor expression on inhibitory interneurons in the hippocampus. This

loss in nicotinic signaling is hypothesized to be a mechanism by which deficits in

P50 gating, a measure of neuronal inhibition that is generated in the

hippocampus (among other brain areas), occur in patients.

Although P50 gating deficits are robust in schizophrenia, they are at best

indirect measures of neuronal inhibition. It may also not be immediately obvious

why hippocampal inhibitory dysfunction might predict attention deficits; indeed, the hippocampus is classically considered a “learning and memory” area. After a brief introduction to the morphology of the hippocampus, this Chapter1 provides an in-depth background discussion of hippocampal pathology in schizophrenia, including 1) structural studies, 2) functional studies, and 3) why pathology may be related to attention deficits in the illness.

1 Copyright note: parts of this Chapter are excerpted from the review: Tregellas, J.R. (2014). Neuroimaging biomarkers for early drug development in schizophrenia. Biol Psychiatry, 76:111-9. I was a major contributor/writer for this review (please see its acknowledgements section) although not officially listed as an author.

Morphology of the Hippocampus

The hippocampus is a curved, tube-like structure located in the medial basal temporal lobe of the brain (Figure 5). The primary route of information flow through the hippocampus originates from the adjacent entorhinal cortex, which projects to the dentate gyrus (DG). The DG in turn projects to the CA3 subfield, which in turn projects to the CA1 subfield via the Schaffer collaterals. The CA1 subfield then projects to the output region of the hippocampus, the subiculum.

The CA2 and CA4 subfields are intermediate regions that are adjacent to the primary route of information flow and modulate its fidelity. The hippocampus also receives inputs from the brainstem and hypothalamus.

Hippocampal Pathology in Schizophrenia: Structural Studies

According to a large meta-analysis, the superior temporal gyrus and

hippocampus are the two brain areas that most consistently show gray matter

loss in schizophrenia (Honea et al. 2005). Loss of hippocampal volume has been

reported in unaffected first-degree relatives, first-episode patients, and subjects

at high risk for psychosis, suggesting that it may not due to antipsychotic

medication and may have a genetic component (Fusar-Poli et al. 2011; Hu et al.

2013). Loss of gray matter volume may also predict cognitive deficits in the

illness (Cocchi et al. 2009; Guo et al. 2014; Minatogawa-Chang et al. 2009;

Wojtalik et al. 2012).

Hippocampal Pathology in Schizophrenia: Functional Studies

Greater hippocampal regional cerebral blood flow in schizophrenia patients, relative to healthy comparison subjects, has been frequently reported

(Lahti et al. 2006; Malaspina et al. 2009; Malaspina et al. 2004; Medoff et al.

2001; Molina et al. 2005; Schobel et al. 2009). Hyperactivity is exacerbated in unmedicated patients and predicts psychotic symptom severity (Lahti et al. 2006;

Malaspina et al. 2009; Schobel et al. 2009). Cerebral blood volume (Schobel et al. 2009) and blood flow (Scheef et al. 2010) studies have replicated and extended this finding. Furthermore, Schobel and colleagues have shown that hippocampal basal blood volume predicts positive and negative symptoms as well as conversion to psychosis in the prodromal state (Schobel et al. 2009).

Increased hippocampal blood flow and baseline activity have also been observed in animal models of schizophrenia, suggesting the phenotype has translational utility (Gozzi et al. 2010; Gozzi et al. 2008; Stevens and Wear 1997). Increased hippocampal blood volume also predicts hippocampal gray matter volume loss in schizophrenia, suggesting a link between functional and structural studies

(Schobel et al. 2013).

Functional magnetic imaging (fMRI) and positron emission tomography

(PET) studies frequently report increased hippocampal response in schizophrenia, particularly during tasks that are designed to isolate sensory

(visual or auditory) processing. These include fixation on a point (Malaspina et al.

2004; Malaspina et al. 1999a), passively viewing fearful faces (Holt et al. 2005),

SPEM (Tregellas et al. 2004b), listening to clicks (Tregellas et al. 2007b), and listening to environmental noise (Tregellas et al. 2009). It is possible that hippocampal hyperactivity during rest or simple sensory processing tasks may reflect a generalized hypersensitivity to sensory input and/or inappropriate information processing, as discussed below.

In schizophrenia, a chronically hyperactive intrinsic state of hippocampal response may contribute to region’s inability to be recruited during tasks that examine memory-related processing, such as encoding and pattern separation/completion (Tamminga et al. 2012). For example, diminished hippocampal recruitment has been observed in schizophrenia during image pair encoding (Achim et al. 2007), deep and shallow word encoding (Heckers et al.

1998; Jessen et al. 2003; Weiss et al. 2003), novel word detection (Weiss et al.

2004), and perceptual closure (Sehatpour et al. 2010).

Our lab has recently examined the relationship between resting (intrinsic) hippocampal activity and cognitive function in schizophrenia (Tregellas et al.

2014). Consistent with previous findings, greater resting hippocampal activity was observed in patients. Furthermore, increased activity predicted negative symptom severity and cognitive dysfunction in patients as measured by the

MCCB (Figure 6). These effects were driven by significant associations between activity and performance on the attention, working memory, and visual learning components of the MCCB (Figure 6). These results suggest that hippocampal

hyperactivity may be a biomarker for cognitive dysfunction in schizophrenia, and that targeting hyperactivity through intervention may have clinical benefit.

Relevance to Attention Deficits in Schizophrenia

Hippocampal hyperactivity may be a pathological feature of schizophrenia, but why might it be related to attention deficits? After all, the cognitive processes most closely associated with the hippocampus are learning and memory.

Attention is rarely considered a hippocampal-dependent process. Indeed, as of

12/4/2015, a PubMed search of “hippocampus AND memory” yields 26210 hits.

A search of “hippocampus AND attention,” on the other hand, yields 2508 hits

(and is likely confounded by frequent non cognitive processing-related usage of

the word “attention”).

One way to conceptualize the relationship of the hippocampus to attention

is to consider how the brain determines what incoming information should be

stored in memory and what should be discarded. An important determinant in this

process is the novelty of the information presented (Bunzeck and Thiel 2015;

Ranganath and Rainer 2003). In essence, novel events are more effectively

encoded into memory than predictable events due to response habituation to

repetitive stimuli (Ranganath and Rainer 2003). More salience (i.e. attention) is

given to these events, increasing the likelihood they will be encoded in memory.

Although the habituation process occurs throughout the brain and

peripheral nervous system, a series of studies published in the 1970s, 80s and

90s by Vinogradova and others suggest that the hippocampus (in particular the

CA3 subfield) is particularly suited to use habituation to help evaluate the novelty of a stimulus. Work in the 1960s and 70s in hippocampectomized animals described a “non habituating orienting reflex”, “insatiable curiosity,” and a

“tendency for incessant exploration” (Glickman et al. 1970; Jarrard and Bunnell

1968; Kim et al. 1970; Roberts et al. 1962). Hippocampal lesions damaged the ability of these animals to transition their perspective of the environment from novel to familiar. Vinogradova and colleagues then conducted a number of experiments to characterize hippocampal responses to repeated stimuli in each subfield. As summarized in a 2001 review, they suggest that 1) the hippocampal

CA3 subfield shows the strongest habituation (response reduction as stimuli are repeated) of any hippocampal area, 2) the CA3 subfield almost exclusively codes for novelty and not other characteristics of stimuli, 3) the CA3 subfield is well positioned to act as a comparator, comparing new input signals from the brainstem with older (previously stored) information from the cortex in order to calculate the “novelty” of the input signal, 4) novelty is encoded by an increase in activity; familiarity by a decrease.

These findings provide a hypothetical framework by which to understand how hippocampal hyperactivity may be related to attention deficits in schizophrenia, particularly during tasks that involve distractors and filtering irrelevant information. Intrinsic hyperactivity due to loss in inhibitory circuit function may impair the ability of the hippocampus to habituate to repetitive

stimuli (e.g. P50 gating deficits, hyperactivity during environmental noise). These stimuli are therefore continuously interpreted as “novel” and given inappropriate salience, making it them more difficult to ignore. As this deficit may be conveyed by the loss of nicotinic receptor expression on interneurons (Miwa et al. 2011), nicotine administration may be expected to restore the circuit and normalize the hyperactive phenotype.

Figure 5. Shape and anatomical location of the human hippocampus.

Figure 6. Associations between cognitive measures and intrinsic hippocampal activity in schizophrenia patients. A: Association with MCCB composite score. B: Significant associations with MCCB domains. Source: Tregellas et al. (2014).

39 CHAPTER IV

ATTENTION NETWORKS IN SCHIZOPHRENIA

Neuroimaging researchers have now established that two primary attention networks exist in the human brain. After an introduction to these networks, this Chapter briefly reviews evidence that they are dysfunctional in schizophrenia.

Attention Networks of the Brain

Imagine having a conversation at a noisy, crowded party. All around you are the sounds of the festivities: music playing, multiple conversations, and the occasional door closing shut. Your attention, however, is focused solely on the person with whom you are conversing. Suddenly, your phone rings, and you politely excuse yourself to answer.

This example illustrates the dichotomy of attentional processing. One aspect, “top down” attention, refers to the ability to attend based on knowledge, experience, and knowledge of task goals. In the brain, this is accomplished by a dorsal attention network (DAN) consisting of the superior parietal cortex

(intraparietal sulcus / superior parietal lobule) and frontal eye fields (FEF)

(Corbetta and Shulman 2002) (Figure 7). The second aspect, “bottom up” attention, refers to the ability to modulate attention based on sensory stimulation.

This process is mediated by a ventral attention network (VAN) consisting of the

temporoparietal junction/inferior parietal lobule/superior temporal gyrus

(TPJ/IPL/STG) and inferior frontal gyrus/middle frontal gyrus (IFG/MFG)

(Corbetta and Shulman 2002) (Figure 7). Attending to a conversation in the midst of ambient noise modulates the DAN (accomplishing the “task goal” of conversing in a noisy environment) as well as the VAN (as ambient noise is filtered out). The novel, unexpected sound of the ringing phone then further activates the VAN, momentarily shifting the focus of attention. Interestingly, the

VAN typically downregulates its activity when distractors are present during challenging cognitive tasks (Shulman et al. 2007; Vossel et al. 2014). This effect is interpreted as a bottom-up mechanism by which the deleterious effects of distractors are minimized.

Network Dysfunction in Schizophrenia

Neuroimaging studies have reported DAN abnormalities in schizophrenia during various attention tasks. A visual sustained attention task (following a moving dot) found decreased DAN activity in patients when targets were both predictable and unpredictable (Keedy et al. 2015). Reduced DAN recruitment in patients relative to controls has also been observed during a visual oddball task

(Wynn et al. 2015) and auditory oddball tasks (Gaebler et al. 2015; Kiehl and

Liddle 2001).

Functional differences in the VAN have also been observed in the illness.

Auditory oddball tasks have generally observed decreased VAN recruitment,

particularly of the TPJ (Kiehl and Liddle 2001; Laurens et al. 2005; Wynn et al.

2015). Increased activity of the IFG during an auditory oddball task, however, has been reported as well (Wolf et al. 2008). Decreased activation of the TPJ has similarly been observed during a CPT task (Salgado-Pineda et al. 2004).

fMRI studies suggest that connectivity within attention networks are disrupted in schizophrenia. A resting state study by Jeong et al. observed decreased connectivity between the IFG and the TPJ (Jeong et al. 2009).

Decreased resting DAN connectivity has been observed in patients as well

(Woodward et al. 2011). During attention tasks, reduced connectivity between nodes of the DAN and VAN and other brain regions (e.g. prefrontal cortex) has been reported as well (Diwadkar et al. 2014; Roiser et al. 2013). VAN disconnectivity has also been observed in other disorders with known attention deficits such as autism and attention-deficit hyperactivity disorder (Fitzgerald et al. 2015; McCarthy et al. 2013).

Our lab has examined how distracting noise affects attention network activity in schizophrenia (Tregellas et al. 2012). I developed a task in which patients were asked to respond (button press) to auditory tone deviants in the presence or absence of distracting environmental noise. I found that patients showed reduced activity of the TPJ node of the VAN during the task with noise stimuli, suggesting that patients were less able to recruit the VAN to respond to deviants when auditory distractors were present. This result suggests that schizophrenia is associated with the inability to recruit the neuronal mechanisms

underlying novel stimulus detection in the presence of noise distraction. No differences in DAN response were observed.

Although the neuronal mechanisms of attention deficits in schizophrenia remain relatively understudied compared to other neurocognitive domains, evidence suggests that disrupted attention network activation and connectivity may play a key role. The TPJ node of the VAN in particular may show abnormal activation during tasks that involve distracting stimuli (Tregellas et al. 2012).

Task-associated functional modulation of the VAN may therefore be a mechanism by which nicotine may target dysfunction in attention-associated processing in schizophrenia.

Figure 7. Attention networks of the human brain. Hubs of the DAN are colored in yellow, and hubs of the VAN in blue (Aboitiz et al. 2014).

44 CHAPTER V

ANALYSIS 1: EFFECTS OF NICOTINE ON HIPPOCAMPAL AND VENTRAL

PARIETAL CORTEX ACTIVITY DURING AUDITORY SELECTIVE ATTENTION

IN SCHIZOPHRENIA

In this Chapter I present the experimental methods and results of a set of analyses designed to test the hypothesis that nicotine will modulate hippocampal and ventral parietal cortex activity during selective attention in schizophrenia. For this experiment I developed an fMRI-compatible task in which subjects are asked to response (button press) to single-digit numbers presented aurally in one ear while ignoring distracting noise stimuli in the opposite ear. I then examined the effects of nicotine (vs. placebo) on task-associated activity in nonsmoking patients with schizophrenia and age/gender-matched healthy control subjects.

Based on evidence presented in the preceding chapters, I hypothesized to observe attention task-associated abnormalities in hippocampal and ventral

parietal response in schizophrenia after placebo administration and normalization

after nicotine.

Materials and Methods

Subjects

37 subjects participated in this study — 17 stable outpatients who had a

primary diagnosis of schizophrenia and 20 healthy comparison subjects.

Demographic and clinical information for participants was assessed by interview and is shown in Table 1. No significant group differences in age, gender, handedness, or ratio of never smokers/former smokers (> 3 months from last cigarette) were observed. No subjects were taking smoking cessation medication

(e.g. varenicline) at the time of the study. Patients were recruited by referral from a University of Colorado psychiatrist. Patients were excluded for a diagnosis of neurological illness, head trauma, current smoking (< 3 months from last cigarette) or substance abuse, poor (inability to hear 60dB SPL 1000 and 1500Hz tones in either ear) or unbalanced (> 10 dB threshold difference between each ear) hearing, failure to pass a physical examination, and magnetic resonance imaging (MRI) exclusion criteria (claustrophobia, weight > 250 lbs, metal in the body). Control subjects were excluded for all of the above as well as a diagnosis of Axis I mental illness or first-degree family history of Axis I mental illness.

Patients were medication stable (> 3 mo. with no change in medication).

Schizophrenia patient comorbidities included 6 subjects with depression not otherwise specified (NOS), 3 with bipolar NOS, 1 with posttraumatic stress disorder, 4 with panic disorder, 1 with social phobia, 2 with generalized anxiety disorder and 1 with obsessive compulsive disorder. 3 patients had a history of alcohol abuse, 1 of cannabis dependence, and 1 of cocaine dependence. None of these patients, however, were substance dependent during or for at least 6 months prior to beginning the study (confirmed by urinalysis). All subjects were required to pass a nicotine tolerance test, in which the nicotine dose used for the

experiment (7 mg) was administered > 3 d prior to the first fMRI scan. Criteria for passing the tolerance test were 1) less than a 20% change in heart rate or blood pressure (BP) for up to 90 minutes post patch-application, 2) no side effects other than mild/minor nausea, headache, lightheadness, clouded thinking, anxiety, or mouth tingling. All participants provided written informed consent in accordance with the principles of the Declaration of Helsinki and could withdraw from the study at any time. Subjects were compensated for participation. The Colorado

Multiple Institutional Review Board approved the study.

Study Design

This was a single-blind, pseudo-randomized, placebo-controlled, crossover study. On each of two study visits, subjects were administered a 7 mg nicotine patch (Nicoderm) or a placebo patch (made in-house) 70 minutes (m) prior to MRI scanning. The nicotine patch administers nicotine at a rate of 1.5 mg

(equivalent to 1.5 cigarettes) worth of nicotine per hour. The order of study visits

(placebo or nicotine) was counterbalanced across subjects. Subjects wore patches throughout scanning. Total time of patch application was approximately

130 m (70 m before scanning, 60m during scanning). The attention task was performed approximately 10 m after the subject was placed in the scanner (~80 m after patch application); the delay was due to localizer, high-order shimming and anatomical scans that preceded the functional scan. The 80 m latent period was used such that the attention task occurred during a time window

corresponding to the peak plasma concentration of nicotine (Dempsey et al.

2013). Based on previous work, the expected nicotine concentration during this period is expected to be approximately 4 ng/ml (Dempsey et al. 2013). The placebo patch was tactilely similar to the nicotine patch and was affixed to the skin (upper arm) in the same manner as the nicotine patch. Subjects were asked to refrain from examining either patch during or after application as the placebo and drug patches were not visually identical. Furthermore, clothing covered patches such that they could not be readily observed after affixation. Patches were removed immediately after scanning. Visits were scheduled > 3 d apart.

Heart rate and BP were monitored immediately prior to patch application, 30 and

60 m after patch application, and up to 60 m after patch removal. Physiological effects of nicotine were analyzed using a mixed-effects model analysis of variance (ANOVA) in SPSS22, with time (pretreatment vs. posttreatment) and drug (placebo vs. nicotine) as within-subjects factors and diagnosis (control vs. patient) as a between-subjects factor.

Auditory Stimuli

For the attention task (see “Task Description”), synthetic audio recordings for the numbers 1-9 were downloaded from www.modeltalker.com. Number

stimuli were adjusted to have the same onset with Adobe Audition.

For task-overlaid noise distraction, environmental, “urban” noise stimuli

were mixed as described previously (Tregellas et al. 2009). Briefly, clips included

segments from two talk radio shows, two classical musical pieces, sounds from a neighborhood block party, which included multiple background conversations and sounds from children playing, traffic sounds, a refrigerator motor cycling on and off, and frequent knocking sounds from glasses being set on countertops.

Volumes of all of these elements were mixed so that no one element was readily identifiable. The subjective experience of the sound mixture was that of standing in a busy crowd of people, in which multiple conversations were occurring, with a low level of indistinguishable background music and other sounds. Urban noise distraction was presented at 80 dB in the ear opposite the task-relevant stimuli with MR-compatible headphones (Resonance Technologies, Inc.).

Task Description

Subjects performed an auditory version of the Sustained Attention to

Response Task (SART) (Seli et al. 2012). For the SART, single-digit numbers were aurally presented one at a time, and the subject was asked to respond (with a button press) (Lumina Response Pad, Cedrus Corp.) after each auditory stimulus (70 dB, presented in either the right or left ear), except for the number

‘3,’ in which case the subject was asked to withhold from responding. Subjects used their dominant hand for motor responses. The ear (right or left) in which the numbers were presented was pseudo-randomized between subjects. Stimulus duration was 250ms and inter-stimulus interval was 900 ms. Subjects performed two variations of the SART, the Ordered SART and the Random SART. In the

Ordered SART, the numbers were presented in order; in the Random SART, the numbers were presented pseudo-randomly. Due to the predictability of Ordered

SART, subjects may be able to correctly respond or withhold responding reflexively to the presence of any auditory stimulus. The unpredictability of

Random SART, however, requires subjects to focus on specific stimulus features before making the appropriate response, increasing attentional demands

(Smucny et al. 2013b). The current SART variation (Ordered or Random) was highlighted and visually presented through MR-compatible goggles (Resonance

Technologies, Inc.) throughout the experiment. The identifier cue was presented

2.3 s before the first set of stimuli, as well 2.3 s before each time the condition switched from Ordered to Random (or vice-versa). The subject was asked to respond as quickly and accurately as possible to help induce attentiveness.

The SART was presented as a block design, with four pseudo-randomly dispersed conditions: Ordered-Silent (ordered numbers with no noise distraction),

Ordered-Noisy (ordered numbers with noise distraction), Random-Silent (random numbers with no noise distraction), and Random-Noisy (random numbers with noise distraction). 72 blocks of 12.65 s each were administered, with 18 blocks

per condition. Each block consisted of 9-11 trials. Baseline data was collected

from six 37.95 s fixation periods interspersed at regular intervals throughout the

experiment. Total task duration was 18 m.

Recorded performance measures on the SART were 1) errors of

commission, or incorrect button presses on ‘3’, 2) errors of omission, or failure to

button press on the numbers 1, 2, and 4-9, and 3) reaction time. Percent correct responses were calculated as 100 – (percent errors commission + percent errors of omission). As a combination of all these measures provides a more accurate assessment of performance than each individual measure (Seli et al. 2013), they were combined into a single measure, “efficiency,” based on a previous SART study in schizophrenia (Chan et al. 2009). Specifically, efficiency was defined as arcsin (√ (Percent Correct Responses / Reaction Time for Correct Responses)).

Efficiency data were analyzed by mixed-effects ANOVA in SPSS22 with drug

(placebo vs. nicotine), SART difficulty (Ordered vs. Random) and distraction level

(Silent vs. Noisy) as within-subjects factors and diagnosis (Control vs. Patient) as a between-subjects factor.

fMRI Scanning Parameters

Functional scans were collected using a clustered volume approach as described previously (Smucny et al. 2013b; Smucny et al. 2013c). Use of the clustered volume approach allowed stimuli to be presented while minimizing scanner noise. This technique has been shown to substantially improve signal detection in fMRI experiments using auditory stimuli, despite reducing the overall number of scans collected per experimental condition (Edmister et al. 1999). I have previously used clustered volume acquisition in a number of auditory tasks in schizophrenia, including the SART (Smucny et al. 2014a; Smucny et al. 2013b;

Smucny et al. 2013c; Tregellas et al. 2007a; Tregellas et al. 2009; Tregellas et al.

2012).

Studies were performed with at 3T GE Signa MR system using a standard quadrature head coil. Functional images were acquired with a gradient-echo T2*

Blood Oxygenation Level Dependent (BOLD) contrast technique, with

TR = 12650 ms (as a clustered volume acquisition of 2000ms, plus an additional

10650 ms silence interval), TE = 30 ms, FOV = 220 mm2, 642 matrix, 38 slices,

3.5 mm thick, 0.5 mm gap, angled parallel to the planum sphenoidale.

Additionally, one inversion recovery echo planar image (IR-EPI) (TI = 505 ms) volume was acquired to improve spatial normalization (see “fMRI

Preprocessing”).

fMRI Preprocessing

Data were preprocessed using SPM8 (Wellcome Dept. of Imaging

Neuroscience, London). Data from each subject were realigned to the first volume, normalized to the Montreal Neurological Institute template using the IR-

EPI as an intermediate to improve coregistration between images, and smoothed with an 8mm FWHM Gaussian kernel.

Region-of-Interest (ROI) Analysis of BOLD Response

To account for both within-group and within-subject variance, a mixed effects analysis was implemented. Parameter estimates were generated for each

individual in a first-level analysis. First-level effects were modeled with a double- gamma function, without temporal derivatives, using the general linear model in

SPM8. A 196 s high pass filter was applied to remove low-frequency fluctuation in the BOLD signal. Rigid-body movement parameters (up/down, left/right, forward/back, roll, pitch, yaw) were entered into the SPM8 design matrix as covariates of no interest. No significant effect of diagnosis, drug treatment, or drug X diagnosis interaction was observed for overall movement. “Task- associated” contrast images were generated for each drug treatment condition

(placebo and nicotine). “Task-associated” response was defined as ((Random

Noisy > Random Silent) > (Ordered Noisy > Ordered Silent)). Fixation periods were used as an implicit baseline.

A priori hypotheses were tested for response in two ROIs, the VPC and

hippocampus. The VPC and hippocampal ROIs consisted of the supramarginal

gyrus and hippocampus delineations in WFU Pickatlas (Maldjian et al. 2003)

respectively. Mean task-associated signal within each ROI was extracted for

each subject using the Marsbar toolbox (Brett et al. 2002) and entered into

SPS22 for ANOVA analysis. The primary contrast of interest, the drug (placebo

vs. nicotine) X diagnosis (patient vs. control) interaction, was evaluated

separately for each ROI, with drug as a within-subjects factor and diagnosis as a

between-subjects factor. Significant interaction effects (omnibus F contrasts)

were followed up by post-hoc one-tailed t-tests in order to describe the directionality of effects.

Whole-Brain Analysis of BOLD Response

An exploratory whole-brain analysis of response was also conducted from first-level task-associated contrast images. The omnibus ANOVA parameters were identical to those used for the ROI analysis. The primary contrast of interest was the diagnosis X drug interaction. Significance was set a threshold of p <

0.001 (uncorrected), k > 10 voxels.

Results

Physiological Effects of Nicotine

Physiological effects of placebo vs. nicotine treatment are presented in

Table 2. Physiological data were not available from one control subject due to an equipment malfunction. No significant time X drug X diagnosis interactions were observed on systolic BP (F(1,34) = 0.60, p = 0.44), diastolic BP (F(1,34) = 1.58, p

= 0.22), or heart rate (F(1,34) = 0.063, p = 0.80). Across all subjects, no significant time (pretreatment vs. 60 m post-treatment) X drug interactions were observed for systolic BP (F(1,34) = 2.84, p = 0.10), diastolic BP (F(1,34) = .070, p

= 0.79), or heart rate F(1,34) = 4.07, p = 0.052).

Behavioral Data

The primary behavioral measure of interest in this study was performance efficiency, a single metric that combines accuracy and reaction time (see

Methods). Efficiency data for each SART condition (Ordered-Silent, Ordered-

Noisy, Random-Silent, Random-Noisy) is presented in Table 3. Using this measure, significant main effects of difficulty (Ordered vs. Random; F(1,35) =

46.4, p < 0.001) and distraction level (Silent vs. Noisy; F(1,35) = 17.2, p < 0.001) were observed, indicative of decreased efficiency during the Random condition and Noisy condition relative to the Ordered and Silent conditions, respectively.

No significant interactions were observed between SART condition and diagnosis

(Control vs. Patient) or drug (Placebo vs. Nicotine).

Base behavioral measures (errors of commission, omission, and reaction times) are presented in Tables 4a-d.

ROI Analysis of BOLD Response

The observed behavioral results suggest that attentional load is greater when number stimuli are random (relative to ordered) as well as during distracting noise (relative to silence). Therefore, I defined task-associated BOLD signal effects as the signal resulting from the contrast ((Random Noisy > Random

Silent) > (Ordered Noisy > Ordered Silent)).

Using mean task-associated BOLD signal within anatomically defined

ROIs as the primary measures of interest (see Methods), significant diagnosis X drug interactions were observed in the left VPC (F(1,35) = 6.98, p = 0.012)

(Figure 8) and left hippocampus (F(1,35) = 4.70, p = 0.037) (Figure 9) but not the right VPC (F(1,35) = 2.17, p = 0.15) or right hippocampus (F(1,35) = 0.31, p =

0.58).

In the left VPC (Figure 8), post-hoc tests revealed increased response in controls after nicotine administration (relative to placebo) (p = 0.015) and a trend towards decreased response in patients after nicotine administration (relative to placebo) (p = 0.073). Between-group comparisons also identified a trend towards greater response in patients (relative to controls) under placebo (p = 0.058) and reduced response in patients (relative to controls) under nicotine (p = 0.036).

In the left hippocampus (Figure 9), post-hoc tests revealed a trend towards increased response in controls after nicotine administration (relative to placebo)

(p = 0.084) and a trend towards decreased response in patients after nicotine administration (relative to placebo) (p = 0.053). Between-group comparisons showed greater response in patients (relative to controls) under placebo (p =

0.044) and no difference in response in patients (relative to controls) under nicotine (p = 0.12).

No significant correlations were observed between nicotinic effects on blood pressure or heart rate and effects on response in any ROI. Task associated-response during placebo did not predict the magnitude of nicotinic effects for either group.

Whole-Brain Analysis

Whole-brain analysis of the diagnosis X drug interaction revealed an

additional cluster in the right VPC (peak F(1,35) = 19.3, peak coordinates x = 51, y = -52, z = 22, cluster size = 21 voxels). The interaction was driven by increased

response in controls after nicotine administration and decreased response in patients.

Behavioral Correlates

I examined correlations between task-associated change in efficiency

(ΔEff) and BOLD response in each ROI. ΔEff was calculated based on the efficiencies for each SART condition using the contrast ((Random > Ordered) >

(Noisy > Silent)), consistent with the previously defined measure for task- associated BOLD response. ΔEff therefore represents the change in performance due to increasing the task difficulty (from Ordered to Random) and distraction level (from Silent to Noisy).

In patients, ΔEff was negatively correlated with task-associated left VPC response (r = -0.54, p = 0.026) (Figure 10) as well as task-associated anterior cingulate response (r = -0.59, p = 0. 013).

Table 1. Demographic and Clinical Data of Participants. Parentheses contain the standard deviation. Abbreviations: BPRS = Brief Psychiatric Rating Scale, SANS = Scale for the Assessment of Negative Symptoms, Typ = # Treated with Typical Antipsychotic Medications, ATyp = # Treated with Atypical Antipsychotic Medications. Controls Schizophrenia Age 38.4 (12) 44 (12) Gender (M/F) 11/9 12/5 Smoking (Never/Former Smokers) 15/5 10/7 Average Total BPRS 36.6 (7.7) Average Total SANS 4.2 (2.9) Meds: Typ/ATyp 1/16

Table 2. Physiological effects of nicotine and placebo patch. Parentheses contain the standard error. Abbreviations: BP = blood pressure, mmHg = mm of mercury, bpm = beats per minute.

Tx Placebo Nicotine Group 60 m 60 m Measure Pre Δ Pre Δ Post Post Controls Systolic BP 128 (4) 121 (3) -7 (3) 127 (3) 125 (2) -2 (2) (mmHg) Diastolic BP 79 (2) 77 (2) -2 (2) 79 (2) 79 (2) 0 (2) (mmHg) Heart Rate 75 (3) 73 (3) -2 (2) 76 (3) 77 (3) 1 (2) (bpm) Patients Systolic BP 135 (4) 130 (4) -5 (5) 128 (4) 125 (3) -3 (2) (mmHg) Diastolic BP 79 (2) 79 (3) 0 (2) 80 (2) 78 (2) -2 (2) (mmHg) Heart Rate 81 (4) 81 (4) 0 (2) 84 (4) 87 (4) 3 (2) (bpm)

Table 3. Performance efficiency for each SART condition. Parentheses contain the standard error. Significant main effects of noise (Noise > Silent) and difficulty (Random > Ordered) were observed (see Results), suggesting that these manipulations decrease behavioral performance.

Controls Patients Condition Placebo Nicotine Placebo Nicotine Ordered-Silent 0.51 (0.018) 0.52 (0.020) 0.51 (0.025) 0.51 (0.027) Ordered-Noise 0.50 (0.017) 0.50 (0.021) 0.48 (0.033) 0.50 (0.026) Random-Silent 0.44 (0.0076) 0.44 (0.0080) 0.43 (0.012) 0.44 (0.015) Random-Noise 0.43 (0.0089) 0.42 (0.010) 0.39 (0.022) 0.42 (0.016)

Table 4A. Percent errors of commission for each task condition. Parentheses contain the standard error.

Controls Patients Condition Placebo Nicotine Placebo Nicotine Ordered-Silent 5.7 (1.3) 5.6 (1.6) 13.0 (2.7) 13.3 (4.5) Ordered-Noise 5.5 (1.5) 5.7 (1.8) 10.7 (2.8) 12.0 (4.7) Random-Silent 16.8 (3.4) 15.3 (2.6) 23.5 (3.4) 36.7 (6.3) Random-Noise 19.5 (4.8) 19.5 (3.7) 26.6 (4.8) 39.3 (6.4)

Table 4B. Percent errors of omission for each task condition. Parentheses contain the standard error.

Controls Patients Condition Placebo Nicotine Placebo Nicotine Ordered-Silent 1.6 (0.7) 2.0 (1.0) 7.8 (2.8) 9.9 (3.7) Ordered-Noise 2.5 (0.9) 3.4 (1.3) 15.7 (5.6) 10.6 (2.8) Random-Silent 0.9 (0.3) 1.2 (0.4) 6.4 (2.8) 5.2 (2.4) Random-Noise 4.1 (0.9) 2.0 (1.0) 7.8 (2.8) 8.6 (2.8)

Table 4C. Reaction times (ms) for correct responses for each task condition. Parentheses contain the standard error.

Controls Patients Condition Placebo Nicotine Placebo Nicotine Ordered-Silent 431 (24) 427 (25) 418 (31) 418 (33) Ordered-Noise 446 (24) 442 (27) 439 (34) 434 (35) Random-Silent 541 (16) 546 (19) 540 (18) 518 (24) Random-Noise 560 (18) 565 (20) 530 (28) 537 (24)

Figure 8. Effects of nicotine on task-associated (see Methods for task definition) neuronal response in the left VPC. A significant drug X diagnosis interaction was observed. For visualization, statistical parametric maps are displayed in the neurologic convention (R on R) and thresholded at p < 0.01, cluster extent (k) > 50 voxels. Error bars represent the standard error. **Significant diagnosis X treatment interaction.

Figure 9. Effects of nicotine on task-associated (see Methods for task definition) neuronal response in the left hippocampus. A significant drug X diagnosis interaction was observed. For visualization, statistical parametric maps are displayed in the neurologic convention (R on R) and thresholded at p < 0.01, cluster extent (k) > 50 voxels. Error bars represent the standard error. *Significantly increased response in patients relative to controls under placebo conditions. **Significant diagnosis X treatment interaction.

Figure 10. Negative correlation between task-associated VPC response and task-associated change in performance efficiency (see Results) in patients.

CHAPTER VI

ANALYSIS 2: EFFECTS OF NICOTINE ON VENTRAL ATTENTION NETWORK

CONNECTIVITY IN SCHIZOPHRENIA

In this Chapter I present the experimental methods and results of a set of analyses designed to test the hypothesis that nicotine will modulate ventral attention network connectivity in schizophrenia. For this analysis I reexamined selective attention task data collected in Analysis 1. I also analyzed resting state fMRI data collected immediately after the SART was performed. I then examined the effects of nicotine (vs. placebo) on task-associated VAN connectivity as well as resting state VAN connectivity. Based on evidence presented in Chapter IV, I hypothesized to observe task-associated disconnectivity of the VAN in schizophrenia under placebo conditions and normalization under nicotine.

Materials and Methods fMRI Scanning Parameters: Resting State

Resting state functional images were acquired with the following parameters: scan time 10 m, TR = 2000 ms, TE = 26 ms, FOV = 220 mm2,

642 matrix, 27 slices, 2.6 mm thick, 1.4 mm gap. The first four volumes of the

300-volume scan were excluded from analysis. Subjects were instructed to rest

with eyes closed and to “not think about anything in particular.”

fMRI Preprocessing (SART and Resting State)

Data were preprocessed using SPM8 (Wellcome Dept. of Imaging

Neuroscience, London). Data from each subject were realigned to the first volume, normalized to the Montreal Neurological Institute template using the IR-

EPI as an intermediate to improve coregistration between images, and smoothed with an 8 mm FWHM Gaussian kernel. The images were motion-corrected using rigid-body motion parameters. No significant effect of diagnosis, drug treatment, or drug X diagnosis interaction was observed for overall movement. White matter and csf signal confounds were removed. Mean overall gray matter signal was not included as a confound as doing so shifted the whole-brain connectivity distribution towards predominantly negative values. The data were detrended and a 0.01 to 0.1 Hz bandpass filter applied to remove low-frequency drifts and physiological high-frequency noise, consistent with previous research using connectivity analysis of sparse acquisition fMRI data (Yakunina et al. 2015).

Connectivity Analysis: Seed and Target ROI Definitions

As I have previously reported task-associated effects of nicotine on BOLD signal in schizophrenia using the anatomically defined ROI of the left VPC in

Wake Forest University Pickatlas (Maldjian et al. 2003; Smucny et al. 2015), I used an identical ROI as a seed in the present analysis. Connectivity was then analyzed between this seed and 6 mm radius spherical target ROIs centered at the coordinates (x,y,z = -45, 36, -6) and (x,y,z = 45, 36, -6), respectively. These

ROIs have been previously identified in the literature as most closely linked to stimulus driven attention reorienting and the ventral attention network (Daselaar et al. 2013). Connectivity was analyzed between the left VPC seed and both the left and right IFG because previous work has shown significant interhemispheric intrinsic connectivity of the VAN (Kucyi et al. 2012).

Connectivity Analysis: Implementation

Psychophysiological interaction (PPI) describes functional connectivity between brain regions contingent on a psychological context (Friston et al. 1997;

Gitelman et al. 2003). Here, I examined PPI of the VAN using the Conn v.15 toolbox (http://www.nitrc.org/projects/conn). A generalized psychophysiological

interaction (gPPI) routine was implemented. Briefly, gPPI allows for an analysis

of task-associated connectivity without the two-condition constraint necessary for

traditional PPI analysis by controlling for the main effects of any number of

conditions across the scanning session in a single model (e.g. Ordered-Silent,

Ordered-Noisy, Random-Silent, and Random-Noisy in this study) (McLaren et al.

2012). “Task-associated” connectivity can therefore be analyzed independent of task-associated effects on BOLD response. Identical to my previous study

(Smucny et al. 2015), task-associated connectivity (Δconnectivity) was defined using the contrast ((Random-Noisy > Random-Silent) > (Ordered-Noisy >

Ordered-Silent)). Connectivity during fixation was used as a baseline and

subtracted from each condition as implemented in a previous gPPI analysis

(McDaniel et al. 2013). Using this contrast, Δconnectivity parameter estimates

(beta weights) between the VPC seed and left/right IFG target ROIs were generated for each individual in a first-level analysis. Because Δconnectivity is

defined as a comparison between conditions, it should not be considered a “pure”

estimate of connectivity (e.g. a negative beta weight should not be interpreted as

a negative correlation between the seed and target ROI). Confounding task-

associated BOLD response was modeled with a double-gamma hemodynamic response function without temporal derivatives.

First level Δconnectivity parameter estimates were analyzed via second level ANOVA with drug (placebo vs. nicotine) as a within-subjects factor and diagnosis (control vs. patient) as a between-subjects factor. A separate ANOVA was performed between the seed ROI (left VPC) and each target ROI (left and right IFG). Significant interaction effects were followed up by analysis of simple main effects to describe the direction of the interactions.

Task-Independent Effects of Nicotine on VAN Connectivity

Task-independent connectivity between the VPC seed and target ROIs was analyzed using data from 10 m resting state sessions that immediately followed the SART task after both placebo and nicotine administration. Resting state data from one control subject could not be analyzed due to a scan ending prematurely. ANOVA was performed in the same manner as the gPPI analysis.

Correlation Analyses

Exploratory correlation analyses were performed with significance threshold set to p < 0.05.

Results gPPI Analysis

gPPI (see Methods) was performed to analyze the effects of nicotine (vs.

placebo) on task-associated change in connectivity (Δconnectivity) between an

anatomically defined VPC seed and the left and right IFG. “Task” was defined

using the contrast ((Random-Noisy > Random-Silent) > (Ordered-Noisy >

Ordered-Silent)). Single-subject Δconnectivity values were then analyzed by

ANOVA using drug (nicotine vs. placebo) as a within-subjects factor and

diagnosis (patient vs. control) as a between-subjects factor.

Average Δconnectivity values (beta weights) for each group (control-

placebo, control-nicotine, patient-placebo, patient-nicotine) during the task are

presented in Table 5a and Figure 11. A significant drug X diagnosis interaction

was observed on Δconnectivity between the left VPC seed and the left IFG

(F(1,35) = 8.03, p < 0.01). The main effect of drug was also significant (F(1,35) =

5.07, p = 0.031). Post-hoc analyses determined the interaction effect was driven

by 1) reduced Δconnectivity in patients (relative to controls) during placebo (Δβ =

-0.074, p = 0.035) and 2) increased Δconnectivity in patients during nicotine

(relative to placebo) (Δβ = 0.12, p < 0.01). Nicotine did not significantly affect

Δconnectivity in control subjects.

No significant drug X diagnosis interaction, main effect of drug, or main effect of diagnosis was observed on Δconnectivity between the left VPC seed and the right IFG.

Resting State Connectivity Analysis

Resting state connectivity values (beta weights) for each group are presented in Table 5b. No significant drug X diagnosis interactions or main effects of drug were observed for connectivity between the seed and either the left or right IFG. A main effect of diagnosis was observed for connectivity between the VPC and right IFG (F(1,34) = 6.80, p = 0.013). This effect was driven by increased connectivity in patients (vs. controls) during placebo (Δβ =

0.10, p < 0.01).

Correlation Analysis

A significant negative correlation was observed between total SANS score and the effect of nicotine on Δconnectivity between the VPC and left IFG (r = -

0.63, p < 0.01, Figure 12), suggesting that patients with the most severe negative symptoms were the least responsive to nicotine. The effect was driven by significant negative correlations with SANS avolition (r = -0.60, p = 0.011) and

SANS asociality (r = -0.61, p < 0.01) subscores. No significant correlations were

observed between behavioral measures and task-associated or resting VAN connectivity.

No significant associations were observed between task-associated TPJ response and VAN connectivity.

Table 5a. Δconnectivity (beta weights) between the VPC seed and left and right IFG. Parentheses contain the standard error.

Controls Patients Target ROI Placebo Nicotine Placebo Nicotine Left IFG 0 (0.023) -0.015 (0.023) -0.075 (0.025) 0.045 (0.026) Right IFG 0.055 (0.020) 0.028 (0.035) -0.033 (0.028) 0.038 (0.043)

Table 5b. Resting connectivity (beta weights) between the VPC seed and left and right IFG. Parentheses contain the standard error.

Controls Patients Target ROI Placebo Nicotine Placebo Nicotine Left IFG 0.23 (0.043) 0.21 (0.036) 0.29 (0.033) 0.25 (0.050) Right IFG 0.11 (0.023) 0.14 (0.026) 0.21 (0.025) 0.22 (0.042)

Figure 11. Effect of nicotine on task-associated connectivity between the VPC and left IFG. Top: Statistical parametric F-map of the drug X diagnosis interaction. Map thresholded at p < 0.01, k > 50 voxels for visualization. Coronal slice displayed in the neurologic convention (R on R). Bottom: Charts illustrating the direction and magnitude of the interaction effect. Beta weights represent relative connectivity between the left VPC seed and the left IFG ROI. *p < 0.05, controls vs. patients during placebo. **p < 0.05, placebo vs. nicotine in patients. ***p < 0.05, drug X diagnosis interaction. Error bars represent the standard error.

Figure 12. Negative correlation between total SANS score and the effect of nicotine on Δconnectivity in patients.

CHAPTER VII

DISCUSSION

Analysis 1 Results Summary

In agreement with the hypothesis, significant diagnosis X drug interactions were observed on task-associated response in the VPC and hippocampus.

These effects were driven by 1) relative hyperactivity of these regions in patients

during the placebo condition, and 2) decreased response in patients after

nicotine administration. Poor task-associated performance was also associated

with VPC hyperactivity in the patient group.

Analysis 2 Results Summary

In agreement with the hypothesis, significant drug X diagnosis interactions

were observed on task-associated VAN connectivity, driven by 1) reduced

Δconnectivity in schizophrenia patients (relative to healthy controls) during placebo administration, and 2) increased Δconnectivity in patients during nicotine. Patients with the least severe negative symptoms also showed the greatest increase in Δconnectivity after nicotine (vs. placebo). No significant interaction effects or main effects of drug were observed on resting state connectivity, despite the observation that patients showed increased connectivity during placebo.

Discussion: Hippocampal Findings

As hypothesized, nicotine modulated task-associated response in the hippocampus in schizophrenia. The effect was driven by hyperactivity in patients during placebo administration and reduced response after nicotine. Hippocampal hyperactivity in patients in this present study is consistent with previous research demonstrating increased response during a variety of tasks, including auditory tasks (see Chapter 3). Hyperactivity of the region during auditory tasks may also related to patient reports of sensory flooding and help explain why patients have difficulty accomplishing tasks in noisy environments.

Pharmacologically, it is possible that nicotine modulates hippocampal response through binding to either α7 or α4β2 receptors. The observed reduction in response observed in this study is consistent with an α7-receptor dependent mechanism on inhibitory interneurons. It is also possible, however, that the observed effects are the result of desensitization, a state in which nicotinic receptors not only no longer respond to exogenous agonists but also endogenous agonists (e.g. acetylcholine). Future studies may examine the effects of α7 agonists (e.g. DMXB) during the task to more closely examine the

pharmacologic basis of the effect.

Vinogradova and others have referred to the hippocampus (specifically the

CA3 subfield) as a comparator, designed to evaluate the novelty of a stimulus

and thereby help determine its salience (Vinogradova 2001). Novel stimuli

activate CA3 and are attended to; non-novel stimuli (e.g. repetitive noise) activate

the region less strongly and are ignored. Consistent with this view, previous neuroimaging studies have demonstrated attention-dependent modulation of the hippocampus (Aly and Turk-Browne 2015; Uncapher and Rugg 2009). Patterns of hippocampal activity may thereby be a reflection of attention level (Aly and

Turk-Browne 2015), and help explain why attended stimuli are better remembered than unattended stimuli (Chun and Turk-Browne 2007).

Hyperactivity of the hippocampus in schizophrenia during auditory tasks may therefore be interpreted as a pathological state in which persistent auditory stimuli (e.g. urban noise distraction) are continuously interpreted as “novel.” As hippocampal function is strongly influenced by inputs from many brain regions, whether this state is entirely due to dysfunctional circuitry within the hippocampus itself or elsewhere cannot be ascertained in the present study. Future studies may also use high-resolution fMRI to examine hippocampal response within the

CA1 and CA3 subfields.

Discussion: VAN Findings

Task-associated effects of nicotine were observed on response in the TPJ

node of the VAN. This result was driven by hyperactivity in patients under

placebo conditions (relative to controls) and reduced activity under nicotine

(relative to placebo). Consistent with previous studies in distracting environments

(reviewed by Vossel et al. 2014), under placebo conditions task-associated

hypoactivation of the TPJ was observed in control subjects. In patients, on the

other hand, task-associated hyperactivity was observed. Downregulation of TPJ activity during distracting environments is hypothesized to be a “bottom up” filtering mechanism in order to minimize the potentially deleterious effects of distractors (Shulman et al. 2007). Abnormal hyperactivation of the region in patients may therefore represent relative dysfunction in this filtering mechanism and may help explain why patients find noisy environments continuously bothersome. The finding that nicotine reversed this abnormality further suggests that nicotine may target the TPJ to help reduce distractibility. A second possibility is that VPC activity is a reflection of task difficulty. The observation that hyperactivity predicted poor task-associated performance in patients supports this hypothesis, although it is incongruent with the observation that nicotine increased VPC activity in controls without affecting performance. The observed correlation between VPC activity and performance also suggests that patients that show VPC hyperactivity may be particularly behaviorally sensitive to distraction.

Interestingly, the directionality of connectivity effects observed in this study was in the opposite direction of activity effects observed in the previous chapter.

Specifically, the previous analysis revealed increased activity of the VPC in

patients during placebo, whereas in the present study decreased connectivity

was observed. Both phenotypes were then reversed by nicotine. One

interpretation of these findings is that reduced connectivity in patients is a

compensatory response to abnormally high VPC response during task. Or,

similarly, greater VPC activity could occur as a result of reduced connectivity.

The observed lack of significant association between connectivity and response, however, is incongruent with these explanations. Another possibility is that the

VAN disconnectivity and VPS activity are less directly related, such that VAN disconnectivity is effectively a separate mechanism by which patients are more distracted in noisy environments, and a second target which nicotine may ameliorate functional attention deficits in schizophrenia. Schizophrenia is frequently referred to as disease of “disconnectivity,” particularly of long-distance connections and networks (Karbasforoushan and Woodward 2012; Uhlhaas

2013). The observed results may therefore be another manifestation of this phenotype.

In contrast to its connectivity during task, VAN hyperconnectivity was observed in patients during the resting state. Nicotine, furthermore, was ineffective at modulating this phenotype. Attention task-specific effects of nicotine are consistent with previous work demonstrating that nicotine improves attentional performance in nonsmoking schizophrenia patients but has little effect on other cognitive domains (Harris et al. 2004). It is also possible the task itself, in which patients are asked to ignore persistent distracting noise, isolates a particularly dysfunctional neuronal system in schizophrenia (sensory filtering) that is amenable to nicotinic modulation. Previous work by Hong et al. (2011) examining the effects of nicotine on a visual sustained attention task did not find

task-specific effects of the drug in patients, possibly because the task did not adequately tap into sensory filtering-related processing systems.

The effectiveness of nicotine at improving VAN connectivity in patients was negatively correlated with negative symptom severity, suggesting that patients with the most severe negative symptoms were the least neuronally responsive to nicotine. Although preliminary, this result suggests that it may be possible to predict nicotine’s effectiveness at normalizing loss of network connectivity. The ability to predict treatment efficacy is a topic of great interest in psychiatry. Previous studies have reported significant interactions between baseline symptom severity and antipsychotic efficacy in schizophrenia (Furukawa et al. 2015) and antidepressant efficacy in depression (Fournier et al. 2010).

Previous work has also demonstrated that first episode patients with higher levels of baseline function benefit more from cognitive behavior therapy (Allott et al.

2011). Our lab has demonstrated that responsiveness to an α7 nicotinic agonist may depend upon the allele expressed near the α7 promoter, possibly due to allelic-driven variation in α7 receptor expression level (Tregellas et al. 2011).

Future studies may more closely examine the ability to predict the neuronal response to nicotine during attention tasks in schizophrenia through a combination of clinical and genetic factors.

The effects of nicotine on VAN connectivity in the present study were task- specific, as no drug effects on resting state connectivity were observed despite the finding that patients showed increased connectivity (relative to controls)

during placebo. The effects of pharmacologic manipulation on resting state connectivity in neuropsychiatric disease is a topic of recently increased interest, due in part to its potential applications in drug development (Smucny and

Tregellas 2013; Smucny et al. 2014b; Wylie et al. 2016). Comparatively few studies, however, have used task-associated connectivity in ascertain the neuronal effects of potential treatment interventions. These results suggest that task-based connectivity should also be considered when developing fMRI-based protocols for evaluating the neuronal effects of investigational compounds.

Effects of Nicotine: Patients vs. Controls

Interestingly, nicotine induced largely opposite task-associated neuronal effects in control subjects compared to patients. Specifically, nicotine increased response of the VPC and hippocampus in controls, while decreasing response in patients. These results are similar to the direction of findings in P50 gating

(paired-click) studies, which show that nicotine decreases response to the second stimulus and improves gating in patients (Adler et al. 1993), and increases overall response to stimuli in healthy subjects that gate normally (Knott et al. 2010). Pharmacologically, nicotine may be expected to have both excitatory and inhibitory effects. For example, in the hippocampus nicotine binds to both high-affinity α4β2 receptors on excitatory pyramidal neurons as well as low- affinity α7 receptors on inhibitory interneurons (Frazier et al. 1998; Papke 2014).

The result that nicotine primarily has an inhibitory task-associated effect in

schizophrenia could be due to several factors, including 1) high activity at baseline in patients, 2) antipsychotic blockade of dopaminergic signaling occluding (Baskys et al. 1993) or otherwise reducing the excitatory effects

(Medoff et al. 2001) of nicotine, and 3) differential expression of nicotinic receptor subtypes in nonsmoking patients vs. control subjects (Freedman et al. 1995;

Mexal et al. 2010). The first possibility is less likely as no correlations were observed between hyperactivity at baseline and the magnitude of nicotinic effects on task-associated response. The second possibility may be examined in future studies that compare the effects of nicotine in unmedicated patients or at-risk populations. Finally, PET studies that examine nicotinic receptor availability may be used to determine the relationship between expression level and nicotinic effects on attention-related processing.

Limitations

A potential limitation of this study was the single-blind design. The experiment was carried out in this manner as the nicotine and placebo patches were not visually identical and therefore it was impractical to blind the experimenter to the treatment. For this reason, subjects were instructed to refrain from examining the patches during the study. Furthermore, nicotine can have physiological effects that may reduce the effectiveness of the blind (Benowitz

1998). It should be noted, however, that 1) nicotine did not have any significant effects on blood pressure or heart rate during scanning in this study, and 2)

subjects most likely to have noticeably adverse reactions to nicotine were excluded by prescreening (see Methods). Although it was somewhat surprising to not observe significant physiological effects of the drug in this study, 1) previous work has found only small physiological effects of 7 mg transdermal nicotine (vs. placebo) in nonsmokers up to 120 min post-treatment (Wignall and de Wit 2011) and 2) exclusion criteria included screening for subjects who showed large physiological effects of nicotine during screening. The latent period (subjects scanned 80 m post-patch application) was chosen as it was expected to capture the peak plasma absorption of nicotine (Dempsey et al. 2013). It remains possible, however, that later time points may show more profound physiological as well as neuronal effects.

An additional limitation of this study was that no significant correlations were observed between the behavioral and neuronal effects of nicotine. This negative finding is not altogether unexpected in that several previous studies have reported neuronal effects of nicotinic agonists during cognitive tasks but no corresponding change in behavior (reviewed by Newhouse et al. 2011). Future studies using larger sample sizes are necessary to more thoroughly examine the relationships between effects of nicotine on brain activity, brain connectivity and behavior during attention tasks.

The present study exclusively focused on nonsmoking patients for several reasons. First, previous work has demonstrated that nicotine improves attention in nonsmokers with schizophrenia (Barr et al. 2008b; Harris et al. 2004). Second,

I wished to avoid potential confounding effects of nicotine withdrawal and level of dependence. Third, a substantial proportion of patients in schizophrenia (30-

40%) are nonsmokers (de Leon and Diaz 2005). Nonetheless, because the majority of schizophrenia patients are smokers, a complete understanding nicotine’s effects in a representative sample of patients will require additional research focusing on this subpopulation. The role of dysfunctional attentional circuitry in driving smoking behavior in schizophrenia may then be more comprehensively evaluated.

Conclusion

Although the nicotinic receptor is one of the most promising therapeutic targets in schizophrenia, clinical trials using nicotinic agonists have shown mixed results in the illness (Deutsch et al. 2013; Freedman 2014). A potential method for improving the probability of success of these and other agents is to examine their effects on brain function using techniques such as fMRI in order to verify that a drug is having its intended biological effect (Tregellas 2014). Furthermore, fMRI-based measures are often more sensitive than behavioral performance in measuring drug effects (Newhouse et al. 2011). Therefore, fMRI validation of drug targeting is unlikely to require the large sample sizes necessary in late- phase clinical trials. Our lab has previously reported that an α7 nicotinic receptor partial agonist may target localized functional abnormalities related to tasks such as SPEM (Tregellas et al. 2010) as well as in task-nonspecific resting networks in

schizophrenia (Tregellas et al. 2011). Other nicotinic receptor signal-promoting agents have also been shown to modulate brain activity on a network level

(Smucny and Tregellas 2013). This study expands upon these findings by showing that regions involved in attention may be pharmacologically targeted in a task-dependent manner and supports further investigation into the functional neurocognitive effects of other nicotinic-based compounds in schizophrenia.

REFERENCES

Aboitiz, F., Ossandon, T., Zamorano, F., Palma, B., Carrasco, X. (2014). Irrelevant stimulus processing in ADHD: catecholamine dynamics and attentional networks. Front Psychol, 5:183.

Achim, A.M., Bertrand, M.C., Sutton, H., Montoya, A., Czechowska, Y., Malla, A.K., Joober, R., Pruessner, J.C., Lepage, M. (2007). Selective abnormal modulation of hippocampal activity during memory formation in first-episode psychosis. Arch Gen Psychiatry, 64:999-1014.

Adler, L.E., Hoffer, L.D., Wiser, A., Freedman, R. (1993). Normalization of auditory physiology by cigarette smoking in schizophrenic patients. Am J Psychiatry, 150:1856-61.

Adler, L.E., Hoffer, L.J., Griffith, J., Waldo, M.C., Freedman, R. (1992). Normalization by nicotine of deficient auditory sensory gating in the relatives of schizophrenics. Biol Psychiatry, 32:607-16.

Adler, L.E., Pachtman, E., Franks, R.D., Pecevich, M., Waldo, M.C., Freedman, R. (1982). Neurophysiological evidence for a defect in neuronal mechanisms involved in sensory gating in schizophrenia. Biol Psychiatry, 17:639-54.

Allott, K., Alvarez-Jimenez, M., Killackey, E.J., Bendall, S., McGorry, P.D., Jackson, H.J. (2011). Patient predictors of symptom and functional outcome following cognitive behaviour therapy or befriending in first-episode psychosis. Schizophr Res, 132:125-30.

Aly, M., Turk-Browne, N.B. (2015). Attention Stabilizes Representations in the Human Hippocampus. Cereb Cortex.

American Psychiatric Association. (2013). Diagnostic and statistical manual of mental disorders. Washington, D.C.

Baier, C.J., Pallares, M.E., Adrover, E., Monteleone, M.C., Brocco, M.A., Barrantes, F.J., Antonelli, M.C. (2015). Prenatal restraint stress decreases the expression of alpha-7 nicotinic receptor in the brain of adult rat offspring. Stress, 18:435-45.

Barch, D.M., Moore, H., Nee, D.E., Manoach, D.S., Luck, S.J. (2012). CNTRICS imaging biomarkers selection: Working memory. Schizophr Bull, 38:43-52.

Barr, A.M., Procyshyn, R.M., Hui, P., Johnson, J.L., Honer, W.G. (2008a). Self- reported motivation to smoke in schizophrenia is related to antipsychotic drug treatment. Schizophr Res, 100:252-60.

Barr, R.S., Culhane, M.A., Jubelt, L.E., Mufti, R.S., Dyer, M.A., Weiss, A.P., Deckersbach, T., Kelly, J.F., Freudenreich, O., Goff, D.C., Evins, A.E. (2008b). The effects of transdermal nicotine on cognition in nonsmokers with schizophrenia and nonpsychiatric controls. Neuropsychopharmacology, 33:480- 90.

Baskys, A., Wang, S., Remington, G., Wojtowicz, J.M. (1993). Haloperidol and loxapine but not clozapine increase synaptic responses in the hippocampus. Eur J Pharmacol, 235:305-7.

Benowitz, N.L. (1998). Nicotine Safety and Toxicity. New York, NY. Oxford University Press.

Bluhm, R.L., Miller, J., Lanius, R.A., Osuch, E.A., Boksman, K., Neufeld, R.W., Theberge, J., Schaefer, B., Williamson, P. (2007). Spontaneous low-frequency fluctuations in the BOLD signal in schizophrenic patients: anomalies in the default network. Schizophr Bull, 33:1004-12.

Breese, C.R., Adams, C., Logel, J., Drebing, C., Rollins, Y., Barnhart, M., Sullivan, B., Demasters, B.K., Freedman, R., Leonard, S. (1997). Comparison of the regional expression of nicotinic acetylcholine receptor alpha7 mRNA and [125I]-alpha-bungarotoxin binding in human postmortem brain. J Comp Neurol, 387:385-98.

Brett, M., Anton, J.-L., Valabregue, R., Poline, J.-B. (2002). Region-of-interest analysis using an SPM toolbox. Neuroimage, 8th International Conference on Functional Mapping of the Human Brain.

Brown, A.S., Derkits, E.J. (2010). Prenatal infection and schizophrenia: a review of epidemiologic and translational studies. Am J Psychiatry, 167:261-80.

Buckner, R.L., Andrews-Hanna, J.R., Schacter, D.L. (2008). The brain's default network: anatomy, function, and relevance to disease. Ann N Y Acad Sci, 1124:1-38.

Bunzeck, N., Thiel, C. (2015). Neurochemical modulation of repetition suppression and novelty signals in the human brain. Cortex.

Chambon, V., Pacherie, E., Barbalat, G., Jacquet, P., Franck, N., Farrer, C. (2011). Mentalizing under influence: abnormal dependence on prior expectations in patients with schizophrenia. Brain, 134:3728-41.

Chan, R.C., Di, X., McAlonan, G.M., Gong, Q.Y. (2011). Brain anatomical abnormalities in high-risk individuals, first-episode, and chronic schizophrenia: an activation likelihood estimation meta-analysis of illness progression. Schizophr Bull, 37:177-88.

Chan, R.C., Wang, Y., Cheung, E.F., Cui, J., Deng, Y., Yuan, Y., Ma, Z., Yu, X., Li, Z., Gong, Q. (2009). Sustained attention deficit along the psychosis proneness continuum: a study on the Sustained Attention to Response Task (SART). Cogn Behav Neurol, 22:180-5.

Chun, M.M., Turk-Browne, N.B. (2007). Interactions between attention and memory. Curr Opin Neurobiol, 17:177-84.

Cocchi, L., Walterfang, M., Testa, R., Wood, S.J., Seal, M.L., Suckling, J., Takahashi, T., Proffitt, T.M., Brewer, W.J., Adamson, C., Soulsby, B., Velakoulis, D., McGorry, P.D., Pantelis, C. (2009). Grey and white matter abnormalities are associated with impaired spatial working memory ability in first-episode schizophrenia. Schizophr Res, 115:163-72.

Corbetta, M., Shulman, G.L. (2002). Control of goal-directed and stimulus-driven attention in the brain. Nat Rev Neurosci, 3:201-15.

Cornblatt, B.A., Keilp, J.G. (1994). Impaired attention, genetics, and the pathophysiology of schizophrenia. Schizophr Bull, 20:31-46.

Court, J., Spurden, D., Lloyd, S., McKeith, I., Ballard, C., Cairns, N., Kerwin, R., Perry, R., Perry, E. (1999). Neuronal nicotinic receptors in dementia with Lewy bodies and schizophrenia: alpha-bungarotoxin and nicotine binding in the thalamus. J Neurochem, 73:1590-7.

Cullum, C.M., Harris, J.G., Waldo, M.C., Smernoff, E., Madison, A., Nagamoto, H.T., Griffith, J., Adler, L.E., Freedman, R. (1993). Neurophysiological and neuropsychological evidence for attentional dysfunction in schizophrenia. Schizophr Res, 10:131-41.

D'Souza, D.C., Esterlis, I., Carbuto, M., Krasenics, M., Seibyl, J., Bois, F., Pittman, B., Ranganathan, M., Cosgrove, K., Staley, J. (2012). Lower beta2*- nicotinic acetylcholine receptor availability in smokers with schizophrenia. Am J Psychiatry, 169:326-34.

D'Souza, M.S., Markou, A. (2012). Schizophrenia and tobacco smoking comorbidity: nAChR agonists in the treatment of schizophrenia-associated cognitive deficits. Neuropharmacology, 62:1564-73.

Dalack, G.W., Meador-Woodruff, J.H. (1996). Smoking, smoking withdrawal and schizophrenia: case reports and a review of the literature. Schizophr Res, 22:133-41.

Daselaar, S.M., Huijbers, W., Eklund, K., Moscovitch, M., Cabeza, R. (2013). Resting-state functional connectivity of ventral parietal regions associated with attention reorienting and episodic recollection. Front Hum Neurosci, 7:38.

Davis, H., Mast, T., Yoshie, N., Zerlin, S. (1966). The slow response of the human cortex to auditory stimuli: recovery process. Electroencephalogr Clin Neurophysiol, 21:105-13.

de Leon, J., Dadvand, M., Canuso, C., White, A.O., Stanilla, J.K., Simpson, G.M. (1995). Schizophrenia and smoking: an epidemiological survey in a state hospital. Am J Psychiatry, 152:453-5. de Leon, J., Diaz, F.J. (2005). A meta-analysis of worldwide studies demonstrates an association between schizophrenia and tobacco smoking behaviors. Schizophr Res, 76:135-57.

Dempsey, D.A., St Helen, G., Jacob, P., 3rd, Tyndale, R.F., Benowitz, N.L. (2013). Genetic and pharmacokinetic determinants of response to transdermal nicotine in white, black, and Asian nonsmokers. Clin Pharmacol Ther, 94:687-94.

Deutsch, S.I., Schwartz, B.L., Schooler, N.R., Brown, C.H., Rosse, R.B., Rosse, S.M. (2013). Targeting alpha-7 nicotinic neurotransmission in schizophrenia: a novel agonist strategy. Schizophr Res, 148:138-44.

Diefendorf, A.R., Dodge, R. (1908). An experimental study of the ocular reactions of the insane from 840 photographic records. Brain, 31:451-489.

Diwadkar, V.A., Bakshi, N., Gupta, G., Pruitt, P., White, R., Eickhoff, S.B. (2014). Dysfunction and Dysconnection in Cortical-Striatal Networks during Sustained Attention: Genetic Risk for Schizophrenia or Bipolar Disorder and its Impact on Brain Network Function. Front Psychiatry, 5:50.

Edmister, W.B., Talavage, T.M., Ledden, P.J., Weisskoff, R.M. (1999). Improved auditory cortex imaging using clustered volume acquisitions. Hum Brain Mapp, 7:89-97.

Ellison-Wright, I., Glahn, D.C., Laird, A.R., Thelen, S.M., Bullmore, E. (2008). The anatomy of first-episode and chronic schizophrenia: an anatomical likelihood estimation meta-analysis. Am J Psychiatry, 165:1015-23.

Esterlis, I., Ranganathan, M., Bois, F., Pittman, B., Picciotto, M.R., Shearer, L., Anticevic, A., Carlson, J., Niciu, M.J., Cosgrove, K.P., D'Souza, D.C. (2014). In vivo evidence for beta2 nicotinic acetylcholine receptor subunit upregulation in smokers as compared with nonsmokers with schizophrenia. Biol Psychiatry, 76:495-502.

Evans, J.D., Heaton, R.K., Paulsen, J.S., Palmer, B.W., Patterson, T., Jeste, D.V. (2003). The relationship of neuropsychological abilities to specific domains of functional capacity in older schizophrenia patients. Biol Psychiatry, 53:422-30.

Fine, R., Zhang, J., Stevens, H.E. (2014). Prenatal stress and inhibitory neuron systems: implications for neuropsychiatric disorders. Mol Psychiatry, 19:641-51.

Fitzgerald, J., Johnson, K., Kehoe, E., Bokde, A.L., Garavan, H., Gallagher, L., McGrath, J. (2015). Disrupted functional connectivity in dorsal and ventral attention networks during attention orienting in autism spectrum disorders. Autism Res, 8:136-52.

Fournier, J.C., DeRubeis, R.J., Hollon, S.D., Dimidjian, S., Amsterdam, J.D., Shelton, R.C., Fawcett, J. (2010). Antidepressant drug effects and depression severity: a patient-level meta-analysis. JAMA, 303:47-53.

Frazier, C.J., Rollins, Y.D., Breese, C.R., Leonard, S., Freedman, R., Dunwiddie, T.V. (1998). Acetylcholine activates an alpha-bungarotoxin-sensitive nicotinic current in rat hippocampal interneurons, but not pyramidal cells. J Neurosci, 18:1187-95.

Freedman, R. (2014). alpha7-nicotinic acetylcholine receptor agonists for cognitive enhancement in schizophrenia. Annu Rev Med, 65:245-61.

Freedman, R., Coon, H., Myles-Worsley, M., Orr-Urtreger, A., Olincy, A., Davis, A., Polymeropoulos, M., Holik, J., Hopkins, J., Hoff, M., Rosenthal, J., Waldo, M.C., Reimherr, F., Wender, P., Yaw, J., Young, D.A., Breese, C.R., Adams, C., Patterson, D., Adler, L.E., Kruglyak, L., Leonard, S., Byerley, W. (1997). Linkage of a neurophysiological deficit in schizophrenia to a chromosome 15 locus. Proc Natl Acad Sci U S A, 94:587-92.

Freedman, R., Hall, M., Adler, L.E., Leonard, S. (1995). Evidence in postmortem brain tissue for decreased numbers of hippocampal nicotinic receptors in schizophrenia. Biol Psychiatry, 38:22-33.

Freedman, R., Leonard, S., Gault, J.M., Hopkins, J., Cloninger, C.R., Kaufmann, C.A., Tsuang, M.T., Farone, S.V., Malaspina, D., Svrakic, D.M., Sanders, A., Gejman, P. (2001). Linkage disequilibrium for schizophrenia at the chromosome 15q13-14 locus of the alpha7-nicotinic acetylcholine receptor subunit gene (CHRNA7). Am J Med Genet, 105:20-2.

Freedman, R., Olincy, A., Buchanan, R.W., Harris, J.G., Gold, J.M., Johnson, L., Allensworth, D., Guzman-Bonilla, A., Clement, B., Ball, M.P., Kutnick, J., Pender, V., Martin, L.F., Stevens, K.E., Wagner, B.D., Zerbe, G.O., Soti, F., Kem, W.R. (2008). Initial phase 2 trial of a nicotinic agonist in schizophrenia. Am J Psychiatry, 165:1040-7.

Freedman, R., Ross, R.G. (2015). Prenatal choline and the development of schizophrenia. Shanghai Arch Psychiatry, 27:90-102.

Freedman, R., Waldo, M., Bickford-Wimer, P., Nagamoto, H. (1991). Elementary neuronal dysfunctions in schizophrenia. Schizophr Res, 4:233-43.

Friston, K.J., Buechel, C., Fink, G.R., Morris, J., Rolls, E., Dolan, R.J. (1997). Psychophysiological and modulatory interactions in neuroimaging. Neuroimage, 6:218-29.

Furukawa, T.A., Levine, S.Z., Tanaka, S., Goldberg, Y., Samara, M., Davis, J.M., Cipriani, A., Leucht, S. (2015). Initial severity of schizophrenia and efficacy of antipsychotics: participant-level meta-analysis of 6 placebo-controlled studies. JAMA Psychiatry, 72:14-21.

Fusar-Poli, P., Borgwardt, S., Crescini, A., Deste, G., Kempton, M.J., Lawrie, S., Mc Guire, P., Sacchetti, E. (2011). Neuroanatomy of vulnerability to psychosis: a voxel-based meta-analysis. Neurosci Biobehav Rev, 35:1175-85.

Gaebler, A.J., Mathiak, K., Koten, J.W., Jr., Konig, A.A., Koush, Y., Weyer, D., Depner, C., Matentzoglu, S., Edgar, J.C., Willmes, K., Zvyagintsev, M. (2015). Auditory mismatch impairments are characterized by core neural dysfunctions in schizophrenia. Brain, 138:1410-23.

Garrity, A.G., Pearlson, G.D., McKiernan, K., Lloyd, D., Kiehl, K.A., Calhoun, V.D. (2007). Aberrant "default mode" functional connectivity in schizophrenia. Am J Psychiatry, 164:450-7.

Gault, J., Hopkins, J., Berger, R., Drebing, C., Logel, J., Walton, C., Short, M., Vianzon, R., Olincy, A., Ross, R.G., Adler, L.E., Freedman, R., Leonard, S. (2003). Comparison of polymorphisms in the alpha7 nicotinic receptor gene and its partial duplication in schizophrenic and control subjects. Am J Med Genet B Neuropsychiatr Genet, 123B:39-49.

George, T.P., Krystal, J.H. (2000). Comorbidity of psychiatric and substance abuse disorders. Curr Opin Psychiatry, 13:327-331.

George, T.P., Vessicchio, J.C., Termine, A., Sahady, D.M., Head, C.A., Pepper, W.T., Kosten, T.R., Wexler, B.E. (2002). Effects of smoking abstinence on visuospatial working memory function in schizophrenia. Neuropsychopharmacology, 26:75-85.

Ghiasi, F., Farhang, S., Farnam, A., Safikhanlou, S. (2013). The short term effect of nicotine abstinence on visuospatial working memory in smoking patients with schizophrenia. Nord J Psychiatry, 67:104-8.

Gitelman, D.R., Penny, W.D., Ashburner, J., Friston, K.J. (2003). Modeling regional and psychophysiologic interactions in fMRI: the importance of hemodynamic deconvolution. Neuroimage, 19:200-7.

Glickman, S.E., Higgins, T.J., Isaacson, R.L. (1970). Some effects of hippocampal lesions on the behavior of Mongolian gerbils. Physiol Behav, 5:931- 8.

Goff, D.C., Henderson, D.C., Amico, E. (1992). Cigarette smoking in schizophrenia: relationship to psychopathology and medication side effects. Am J Psychiatry, 149:1189-94.

Gordon, E.M., Stollstorff, M., Vaidya, C.J. (2012). Using spatial multiple regression to identify intrinsic connectivity networks involved in working memory performance. Hum Brain Mapp, 33:1536-52.

Gottesman, II, Shields, J. (1967). A polygenic theory of schizophrenia. Proc Natl Acad Sci U S A, 58:199-205.

Gozzi, A., Crestan, V., Turrini, G., Clemens, M., Bifone, A. (2010). Antagonism at serotonin 5-HT(2A) receptors modulates functional activity of frontohippocampal circuit. Psychopharmacology (Berl), 209:37-50.

Gozzi, A., Herdon, H., Schwarz, A., Bertani, S., Crestan, V., Turrini, G., Bifone, A. (2008). Pharmacological stimulation of NMDA receptors via co-agonist site suppresses fMRI response to phencyclidine in the rat. Psychopharmacology (Berl), 201:273-84.

Gradin, V.B., Waiter, G., O'Connor, A., Romaniuk, L., Stickle, C., Matthews, K., Hall, J., Douglas Steele, J. (2012). Salience network-midbrain dysconnectivity and blunted reward signals in schizophrenia. Psychiatry Res.

Green, M.F. (1996). What are the functional consequences of neurocognitive deficits in schizophrenia? Am J Psychiatry, 153:321-30.

Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V. (2003). Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proc Natl Acad Sci U S A, 100:253-8.

Grunwald, T., Boutros, N.N., Pezer, N., von Oertzen, J., Fernandez, G., Schaller, C., Elger, C.E. (2003). Neuronal substrates of sensory gating within the human brain. Biol Psychiatry, 53:511-9.

Guo, X., Li, J., Wang, J., Fan, X., Hu, M., Shen, Y., Chen, H., Zhao, J. (2014). Hippocampal and orbital inferior frontal gray matter volume abnormalities and cognitive deficit in treatment-naive, first-episode patients with schizophrenia. Schizophr Res, 152:339-43.

Hahn, B., Robinson, B.M., Kaiser, S.T., Matveeva, T.M., Harvey, A.N., Luck, S.J., Gold, J.M. (2012). Kraepelin and Bleuler had it right: people with schizophrenia have deficits sustaining attention over time. J Abnorm Psychol, 121:641-8.

Hales, R.E., Yudofsky, S.C., Weiss Roberts, L. (2014). The American Psychiatric Publishing Textbook of Psychiatry Washington, D.C. American Psychiatric Publishing.

Harris, J.G., Kongs, S., Allensworth, D., Martin, L., Tregellas, J., Sullivan, B., Zerbe, G., Freedman, R. (2004). Effects of nicotine on cognitive deficits in schizophrenia. Neuropsychopharmacology, 29:1378-85.

Heckers, S., Rauch, S.L., Goff, D., Savage, C.R., Schacter, D.L., Fischman, A.J., Alpert, N.M. (1998). Impaired recruitment of the hippocampus during conscious recollection in schizophrenia. Nat.Neurosci, 1:318-323.

Herran, A., de Santiago, A., Sandoya, M., Fernandez, M.J., Diez-Manrique, J.F., Vazquez-Barquero, J.L. (2000). Determinants of smoking behaviour in outpatients with schizophrenia. Schizophr Res, 41:373-81.

Holt, D.J., Cassidy, B.S., Andrews-Hanna, J.R., Lee, S.M., Coombs, G., Goff, D.C., Gabrieli, J.D., Moran, J.M. (2011). An anterior-to-posterior shift in midline cortical activity in schizophrenia during self-reflection. Biol Psychiatry, 69:415-23.

Holt, D.J., Weiss, A.P., Rauch, S.L., Wright, C.I., Zalesak, M., Goff, D.C., Ditman, T., Welsh, R.C., Heckers, S. (2005). Sustained activation of the hippocampus in response to fearful faces in schizophrenia. Biol Psychiatry, 57:1011-9.

Honea, R., Crow, T.J., Passingham, D., Mackay, C.E. (2005). Regional deficits in brain volume in schizophrenia: a meta-analysis of voxel-based morphometry studies. Am J Psychiatry, 162:2233-45.

Hong, L.E., Schroeder, M., Ross, T.J., Buchholz, B., Salmeron, B.J., Wonodi, I., Thaker, G.K., Stein, E.A. (2011). Nicotine enhances but does not normalize visual sustained attention and the associated brain network in schizophrenia. Schizophr Bull, 37:416-25.

Hu, M., Li, J., Eyler, L., Guo, X., Wei, Q., Tang, J., Liu, F., He, Z., Li, L., Jin, H., Liu, Z., Wang, J., Liu, F., Chen, H., Zhao, J. (2013). Decreased left middle temporal gyrus volume in antipsychotic drug-naive, first-episode schizophrenia patients and their healthy unaffected siblings. Schizophr Res, 144:37-42.

Iversen, L.L. (2009). Introduction to Neuropsychopharmacology. New York. Oxford University Press.

Jacobsen, L.K., D'Souza, D.C., Mencl, W.E., Pugh, K.R., Skudlarski, P., Krystal, J.H. (2004). Nicotine effects on brain function and functional connectivity in schizophrenia. Biol Psychiatry, 55:850-8.

Jafri, M.J., Pearlson, G.D., Stevens, M., Calhoun, V.D. (2008). A method for functional network connectivity among spatially independent resting-state components in schizophrenia. Neuroimage, 39:1666-81.

Jarrard, L.E., Bunnell, B.N. (1968). Open-field behavior of hippocampal-lesioned rats and hamsters. J Comp Physiol Psychol, 66:500-2.

Jeong, B., Kubicki, M. (2010). Reduced task-related suppression during semantic repetition priming in schizophrenia. Psychiatry Res, 181:114-20.

Jeong, B., Wible, C.G., Hashimoto, R., Kubicki, M. (2009). Functional and anatomical connectivity abnormalities in left inferior frontal gyrus in schizophrenia. Hum Brain Mapp, 30:4138-51.

Jessen, F., Scheef, L., Germeshausen, L., Tawo, Y., Kockler, M., Kuhn, K.U., Maier, W., Schild, H.H., Heun, R. (2003). Reduced Hippocampal Activation During Encoding and Recognition of Words in Schizophrenia Patients. American Journal of Psychiatry, 160:1305-1312.

Karbasforoushan, H., Woodward, N.D. (2012). Resting-state networks in schizophrenia. Curr Top Med Chem, 12:2404-14.

Keedy, S.K., Reilly, J.L., Bishop, J.R., Weiden, P.J., Sweeney, J.A. (2015). Impact of antipsychotic treatment on attention and motor learning systems in first-episode schizophrenia. Schizophr Bull, 41:355-65.

Keefe, R.S., Harvey, P.D. (2012). Cognitive impairment in schizophrenia. Handb Exp Pharmacol:11-37.

Kiehl, K.A., Liddle, P.F. (2001). An event-related functional magnetic resonance imaging study of an auditory oddball task in schizophrenia. Schizophr Res, 48:159-71.

Kim, C., Choi, H., Kim, J.K., Chang, H.K., Park, R.S., Kang, I.Y. (1970). General behavioral activity and its component patterns in hippocampectomized rats. Brain Res, 19:379-94.

Kimhy, D., Vakhrusheva, J., Bartels, M.N., Armstrong, H.F., Ballon, J.S., Khan, S., Chang, R.W., Hansen, M.C., Ayanruoh, L., Lister, A., Castren, E., Smith, E.E., Sloan, R.P. (2015). The Impact of Aerobic Exercise on Brain-Derived Neurotrophic Factor and Neurocognition in Individuals With Schizophrenia: A Single-Blind, Randomized Clinical Trial. Schizophr Bull, 41:859-68.

Knott, V.J., Fisher, D.J., Millar, A.M. (2010). Differential effects of nicotine on P50 amplitude, its gating, and their neural sources in low and high suppressors. Neuroscience, 170:816-26.

Koutsouleris, N., Gaser, C., Jager, M., Bottlender, R., Frodl, T., Holzinger, S., Schmitt, G.J., Zetzsche, T., Burgermeister, B., Scheuerecker, J., Born, C., Reiser, M., Moller, H.J., Meisenzahl, E.M. (2008). Structural correlates of psychopathological symptom dimensions in schizophrenia: a voxel-based morphometric study. Neuroimage, 39:1600-12.

Kucyi, A., Hodaie, M., Davis, K.D. (2012). Lateralization in intrinsic functional connectivity of the temporoparietal junction with salience- and attention-related brain networks. J Neurophysiol, 108:3382-92.

Lahti, A.C., Weiler, M.A., Holcomb, H.H., Tamminga, C.A., Carpenter, W.T., McMahon, R. (2006). Correlations between rCBF and symptoms in two independent cohorts of drug-free patients with schizophrenia. Neuropsychopharmacology, 31:221-230.

Laurens, K.R., Kiehl, K.A., Ngan, E.T., Liddle, P.F. (2005). Attention orienting dysfunction during salient novel stimulus processing in schizophrenia. Schizophr Res, 75:159-71.

Laurens, K.R., Ngan, E.T., Bates, A.T., Kiehl, K.A., Liddle, P.F. (2003). Rostral anterior cingulate cortex dysfunction during error processing in schizophrenia. Brain, 126:610-22.

Laurent, A., Saoud, M., Bougerol, T., d'Amato, T., Anchisi, A.M., Biloa-Tang, M., Dalery, J., Rochet, T. (1999). Attentional deficits in patients with schizophrenia and in their non-psychotic first-degree relatives. Psychiatry Res, 89:147-59.

Le Houezec, J. (1998). Nicotine: abused substance and therapeutic agent. J Psychiatry Neurosci, 23:95-108.

Leonard, S., Breese, C., Adams, C., Benhammou, K., Gault, J., Stevens, K., Lee, M., Adler, L., Olincy, A., Ross, R., Freedman, R. (2000). Smoking and schizophrenia: abnormal nicotinic receptor expression. Eur J Pharmacol, 393:237-42.

Leonard, S., Freedman, R. (2006). Genetics of chromosome 15q13-q14 in schizophrenia. Biol Psychiatry, 60:115-22.

Leonard, S., Gault, J., Hopkins, J., Logel, J., Vianzon, R., Short, M., Drebing, C., Berger, R., Venn, D., Sirota, P., Zerbe, G., Olincy, A., Ross, R.G., Adler, L.E., Freedman, R. (2002). Association of promoter variants in the alpha7 nicotinic acetylcholine receptor subunit gene with an inhibitory deficit found in schizophrenia. Arch Gen Psychiatry, 59:1085-96.

Levy, D.L., Holzman, P.S., Matthysse, S., Mendell, N.R. (1993). Eye tracking dysfunction and schizophrenia: a critical perspective. Schizophr Bull, 19:461-536.

Levy, D.L., Sereno, A.B., Gooding, D.C., O'Driscoll, G.A. (2010). Eye tracking dysfunction in schizophrenia: characterization and pathophysiology. Curr Top Behav Neurosci, 4:311-47.

Lieberman, J.A., Dunbar, G., Segreti, A.C., Girgis, R.R., Seoane, F., Beaver, J.S., Duan, N., Hosford, D.A. (2013). A randomized exploratory trial of an alpha-7 nicotinic receptor agonist (TC-5619) for cognitive enhancement in schizophrenia. Neuropsychopharmacology, 38:968-75.

Liu, H., Kaneko, Y., Ouyang, X., Li, L., Hao, Y., Chen, E.Y., Jiang, T., Zhou, Y., Liu, Z. (2012). Schizophrenic patients and their unaffected siblings share increased resting-state connectivity in the task-negative network but not its anticorrelated task-positive network. Schizophr Bull, 38:285-94.

Luck, S.J., Ford, J.M., Sarter, M., Lustig, C. (2012). CNTRICS final biomarker selection: Control of attention. Schizophr Bull, 38:53-61.

Mackowick, K.M., Barr, M.S., Wing, V.C., Rabin, R.A., Ouellet-Plamondon, C., George, T.P. (2014). Neurocognitive endophenotypes in schizophrenia: modulation by nicotinic receptor systems. Prog Neuropsychopharmacol Biol Psychiatry, 52:79-85.

Malaspina, D., Corcoran, C., Schobel, S., Small, S., Kimby, D., Lewandowski, N. (2009). Hippocampal hyperactivity is associated with positive symptoms (abstract). Schizophr Bull, (sup 1):160.

Malaspina, D., Harkavy-Friedman, J., Corcoran, C., Mujica-Parodi, L., Printz, D., Gorman, J.M., Van Heertum, R. (2004). Resting neural activity distinguishes subgroups of schizophrenia patients. Biological Psychiatry, 56:931-7.

Malaspina, D., Storer, S., Furman, V., Esser, P., Printz, D., Berman, A., Lignelli, A., Gorman, J., Van Heertum, R. (1999a). SPECT study of visual fixation in schizophrenia and comparison subjects. Biol.Psychiatry, 46:89-93.

Malaspina, D., Storer, S., Furman, V., Esser, P., Printz, D., Berman, A., Lignelli, A., Gorman, J., Van Heertum, R. (1999b). SPECT study of visual fixation in schizophrenia and comparison subjects. Biol Psychiatry, 46:89-93.

Maldjian, J.A., Laurienti, P.J., Kraft, R.A., Burdette, J.H. (2003). An automated method for neuroanatomic and cytoarchitectonic atlas-based interrogation of fMRI data sets. Neuroimage, 19:1233-9.

Mandavilli, A. (2004). Nicotine fix. Nat Med, 10:660-1.

Marutle, A., Zhang, X., Court, J., Piggott, M., Johnson, M., Perry, R., Perry, E., Nordberg, A. (2001). Laminar distribution of nicotinic receptor subtypes in cortical regions in schizophrenia. J Chem Neuroanat, 22:115-26.

Massey, K.A., Zago, W.M., Berg, D.K. (2006). BDNF up-regulates alpha7 nicotinic acetylcholine receptor levels on subpopulations of hippocampal interneurons. Mol Cell Neurosci, 33:381-8.

Mayer, A.R., Hanlon, F.M., Teshiba, T.M., Klimaj, S.D., Ling, J.M., Dodd, A.B., Calhoun, V.D., Bustillo, J.R., Toulouse, T. (2015). An fMRI study of multimodal selective attention in schizophrenia. Br J Psychiatry, 207:420-8.

McCarthy, H., Skokauskas, N., Mulligan, A., Donohoe, G., Mullins, D., Kelly, J., Johnson, K., Fagan, A., Gill, M., Meaney, J., Frodl, T. (2013). Attention network hypoconnectivity with default and affective network hyperconnectivity in adults diagnosed with attention-deficit/hyperactivity disorder in childhood. JAMA Psychiatry, 70:1329-37.

McDaniel, M.A., Lamontagne, P., Beck, S.M., Scullin, M.K., Braver, T.S. (2013). Dissociable neural routes to successful prospective memory. Psychol Sci, 24:1791-800.

McGhie, A., Chapman, J. (1961). Disorders of attention and perception in early schizophrenia. Br J Med Psychol, 34:103-16.

McLaren, D.G., Ries, M.L., Xu, G., Johnson, S.C. (2012). A generalized form of context-dependent psychophysiological interactions (gPPI): a comparison to standard approaches. Neuroimage, 61:1277-86.

Medoff, D.R., Holcomb, H.H., Lahti, A.C., Tamminga, C.A. (2001). Probing the human hippocampus using rCBF: contrasts in schizophrenia. Hippocampus, 11:543-550.

Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 15:483-506.

Mexal, S., Berger, R., Logel, J., Ross, R.G., Freedman, R., Leonard, S. (2010). Differential regulation of alpha7 nicotinic receptor gene (CHRNA7) expression in schizophrenic smokers. J Mol Neurosci, 40:185-95.

Meyer-Lindenberg, A.S., Olsen, R.K., Kohn, P.D., Brown, T., Egan, M.F., Weinberger, D.R., Berman, K.F. (2005). Regionally specific disturbance of dorsolateral prefrontal-hippocampal functional connectivity in schizophrenia. Arch Gen Psychiatry, 62:379-86.

Minatogawa-Chang, T.M., Schaufelberger, M.S., Ayres, A.M., Duran, F.L., Gutt, E.K., Murray, R.M., Rushe, T.M., McGuire, P.K., Menezes, P.R., Scazufca, M., Busatto, G.F. (2009). Cognitive performance is related to cortical grey matter volumes in early stages of schizophrenia: a population-based study of first- episode psychosis. Schizophr Res, 113:200-9.

Mitchell, R.L., Elliott, R., Barry, M., Cruttenden, A., Woodruff, P.W. (2004). Neural response to emotional prosody in schizophrenia and in bipolar affective disorder. Br J Psychiatry, 184:223-30.

Miwa, J.M., Freedman, R., Lester, H.A. (2011). Neural systems governed by nicotinic acetylcholine receptors: emerging hypotheses. Neuron, 70:20-33.

Molina, V., Sanz, J., Sarramea, F., Benito, C., Palomo, T. (2005). Prefrontal atrophy in first episodes of schizophrenia associated with limbic metabolic hyperactivity. J Psychiatr Res, 39:117-27.

Moran, L.V., Sampath, H., Stein, E.A., Hong, L.E. (2012). Insular and anterior cingulate circuits in smokers with schizophrenia. Schizophr Res, 142:223-9.

Mosso, A. (1881). Concerning the circulation of the blood in the human brain. Liepzig. Verlag von Viet & Company.

Newhouse, P.A., Potter, A.S., Dumas, J.A., Thiel, C.M. (2011). Functional brain imaging of nicotinic effects on higher cognitive processes. Biochem Pharmacol, 82:943-51.

O'Driscoll, G.A., Callahan, B.L. (2008). Smooth pursuit in schizophrenia: a meta- analytic review of research since 1993. Brain Cogn, 68:359-70.

Ogawa, S., Lee, T.M., Kay, A.R., Tank, D.W. (1990). Brain magnetic resonance imaging with contrast dependent on blood oxygenation. Proc Natl Acad Sci U S A, 87:9868-72.

Olincy, A., Ross, R.G., Young, D.A., Roath, M., Freedman, R. (1998). Improvement in smooth pursuit eye movements after cigarette smoking in schizophrenic patients. Neuropsychopharmacology, 18:175-85.

Olincy, A., Young, D.A., Freedman, R. (1997). Increased levels of the nicotine metabolite cotinine in schizophrenic smokers compared to other smokers. Biol Psychiatry, 42:1-5.

Palaniyappan, L., Liddle, P.F. (2012). Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci, 37:17-27.

Palaniyappan, L., Mallikarjun, P., Joseph, V., White, T.P., Liddle, P.F. (2011). Reality distortion is related to the structure of the salience network in schizophrenia. Psychol Med, 41:1701-8.

Palaniyappan, L., White, T.P., Liddle, P.F. (2012). The Concept Of Salience Network Dysfunction In Schizophrenia: From Neuroimaging Observations To Therapeutic Opportunities. Curr Top Med Chem.

Papke, R.L. (2014). Merging old and new perspectives on nicotinic acetylcholine receptors. Biochem Pharmacol, 89:1-11.

Paterson, D., Nordberg, A. (2000). Neuronal nicotinic receptors in the human brain. Prog Neurobiol, 61:75-111.

Phillips, C., Baktir, M.A., Srivatsan, M., Salehi, A. (2014). Neuroprotective effects of physical activity on the brain: a closer look at trophic factor signaling. Front Cell Neurosci, 8:170.

Phillips, M.L., Williams, L., Senior, C., Bullmore, E.T., Brammer, M.J., Andrew, C., Williams, S.C., David, A.S. (1999). A differential neural response to threatening and non-threatening negative facial expressions in paranoid and non- paranoid schizophrenics. Psychiatry Res, 92:11-31.

Picciotto, M.R., Higley, M.J., Mineur, Y.S. (2012). Acetylcholine as a neuromodulator: cholinergic signaling shapes nervous system function and behavior. Neuron, 76:116-29.

Pocivavsek, A., Icenogle, L., Levin, E.D. (2006). Ventral hippocampal alpha7 and alpha4beta2 nicotinic receptor blockade and clozapine effects on memory in female rats. Psychopharmacology (Berl), 188:597-604.

Polli, F.E., Barton, J.J., Thakkar, K.N., Greve, D.N., Goff, D.C., Rauch, S.L., Manoach, D.S. (2008). Reduced error-related activation in two anterior cingulate circuits is related to impaired performance in schizophrenia. Brain, 131:971-86.

Pomarol-Clotet, E., Salvador, R., Sarro, S., Gomar, J., Vila, F., Martinez, A., Guerrero, A., Ortiz-Gil, J., Sans-Sansa, B., Capdevila, A., Cebamanos, J.M., McKenna, P.J. (2008). Failure to deactivate in the prefrontal cortex in schizophrenia: dysfunction of the default mode network? Psychol Med, 38:1185- 93.

Potter, D., Summerfelt, A., Gold, J., Buchanan, R.W. (2006). Review of clinical correlates of P50 sensory gating abnormalities in patients with schizophrenia. Schizophr Bull, 32:692-700.

Preskorn, S.H., Gawryl, M., Dgetluck, N., Palfreyman, M., Bauer, L.O., Hilt, D.C. (2014). Normalizing effects of EVP-6124, an alpha-7 nicotinic partial agonist, on event-related potentials and cognition: a proof of concept, randomized trial in patients with schizophrenia. J Psychiatr Pract, 20:12-24.

Radek, R.J., Kohlhaas, K.L., Rueter, L.E., Mohler, E.G. (2010). Treating the cognitive deficits of schizophrenia with alpha4beta2 neuronal nicotinic receptor agonists. Curr Pharm Des, 16:309-22.

Ranganath, C., Rainer, G. (2003). Neural mechanisms for detecting and remembering novel events. Nat Rev Neurosci, 4:193-202.

Repovs, G., Barch, D.M. (2012). Working memory related brain network connectivity in individuals with schizophrenia and their siblings. Front Hum Neurosci, 6:137.

Roberts, W.W., Dember, W.N., Brodwick, M. (1962). Alternation and exploration in rats with hippocampal lesions. J Comp Physiol Psychol, 55:695-700.

Roiser, J.P., Wigton, R., Kilner, J.M., Mendez, M.A., Hon, N., Friston, K.J., Joyce, E.M. (2013). Dysconnectivity in the frontoparietal attention network in schizophrenia. Front Psychiatry, 4:176.

Role, L.W. (1992). Diversity in primary structure and function of neuronal nicotinic acetylcholine receptor channels. Curr Opin Neurobiol, 2:254-62.

Roy, C.S., Sherrington, C.S. (1890). On the Regulation of the Blood-supply of the Brain. J Physiol, 11:85-158 17.

Rushforth, S.L., Steckler, T., Shoaib, M. (2011). Nicotine improves working memory span capacity in rats following sub-chronic ketamine exposure. Neuropsychopharmacology, 36:2774-81.

Sacco, K.A., Termine, A., Seyal, A., Dudas, M.M., Vessicchio, J.C., Krishnan- Sarin, S., Jatlow, P.I., Wexler, B.E., George, T.P. (2005). Effects of cigarette smoking on spatial working memory and attentional deficits in schizophrenia: involvement of nicotinic receptor mechanisms. Arch Gen Psychiatry, 62:649-59.

Salgado-Pineda, P., Junque, C., Vendrell, P., Baeza, I., Bargallo, N., Falcon, C., Bernardo, M. (2004). Decreased cerebral activation during CPT performance: structural and functional deficits in schizophrenic patients. Neuroimage, 21:840-7.

100

Scheef, L., Manka, C., Daamen, M., Kuhn, K.U., Maier, W., Schild, H.H., Jessen, F. (2010). Resting-state perfusion in nonmedicated schizophrenic patients: a continuous arterial spin-labeling 3.0-T MR study. Radiology, 256:253-60.

Schobel, S.A., Chaudhury, N.H., Khan, U.A., Paniagua, B., Styner, M.A., Asllani, I., Inbar, B.P., Corcoran, C.M., Lieberman, J.A., Moore, H., Small, S.A. (2013). Imaging patients with psychosis and a mouse model establishes a spreading pattern of hippocampal dysfunction and implicates glutamate as a driver. Neuron, 78:81-93.

Schobel, S.A., Lewandowski, N.M., Corcoran, C.M., Moore, H., Brown, T., Malaspina, D., Small, S.A. (2009). Differential targeting of the CA1 subfield of the hippocampal formation by schizophrenia and related psychotic disorders. Arch Gen Psychiatry, 66:938-46.

Sehatpour, P., Dias, E.C., Butler, P.D., Revheim, N., Guilfoyle, D.N., Foxe, J.J., Javitt, D.C. (2010). Impaired visual object processing across an occipital-frontal- hippocampal brain network in schizophrenia: an integrated neuroimaging study. Arch Gen Psychiatry, 67:772-82.

Seiferth, N.Y., Pauly, K., Kellermann, T., Shah, N.J., Ott, G., Herpertz-Dahlmann, B., Kircher, T., Schneider, F., Habel, U. (2009). Neuronal correlates of facial emotion discrimination in early onset schizophrenia. Neuropsychopharmacology, 34:477-87.

Selden, N.R., Gitelman, D.R., Salamon-Murayama, N., Parrish, T.B., Mesulam, M.M. (1998). Trajectories of cholinergic pathways within the cerebral hemispheres of the human brain. Brain, 121 ( Pt 12):2249-57.

Seli, P., Cheyne, J.A., Barton, K.R., Smilek, D. (2012). Consistency of sustained attention across modalities: comparing visual and auditory versions of the SART. Can J Exp Psychol, 66:44-50.

Seli, P., Jonker, T.R., Solman, G.J., Cheyne, J.A., Smilek, D. (2013). A methodological note on evaluating performance in a sustained-attention-to- response task. Behav Res Methods, 45:355-63.

Shamsi, S., Lau, A., Lencz, T., Burdick, K.E., DeRosse, P., Brenner, R., Lindenmayer, J.P., Malhotra, A.K. (2011). Cognitive and symptomatic predictors of functional disability in schizophrenia. Schizophr Res, 126:257-64.

Shim, J.C., Jung, D.U., Jung, S.S., Seo, Y.S., Cho, D.M., Lee, J.H., Lee, S.W., Kong, B.G., Kang, J.W., Oh, M.K., Kim, S.D., McMahon, R.P., Kelly, D.L. (2012). Adjunctive varenicline treatment with antipsychotic medications for cognitive impairments in people with schizophrenia: a randomized double-blind placebo- controlled trial. Neuropsychopharmacology, 37:660-8.

101

Shulman, G.L., Astafiev, S.V., McAvoy, M.P., d'Avossa, G., Corbetta, M. (2007). Right TPJ deactivation during visual search: functional significance and support for a filter hypothesis. Cereb Cortex, 17:2625-33.

Sinkus, M.L., Lee, M.J., Gault, J., Logel, J., Short, M., Freedman, R., Christian, S.L., Lyon, J., Leonard, S. (2009). A 2-base pair deletion polymorphism in the partial duplication of the alpha7 nicotinic acetylcholine gene (CHRFAM7A) on chromosome 15q14 is associated with schizophrenia. Brain Res, 1291:1-11.

Skudlarski, P., Jagannathan, K., Anderson, K., Stevens, M.C., Calhoun, V.D., Skudlarska, B.A., Pearlson, G. (2010). Brain connectivity is not only lower but different in schizophrenia: a combined anatomical and functional approach. Biol Psychiatry, 68:61-9.

Smith, A.K., Edgar, J.C., Huang, M., Lu, B.Y., Thoma, R.J., Hanlon, F.M., McHaffie, G., Jones, A.P., Paz, R.D., Miller, G.A., Canive, J.M. (2010). Cognitive abilities and 50- and 100-msec paired-click processes in schizophrenia. Am J Psychiatry, 167:1264-75.

Smith, C.W., Cornblatt, B.A. (2005). Attention deficits in the development of schizophrenia: Recent evidence from genetic high-risk and prodromal studies. Curr Psychos Ther Rep, 3:152-156.

Smith, R.C., Warner-Cohen, J., Matute, M., Butler, E., Kelly, E., Vaidhyanathaswamy, S., Khan, A. (2006). Effects of nicotine nasal spray on cognitive function in schizophrenia. Neuropsychopharmacology, 31:637-43.

Smucny, J., Olincy, A., Eichman, L.C., Lyons, E., Tregellas, J.R. (2013a). Early sensory processing deficits predict sensitivity to distraction in schizophrenia. Schizophr Res, 147:196-200.

Smucny, J., Olincy, A., Eichman, L.S., Tregellas, J.R. (2014a). Neuronal effects of nicotine during auditory selective attention. Psychopharmacology (Berl).

Smucny, J., Olincy, A., Rojas, D.C., Tregellas, J.R. (2015). Neuronal effects of nicotine during auditory selective attention in schizophrenia. Hum Brain Mapp.

Smucny, J., Rojas, D.C., Eichman, L.C., Tregellas, J.R. (2013b). Neural effects of auditory distraction on visual attention in schizophrenia. PLoS One, 8:e60606.

Smucny, J., Rojas, D.C., Eichman, L.C., Tregellas, J.R. (2013c). Neuronal effects of auditory distraction on visual attention. Brain Cogn, 81:263-70.

Smucny, J., Tregellas, J. (2013). Nicotinic modulation of intrinsic brain networks in schizophrenia. Biochem Pharmacol, 86:1163-72.

102

Smucny, J., Wylie, K.P., Tregellas, J.R. (2014b). Functional magnetic resonance imaging of intrinsic brain networks for translational drug discovery. Trends Pharmacol Sci, 35:397-403.

Stein, E.A., Pankiewicz, J., Harsch, H.H., Cho, J.K., Fuller, S.A., Hoffmann, R.G., Hawkins, M., Rao, S.M., Bandettini, P.A., Bloom, A.S. (1998). Nicotine-induced limbic cortical activation in the human brain: a functional MRI study. Am J Psychiatry, 155:1009-15.

Stephens, S.H., Logel, J., Barton, A., Franks, A., Schultz, J., Short, M., Dickenson, J., James, B., Fingerlin, T.E., Wagner, B., Hodgkinson, C., Graw, S., Ross, R.G., Freedman, R., Leonard, S. (2009). Association of the 5'-upstream regulatory region of the alpha7 nicotinic acetylcholine receptor subunit gene (CHRNA7) with schizophrenia. Schizophr Res, 109:102-12.

Stevens, K.E., Wear, K.D. (1997). Normalizing effects of nicotine and a novel nicotinic agonist on hippocampal auditory gating in two animal models. Pharmacol.Biochem.Behav., 57:869-874.

Susser, E., Neugebauer, R., Hoek, H.W., Brown, A.S., Lin, S., Labovitz, D., Gorman, J.M. (1996). Schizophrenia after prenatal famine. Further evidence. Arch Gen Psychiatry, 53:25-31.

Suwa, H., Matsushima, E., Ohta, K., Mori, K. (2004). Attention disorders in schizophrenia. Psychiatry Clin Neurosci, 58:249-56.

Tamminga, C.A., Southcott, S., Sacco, C., Wagner, A.D., Ghose, S. (2012). Glutamate Dysfunction in Hippocampus: Relevance of Dentate Gyrus and CA3 Signaling. Schizophr Bull.

Torrey, E.F. (2006). Surviving Schizophrenia: A Manual for Families, Patients, and Providers. . New York, NY. Harper Collins.

Tregellas, J.R. (2014). Neuroimaging biomarkers for early drug development in schizophrenia. Biol Psychiatry, 76:111-9.

Tregellas, J.R., Davalos, D.B., Rojas, D.C., Waldo, M.C., Gibson, L., Wylie, K., Du, Y.P., Freedman, R. (2007a). Increased hemodynamic response in the hippocampus, thalamus and prefrontal cortex during abnormal sensory gating in schizophrenia. Schizophr Res, 92:262-72.

Tregellas, J.R., Davalos, D.B., Rojas, D.C., Waldo, M.C., Gibson, L., Wylie, K., Du, Y.P., Freedman, R. (2007b). Increased hemodynamic response in the hippocampus, thalamus and prefrontal cortex during abnormal sensory gating in schizophrenia. Schizophr Res., 92:262-272.

103

Tregellas, J.R., Ellis, J., Shatti, S., Du, Y.P., Rojas, D.C. (2009). Increased hippocampal, thalamic, and prefrontal hemodynamic response to an urban noise stimulus in schizophrenia. Am J Psychiatry, 166:354-60.

Tregellas, J.R., Olincy, A., Johnson, L., Tanabe, J., Shatti, S., Martin, L.F., Singel, D., Du, Y.P., Soti, F., Kem, W.R., Freedman, R. (2010). Functional magnetic resonance imaging of effects of a nicotinic agonist in schizophrenia. Neuropsychopharmacology, 35:938-42.

Tregellas, J.R., Smucny, J., Eichman, L., Rojas, D.C. (2012). The effect of distracting noise on the neuronal mechanisms of attention in schizophrenia. Schizophr Res, 142:230-6.

Tregellas, J.R., Smucny, J., Harris, J.G., Olincy, A., Maharajh, K., Kronberg, E., Eichman, L.C., Lyons, E., Freedman, R. (2014). Intrinsic hippocampal activity as a biomarker for cognition and symptoms in schizophrenia. Am J Psychiatry, 171:549-56.

Tregellas, J.R., Tanabe, J., Rojas, D.C., Shatti, S., Olincy, A., Johnson, L., Martin, L.F., Soti, F., Kem, W.R., Leonard, S., Freedman, R. (2011). Effects of an alpha 7-nicotinic agonist on default network activity in schizophrenia. Biol Psychiatry, 69:7-11.

Tregellas, J.R., Tanabe, J.L., Martin, L.F., Freedman, R. (2005). FMRI of response to nicotine during a smooth pursuit eye movement task in schizophrenia. Am J Psychiatry, 162:391-3.

Tregellas, J.R., Tanabe, J.L., Miller, D.E., Freedman, R. (2002). Monitoring eye movements during fMRI tasks with echo planar images. Hum Brain Mapp, 17:237-43.

Tregellas, J.R., Tanabe, J.L., Miller, D.E., Ross, R.G., Olincy, A., Freedman, R. (2004a). Neurobiology of smooth pursuit eye movement deficits in schizophrenia: an fMRI study. Am J Psychiatry, 161:315-21.

Tregellas, J.R., Tanabe, J.L., Miller, D.E., Ross, R.G., Olincy, A., Freedman, R. (2004b). Neurobiology of smooth pursuit eye movement deficits in schizophrenia: an fMRI study. Am.J Psychiatry, 161:315-321.

Tu, P., Buckner, R.L., Zollei, L., Dyckman, K.A., Goff, D.C., Manoach, D.S. (2010). Reduced functional connectivity in a right-hemisphere network for volitional ocular motor control in schizophrenia. Brain, 133:625-37.

104

Tu, P.C., Hsieh, J.C., Li, C.T., Bai, Y.M., Su, T.P. (2012). Cortico-striatal disconnection within the cingulo-opercular network in schizophrenia revealed by intrinsic functional connectivity analysis: a resting fMRI study. Neuroimage, 59:238-47.

Uhlhaas, P.J. (2013). Dysconnectivity, large-scale networks and neuronal dynamics in schizophrenia. Curr Opin Neurobiol, 23:283-90.

Umbricht, D., Keefe, R.S., Murray, S., Lowe, D.A., Porter, R., Garibaldi, G., Santarelli, L. (2014). A randomized, placebo-controlled study investigating the nicotinic alpha7 agonist, RG3487, for cognitive deficits in schizophrenia. Neuropsychopharmacology, 39:1568-77.

Uncapher, M.R., Rugg, M.D. (2009). Selecting for memory? The influence of selective attention on the mnemonic binding of contextual information. J Neurosci, 29:8270-9.

Venables, P.H. (1964). Input Dysfunction in Schizophrenia. Prog Exp Pers Res, 72:1-47.

Verleger, R., Talamo, S., Simmer, J., Smigasiewicz, K., Lencer, R. (2013). Neurophysiological sensitivity to attentional overload in patients with psychotic disorders. Clin Neurophysiol, 124:881-92.

Vinogradova, O.S. (2001). Hippocampus as comparator: role of the two input and two output systems of the hippocampus in selection and registration of information. Hippocampus, 11:578-98.

Vinogravoda, O.S. (1975). Functional organization of the limbic system in the process of registration of information: facts and hypothesis. New York. Plenum Press.

Vossel, S., Geng, J.J., Fink, G.R. (2014). Dorsal and ventral attention systems: distinct neural circuits but collaborative roles. Neuroscientist, 20:150-9.

Weiss, A.P., Schacter, D.L., Goff, D.C., Rauch, S.L., Alpert, N.M., Fischman, A.J., Heckers, S. (2003). Impaired hippocampal recruitment during normal modulation of memory performance in schizophrenia. Biol Psychiatry, 53:48-55.

Weiss, A.P., Zalesak, M., DeWitt, I., Goff, D., Kunkel, L., Heckers, S. (2004). Impaired hippocampal function during the detection of novel words in schizophrenia. Biol Psychiatry, 55:668-75.

White, T.P., Joseph, V., Francis, S.T., Liddle, P.F. (2010). Aberrant salience network (bilateral insula and anterior cingulate cortex) connectivity during information processing in schizophrenia. Schizophr Res, 123:105-15.

105

Whitfield-Gabrieli, S., Ford, J.M. (2012). Default mode network activity and connectivity in psychopathology. Annu Rev Clin Psychol, 8:49-76.

Whitfield-Gabrieli, S., Thermenos, H.W., Milanovic, S., Tsuang, M.T., Faraone, S.V., McCarley, R.W., Shenton, M.E., Green, A.I., Nieto-Castanon, A., LaViolette, P., Wojcik, J., Gabrieli, J.D., Seidman, L.J. (2009). Hyperactivity and hyperconnectivity of the default network in schizophrenia and in first-degree relatives of persons with schizophrenia. Proc Natl Acad Sci U S A, 106:1279-84.

Wignall, N.D., de Wit, H. (2011). Effects of nicotine on attention and inhibitory control in healthy nonsmokers. Exp Clin Psychopharmacol, 19:183-91.

Williams, T.J., Nuechterlein, K.H., Subotnik, K.L., Yee, C.M. (2011). Distinct neural generators of sensory gating in schizophrenia. Psychophysiology, 48:470- 8.

Winterer, G. (2010). Why do patients with schizophrenia smoke? Curr Opin Psychiatry, 23:112-9.

Winterer, G., Gallinat, J., Brinkmeyer, J., Musso, F., Kornhuber, J., Thuerauf, N., Rujescu, D., Favis, R., Sun, Y., Franc, M.A., Ouwerkerk-Mahadevan, S., Janssens, L., Timmers, M., Streffer, J.R. (2013). Allosteric alpha-7 nicotinic receptor modulation and P50 sensory gating in schizophrenia: a proof-of- mechanism study. Neuropharmacology, 64:197-204.

Wojtalik, J.A., Eack, S.M., Pollock, B.G., Keshavan, M.S. (2012). Prefrontal gray matter morphology mediates the association between serum anticholinergicity and cognitive functioning in early course schizophrenia. Psychiatry Res, 204:61- 7.

Wolf, D.H., Turetsky, B.I., Loughead, J., Elliott, M.A., Pratiwadi, R., Gur, R.E., Gur, R.C. (2008). Auditory Oddball fMRI in Schizophrenia: Association of Negative Symptoms with Regional Hypoactivation to Novel Distractors. Brain Imaging Behav, 2:132-145.

Wonnacott, S., Barik, J., Dickinson, J., Jones, I.W. (2006). Nicotinic receptors modulate transmitter cross talk in the CNS: nicotinic modulation of transmitters. J Mol Neurosci, 30:137-40.

Woodward, N.D., Rogers, B., Heckers, S. (2011). Functional resting-state networks are differentially affected in schizophrenia. Schizophr Res, 130:86-93.

Wylie, K.P., Smucny, J., Legget, K.T., Tregellas, J.R. (2016). Targeting Functional Biomarkers in Schizophrenia with Neuroimaging. Curr Pharm Des.

106

Wylie, K.P., Tregellas, J.R. (2010). The role of the insula in schizophrenia. Schizophr Res, 123:93-104.

Wynn, J.K., Jimenez, A.M., Roach, B.J., Korb, A., Lee, J., Horan, W.P., Ford, J.M., Green, M.F. (2015). Impaired target detection in schizophrenia and the ventral attentional network: Findings from a joint event-related potential-functional MRI analysis. Neuroimage Clin, 9:95-102.

Yakunina, N., Kim, T.S., Tae, W.S., Kim, S.S., Nam, E.C. (2015). Applicability of the Sparse Temporal Acquisition Technique in Resting-State Brain Network Analysis. AJNR Am J Neuroradiol.

107 APPENDIX A

EFFECTS OF SMOKING STATUS ON P50 GATING AND COGNITIVE

PERFORMANCE (In Fulfillment of CCTSI Requirements)

Statement

In fulfillment of the clinical experience requirement for the Colorado

Clinical and Translational Sciences Institute predoctoral fellowship, I completed the following analysis of the effects of smoking status and nicotine dependence on P50 gating and cognitive performance in an otherwise healthy subject population.

Rationale

Although the neurocognitive effects of smoking withdrawal and reinstatement have been well studied, relatively few studies have examined cognitive performance using standardized batteries in smoking patients allowed to smoke freely before testing and nonsmokers. Furthermore, relatively few studies have examined differences in P50 gating between healthy non-deprived

(freely smoking) smokers and nonsmokers.

A study by Tan et al. (2014) found that non-deprived smokers performed worse compared to non-smokers on the Stroop Test (a test of processing speed, selective attention, and executive function) and Brief Visuospatial Memory Test-

Revised (BVMT-R; a test of working memory and short-term (30 minute period

108

between presentation and recall) memory tests of the MCCB. No differences

were observed between groups on P50 gating. In a more recent study by Zhang

et al. (2016), significantly impaired scores were observed on the Immediate

Memory, Attention, and Language indices of the Repeatable Battery for the

Assessment of Neuropsychological Status (RBANS) as well as RBANS total

score in smokers compared to non-smokers who also did not drink alcohol. Both

of these studies examined performance in a Chinese population and therefore

used batteries translated to Chinese.

The goal of the present pilot study was to examine neurocognitive

performance on the RBANS as well as P50 gating in an English-speaking

population of healthy non-deprived smokers and nonsmokers. Based on previous

findings I hypothesized that smokers would perform more poorly on the RBANS

than nonsmokers, driven by deficits in the immediate memory, attention, and

language indices. Furthermore, based on previous findings I hypothesized to

observe no differences in P50 gating between groups. As an additional

exploratory measure, I also examined correlations between RBANS scores and

P50 gating across all subjects.

Materials and Methods

Subjects

24 nonsmoking subjects (12 male, 12 female, average age 33 years,

average education 16 years) and 8 smoking subjects (6 male, 2 female, average

109

age 30 years, average education 15 years) participated in this study. Smokers were classified by self-report (minimum 2 cigarettes per day). Subjects were excluded for neurological illness, psychiatric illness, mental retardation, pregnancy, alcohol or drug abuse, abnormal physical examination, and abnormal electrocardiogram. No significant differences were observed between smokers and nonsmokers for age (t = 0.80, p = 0.44), gender (Pearson χ2 = 1.5, p = 0.22), or education level (t = 0.95, p = 0.37). Smokers smoked 0.35 packs a day and smoked for 6.42 years on average. Smokers were allowed to freely smoke on testing day, and their average carbon monoxide level on testing day was 13.6 ppm.

RBANS

The RBANS is a validated, brief (30 minute) neuropsychological screening tool used to evaluate cognitive function in adults. The test contains the following indices: Immediate memory, Visuospatial/Constructional, Attention, Language, and Delayed Memory. The Immediate Memory Index consists of List Learning and Story Memory subtests; the Attention Index consists of Digit Span and

Coding subtests; the Visuospatial/Constructional Index consists of Figure Copy and Line Orientation subtests; the Language Index consists of Picture Naming and Semantic Fluency subtests; the Delayed Memory Index consists of List

Recall, Story Recall, List Recognition, and Figure Recall subtests. Brief descriptions of each subtest are as follows (Bartels et al. 2010):

110

List Learning (Immediate Memory subtest): A list of 10 words is orally presented

and subjects are asked to immediately recall the list. The process is repeated

three times.

Story Memory (Immediate Memory subtest): A short passage is read and

subjects are asked to recall details of the passage. The process is repeated

once.

Digit Span (Attention subtest): The subject is asked to immediately repeat

random number sequences read aloud. The sequences range in length from 2 to

9 numbers.

Coding (Attention subtest): On a work sheet, pairs of symbols and digits from 1 to

9 are presented, followed by rows of symbols without the respective digit. Within

a time limit of 90 seconds, test subjects are asked to assign as many numbers to

symbols as they can.

Figure Copy (Visuospatial/Constructional subtest): Subjects are asked to copy a

complex figure within a 4-minute time limit.

Line Orientation (Visuospatial/Constructional subtest): The subject is asked to

visually match a pair of angled lines to a display of 13 lines forming a semicircle.

10 pairs of angled lines are presented.

Picture Naming (Language subtest): Picture naming is a simple task of naming

10 familiar objects. If the subject is unable to give the correct name, cues are

allowed.

111

Semantic Fluency (Language subtest): The subject is asked to spontaneously

generate as many words as possible for a category (e.g. zoo animals) within 1

minute.

List Recall (Delayed Memory subtest): ~20 minutes after initial presentation of the

word list in the List Learning test, the subject is again asked to recall as many

words as possible from the list.

Story Recall (Delayed Memory subtest): ~20 minutes after initial presentation of

the story in the Story Memory test, subjects are asked to freely recall details of

the story.

List Recognition (Delayed Memory subtest): This test is conducted immediately

after the List Recall test. In this test, the ten words presented in List Learning

have to be identified amongst ten other distractor words.

Figure Recall (Delayed Memory subtest): ~20 minutes after initial presentation,

the complex figure copied during the Figure Copy test has to be recalled and

drawn from memory.

RBANS index scoring is based on a mean 100-point scale (Table 1).

P50 Gating

P50 gating examination was administered immediately following the

RBANS. P50 auditory sensory gating was measured and analyzed as described

previously (Smucny et al. 2013). Briefly, EEG activity was recorded from a gold

disk electrode affixed to the vertex, referenced to linked ears. The

112

electroculogram was simultaneously recorded between the right superior orbit and lateral canthus. EEG recording and analysis was performed using LEA2003 software (Rave Wave Systems, Inc.). The P50 wave of the auditory evoked response was measured in a paired-stimulus (conditioning-testing) paradigm, measuring the P50 wave amplitudes, and then calculating the P50 ratio (the ratio of the test amplitude to the conditioning amplitude), with lower values indicative of better sensory gating.

Clicks were presented binaurally with headphones (Sony MDR V-600) and

LEA2003 hardware (Rave Wave Systems, Inc.). The click stimulus intensity was

set to 50 dB above the subject's threshold to optimize the evoked potential

response while minimizing startle. The clicks were presented in pairs separated

by 500 ms, with 10 s between click pairs. Click pairs were presented in five

blocks of 16 conditioning-test trials. Total recording time was approximately 30

min.

Trials (evoked response pairs) were rejected during recording if they

contained muscle startle artifact or eye blinks as indicated by an EEG or electro-

oculographic voltage of ±35 μV at 50 ms after stimulus or if there was evidence

of drowsiness in the EEG, as indicated by repetitive waves greater than 20

μV. The average waveform for each block of trials was then traced; tracings were

rejected if they lacked an identifiable P50 waveform in the conditioning block, or if

the electrocular waveform grossly outweighed (1.5x or greater average

amplitude) the P50 waveform in the conditioning block. The grand average P50

113

potential of accepted blocks of trials for each subject was identified and measured by using a previously described computer algorithm (Adler et al. 2004).

Subjects were given no special instructions concerning the clicks they were hearing.

Data Analysis

Between-group (smokers vs. nonsmokers) comparisons of RBANS scores and P50 data were analyzed using independent samples t-tests. Correlations between RBANS scores and P50 data were examined using Pearson’s correlation coefficients. As the sample size in this study was relatively low (n = 8 smokers), significance threshold was set at p < 0.05, uncorrected. Due the liberal threshold the results of this study should be considered exploratory.

Results

Results for cognitive and P50-related measures in smokers and nonsmokers are summarized in Table 2. Both groups scored within the “average” range on the RBANS.

RBANS

Generally lower scores were observed on the RBANS in smokers relative to non-smokers, although the difference in RBANS total score between groups was not significant (Table 2). Performance decrements in smokers were most

114

prevalent on the Immediate Memory, Visuospatial/Constructional, and Delayed

Memory indices of the RBANS (Table 2), although only the Delayed Memory index showed a significant difference (p = 0.036).

P50 Gating

No significant differences between groups were observed on S1 amplitude, S2 amplitude, or P50 ratio.

RBANS and P50: Correlation Analyses

Across all subjects, no significant correlations were observed between S1 amplitude, S2 amplitude, or P50 ratio and total RBANS score or any RBANS index.

Comment

In contrast to my hypothesis, in this study significantly lower scores were not observed on RBANS total score or the RBANS Immediate Memory, Attention, or Language indices in smokers compared to nonsmokers. Indeed, the only significant deficit in score was observed on the Delayed Memory index. In agreement with my hypothesis, however, no differences were observed between groups on P50 gating or P50-related measures. P50 gating also did not predict performance on any RBANS-related measure.

115

Although the neurocognitive results of this study were largely nonsignificant, mean scores for smokers were lower in the majority of RBANS domains as well as in total RBANS score. It is possible, therefore, that the lack of significant effect was due the relatively low sample size in this pilot study (n = 8 smokers). To test this possibility, additional subjects may be added in order to bring the sample size of smokers more in-line with the nonsmoking group (n =

24).

Lack of an association between P50 gating and neurocognitive performance is consistent with previous findings (Sanchez-Morla et al. 2013).

This result is not altogether surprising given that P50 gating is hypothesized be a measure of sensory filtering, and no RBANS measures substantially tax filtering- related processing (e.g. selective attention).

In conclusion, the results of this small pilot study suggest that non- deprived smokers and nonsmokers do not significantly differ in overall cognitive performance. Future studies with larger sample sizes will be needed to more thoroughly compare neuropsychological measures between these groups.

116

Table 1. Qualitative and expected distribution of RBANS scores in a healthy population (Kimbell 2013).

Index Score Classification Theoretical Normal Curve 130+ Very Superior 2.2% 120-129 Superior 6.7% 110-119 High Average 16.1% 90-109 Average 50% 80-89 Low Average 16.1% 70-79 Borderline 6.7% 69 and below Extremely low 2.2%

117

Table 2. Cognitive and electrophysiological (P50) summary results. Abbreviations: Amp – Amplitude; Attn – Attention Index; DM – Delayed Memory Index; IM – Immediate Memory Index; Lang – Language Index; RBANS – Repeatable Battery for the Assessment of Neuropsychological Status; S1 – First Stimulus in P50 Paired Click Paradigm; S2 – Second Stimulus in P50 Paired Click Paradigm; SE: Standard Error of the Mean; VS – Visuospatial/Constructional Index.

Measure Mean (SE) Mean (SE) t value p value Nonsmokers Smokers (two- tailed) RBANS Total 102 (2.55) 95 (3.40) 1.56 0.14 RBANS IM 107 (2.72) 99 (5.21) 1.37 0.20 RBANS VS 97 (4.00) 87 (5.94) 1.38 0.19 RBANS Lang 101 (2.27) 102 (3.67) -0.25 0.81 RBANS Attn 102 (3.19) 103 (3.90) -0.074 0.94 RBANS DM 101 (1.83) 94 (2.71) 2.32 0.036 P50 S1 Amp 2.82 (0.24) 2.93 (0.24) -0.31 0.76 P50 S2 Amp 0.94 (0.17) 1.20 (0.23) -0.90 -0.38 P50 Ratio 0.37 (0.068) 0.43 (0.084) -0.50 0.62

118

REFERENCES

Adler, L.E., Olincy, A., Cawthra, E.M., McRae, K.A., Harris, J.G., Nagamoto, H.T., Waldo, M.C., Hall, M.H., Bowles, A., Woodward, L., Ross, R.G., Freedman, R. (2004). Varied effects of atypical neuroleptics on P50 auditory gating in schizophrenia patients. Am J Psychiatry, 161:1822-8.

Bartels, C., Wegrzyn, M., Wiedl, A., Ackermann, V., Ehrenreich, H. (2010). Practice effects in healthy adults: a longitudinal study on frequent repetitive cognitive testing. BMC Neurosci, 11:118.

Kimbell, A.-M. 2013. Cognitive Testing Using the RBANS Update [Presentation].

Sanchez-Morla, E.M., Santos, J.L., Aparicio, A., Garcia-Jimenez, M.A., Soria, C., Arango, C. (2013). Neuropsychological correlates of P50 sensory gating in patients with schizophrenia. Schizophr Res, 143:102-6.

Smucny, J., Olincy, A., Eichman, L.C., Lyons, E., Tregellas, J.R. (2013). Early sensory processing deficits predict sensitivity to distraction in schizophrenia. Schizophr Res, 147:196-200.

Tan, S.P., Jie-Feng, C., Fan, F.M., Zhao, Y.L., Chen, N., Fan, H.Z., Zhang, J.G., Wang, Y.H., Yoon, J.H., Soares, J.C., Zou, Y.Z., Zhang, X.Y. (2014). Smoking, MATRICS consensus cognitive battery and P50 sensory gating in a Han Chinese population. Drug Alcohol Depend, 143:51-7.

Zhang, X.Y., Tan, Y.L., Chen, D.C., Tan, S.P., Yang, F.D., Zunta-Soares, G.B., Soares, J.C. (2016). Effects of cigarette smoking and alcohol use on neurocognition and BDNF levels in a Chinese population. Psychopharmacology (Berl), 233:435-45.

119 APPENDIX B

NICOTINIC MODULATION OF SALIENCE NETWORK CONNECTIVITY AND

CENTRALITY IN SCHIZOPHRENIA

Introduction

The brain is constantly bombarded by information from the external environment and internal sources. In order to produce an appropriate response, the brain must be able to filter, integrate, and evaluate this information using knowledge of past events, the current situation, and future plans. This moment- to-moment evaluation is a major function of the salience network, a functionally

(Menon 2015) and structurally (Uddin et al. 2011; van den Heuvel et al. 2009) connected set of brain areas that includes the anterior insular and anterior cingulate cortices (ACC). The salience network is able to accomplish this task through its functional connectivity to diverse brain areas. These include regions involved in executive function (e.g. prefrontal cortex and superior parietal cortex) as well as to areas that comprise the “default mode network” (DMN) (e.g. the posterior cingulate cortex) (Menon and Uddin 2010). Indeed, due to its patterns of intrinsic connectivity, the salience network may be involved in switching between executive and default-mode dominant states based on task demands

(Menon 2011; Menon and Uddin 2010; Palaniyappan and Liddle 2012).

Based on its functionality, it is reasonable to theorize that salience network dysfunction may contribute to symptoms of schizophrenia. These symptoms

120

include positive symptoms (e.g. delusional thoughts suggestive of misattributed salience), cognitive symptoms (perhaps due to a failure to appropriately switch between networks), and negative symptoms (hinting at deficits in the ability to act appropriately based on circumstances). Indeed, previous work has demonstrated that the salience network is functionally, structurally, and neurochemically abnormal in schizophrenia (reviewed by Palaniyappan and Liddle (2012);

Palaniyappan et al. (2012); Smucny and Tregellas (2013); Wylie and Tregellas

(2010)). Resting-state functional magnetic resonance imaging (fMRI) studies have reported abnormal salience network connectivity in schizophrenia, including within the network (Kraguljac et al. 2016a; Pu et al. 2012) and between the network and other networks (Manoliu et al. 2014; Moran et al. 2013b;

Palaniyappan et al. 2013; Pelletier-Baldelli et al. 2015). In addition, salience network dysfunction has been linked to all three domains of symptoms in schizophrenia (Kuhn and Gallinat 2012; Lahti et al. 2006; Manoliu et al. 2013;

Palaniyappan et al. 2013).

Given that the salience network may play a key role in understanding the symptoms of schizophrenia, it follows that pharmacologically targeting the network may have clinical utility. One highly studied class of drugs in schizophrenia is nicotinic agonists. Interest in these drugs is due to high rates of smoking (~70%) in the illness (Winterer 2010) leading researchers to hypothesize that nicotine may be a form of “self medication” (Winterer 2010).

Acute nicotine has been shown to improve cognition in schizophrenia (Barr et al.

121

2008; Harris et al. 2004) as well target abnormal brain function (Smucny et al.

2016a; Smucny et al. 2016b; Smucny et al. 2015; Tregellas et al. 2011; Wylie et

al. 2016). Conversely, the nicotinic antagonist mecamylamine worsens cognitive

performance in patients (Roh et al. 2014). Aberrant salience network function is

associated with smoking status in schizophrenia (Moran et al. 2013a), targeted

by nicotine in healthy deprived cigarette smokers (Hong et al. 2009; Sutherland

et al. 2013), and may be a critical system underlying nicotine addition (reviewed

by Sutherland et al. 2012). Finally, all three nodes of the salience network highly

express nicotinic receptors (Breese et al. 1997; Paterson and Nordberg 2000),

suggesting the network may be effectively targeted by nicotine and other nicotinic

agents.

To examine the effects of pharmacologic treatment on brain network

connectivity, researchers most frequently adopt seed-based (connectivity

between a seed and other regions) or multivariate (e.g. independent components

analysis (ICA)) approaches. To take these analyses a step further, topological

analysis or “graph theory” can be used to ascertain the organizational principles

that underlie functional intrinsic networks. One interesting topological metric is

betweenness centrality, a term that describes how frequently a brain region is

used to enable one area to communicate with another. A node (e.g. brain region)

with high betweenness centrality is frequently used to traverse from any region in

a network of brain regions to any other region (Fig. 1, top). Related to this point,

the relatively high betweenness centrality of the salience network may drive its

122

ability to integrate information and process salience (van den Heuvel and Sporns

2013). Furthermore, previous studies suggest that betweenness centrality of the

ACC may be disrupted in schizophrenia and in at-risk populations (Lord et al.

2012; Lord et al. 2011; van den Heuvel et al. 2010).

In contrast to betweenness centrality, analysis of local efficiency examines communication solely between a node (e.g. brain region) and its "neighbors"

(other regions directly connected to that region) and is therefore a measure of local (rather than global) information integration. Neighbors surrounding a node with high local efficiency are able to communicate between themselves without having to traverse between many other nodes (Figure 1, bottom). Disrupted local efficiency has been observed in schizophrenia patients in a number of areas, including the ACC (Smucny and Tregellas 2013; Yan et al. 2015).

Despite the links between the salience network, schizophrenia, and nicotine, little is known about the effects of the drug on salience network connectivity and topology in the illness, particularly in nonsmokers. Filling in this knowledge gap is important as a substantial fraction (~30%) of schizophrenia patients do not smoke (Winterer 2010). Studying nonsmokers, furthermore, circumvents the unavoidable confounding effects of withdrawal associated with studying a smoking population. The goals of this study, therefore, were to 1) examine the effects of acute nicotine administration (vs. placebo) on connectivity between the three cortical nodes of the salience network (ACC, left and right anterior insula) and the rest of the brain in patients, and 2) examine the effects of

123

nicotine (vs. placebo) on betweenness centrality and local efficiency of the three salience network nodes. I hypothesized abnormal connectivity between the salience network and regions associated with the DMN and executive networks

(e.g. the posterior cingulate and prefrontal cortex) as well as disrupted betweenness centrality and local efficiency of salience network nodes in patients under placebo administration and amelioration of these differences after nicotine.

Materials and Methods

Subjects

36 subjects participated in this study — 17 stable outpatients who had a primary diagnosis of schizophrenia and 19 healthy comparison subjects.

Demographic and clinical (Brief Psychiatric Rating Scale (BPRS, 24 point)

(Ventura et al. 1993), Scale for the Assessment of Negative Symptoms (SANS, 4 factor) (Andreasen 1983), and Global Assessment of Function (GAF) (Jones et al. 1995)) information for participants was assessed by interview and is shown in

Table 1. No significant group differences in age or gender were observed. No subjects were taking smoking cessation medication (e.g. varenicline) at the time of the study. Controls were recruited by advertisement. Patients were recruited by referral from a University of Colorado psychiatrist. Patients were excluded for a diagnosis of neurological illness, head trauma, current smoking (< 3 months from last cigarette) or substance abuse, failure to pass a physical examination, and magnetic resonance imaging (MRI) exclusion criteria (claustrophobia, weight

124

> 250 lbs, metal in the body). Control subjects were excluded for all of the above as well as a diagnosis of Axis I mental illness or first-degree family history of Axis

I mental illness. Patients were antipsychotic medication stable (> 3 mo with no change in medication). All subjects were required to pass a nicotine tolerance test, in which the nicotine dose used for the experiment (7 mg) was administered

> 3 d prior to the first fMRI scan. Criteria for passing the tolerance test were 1) less than a 20% change in heart rate or blood pressure (BP) for up to 90 minutes post patch-application, 2) no side effects other than mild/minor nausea, headache, lightheadness, clouded thinking, anxiety, or mouth tingling. All participants provided written informed consent in accordance with the principles of the Declaration of Helsinki and could withdraw from the study at any time.

Subjects were compensated for participation. The Colorado Multiple Institutional

Review Board approved the study.

Study Design and Drug Administration

This was a single-blind, randomized, placebo-controlled, crossover study.

On each of two study visits, subjects were administered either a 7 mg nicotine patch (NicoDerm CQ, GlaxoSmithKline, Middlesex, UK) or placebo patch (made in-house). The order of study visits (placebo-nicotine or nicotine-placebo) was counterbalanced across subjects. Visits were scheduled > 3 d apart. The placebo and nicotine patches were tactilely identical, and the placebo patch was affixed to the skin in the same manner as the nicotine patch. Subjects were asked to refrain

125

from examining either patch, however, during or after application as the placebo and drug patches (although visually similar) were not visually identical.

Participants’ clothing (sleeves) also covered the patches such that they could not be readily observed after affixation. Patches remained affixed from application to the end of the scan.

Resting-state scans were performed ~120 m after patch application. The

latent period for this study was used such that the scans occurred during a time

window corresponding to the peak plasma concentration of nicotine (Dempsey et

al. 2013). Based on previous work, the nicotine concentration during this period is

expected to be approximately 4.5 ng/ml (Dempsey et al. 2013).

Physiological effects (heart rate (HR) and blood pressure (BP)) were

monitored immediately prior to 1) patch application and 2) entering the MR

scanner. Physiological effects were analyzed using a mixed-effects model

analysis of variance (ANOVA) in SPSS v. 22 (IBM Corp., Armonk, NY, USA), with

time (pretreatment vs. posttreatment) and drug (placebo vs. nicotine) as within-

subjects factors and diagnosis (control vs. patient) as a between-subjects factor.

fMRI Acquisition

Resting state functional images were acquired on a 3T MR scanner

(General Electric, Milwaukee, WI, USA) using a standard quadrature head coil.

An inversion-recovery echoplanar image (IR-EPI; TI = 505 ms) was collected to improve coregistration of functional images. Images were acquired with the

126

following parameters: scan time 10 m, TR = 2000 ms, TE = 26 ms, FOV = 220 mm2, 642 matrix, 27 slices, 2.6 mm thick, 1.4 mm gap, interleaved, flip angle 70°,

300 volumes. Subjects were instructed to rest with eyes closed and to “not think

about anything in particular.”

fMRI Preprocessing – Realignment, Coregistration and Smoothing

fMRI data realignment, coregistration and smoothing were performed

using SPM8 (Wellcome Dept. of Imaging Neuroscience, London) in Matlab 2012a

(MathWorks, Natick, MA, USA). The first four images were excluded for

saturation effects. Data from each subject were realigned to the first volume and

normalized to the Montreal Neurological Institute template using the IR-EPI as an

intermediate to improve coregistration between images. During spatial

normalization, data were resliced to a 3 mm3 voxel size. Finally, data were smoothed with an 8 mm full width half maximum (FWHM) Gaussian kernel.

Functional Connectivity Analysis

A hybrid ICA / seed-based connectivity approach was utilized (Kelly et al.

2010). This methodology first uses ICA to extract group level networks of interest

(in this study, the salience network). Focal signal peaks from that network are

then used as seeds in a whole-brain seed-to-voxel connectivity analysis. This

approach provides a data-driven, unbiased estimate of connectivity between

specific regions within the salience network and the rest of the brain, as it does

127

not require prespecification of anatomically-based seeds of unknown reliability and validity (Zilles and Amunts 2010). Importantly, no statistical analysis was performed on ICA results to avoid potentially performing circular analyses (i.e.

“double dipping”) (Kriegeskorte et al. 2009).

Preprocessing for connectivity analysis was conducted using in-house

Matlab 2012a scripts according to suggested guidelines (Murphy et al. 2013).

Signal associated with white matter, CSF, and the main effects of treatment (drug

or placebo) were included as covariates of no interest (confounders). Mean

overall gray matter signal was not included as a confounder as doing so shifted

the whole-brain connectivity distribution towards predominantly negative values.

The data were linearly detrended and a 0.01 to 0.1 Hz bandpass filter applied to

remove low-frequency drifts and physiological high-frequency noise.

The effects of motion on resting state connectivity remain a contentious

issue, and no universal standard exists regarding if and how to correct for motion

effects (Murphy et al. 2013). Therefore, motion effects were accounted for using

two pipelines. For the first approach, rigid-body motion parameters (forward/back,

left/right, up/down, pitch, yaw and roll) were simply included as covariates of no

interest. No difference was observed between patients and controls for overall

movement. For the second approach, the same parameters were again included

as covariates. Censoring was then performed in which adjacent volumes that

showed scan-to-scan differences of > 0.5 mm (translational displacement), > 0.2 rad (rotational displacement), or > 9 (global signal z-value) were removed before

128

analysis. All subjects had < 50% of frames removed after censoring. Censoring was performed using the ART toolbox (www.nitrc.org/projects/artifact_detect).

Group spatial ICA was performed using the GIFT software v1.3g

(icatb.sourceforge.net). A single group ICA was performed across all subjects

(controls and patients) and treatment conditions (placebo and nicotine). ICA parameters have been described previously (Tregellas et al. 2014). Briefly, data were intensity-normalized, their dimensionality reduced using principle component analysis, and twenty independent sources estimated using the

Infomax algorithm (Bell and Sejnowski 1995). The component containing the

salience network was identified by selecting the component with the highest

spatial correlation with an anterior salience network mask (Shirer et al. 2012).

Consistent with previous ICA findings (Seeley et al. 2007; Sridharan et al. 2008), the extracted network included the left and right anterior insula as well as the

ACC (Figure 2, top). The extracted peak coordinates were {x, y, z} = {-42, 20, 5}

(left insula), {39, 26, -8} (right insula), {-3, 17, 58} (ACC). Consequently, 5 mm radius spherical ROIs were centered on these peaks and used as seeds for whole brain seed-to-voxel connectivity analysis (Kelly et al. 2010) (Figure 2, bottom).

Seed-based connectivity analysis was performed using the Conn v.15 toolbox (www.nitrc.org/projects/conn). Second-level random effects mixed-model

ANOVA analyses were used to create within group statistical parametric maps for

each seed and to examine connectivity differences between groups. For these

129

analyses, treatment condition (placebo vs. nicotine) was entered as a within- subjects factor and diagnosis (control vs. patient) was entered as a between- subjects factor. The primary contrasts of interest were the directional interaction contrast (Patient Nicotine > Patient Placebo) > (Control Nicotine > Control

Placebo) (i.e. increased connectivity during nicotine administration (vs. placebo) in patients vs. control) and the opposite interaction contrast (Patient Placebo >

Patient Nicotine) > (Control Placebo > Control Nicotine) (i.e. decreased connectivity during nicotine administration (vs. placebo) in patients vs. control).

Second-level connectivity maps were thresholded at p < 0.01 (voxelwise), p <

0.05 (cluster false discovery rate-corrected) (Genovese et al. 2002). To fully characterize interaction effects, significant interactions were followed up by post- hoc tests of simple main effects using the mean connectivity between the seed and each significant cluster, as described previously (Dodhia et al. 2014).

Topological Analysis

Betweenness centrality and local efficiency are analyzed in a topological framework, in which the brain is parcellated into anatomically defined regions, or

“nodes”, and metrics calculated for ROIs (in this study, the ROIs consisted of the three salience network peaks described previously). Connectivity between nodes is then calculated to provide the basis for drawing “edges” (lines representing connections) of the graph.. All calculations were performed using the Brain

Connectivity Toolbox (Rubinov and Sporns 2010).

130

The first step of a topological analysis is to parcellate the brain into functional regions. To accomplish this, I used a previously published atlas of 264 regions classified according to their putative functionality via a meta-analysis of task and resting-state imaging studies (Power et al. 2011). Spherical ROIs (5 mm radius) were centered on coordinates provided by the atlas. ROIs with “unknown” functionality as defined by this analysis were not included, nor those spheres that overlapped with white matter or CSF. I then combined this atlas with the three

salience network ROIs (ACC, left and right insula), removing any ROIs from the

Power et al. (2011) atlas that overlapped with the ICA-extracted ROIs. Taken

together, these ROIs represent “nodes” that constitute a “graph” for which

betweenness centrality can be analyzed. It is worthwhile to note that, as

previously alluded to in “Functional Connectivity Analysis”, a limitation of using

this type of atlas-based approach is that it requires prespecification of node coordinates. Nonetheless, I considered this method appropriate for the present analysis because I were interested in analyzing topology between salience network nodes and the rest of the brain (or, in the case of local efficiency, topologically adjacent neighbors) as a whole, but not between salience network nodes and specific brain areas.

Functional time series were then extracted by taking the mean signal over time from within each node. Time series were detrended, bandpass filtered, and white matter/CSF/motion confounders removed. Analysis was conducted with and without motion censoring.

131

Correlation matrices (i.e., connectivity matrices) for each subject were generated by calculating the absolute value of the Fisher transformation of the correlation in BOLD signal over time (the time series) between each pair of nodes. The diagonal elements of each matrix were set to zero to assure compliance with Brain Connectivity Toolbox functions.

In a graph theory-based framework, edges between nodes represent “real” connections. Conversely, the absence of an edge between nodes represents the lack of a connection (or “spurious” connection) between them. In order to construct such graphs, cost-based thresholding is performed in which an edge is only placed between nodes with connectivity stronger than the threshold (e.g. the strongest 10% of possible of connections). The procedure is termed “cost” based thresholding because as the connectivity threshold decreases, the number of connections increases, increasing the wiring or topological cost needed in order to construct the graph. As there is no universally accepted threshold that best represents the brain’s “true” connections while ignoring “spurious” connections, graph-based metrics were calculated from individual subject graphs across a range of thresholds. Specifically, I calculated betweenness centrality and local efficiency from graphs thresholded from 10% to 50% of possible connections

(based on connectivity strength) and ignoring all weaker connections. This range was used because 1) a cost of 10% is typically the lowest cost of a fully connected brain network and 2) connections weaker than the strongest 50% are likely to be non-neuronal and/or strongly influenced by noise (Achard and

132

Bullmore 2007; Kaiser and Hilgetag 2006). This cost range is also consistent with previous graph theory-based fMRI studies from our lab and others (Berman et al.

In Press; Bullmore and Bassett 2011; Whitlow et al. 2011). Metrics from each

threshold were then integrated over the cost range in order to provide a “cost-

integrated” value for each subject to be used for group analyses.

Analyses were performed using both binary and weighted graphs. For

binary graphs, all potential connections that met the cost threshold were set to 1

(connection exists) and all other potential connections set to 0 (connection does

not exist). For weighted graphs, connectivity strength was preserved for all

connections above the cost threshold and all other potential connections set to 0.

In graph theory, a “path” is a sequence of edges (i.e. connections) that connect a

sequence of nodes (e.g. ROIs). The length of a path between nodes is the

topological distance between them. For binary graphs, this is simply the number

of nodes along the path between a starting node and the destination node, as the

distance between any two adjacently connected nodes is 1. For weighted graphs,

the distance between two adjacent nodes is proportional to the connectivity

strength between them.

The betweenness centrality of a node is defined as the number of shortest

paths from all nodes to all others that pass through the node, and is a measure of

the amount of information that is transferred through it relative to other brain

areas. Brain regions with high BTC are typically referred to as “hubs” with high

133

“centrality” and are essential for maintaining overall fidelity of information flow

(Freeman 1977).

Formally, the betweenness centrality of a node ν is given by the

expression

� (�) �� = !" �!" ! !! !!

� where σ!" is the total number of shortest paths from node to node �, and σ!"(ν)

is the number of those paths that pass through ν. Betweenness centrality is then

typically divided by the number of node pairs in the graph excluding ν: ((V-1)(V-

2)) / 2, where V is the total number of nodes in the connected graph that ν belongs to. Thus, betweenness centrality of a node is always between 0 and 1. Betweenness centrality scores at each cost threshold were converted to z- scores (within-subject normalized) before cost-integration.

2.6.6. Local Efficiency: Formalism

The efficiency of a network is a measure of its ability to transfer information without having to traverse large topographical distances over many nodes (Stam and Reijneveld 2007). Formally, the efficiency of a network � is defined by the equation

2 1 � � = � (� − 1) �(�, �) !!!∈! where n denotes the total nodes in a network and d(i, j) denotes the length of the

134

shortest path between a node i and another node j. Thus, efficiency is comparable to the inverse of the shortest path length of a network.

Efficiency can be calculated on a global scale (efficiency of an entire network) or local scale (efficiency of a subgraph of a network, i.e. a node and its neighbors). On the local scale, the local efficiency of a node is defined as the inverse of the shortest path length connecting all neighbors of that node, and is a measure of local communication between that node and those next to it.

Cost-integrated betweenness centrality and local efficiency scores at the

three 5 mm radius salience network nodes (ACC, left and right insula) for each

subject and treatment condition were entered into separate mixed model

ANOVAs with treatment condition (placebo vs. nicotine) as a within-subjects

factor and diagnosis (control vs. patient) as a between-subjects factor. To fully

characterize interaction effects, significant interactions were followed up by post-

hoc tests of simple main effects. The procedure was conducted for 1) binary

graphs without movement censoring, 2) weighted graphs without movement

censoring, 3) binary graphs with movement censoring, and 4) weighted graphs

with movement censoring.

Correlation Analyses

Connectivity (mean between each seed and significant cluster) and

topological metrics for each patient were tested with a Pearson’s correlation

coefficient for relationships with symptoms (BPRS, SANS, GAF) and global

135

functioning. Correlations with subscales (e.g. SANS Alogia) were only examined if a significant association was observed between the corresponding total score on a scale (e.g. SANS Total) and fMRI-related metrics. Due to the low sample size, I used a liberal significance threshold in which correlations with p < 0.05 were considered significant and p < 0.10 trend-level. These analyses should therefore be considered exploratory.

Results

Physiological Effects of Nicotine

Physiological effects of nicotine are presented in Table 2. Physiological data were not available from one control subject due to an equipment malfunction. No significant time X drug X diagnosis interactions were observed for systolic BP (F(1,33) = 0.55, p = 0.47), diastolic BP (F(1,33) = 2.01, p = 0.17), or heart rate (F(1,33) = 0.060, p = 0.81). Across all subjects, no significant time

(pretreatment vs. 60 m post-treatment) X drug interactions were observed for systolic BP (F(1,34) = 2.74, p = 0.11) or diastolic BP (F(1,34) = 0.22, p = 0.64). A trend-level interaction was observed for heart rate F(1,34) = 3.92, p = 0.056).

Whole-Brain Seed to Voxel Connectivity Analysis: Overview

To understand how nicotine affects connectivity between the salience network and the rest of the brain, 5 mm radius spherical ROIs were centered on the three salience network peaks extracted by ICA (ACC, left insula, and right

136

insula; see Methods for coordinates) and used for whole brain seed-to-voxel connectivity analysis (see Methods).

Seed-to-Voxel Connectivity Results: ACC Seed

The directional interaction contrast (Patient Nicotine > Patient Placebo) >

(Control Nicotine > Control Placebo) yielded significant clusters in the left superior parietal lobule (peak coordinates {x, y, z} = {-42, -55, 64}, t = 4.35, k =

139 voxels), right dorsolateral prefrontal cortex (peak coordinates {x, y, z} = {39,

20, 37}, t = 3.79, k = 103 voxels), and right ventrolateral prefrontal cortex (peak

coordinates {x, y, z} = {42, 26, 19}, t = 3.62, k = 131 voxels) (Figure 3). Clusters

in the left prefrontal cortex and right superior parietal lobule were not large

enough to meet criteria for significance. Post-hoc tests revealed significant

interaction effects were driven by decreased connectivity in patients (vs. controls)

under placebo conditions, decreased connectivity in controls under nicotine

administration (vs. placebo), and increased connectivity in patients under nicotine

administration (vs. placebo) (Figure 3; Table 3a).

The opposite directional interaction contrast (Patient Placebo > Patient

Nicotine) > (Control Placebo > Control Nicotine) yielded a significant cluster in the

posterior cingulate (peak coordinates {x, y, z} = {-9, -58, 25}, t = 4.02, k = 180

voxels) (Figure 4). Post-hoc tests revealed the effect was driven by increased

connectivity in patients (vs. controls) under placebo conditions, increased

connectivity in controls under nicotine administration (vs. placebo), and

137

decreased connectivity in patients under nicotine administration (vs. placebo)

(Figure 4; Table 3a).

Seed-to-Voxel Connectivity Results: Left Insula Seed

The directional interaction contrast (Patient Placebo > Patient Nicotine) >

(Control Placebo > Control Nicotine) yielded a significant cluster in the middle cingulate cortex (peak coordinates {x, y, z} = {15, -22, 52}, t = 491, k = 315 voxels) (Figure 5). Post-hoc tests revealed the effects were driven by increased connectivity in patients (vs. controls) under placebo conditions, increased connectivity in controls under nicotine administration (vs. placebo), and decreased connectivity in patients under nicotine administration (vs. placebo)

(Table 3b).

Seed-to-Voxel Connectivity Results: Right Insula Seed

No significant drug X diagnosis interaction effects were observed on connectivity between the right insula seed and remainder of the brain.

Graph Analysis – Binary Graphs

Drug X diagnosis interaction effects on betweenness centrality were analyzed at each of the three salience network nodes using cost-thresholded binary graphs. Betweenness centrality was cost-integrated over a range of 10% to 50% of possible connections (see Methods). A significant interaction was

138

observed on betweenness centrality in the ACC (F(1,34) = 10.66, p < 0.01), driven by decreased centrality in patients (vs. controls) under placebo administration (p = 0.010), decreased centrality in controls under nicotine administration (vs. placebo) (p = 0.047), and increased centrality in patients under nicotine administration (vs. placebo (p = 0.015) (Figure 6). No significant interaction effects or main effects of drug were observed on betweenness centrality for either the left or right insula node (Figure 6).

No significant drug X diagnosis interactions were observed for local efficiency of subgraphs centered on the ACC (F(1,34) = 0.11, p = 0.74), left insula (F(1,34) = 2.23, p = 0.15), or right insula (F(1,34) = 0.06, p = 0.81) (Table

4a).

Graph Analysis – Weighted Graphs

To determine if graph theory-based results were influenced by connectivity strength and to increase the generalizability of the findings, analyses were repeated using cost-thresholded weighted graphs. Results were similar to the previous analysis using binary graphs. Specifically, a significant drug X diagnosis interaction was observed on betweenness centrality of the ACC (F(1,34) = 11.2, p < 0.01), driven by decreased centrality in patients (vs. controls) under placebo administration (p < 0.01) and increased centrality in patients under nicotine administration (vs. placebo (p = 0.011) (Figure 7). No significant interaction effects or main effects of drug were observed on betweenness centrality for

139

either the left or right insula node (Figure 7). No significant drug X diagnosis

interactions were observed for local efficiency of subgraphs centered on the ACC

(F(1,34) = 0.44 , p = 0.51), left insula (F(1,34) = 3.43, p = 0.073), or right insula

(F(1,34) = 1.21, p = 0.28) (Table 4b).

Effects of Movement-Based Volume Censoring

Due to potential confounding effects of subject motion on the present

analyses (Murphy et al. 2013; Power et al. 2012; Yan et al. 2013), I reanalyzed

data after movement-based volume censoring (see Methods). Connectivity

results were largely similar to the previous uncensored analyses. The only

difference of note was observed using the ACC seed and the interaction contrast

(Patient Nicotine > Patient Placebo) > (Control Nicotine > Control Placebo), in

which the cluster in the right dorsolateral prefrontal cortex was no longer large

enough to meet significance criteria (k = 63 voxels). Betweenness centrality and

local efficiency results were not appreciably altered.

Clinical Correlates

Connectivity between the left insula and middle cingulate cortex during

placebo administration was associated with higher total SANS score (i.e. more

severe negative symptoms) in schizophrenia (r = 0.54, p = 0.027). The effect was

primarily driven by an association between connectivity and SANS

Anhedonia/Asociality (r = 0.52, p = 0.034). A positive trend was observed

140

between connectivity between the ACC and dorsolateral prefrontal cortex during placebo administration in patients and higher GAF score (i.e. higher global functioning) (r = 0.43, p = 0.084). Conversely, a negative trend was observed between connectivity between the ACC and posterior cingulate cortex during placebo administration in patients and GAF score (r = -0.42, p = 0.091).

Discussion

In agreement with my hypothesis, significant drug X diagnosis interactions were observed between the ACC node of the salience network and brain areas associated with the executive (prefrontal cortex and superior parietal lobule) and default mode (posterior cingulate cortex) networks. A significant drug X diagnosis interaction was also observed between the insula and the middle cingulate cortex. In regards to graph theory-based metrics, a significant interaction effect was observed on betweenness centrality of the ACC node. No significant interactions were observed on betweenness centrality of the insula nodes, however, or for local efficiency of subgraphs centered on any salience network nodes. Significant effects were driven by relative hyper- or hypoconnectivity and reduced betweenness centrality in patients during placebo administration, and amelioration of these abnormalities after nicotine administration.

Hyperconnectivity between the insula and middle cingulate cortex predicted severity of negative symptoms including asociality in patients. These results suggest that abnormal connectivity and centrality of the salience network

141

(particularly the ACC component) may be targeted by nicotinic agonists in schizophrenia.

The pattern of connectivity abnormalities observed in the present study suggests that the ACC node of the salience network may be hyperconnected to the DMN (specifically, the posterior cingulate) and hypoconnected to executive- network areas (e.g. the superior parietal lobule) during the resting state in schizophrenia. Previous resting state studies have demonstrated abnormalities between components of the salience network, DMN, and executive networks in schizophrenia, although the direction and magnitude of these results (e.g. hypo vs. hyperconnectivity) are not consistent (Chen et al. 2016; Cui et al. 2015;

Manoliu et al. 2013; Manoliu et al. 2014; Minzenberg et al. 2015; Pelletier-Baldelli

et al. 2015; Wang et al. 2015; Woodward et al. 2011; Wotruba et al. 2014).

Differences in findings across studies may be due to a number of factors,

including medication status, smoking status, or even variable levels of MR

scanner noise (patients are hypersensitive to auditory stimuli, potentially due in

part to salience network dysfunction (Gaebler et al. 2015; Javitt 2009; McGhie

and Chapman 1961)). Future studies will be needed to examine which of these

factors, if any, contribute the observed differences in salience network function

between patients and controls as well as to the efficacy of nicotinic drugs on

these groups.

In addition to connectivity, nicotine targeted aberrant centrality of the

salience network, particularly of the ACC node. In contrast, no interaction effect

142

was observed on local efficiency around any salience network node. This result suggests that nicotinic agonists may affect topological organization of the salience network in schizophrenia on a global, integrative level (as opposed to a local level). Previous work has demonstrated that the salience network has relatively high betweenness centrality, contributing to its ability to act as an indispensable brain “hub” (Cole et al. 2010; Lavin et al. 2013; van den Heuvel and Sporns 2011). Previous work has also demonstrated that salience network centrality may be disrupted in schizophrenia and in populations at risk for the illness (Crossley et al. 2014; Lord et al. 2012; Lord et al. 2011; van den Heuvel et

al. 2010). As a theorized function of the salience network is to integrate

information from other major brain networks (Menon 2015) and betweenness

centrality is a surrogate measure of a region’s capacity for this process, the

results of this study suggest that nicotine may topologically reorganize brain

function by restoring salience network integrity. The hypothesized role of the

salience network in switching between task-positive and task-negative network-

dominant states as a function of cognitive demands (Menon 2011; Palaniyappan

and Liddle 2012) suggests that nicotine may improve cognition in schizophrenia

(Barr et al. 2008; Harris et al. 2004) via its ability to increase the integrative

capacity of the network. As cognition was not measured as part of this study,

future studies may examine the relationships between salience network

connectivity and performance in various cognitive domains.

The ability of nicotine to increase betweenness centrality of the ACC may

143

be related to the presence of specialized neurons in the area called von

Economo or “spindle” neurons. Von Economo neurons are unusually long (160-

200 microns or more) neurons that are exclusively present in the ACC and

insular cortices of large-brained mammals such as elephants, whales, great apes

and humans (Butti et al. 2013). The unique morphology of these cells is thought to enable these brain areas to communicate with distal sites, facilitating their ability to integrate information from many sources to aid in complex computations associated with high-level cognitive functions, e.g. social behavior (Butti et al.

2013). Interestingly, Brune et al. (2010) reported a reduction in density of these neurons in the ACC in schizophrenia patients, as well as inverse associations between von Economo neuron density, illness onset, and length of illness. It is unknown, however, if loss of von Economo neuron signaling is associated with symptomatology or can be pharmacologically targeted by nicotine or other agents to affect network function.

A significant association was noted between left insula – middle cingulate cortex connectivity during placebo administration and negative symptoms in schizophrenia patients, driven primarily by the Anhedonia/Asociality subscale of the SANS. This subscale measures the degree to which a patient shows relationships with friends and peers (among other factors) (Andreasen 1983).

Related to this finding, social information processing may depend on the middle cingulate. Research in non-human primates has demonstrated that middle cingulate cortex lesions impair social cognition and reduce contact with others

144

(Hadland et al. 2003; Rudebeck et al. 2006). In humans, the middle cingulate is recruited during tasks that involve monitoring the consequences of actions taken by others and may therefore predict outcomes during social interaction (Apps et al. 2013a; Apps et al. 2013b; Behrens et al. 2008). Consequently, it is reasonable to suggest that the middle cingulate cortex is dysfunctional in diseases that feature social cognitive deficits (Apps et al. 2013b). In regards to the present study, the finding that hyperconnectivity between the middle cingulate and insula predicted anhedonia/asociality in patients combined with the result that nicotine targeted this functional abnormality suggests that targeting this circuit via nicotinic agents may have therapeutic benefit. Indeed, one behavioral study observed improved social cognition after acute nicotine administration in nonsmoking schizophrenia patients (Quisenaerts et al. 2013). Another study, however, found no neuronal or behavioral effects of the drug during social cognition in the illness (Drusch et al. 2013). As this area remains understudied, additional research is needed to clarify the role of the middle cingulate in social cognition deficits in schizophrenia as well as to examine the ability of nicotine and other drugs to target the associated circuitry.

When interpreting the results of the present research, an important consideration is the effects of antipsychotic medication. All of the patients in this study were being treated with either first or second-generation antipsychotics, the majority of which are dopaminergic receptor antagonists. DA is a primary neurochemical mediator of many insula functions, such as novelty-seeking

145

(Suhara et al. 2001) and risk-taking (Clark and Dagher 2014; Rutledge et al.

2015). Neuroimaging studies suggest that areas of the SN are modulated by

dopaminergic treatment in schizophrenia patients during both task (Dolan et al.

1995; Keedy et al. 2009; Snitz et al. 2005) and at rest (Kraguljac et al. 2016b;

Lahti et al. 2009). It is possible, therefore, that medication effects were driving

factors behind the observed between-group differences. Nicotine also has known

neurocognitive and physiological interactions with dopaminergic systems (Levin

and Rezvani 2007). Nonetheless, differences in the neuronal response to

nicotinic in unmedicated vs. medicated patients with schizophrenia are largely

unknown and an important area for future investigation.

A potential limitation of this study is that nicotine can have physiological

effects that may reduce the effectiveness of the blind (Benowitz 1998). It should

be noted, however, that 1) nicotine did not have any significant effects on blood

pressure or heart rate during scanning in this study, and 2) subjects most likely to

have noticeably adverse reactions to nicotine were excluded by prescreening

(see Methods). Although it was somewhat surprising to not observe significant

physiological effects of the drug, previous work has found only small

physiological effects of 7 mg transdermal nicotine (vs. placebo) in nonsmokers up

to 120 min post-treatment (Wignall and de Wit 2011). The latent period (resting

state scans acquired ~120 m post-patch application) was chosen as it was

expected to capture the peak plasma absorption of nicotine (Dempsey et al.

2013). It remains possible, however, that later time points may show more

146

profound physiological as well as neuronal effects. Finally, to help preserve the blind, placebo and nicotine patches were covered by clothing, tactilely identical and visually similar, and subjects instructed to refrain from examining the patches.

The ability of nicotinic agents to pharmacologically target intrinsic network dysfunction in schizophrenia remains a priority for psychiatry research. This study identifies functional salience network abnormalities as potential nicotinic targets in schizophrenia. Future imaging studies may investigate the ability of nicotine and nicotinic agonists to target this network in other schizophrenia- associated populations, such as smokers, first-degree relatives, and at-risk individuals.

147

Figure 1. Top: Graphical illustration of betweenness centrality. Betweenness centrality is defined as the proportion of shortest paths of a network that contain a given node. Nodes with low betweenness centrality are colored in gray, nodes with medium betweenness centrality colored in green, and the node with the highest betweenness centrality colored in red. Node 3 (the red node) participates in the highest number of shortest paths between each pair of all other nodes in the network and therefore has the highest betweenness centrality. In the present framework, nodes represent brain regions and edges represent connections between regions. Bottom: Graphical illustration of local efficiency. Local efficiency is a measure of ability of a node and its neighbors to transfer information between themselves. The graph on the left has low local efficiency of the green node and its neighbors. The graph on the right has high local efficiency of the green node and its neighbors due to increased connections between the neighbors.

148

Figure 2. Top: Mean salience network component extracted by independent components analysis. Significant clusters were centered on the anterior cingulate and bilateral insula. Statistical parametric map thresholded at whole-brain voxelwise cluster family-wise error rate corrected p < 0.05 purely for visualization purposes. Images are presented in the neurologic convention (R on R). Bottom: Location of salience network peaks (5 mm spheres) used as seeds for connectivity analysis.

149

Figure 3. Top: Anterior cingulate seed to voxelwise whole-brain connectivity result using the directional drug X diagnosis interaction contrast (Patient Nicotine > Patient Placebo) > (Control Nicotine > Control Placebo). Significant clusters were observed in the superior parietal lobule, dorsolateral prefrontal cortex, and ventrolateral prefrontal cortex. Statistical parametric map thresholded at p < 0.01, k > 75 voxels purely for visualization purposes. Images are presented in the neurologic convention (R on R). Abbreviations: ACC – anterior cingulate cortex; DLPFC – dorsolateral prefrontal cortex; SPL – superior parietal lobule; VLPFC – ventrolateral prefrontal cortex. Bottom: Chart displaying the nature of the drug X diagnosis interaction. Cluster beta weight values represent the mean taken from the superior parietal lobule cluster. *p < 0.05 vs. control placebo (post-hoc test). **p < 0.05 vs. control nicotine. ***p < 0.05 vs. patient placebo.

150

Figure 4. Top: Anterior cingulate seed to voxelwise whole-brain connectivity result using the directional drug X diagnosis interaction contrast (Patient Placebo > Patient Nicotine) > (Control Placebo > Control Nicotine). A significant cluster was observed in the posterior cingulate cortex. Statistical parametric map thresholded at p < 0.01, k > 75 voxels purely for visualization purposes. Images are presented in the neurologic convention (R on R). Abbreviations: ACC – anterior cingulate cortex; PCC – posterior cingulate cortex. Bottom: Chart displaying the nature of the drug X diagnosis interaction. Cluster beta weight values represent the mean taken from the cluster. *p < 0.05 vs. control placebo (post-hoc test). **p < 0.05 vs. control nicotine. ***p < 0.05 vs. patient placebo.

151

Figure 5. Left: Left insula seed to voxelwise whole- brain connectivity result using the directional drug X diagnosis interaction contrast (Patient Placebo > Patient Nicotine) > (Control Placebo > Control Nicotine). A significant cluster was observed in the middle cingulate cortex. Statistical parametric map thresholded at p < 0.01, k > 75 voxels purely for visualization purposes. Images are presented in the neurologic convention (R on R). Abbreviations: MCC – middle cingulate cortex. Right: Chart displaying the nature of the drug X diagnosis interaction. Cluster beta weight values represent the mean taken from the cluster. *p < 0.05 vs. control placebo (post-hoc test). **p < 0.05 vs. control nicotine. ***p < 0.05 vs. patient placebo.

152

Figure 6. Drug x diagnosis interaction effects on betweenness centrality for the anterior cingulate cortex (ACC), left insula, and right insula nodes using binary network analysis. A significant interaction effect was observed for the ACC but not the insula nodes. *p < 0.05 vs. control placebo. ** p < 0.05 vs. control placebo.

153

Figure 7: Drug x diagnosis interaction effects on betweenness centrality for the ACC, left insula, and right insula nodes using weighted network analysis. A significant interaction effect was observed for the ACC but not the insula nodes. . *p < 0.05 vs. control placebo. ***p < 0.05 vs. patient placebo.

154

Table 1. Demographic and Clinical Data of Participants. Parentheses contain the standard deviation. Abbreviations: C = Caucasian, AA = African American, HL = Hispanic or Latino, BPRS = Brief Psychiatric Rating Scale, SANS = Scale for the Assessment of Negative Symptoms, Typ = # Treated with Typical Antipsychotic Medications, ATyp = # Treated with Atypical Antipsychotic Medications.

Controls Schizophrenia Test Statistic (p) Age 37.4 (12) 44 (12) t = 1.61 (0.12) Gender (M/F) 10/9 12/5 Χ2 = 1.22 (0.32) Average Total BPRS 36.6 (7.7) n/a Average Total SANS 4.59 (3.4) n/a Meds: Typ/ATyp 1/16 n/a

155

Table 2. Physiological effects of nicotine and placebo patch. Parentheses contain the standard error. Abbreviations: BP = blood pressure, mmHg = mm of mercury, bpm = beats per minute.

Tx Placebo Nicotine Group Pretreat 60m Post- Pretreat 60m Post- Measure Δ Δ ment treatment ment treatment Controls Systolic -8 -2 BP 129 (4) 121 (4) 128 (3) 125 (2) (2) (2) (mmHg) Diastolic -3 1 BP 79 (2) 76 (2) 79 (2) 80 (2) (2) (2) (mmHg) Heart -3 0 Rate 73 (2) 70 (2) 76 (3) 76 (3) (2) (1) (bpm) Patients Systolic -5 -3 BP 135 (4) 130 (4) 128 (4) 125 (3) (5) (2) (mmHg) Diastolic 0 -2 BP 79 (2) 79 (3) 80 (2) 78 (2) (2) (2) (mmHg) Heart 0 3 Rate 81 (4) 81 (4) 84 (4) 87 (4) (2) (2) (bpm)

156

Table 3a. Connectivity (beta weights) between the anterior cingulate cortex seed and significant (p < 0.01 (voxelwise), p < 0.05 (cluster FDR-corrected)) clusters drawn from whole-brain drug X diagnosis interaction contrasts. P values are drawn from post-hoc tests of simple main effects (see Methods) and are displayed to describe the nature of the interactions. Values in parentheses represent the standard error. Abbreviations: DLPFC – dorsolateral prefrontal cortex, PCC – posterior cingulate cortex, SPL – superior parietal lobule, VLPFC – ventrolateral prefrontal cortex.

Brain Region (Cluster Controls Patients Location) p p (patient (nicotine vs. p (nicotine vs. Placebo Nicotine Placebo Nicotine vs. control placebo) placebo) placebo) 0.10 0.002 -0.07 0.07 SPL < 0.01 < 0.01 < 0.01 (0.03) (0.03) (0.03) (0.03) 0.15 -0.04 0.03 0.10 DLPFC < 0.01 0.012 0.070 (0.03) (0.03) (0.03) (0.04) 0.05 -0.09 -0.07 0.08 VLPFC < 0.01 < 0.01 < 0.01 (0.03) (0.03) (0.03) (004) -0.07 0.08 0.02 -0.08 PCC < 0.01 0.030 0.011 (0.03) (0.03) (0.03) (0.03)

Table 3b. Connectivity (beta weights) between the left insula seed and the significant (p < 0.01 (voxelwise), p < 0.05 (cluster FDR-corrected)) cluster drawn from whole-brain drug X diagnosis interaction contrasts. P values are drawn from post-hoc tests of simple main effects (see Methods) and are displayed to describe the nature of the interaction. Values in parentheses represent the standard error. Abbreviations: MCC – middle cingulate cortex.

Brain Region (Cluster Controls Patients Location) p p (nicotine p (patient (nicotine Placebo Nicotine vs. Placebo Nicotine vs. control vs. placebo) placebo) placebo) -0.09 0.05 0.10 -0.13 MCC < 0.01 < 0.01 < 0.01 (0.03) (0.03) (0.03) (0.03)

157

Table 4a. Local efficiency of binary subgraphs centered on each salience network node. Abbreviations: ACC – anterior cingulate cortex. Values in parentheses represent the standard error.

Brain Region Controls Patients Placebo Nicotine Placebo Nicotine ACC 0.30 (0.01) 0.31 (0.01) 0.30 (0.02) 0.32 (0.01) Left Insula 0.31 (0.01) 0.31 (0.01) 0.32 (0.01) 0.29 (0.01) Right Insula 0.31 (0.01) 0.32 (0.01) 0.31 (0.02) 0.32 (0.01)

Table 4b. Local efficiency of weighted subgraphs centered on each salience network node. Abbreviations: ACC – anterior cingulate cortex. Values in parentheses represent the standard error.

Brain Region Controls Patients Placebo Nicotine Placebo Nicotine ACC 0.33 (0.03) 0.37 (0.04) 0.38 (0.04) 0.39 (0.04) Left Insula 0.35 (0.03) 0.36 (0.02) 0.42 (0.03) 0.35 (0.03) Right Insula 0.36 (0.03) 0.39 (0.03) 0.39 (0.04) 0.37 (0.02)

158

REFERENCES

Achard, S., Bullmore, E. (2007). Efficiency and cost of economical brain functional networks. PLoS Comp Biol, 3:174-183.

Andreasen, N. (1983). The scale for the assessment of negative symptoms (SANS). Iowa City. University of Iowa.

Apps, M.A., Green, R., Ramnani, N. (2013a). Reinforcement learning signals in the anterior cingulate cortex code for others' false beliefs. Neuroimage, 64:1-9.

Apps, M.A., Lockwood, P.L., Balsters, J.H. (2013b). The role of the midcingulate cortex in monitoring others' decisions. Front Neurosci, 7:251.

Barr, R.S., Culhane, M.A., Jubelt, L.E., Mufti, R.S., Dyer, M.A., Weiss, A.P., Deckersbach, T., Kelly, J.F., Freudenreich, O., Goff, D.C., Evins, A.E. (2008). The effects of transdermal nicotine on cognition in nonsmokers with schizophrenia and nonpsychiatric controls. Neuropsychopharmacology, 33:480- 90.

Behrens, T.E., Hunt, L.T., Woolrich, M.W., Rushworth, M.F. (2008). Associative learning of social value. Nature, 456:245-9.

Bell, A.J., Sejnowski, T.J. (1995). An information-maximization approach to blind separation and blind deconvolution. Neural Comput, 7:1129-59.

Benowitz, N.L. (1998). Nicotine Safety and Toxicity. New York, NY. Oxford University Press.

Berman, B.D., Smucny, J., Wylie, K.P., Shelton, E., Kronberg, E., Leehey, M.A., Tregellas, J.R. (In Press). Levodopa modulates small-world architecture of functional brain networks in Parkinson disease. Movement Dis, In Press.

Brune, M., Schobel, A., Karau, R., Benali, A., Faustmann, P.M., Juckel, G., Petrasch-Parwez, E. (2010). Von Economo neuron density in the anterior cingulate cortex is reduced in early onset schizophrenia. Acta Neuropathol, 119:771-8.

Bullmore, E.T., Bassett, D.S. (2011). Brain graphs: graphical models of the human brain connectome. Annu Rev Clin Psychol, 7:113-40.

159

Butti, C., Santos, M., Uppal, N., Hof, P.R. (2013). Von Economo neurons: clinical and evolutionary perspectives. Cortex, 49:312-26.

Chen, Q., He, X., Chen, X., Wang, L., Wang, K., Qiuac, B. (2016). Aberrant structural and functional connectivity in the salience network and central executive network circuit in schizophrenia. Neurosci Lett.

Clark, C.A., Dagher, A. (2014). The role of dopamine in risk taking: a specific look at Parkinson's disease and gambling. Front Behav Neurosci, 8:196.

Cole, M.W., Pathak, S., Schneider, W. (2010). Identifying the brain's most globally connected regions. Neuroimage, 49:3132-48.

Crossley, N.A., Mechelli, A., Scott, J., Carletti, F., Fox, P.T., McGuire, P., Bullmore, E.T. (2014). The hubs of the human connectome are generally implicated in the anatomy of brain disorders. Brain, 137:2382-95.

Cui, L.B., Liu, J., Wang, L.X., Li, C., Xi, Y.B., Guo, F., Wang, H.N., Zhang, L.C., Liu, W.M., He, H., Tian, P., Yin, H., Lu, H. (2015). Anterior cingulate cortex- related connectivity in first-episode schizophrenia: a spectral dynamic causal modeling study with functional magnetic resonance imaging. Front Hum Neurosci, 9:589.

Dodhia, S., Hosanagar, A., Fitzgerald, D.A., Labuschagne, I., Wood, A.G., Nathan, P.J., Phan, K.L. (2014). Modulation of resting-state amygdala-frontal functional connectivity by oxytocin in generalized social anxiety disorder. Neuropsychopharmacology, 39:2061-9.

Dolan, R.J., Fletcher, P., Frith, C.D., Friston, K.J., Frackowiak, R.S., Grasby, P.M. (1995). Dopaminergic modulation of impaired cognitive activation in the anterior cingulate cortex in schizophrenia. Nature, 378:180-2.

Drusch, K., Lowe, A., Fisahn, K., Brinkmeyer, J., Musso, F., Mobascher, A., Warbrick, T., Shah, J., Ohmann, C., Winterer, G., Wolwer, W. (2013). Effects of nicotine on social cognition, social competence and self-reported stress in schizophrenia patients and healthy controls. Eur Arch Psychiatry Clin Neurosci, 263:519-27.

Freeman, L.C. (1977). A Set of Measures of Centrality Based on Betweenness. Sociometry, 40:35-41.

160

Genovese, C.R., Lazar, N.A., Nichols, T. (2002). Thresholding of statistical maps in functional neuroimaging using the false discovery rate. Neuroimage, 15:870-8.

Hadland, K.A., Rushworth, M.F., Gaffan, D., Passingham, R.E. (2003). The effect of cingulate lesions on social behaviour and emotion. Neuropsychologia, 41:919- 31.

Hong, L.E., Gu, H., Yang, Y., Ross, T.J., Salmeron, B.J., Buchholz, B., Thaker, G.K., Stein, E.A. (2009). Association of nicotine addiction and nicotine's actions with separate cingulate cortex functional circuits. Arch Gen Psychiatry, 66:431- 41.

Javitt, D.C. (2009). When doors of perception close: bottom-up models of disrupted cognition in schizophrenia. Annu Rev Clin Psychol, 5:249-75.

Jones, S.H., Thornicroft, G., Coffey, M., Dunn, G. (1995). A brief mental health outcome scale-reliability and validity of the Global Assessment of Functioning (GAF). Br J Psychiatry, 166:654-9.

Kaiser, M., Hilgetag, C.C. (2006). Nonoptimal component placement, but short processing paths, due to long-distance projections in neural systems. PLoS Comput Biol, 2:e95.

Keedy, S.K., Rosen, C., Khine, T., Rajarethinam, R., Janicak, P.G., Sweeney, J.A. (2009). An fMRI study of visual attention and sensorimotor function before and after antipsychotic treatment in first-episode schizophrenia. Psychiatry Res, 172:16-23.

Kelly, R.E., Wang, Z., Alexopoulos, G.S., Gunning, F.M., Murphy, C.F., Morimoto, S.S., Kanellopoulos, D., Jia, Z., Lim, K.O., Hoptman, M.J. (2010). Hybrid ICA- Seed-Based Methods for fMRI Functional Connectivity Assessment: A Feasibility Study. Int J Biomed Imaging, 2010.

Kraguljac, N.V., White, D.M., Hadley, J.A., Visscher, K., Knight, D., ver Hoef, L., Falola, B., Lahti, A.C. (2016a). Abnormalities in large scale functional networks in unmedicated patients with schizophrenia and effects of risperidone. Neuroimage Clin, 10:146-58.

161

Kraguljac, N.V., White, D.M., Hadley, N., Hadley, J.A., Ver Hoef, L., Davis, E., Lahti, A.C. (2016b). Aberrant Hippocampal Connectivity in Unmedicated Patients With Schizophrenia and Effects of Antipsychotic Medication: A Longitudinal Resting State Functional MRI Study. Schizophr Bull.

Kriegeskorte, N., Simmons, W.K., Bellgowan, P.S., Baker, C.I. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nat Neurosci, 12:535-40.

Kuhn, S., Gallinat, J. (2012). Quantitative meta-analysis on state and trait aspects of auditory verbal hallucinations in schizophrenia. Schizophr Bull, 38:779-86.

Lahti, A.C., Weiler, M.A., Holcomb, H.H., Tamminga, C.A., Cropsey, K.L. (2009). Modulation of limbic circuitry predicts treatment response to antipsychotic medication: a functional imaging study in schizophrenia. Neuropsychopharmacology, 34:2675-90.

Lavin, C., Melis, C., Mikulan, E., Gelormini, C., Huepe, D., Ibanez, A. (2013). The anterior cingulate cortex: an integrative hub for human socially-driven interactions. Front Neurosci, 7:64.

Levin, E.D., Rezvani, A.H. (2007). Nicotinic interactions with antipsychotic drugs, models of schizophrenia and impacts on cognitive function. Biochem Pharmacol, 74:1182-91.

Lord, L.D., Allen, P., Expert, P., Howes, O., Broome, M., Lambiotte, R., Fusar- Poli, P., Valli, I., McGuire, P., Turkheimer, F.E. (2012). Functional brain networks before the onset of psychosis: A prospective fMRI study with graph theoretical analysis. Neuroimage Clin, 1:91-8.

Lord, L.D., Allen, P., Expert, P., Howes, O., Lambiotte, R., McGuire, P., Bose, S.K., Hyde, S., Turkheimer, F.E. (2011). Characterization of the anterior cingulate's role in the at-risk mental state using graph theory. Neuroimage, 56:1531-9.

Manoliu, A., Riedl, V., Doll, A., Bauml, J.G., Muhlau, M., Schwerthoffer, D., Scherr, M., Zimmer, C., Forstl, H., Bauml, J., Wohlschlager, A.M., Koch, K., Sorg, C. (2013). Insular Dysfunction Reflects Altered Between-Network Connectivity and Severity of Negative Symptoms in Schizophrenia during Psychotic Remission. Front Hum Neurosci, 7:216.

162

Manoliu, A., Riedl, V., Zherdin, A., Muhlau, M., Schwerthoffer, D., Scherr, M., Peters, H., Zimmer, C., Forstl, H., Bauml, J., Wohlschlager, A.M., Sorg, C. (2014). Aberrant dependence of default mode/central executive network interactions on anterior insular salience network activity in schizophrenia. Schizophr Bull, 40:428-37.

McGhie, A., Chapman, J. (1961). Disorders of attention and perception in early schizophrenia. Br J Med Psychol, 34:103-16.

Menon, V. (2011). Large-scale brain networks and psychopathology: a unifying triple network model. Trends Cogn Sci, 15:483-506.

Menon, V. (2015) Salience Network. In: Toga, A.W., editor. Brain Mapping: An Encyclopedic Reference: Academic Press: Elsevier. p 597-611.

Menon, V., Uddin, L.Q. (2010). Saliency, switching, attention and control: a network model of insula function. Brain Struct Funct, 214:655-67.

Minzenberg, M.J., Lesh, T., Niendam, T., Yoon, J.H., Cheng, Y., Rhoades, R., Carter, C.S. (2015). Conflict-related anterior cingulate functional connectivity is associated with past suicidal ideation and behavior in recent-onset schizophrenia. J Psychiatr Res, 65:95-101.

Moran, L.V., Sampath, H., Kochunov, P., Hong, L.E. (2013a). Brain circuits that link schizophrenia to high risk of cigarette smoking. Schizophr Bull, 39:1373-81.

Moran, L.V., Tagamets, M.A., Sampath, H., O'Donnell, A., Stein, E.A., Kochunov, P., Hong, L.E. (2013b). Disruption of anterior insula modulation of large-scale brain networks in schizophrenia. Biol Psychiatry, 74:467-74.

Murphy, K., Birn, R.M., Bandettini, P.A. (2013). Resting-state fMRI confounds and cleanup. Neuroimage, 80:349-59.

Palaniyappan, L., Liddle, P.F. (2012). Does the salience network play a cardinal role in psychosis? An emerging hypothesis of insular dysfunction. J Psychiatry Neurosci, 37:17-27.

Palaniyappan, L., Simmonite, M., White, T.P., Liddle, E.B., Liddle, P.F. (2013). Neural primacy of the salience processing system in schizophrenia. Neuron, 79:814-28.

Palaniyappan, L., White, T.P., Liddle, P.F. (2012). The concept of salience network dysfunction in schizophrenia: from neuroimaging observations to therapeutic opportunities. Curr Top Med Chem, 12:2324-38.

163

Paterson, D., Nordberg, A. (2000). Neuronal nicotinic receptors in the human brain. Prog Neurobiol, 61:75-111.

Pelletier-Baldelli, A., Bernard, J.A., Mittal, V.A. (2015). Intrinsic Functional Connectivity in Salience and Default Mode Networks and Aberrant Social Processes in Youth at Ultra-High Risk for Psychosis. PLoS One, 10:e0134936.

Power, J.D., Barnes, K.A., Snyder, A.Z., Schlaggar, B.L., Petersen, S.E. (2012). Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion. Neuroimage, 59:2142-54.

Power, J.D., Cohen, A.L., Nelson, S.M., Wig, G.S., Barnes, K.A., Church, J.A., Vogel, A.C., Laumann, T.O., Miezin, F.M., Schlaggar, B.L., Petersen, S.E. (2011). Functional network organization of the human brain. Neuron, 72:665-78.

Pu, W., Li, L., Zhang, H., Ouyang, X., Liu, H., Zhao, J., Li, L., Xue, Z., Xu, K., Tang, H., Shan, B., Liu, Z., Wang, F. (2012). Morphological and functional abnormalities of salience network in the early-stage of paranoid schizophrenia. Schizophr Res, 141:15-21.

Quisenaerts, C., Morrens, M., Hulstijn, W., de Boer, P., Timmers, M., Sabbe, B., de Bruijn, E.R. (2013). Acute nicotine improves social decision-making in nonsmoking but not in smoking schizophrenia patients. Front Neurosci, 7:197.

Roh, S., Hoeppner, S.S., Schoenfeld, D., Fullerton, C.A., Stoeckel, L.E., Evins, A.E. (2014). Acute effects of mecamylamine and varenicline on cognitive performance in non-smokers with and without schizophrenia. Psychopharmacology (Berl), 231:765-75.

Rubinov, M., Sporns, O. (2010). Complex network measures of brain connectivity: uses and interpretations. Neuroimage, 52:1059-69.

Rudebeck, P.H., Buckley, M.J., Walton, M.E., Rushworth, M.F. (2006). A role for the macaque anterior cingulate gyrus in social valuation. Science, 313:1310-2.

Rutledge, R.B., Skandali, N., Dayan, P., Dolan, R.J. (2015). Dopaminergic Modulation of Decision Making and Subjective Well-Being. J Neurosci, 35:9811- 22.

Seeley, W.W., Menon, V., Schatzberg, A.F., Keller, J., Glover, G.H., Kenna, H., Reiss, A.L., Greicius, M.D. (2007). Dissociable intrinsic connectivity networks for salience processing and executive control. J Neurosci, 27:2349-56.

Shirer, W.R., Ryali, S., Rykhlevskaia, E., Menon, V., Greicius, M.D. (2012). Decoding subject-driven cognitive states with whole-brain connectivity patterns. Cereb Cortex, 22:158-65.

164

Smucny, J., Olincy, A., Rojas, D.C., Tregellas, J.R. (2016a). Neuronal effects of nicotine during auditory selective attention in schizophrenia. Hum Brain Mapp, 37:410-21.

Smucny, J., Olincy, A., Tregellas, J.R. (2016b). Nicotine restores functional connectivity of the ventral attention network in schizophrenia. Neuropharmacology, 108:144-151.

Smucny, J., Stevens, K.E., Olincy, A., Tregellas, J.R. (2015). Translational utility of rodent hippocampal auditory gating in schizophrenia research: a review and evaluation. Transl Psychiatry, 5:e587.

Smucny, J., Tregellas, J. (2013). Nicotinic modulation of intrinsic brain networks in schizophrenia. Biochem Pharmacol, 86:1163-72.

Snitz, B.E., MacDonald, A., 3rd, Cohen, J.D., Cho, R.Y., Becker, T., Carter, C.S. (2005). Lateral and medial hypofrontality in first-episode schizophrenia: functional activity in a medication-naive state and effects of short-term atypical antipsychotic treatment. Am J Psychiatry, 162:2322-9.

Sridharan, D., Levitin, D.J., Menon, V. (2008). A critical role for the right fronto- insular cortex in switching between central-executive and default-mode networks. Proc Natl Acad Sci U S A, 105:12569-74.

Stam, C.J., Reijneveld, J.C. (2007). Graph theoretical analysis of complex networks in the brain. Nonlinear Biomed Phys, 1:3.

Suhara, T., Yasuno, F., Sudo, Y., Yamamoto, M., Inoue, M., Okubo, Y., Suzuki, K. (2001). Dopamine D2 receptors in the insular cortex and the personality trait of novelty seeking. Neuroimage, 13:891-5.

Sutherland, M.T., Carroll, A.J., Salmeron, B.J., Ross, T.J., Hong, L.E., Stein, E.A. (2013). Down-regulation of amygdala and insula functional circuits by varenicline and nicotine in abstinent cigarette smokers. Biol Psychiatry, 74:538-46.

Sutherland, M.T., McHugh, M.J., Pariyadath, V., Stein, E.A. (2012). Resting state functional connectivity in addiction: Lessons learned and a road ahead. Neuroimage, 62:2281-95.

165

Uddin, L.Q., Supekar, K.S., Ryali, S., Menon, V. (2011). Dynamic reconfiguration of structural and functional connectivity across core neurocognitive brain networks with development. J Neurosci, 31:18578-89. van den Heuvel, M.P., Mandl, R.C., Kahn, R.S., Hulshoff Pol, H.E. (2009). Functionally linked resting-state networks reflect the underlying structural connectivity architecture of the human brain. Hum Brain Mapp, 30:3127-41. van den Heuvel, M.P., Mandl, R.C., Stam, C.J., Kahn, R.S., Hulshoff Pol, H.E. (2010). Aberrant frontal and temporal complex network structure in schizophrenia: a graph theoretical analysis. J Neurosci, 30:15915-26. van den Heuvel, M.P., Sporns, O. (2011). Rich-club organization of the human connectome. J Neurosci, 31:15775-86. van den Heuvel, M.P., Sporns, O. (2013). Network hubs in the human brain. Trends Cogn Sci, 17:683-96.

Ventura, J., Lukoff, D., Nuechterlein, K.H., Liberman, R.P., Green, M.F., Shaner, A. (1993). Appendix 1: Brief Psychiatric Rating Scale (BPRS) Expanded version (4.0) scales, anchor points and administration manual. Int J Methods Psychiatr Res, 3:227-244.

Wang, D., Zhou, Y., Zhuo, C., Qin, W., Zhu, J., Liu, H., Xu, L., Yu, C. (2015). Altered functional connectivity of the cingulate subregions in schizophrenia. Transl Psychiatry, 5:e575.

Whitlow, C.T., Casanova, R., Maldjian, J.A. (2011). Effect of resting-state functional MR imaging duration on stability of graph theory metrics of brain network connectivity. Radiology, 259:516-24.

Wignall, N.D., de Wit, H. (2011). Effects of nicotine on attention and inhibitory control in healthy nonsmokers. Exp Clin Psychopharmacol, 19:183-91.

Winterer, G. (2010). Why do patients with schizophrenia smoke? Curr Opin Psychiatry, 23:112-9.

Woodward, N.D., Rogers, B., Heckers, S. (2011). Functional resting-state networks are differentially affected in schizophrenia. Schizophr Res, 130:86-93.

166

Wotruba, D., Michels, L., Buechler, R., Metzler, S., Theodoridou, A., Gerstenberg, M., Walitza, S., Kollias, S., Rossler, W., Heekeren, K. (2014). Aberrant coupling within and across the default mode, task-positive, and salience network in subjects at risk for psychosis. Schizophr Bull, 40:1095-104.

Wylie, K.P., Smucny, J., Legget, K.T., Tregellas, J.R. (2016). Targeting Functional Biomarkers in Schizophrenia with Neuroimaging. Curr Pharm Des, 22:2117-23.

Wylie, K.P., Tregellas, J.R. (2010). The role of the insula in schizophrenia. Schizophr Res, 123:93-104.

Yan, C.G., Craddock, R.C., He, Y., Milham, M.P. (2013). Addressing head motion dependencies for small-world topologies in functional connectomics. Front Hum Neurosci, 7:910.

Yan, H., Tian, L., Wang, Q., Zhao, Q., Yue, W., Yan, J., Liu, B., Zhang, D. (2015). Compromised small-world efficiency of structural brain networks in schizophrenic patients and their unaffected parents. Neurosci Bull, 31:275-87.

Zilles, K., Amunts, K. (2010). Centenary of Brodmann's map--conception and fate. Nat Rev Neurosci, 11:139-45.

167