Common neurobiological and psychological underpinnings of gambling and substance-use disorders

Journal Pre-proof Common neurobiological and psychological underpinnings of gambling and substance-use disorders

Iris M. Balodis, Marc N. Potenza PII:

S0278-5846(19)30537-8

DOI:

https://doi.org/10.1016/j.pnpbp.2019.109847

Reference:

PNP 109847

To appear in:

Progress in Neuropsychopharmacology & Biological Psychiatry

Received date:

2 July 2019

Revised date:

11 December 2019

Accepted date:

16 December 2019

Please cite this article as: I.M. Balodis and M.N. Potenza, Common neurobiological and psychological underpinnings of gambling and substance-use disorders, Progress in Neuropsychopharmacology & Biological Psychiatry(2019), https://doi.org/10.1016/ j.pnpbp.2019.109847

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© 2019 Published by Elsevier.

Journal Pre-proof Common neurobiological and psychological underpinnings of gambling and substance -use disorders Iris M. Balodis1* , Marc N. Potenza2,3,4 1

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Departments of Psychiatry, Neuroscience and Child Study, Yale University School of Medicine, New Haven, CT, USA 3 Connecticut Council on Problem Gambling, Wethersfield, CT, USA 4 Connecticut Mental Health Center, New Haven, CT, USA

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*Corresponding Author Iris M. Balodis, PhD Peter Boris Centre for Addictions Research Department of Psychiatry and Behavioural Neurosciences McMaster University D113, 100 West 5th Street Hamilton, ON L8N 4K7 Canada [email protected]

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Peter Boris Centre for Addictions Research, Department of Psychiatry and Behavioural Neuroscience, McMaster University

Abstract

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Journal Pre-proof Both psychological and neurobiological studies in gambling disorder have increased in the past 10-15 years. This review examines the current state of the literature, with a focus on recent magnetic resonance imaging (MRI) neuroimaging studies in gambling disorder. The review compares and contrasts findings across gambling and substance-use disorders. Additionally, features with arguably particular relevance to gambling disorder (e.g., "near-miss" processing) are described, as well as their relationship to choice behaviours. More broadly, the review

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to decision- making and key features of addictive disorders.

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informs on how these studies advance our understanding of brain-behavior relationships relating

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Keywords: Gambling, Addiction, Near-Miss, Striatum, rTMS

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INTRODUCTION

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The application of neuroimaging to examine gambling disorder (GD) began approximately 20 years ago: the first decade of neuroimaging studies produced 5 original research investigations in

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this population. Since 2010, dozens of neuroimaging studies have examined neural processes in

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GD populations (this does not include hundreds of neuroimaging studies examining gamblingrelated tasks or other phenomena such as loss-chasing or risk-taking). In the past 5 years, there has been an increase not only in the number of neuroimaging studies in GD, but also a refinement in the cognitive constructs examined, as well as sophistication in the types of comparisons made. The current literature review examines recent findings in human GD studies using different neuroimaging methods with a focus on magnetic resonance imaging (MRI). The review focuses first on the construct of appetitive processing – one of the most researched GD areas. This review also appraises recent studies using clinical comparison groups and work

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Journal Pre-proof examining the neurobiology of specific gambling feature, near-misses. The review also examines other neuroimaging modalities, namely structural-based studies as well as the application of repetitive transcranial magnetic imaging (rTMS) for GD treatment. The first author searched the literature for relevant manuscripts on the topic, and supplemented this approach with knowledge from abstracts and citations in relevant articles. The purpose of this literature review is to shed

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light on recent findings in GD and gambling neurobiology, but is not necessarily exhaustive.

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The advantages of including a GD model in addiction neurobiology

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The interest in using neuroimaging to study GD is multifold. First, it provides a means to understand brain mechanisms involved in this disorder. Second, as the first formal ‘non-

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substance’ or ‘behavioral’ addiction, GD has potential to clarify addiction risk factors by

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disentangling addiction mechanisms from drug-use confounds. Many features are shared

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between substance-based and non-substance-based addictive disorders including urges/craving, withdrawal and tolerance. Additionally, core concepts and transdiagnostic constructs including

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impulsivity and compulsivity show similar relationships with these disorders. While it is possible

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that brain alterations (both in function and structure) may occur as a consequence of disordered gambling, recent GD studies, through careful subgrouping of co-occurring drug use, are beginning to isolate brain changes that may relate uniquely to gambling from those that may relate to alcohol or polysubstance use (Zois et al. 2017). This is particularly relevant as other problematic behaviors, including internet gaming, binge-eating and compulsive sexual behaviors, are increasingly discussed and debated in the context of ‘behavioral addictions’(Fineberg et al. 2010, el-Guebaly et al. 2012, Leeman and Potenza 2013, Fineberg et al. 2014, Long et al. 2015, Kessler et al. 2016, Kraus et al. 2016, Kuhn and Gallinat 2016, Gentile et al. 2017, Hutson et al.

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Journal Pre-proof 2018, Kowalewska et al. 2018, Vaccaro and Potenza 2019, Brand et al. 2019). With comparable features as substance-use disorders (SUDs; e.g., urges, tolerance, withdrawal), GD offers an important condition for understanding neural mechanisms, both shared and unique features. As such, non-substance-based addictions like GD may help delineate boundaries and overlaps in addiction-like features. Additionally, transitions from impulsive to compulsive behaviours may share neural substrates (el-Guebaly et al., 2012; Fineberg et al.,

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2010; Fineberg et al., 2014; Brand et al., 2019). A recent systematic review and meta-analysis of

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compulsivity-related neurocognitive performance in GD found deficits in specific subdomains of

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this construct (van Timmeren et al. 2018). In particular, on tasks assessing attentional set-

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shifting, such as the Wisconsin Card Sorting Task (WCST), individuals with GD demonstrate impairments in cognitive flexibility which are often additionally related to problem-gambling

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severity, gambling frequency and gambling urges (Leppink et al. 2016, van Timmeren et al.

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2018). Neuroimaging studies in GD reveal alterations in prefrontal cortical areas during cognitive control including the dorsolateral prefrontal cortex, the anterior cingulate cortex and

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the ventromedial/orbitofrontal cortex (Leppink et al. 2016). These alterations may express

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themselves as generalized compulsive propensities that could relate to difficulties in flexibly moving attention and in perseverative tendencies that maintain maladaptive gambling behaviors (van Timmeren et al. 2018). It has been proposed that greater dorsal striatal activity to some gambling-related cues may relate to an increased susceptibility to form action-outcome associations in GD populations (Balodis et al. 2012). Combined with heightened impulsive tendencies well documented in GD populations, transitions to compulsive behaviors may be accelerated in this population; such impulsivity-compulsivity transitions represent an important

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Journal Pre-proof future direction for disordered gambling research (Balodis et al. 2012). Future studies should directly examine this possibility both in humans and animal models. Early neuroimaging studies in GD examined gambling-related cue-reactivity, particularly in the context of reward-based decision-making. However, recent studies have examined arguably more specific GD cognitive constructs, such as anticipatory processing, near-misses and value-based decision-making. Additionally, current neuroimaging studies have directly

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compared clinical groups with addictive disorders, including evaluations not only with SUDs but

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also with other disorders of impulse control such as binge-eating disorder (BED) or food

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addiction. These direct comparisons are in line with research efforts to consider constructs

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beyond diagnostic boundaries and provide a deeper understanding of pathophysiological mechanisms of addiction. One study has contrasted disorders with phenotypically different

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behavioural patterns (Majuri et al. 2017), having implications for disorder conceptualization. In

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addition, study stimuli are also moving beyond monetary cues to include natural rewards such as food or erotic stimuli (Sescousse et al. 2013, Majuri et al. 2017). These studies have the potential

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to highlight alterations in generalized reward processing that may act as precursor or

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maintenance factors in GD. Some GD studies already suggest differential reward sensitivities at both the motivational and hedonic levels to natural rewards (e.g. food or erotic cues), which provide some indication of core pathophysiological mechanisms in behavioural addictions (Sescousse et al. 2013), and similar approaches have been applied successfully to other behaviors proposed to have addictive potential like problematic pornography viewing (Gola et al. 2017). This review will highlight recent neuroimaging studies in GD, including relevant constructs and novel neuromodulation techniques under study in GD.

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Journal Pre-proof FMRI TASK-R ELATED

STUDIES

Appetitive Processing Studies: Seemingly Conflicting Findings and Ongoing Discussions To date, most neuroimaging studies in GD have focused on appetitive processing, in particular with monetary cues and rewards. Some of the first fMRI studies in GD involved cueexposures using tasks that were successful in increasing ratings of gambling urges; however, neuroimaging findings have been seemingly mixed. Early studies reported relatively diminished

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striatal and ventromedial prefrontal cortex (vmPFC) recruitment (Potenza et al. 2003, Reuter et

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al. 2005, de Greck et al. 2010) during cue exposure and simulated gambling in individuals with

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GD compared to those without. The vmPFC, together with striatal regions, contributes

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importantly to value representation and emotion regulation (Hiser and Koenigs 2017). There is also evidence for increased activity in more dorsal areas, including the

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dorsolateral prefrontal cortex (dlPFC) and anterior cingulate cortex (ACC), as well as subcortical

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areas including the amygdala (Crockford et al. 2005, Goudriaan et al. 2010). While reduced fronto-striatal recruitment is consistent with a blunted reward system in GD, heightened activity

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in visual processing areas suggests recruitment of attentional resources and that gambling cues

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are more salient in GD, similar to findings in SUDs (Goudriaan et al. 2010). The identified areas include those within a salience network that includes cortico-striato-thalamic-cortical loops (Menon 2015, Peters et al. 2016) and demonstrates altered responses to environmental cues and reward signaling. While there is no universally-accepted definition of the reward system, frontostriatal brain regions (Chau et al. 2004) overlap with salience-network areas and contribute to allocation of attention to relevant stimuli (Menon 2015). In this context, this network may contribute to signaling rewarding or emotionally engaging stimuli, thereby influencing approach behaviours.

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Journal Pre-proof Animal studies examining reward and saliency processing also support roles for the orbitofrontal cortex (OFC) and mPFC in risky decision-making, with an important role for the OFC in valuation signaling, particularly during risky decision- making with high learning demands (Winstanley and Clark 2016). For example, medial OFC inactivation in rats can increase risky choices and perseverative responding (Stopper et al. 2014). In animal models, lesions in the OFC and basolateral amygdala impair optimal choice selection (Zeeb et al. 2010,

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Zeeb and Winstanley 2011, Zeeb and Winstanley 2013). The anterior insula also contributes to

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cost-benefit decision-making; similar to its role in humans, this area plays a role in guiding

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behaviour according to internal states (Daniel et al. 2017).

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Over the past two decades, there have been advances in dissociating neural substrates underlying different components of reward, particularly those involved with motivational versus

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hedonic processing (Knutson et al. 2001, Knutson et al. 2001b). An ongoing discussion in GD

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research is whether reward processing in disordered gambling populations is best characterized as hyporesponsivity or hyperresponsivity during anticipation and receipt of monetary rewards

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(Luijten et al. 2017, van Holst et al. 2017). Characterizing anticipatory signaling is particularly

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important in decision-making given its temporal placement immediately prior to choice (Knutson et al. 2001, Knutson et al. 2001b). Early neuroimaging studies in non-clinical populations demonstrated how probabilistic reward delivery may increase anticipatory ventral striatal signaling (Knutson et al. 2001, Knutson et al. 2001b). Accordingly, several addiction theories are based on how this signaling might affect addictive tendencies and may change over the course of addiction (Koob and Le Moal 2005, Goldstein et al. 2009, Koob 2009, Goldstein and Volkow 2011, Berridge and Robinson 2016). In GD, alterations in anticipatory signaling could alter the perceived value of a reinforcer and thereby lead to deficient learning and maladaptive choices.

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Journal Pre-proof Some GD studies demonstrate increased anticipatory processing (van Holst et al. 2012), while others show blunted responses, particularly in prefrontal cortex and striatal areas, during reward anticipation (Balodis et al. 2012, Choi et al. 2012). In 2017, Luijten and colleagues published a meta-analysis of reward-task studies using monetary reward anticipation and/or outcome across SUDs and GD (Luijten et al. 2017). Using whole-brain T maps from separate neuroimaging studies in addicted populations, the researchers separated anticipatory processing

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from outcome reward processing across both SUD and GD groups. Across 20 studies,

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contrasting 526 individuals with addictive disorders and 505 control individuals, Luijten and

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colleagues found diminished anticipatory reward responses in the striatum bilaterally. This

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reduced anticipatory signaling held within both the SUD and GD groups. During the outcome phase, however, the SUD and GD groups diverged in both the direction and the location of

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activity: the SUD group showed increased ventral striatal activity during reward outcome relative

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to the non-addiction comparison group, whereas the GD group displayed decreased activity bilaterally in more dorsal striatal areas (Luijten et al. 2017). These meta-analytic findings

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provide a timely summary of a burgeoning monetary-reward-processing literature in GD;

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however, task heterogeneity across studies included in the meta-analysis complicates interpretation (van Holst et al. 2017). While task heterogeneity is a concern across most neuroimaging studies, Luijten and colleagues focused on consistent contrasts of the metaanalytic data that would capture distinct anticipatory and outcome phases. Nevertheless, the ability for a task to engage a participant and draw their attention may vary between tasks; in this way, a person’s reward and motivational systems may be stimulated or remain unaffected. Examining other behavioral and self-report measures confirming construct validity as well as effectiveness and clinical utility (e.g., in eliciting craving) is important as findings could possibly

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Journal Pre-proof reflect poor motivational or attentional processes rather than disorder-specific neuropathology. Another important consideration, particularly given the central role of learning in addiction theories, is to provide information on practice trials or group differences in achieving performance. Risk-reward decision-making may be guided by internal responses to losses and cues, and the Iowa Gambling Task involves decision-making that is influenced over time by winning

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and losing experiences (Bechara et al. 1994). Individuals with GD have shown differences on

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task performance (Cavedini et al. 2002) although not consistently (Balodis et al. 2018). In a

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study of 28 individuals with GD and 28 comparison subjects without GD (Balodis et al. 2018),

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ventral striatal activation during the prospect phase of both losing and winning conditions on the monetary incentive delay task was associated with out-of-the-scanner performance on the Iowa

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Gambling Task. The findings suggest that decision-making involving translating learned risk-

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reward likelihoods may operate in a transdiagnostic fashion. Nevertheless, to date, few direct neuroimaging comparisons exist between SUD and GD

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groups. The above-mentioned meta-analysis (Luijten et al. 2017) did not include a direct

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comparison between SUD and GD groups. These direct contrasts are much needed to clarify reward-signaling similarities and differences across substance-based and non-substance-based addictions. Clinical Reference Group Studies Several neuroimaging studies now include other clinical reference groups in the study of GD (see Table 1). Choi and colleagues (Choi et al. 2012) included a group with obsessivecompulsive disorder (OCD) in a reward-processing study and found reduced anticipatory striatal processing in the GD relative to the OCD group (Choi et al. 2012). While other studies in SUD

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Journal Pre-proof and at-risk populations report blunted anticipatory reward signaling (Beck et al. 2009, Andrews et al. 2011), no neuroimaging study has directly compared GD with SUD groups during anticipatory processing on the monetary incentive delay task. While much attention has focused on reward processing in GD, there is also a need to understand ‘top-down’ processing, including monitoring and response inhibition, processes that are also fundamental to goal-directed behavior. Inhibitory control alterations are linked to GD,

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and neuroimaging studies provide insights into underlying neural mechanisms. Assessing aspects

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of executive control, response-inhibition studies may provide information on multiple

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components including performance monitoring and behavioral adjustment.

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One study compared GD and tobacco-smoking groups (de Ruiter et al. 2012) on an inhibitory control task; both the GD and the smoking groups demonstrated reduced dorsomedial

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prefrontal cortex and dorsal anterior cingulate (dACC) activity relative to a comparison group.

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These hypoactivations overlapped between the groups for both successful and failed inhibition trials, thereby demonstrating similar alterations in inhibitory processing across substance- and

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non-substance based addictions. Additionally, in the GD group, the dACC hypoactivity

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correlated with problem-gambling severity, thereby linking problematic behaviors with neural correlates of inhibitory control. Nonetheless, it is noteworthy that with a motivational component on the task, recruitment of brain regions in GD may be more robust, even surpassing that in non-addicted control subjects and being associated with improved performance. Van Holst and colleagues (2012), using an affective Go/NoGo task with positive, gambling-related and negative stimuli demonstrated how salience may increase performance, which in turn is related to greater dlPFC, ACC and ventral striatal activity in the GD group (van Holst et al. 2012). The motivational component of a cue,

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Journal Pre-proof whether addiction-related or otherwise linked with an incentive motivational state, could play a central role in the fronto-striatal hypo- or hyper-responsivity observed (Leyton and Vezina 2013).

Near-Misses Relative to appetitive processing studies, few neuroimaging studies of GD to date have

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examined negative valence processing. A ‘near-miss’ is a unique type of loss in gambling

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research whereby an individual loses the trial, may experience negative affect, but may feel as if

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a win is near. This type of loss may produce a motivating effect to continue gambling and

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potentially increase a sense of control (Chase and Clark 2010, Clark et al. 2012, Sescousse et al. 2016). In individuals who gamble regularly, near-misses activate striatal and midbrain regions

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(Clark et al. 2009, Chase and Clark 2010). Two neuroimaging studies now report enhanced

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striatal responses to near-misses in GD populations (Worhunsky et al. 2014, Sescousse et al. 2016), potentially suggestive of a greater reward expectancy than loss expectancy. The inclusion

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of a cocaine dependent (CD) clinical comparison group during the slot-machine task also

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demonstrated increased anticipatory responding in similar fronto-striatal circuitry (Worhunsky et al. 2014), again demonstrating similarities in anticipatory signaling across SUD and GD populations. During near-miss loss outcomes, however, the GD group showed blunted responses in medial frontal, anterior cingulate and inferior temporal regions, relative to both the CD and control groups (Worhunsky et al. 2014), suggesting that these trials may be processed as less salient in this population. These findings suggest shared common alterations in near-miss signaling across SUD and GD groups, with divergences during outcome processing – consistent

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Journal Pre-proof with anticipatory-outcome findings reported in the reward-processing meta-analysis (Luijten et al. 2017).

FMRI BASED FUNCTIONAL

CONNECTIVITY

Moving Beyond Monetary/Gambling Cues To date, few neuroimaging studies of GD have directly examined reactivity to non-

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monetary/gambling cues. These types of studies are important as they shed light on whether

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imbalances in cue reactivity may be driven by hypersensitivity to monetary cues or reduced

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sensitivity to other types of reward. This has implications for understanding developmental and

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maintenance factors in GD. One study applied a modified incentive delay task in which participants anticipated viewing gambling or erotic cues (Sescousse et al. 2013). Relative to

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healthy controls, the GD group demonstrated altered bilateral ventral striatum responding which

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was related to decreased responsiveness to erotic cues, rather than increased response to gambling ones, which in turn was positively correlated with problem-gambling-severity scores

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(Sescousse et al. 2013). These findings suggest an imbalance in addiction-related versus non-

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addiction-related stimuli, particularly as problem-gambling severity was related to the difference in striatal signaling to gambling-related versus erotic cues and as the GD group responded more rapidly to the gambling cues (Sescousse et al. 2013). Despite similar subjective ratings to erotic arousability, blunted striatal signaling to erotic cues suggests that the GD group may not effectively encode the hedonic value of the erotic cues, thereby providing a possible neural explanation for the frequent co-occurrence of GD and compulsive sexual behaviors (Leeman et al. 2019) and indicating potential impairment in assessing relative value. Differences in subjective reward valuation across a task was highlighted by Miedl and colleagues (Miedl et al.

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Journal Pre-proof 2012) administering a delay-discounting task to a GD population; they found that while this task recruited ventral striatal and vmPFC areas, these signals appeared modulated by individual subjective values. When only categorical comparisons are made, differences in subjective valuation may be obscured (Miedl et al. 2012). A study has directly compared GD, CD and healthy control groups on a double-ornothing task designed to model the loss-chasing criterion in GD (Worhunsky et al. 2017). During

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the decision to quit chasing losses, individuals with GD as compared to those with CD or neither

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disorder recruited medial frontal circuitry implicated in executive control to a greater degree.

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However, those with CD demonstrated altered engagement of an amygdalar-striatal circuit,

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including during the processing of losses and during decision-making. This study highlights that loss-chasing behaviours might be driven by differential engagement of cortical and subcortical

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circuits across disorders.

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In another study directly comparing neural similarities and differences in craving in GD and SUD populations, individuals with GD, those with CD and those with neither were imaged

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as they viewed videos depicting gambling, cocaine use, or sad scenarios (Kober et al. 2016).

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Both the GD and CD groups reported increased gambling and cocaine urges following the gambling and cocaine videos, respectively. The CD group showed increased ACC/mPFC activity when initially viewing cocaine videos, relative to both the control and GD group. During the final period of video viewing, however, each experimental group demonstrated heightened activity in the dACC/dmPFC to their respective videos; the GD group showed heightened activity while viewing the gambling videos, the CD participants while viewing cocaine videos and the non-addicted control group while viewing sad videos.

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Journal Pre-proof Another study examined cue-reactivity and craving by using individually tailored cues for gambling preferences as well as images of highly palatable food (Limbrick-Oldfield et al. 2017). The investigators found heightened reactivity to gambling cues, but no difference to highly palatable food cues (Limbrick-Oldfield et al. 2017). The individually tailored gambling cues contrasted with gambling- matched neutral cues showed in the GD group increased activity in the insula, ACC, and superior frontal regions relative to the control group. Within the GD

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group, the bilateral insula and nucleus accumbens activity were positively correlated with

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craving scores. Analysis of functional connectivity identified increased connectivity between the

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nucleus accumbens and insula, which additionally was related to craving and depression scores.

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In the GD group, higher craving in response to gambling cues was inversely related to nucleus accumbens connectivity with the medial PFC. When contrasting food and food-matched neutral

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cues, there were no significant group differences. This study shows changes in activity and

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responses.

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connectivity related to gambling-related stimuli over natural rewards and linked with craving

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MRI-BASED STRUCTURAL STUDIES

Initial grey-matter volume studies in GD presented seemingly ambiguous findings. Several studies reported no differences with healthy control groups (van Holst et al. 2012, Fuentes et al. 2015) or increased ventral striatum and prefrontal cortex volumes using voxel-based morphometry (VBM) (Koehler et al. 2015). A region-of- interest approach identified smaller amygdalar and hippocampal volumes in GD that were linked to behavioral- inhibition tendencies (Rahman et al. 2014). There may also be a relationship between brain morphology and

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Journal Pre-proof intertemporal choice, with one study detecting positive relationships between delay-discounting k values and left insular and right occipital cortical volumes in GD (Mohammadi et al. 2016). One large VBM studied individuals with GD, CD and those with neither disorder found that the CD group differed from the other two by showing relatively decreased volumes in ventral to dorsal regions of the PFC (Yip et al. 2018). While these findings may suggest potential neurotoxic effects related to cocaine use (Beveridge et al. 2008), findings from a meta-analysis

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of internet gaming disorder showed lower PFC volumes in overlapping regions (Yao et al. 2017).

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As such, longitudinal studies should examine whether the identified PFC volumetric differences

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precede or are subsequent to addictive behaviors. Furthermore, across GD, CD and control

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groups, self-reported impulsivity was inversely correlated with volumes in the insula and subcortical regions including the amygdala and hippocampus, further highlighting the

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importance of considering relevant transdiagnostic measures in understanding neurobiological

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correlates relevant to human health and functioniing (Yip et al., 2018). Another large morphology study (Zois et al. 2017) investigated grey-matter volume

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alterations in 107 individuals with GD, subdividing individuals by SUD status. Specifically,

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individuals were subdivided into a ‘pure’ group of sixty GD individuals with no comorbid conditions, an ‘alcohol’ and a ‘polysubstance’ group. The ‘pure’ GD group, (consisting of males with mostly electronic-gambling- machine problems) showed decreases in grey-matter volume in superior medial and orbitofrontal cortices independent of SUDs. This large study presents evidence for frontal morphometric alterations in GD that appear not to be related to SUDs. Nonetheless, these findings are in contrast to volume reductions noted in SUDs in striatal areas (Kubota et al. 2001, Sullivan et al. 2005, Wrase et al. 2008) and therefore potentially clarify some neurotoxic effects of alcohol on the brain. Nevertheless, the seemingly contradicting

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Journal Pre-proof findings within the GD studies highlight the importance for larger cohorts matched for other drug use, particularly controlling for alcohol and cigarette use. Several studies have reported white-matter-related differences in main white-matter tracts and prefrontal regions in GD, suggestive of early neuropathology prior to larger volume changes (Joutsa et al. 2012, Mohammadi et al. 2016, Majuri et al. 2017, Yip et al. 2017), although more research is necessary to understand whether white-matter integrity changes over time and if so

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how these changes may related to cognitive functioning in GD. Recently, a larger study

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investigated white-matter architecture, examining crossing fibers and white-matter

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microstructural features, including diffusion estimates of primary and secondary fiber

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orientations (Yip et al. 2017). Both GD and CD groups showed reduced anisotropy in corticolimbic tracts, including the internal capsule, forceps major and thalamic radiation, relative

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to a control group. Notably, alcohol-use disorders did not account for the findings and the GD

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and CD groups did not differ from one another in these regions. Similarly, Mohammadi et al. found reduced fractional anistropy in the GD group in similar tracts – with an inverse

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relationship between fractional anistropy in the inferior fronto-occipital fascicle and delay-

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discounting k values (Mohammadi et al. 2016). Altogether these findings suggest reduced whitematter integrity relates importantly to GD. Given that many of these studies included individuals with few comorbid conditions, these findings lend support to the idea of reduced integrity is not exclusively accounted for by the direct effects of drugs of abuse on these tracts.

Treatment Studies In the past 5 years, neuroimaging studies have begun to investigate neural correlates of treatment outcome and to apply neuromodulatory techniques to treat GD symptoms. Noted

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Journal Pre-proof alterations in prefrontal circuitry in GD make the PFC a potential target area for treatment. The first study applying neuromodulation as a treatment option for GD used repetitive transcranial magnetic stimulation (rTMS) (Rosenberg et al. 2013), applying inhibitory frequency of 1 Hz to the left PFC. The rationale was based on previous neuroimaging studies reporting dlPFC activity during cue-exposure in GD as well as studies in healthy populations where low-frequency, repetitive TMS to the dlPFC affected inhibitory control resulting in risky decision-making

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(Knoch et al. 2006). The GD pilot study consisted of a very small sample (N=5), and although

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patients initially described decreases in addiction-related symptomology, following several

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weeks of TMS to the dlPFC, there were no significant therapeutic effects as all patients

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continued gambling (Rosenberg et al. 2013). As such, questions remained regarding the longterm efficacy of rTMS.

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A more recent rTMS study (Zack et al. 2016) used a sham-controlled, repeated-measures,

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counter-balanced design in 9 males with GD as they performed delay-discounting, risky decision-making and Stroop tasks. In contrast to the Rosenberg study, these investigators applied

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active stimulation: high-frequency rTMS to the mPFC and continuous theta burst stimulation

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(cTBS) to the right dlPFC. These brain areas were targeted based on previous studies in healthy individuals demonstrating dlPFC stimulation effects on cognitive-executive functions and mPFC stimulation influence on affective-motivational functions (Steele and Lawrie 2004). Relative to the sham condition, rTMS and cTBS did not alter delay discounting, nor bet size or speed of play on a slot-machine task. Altogether, the inability of tDCS and cTBS to alter these cognitive constructs in GD, despite previous stimulation studies altering delay discounting in non-GD groups, suggests that altered cognitive functioning in GD may not be readily amenable to these interventions. Further, on the Stroop task, both forms of active stimulation increased Stroop

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Journal Pre-proof interference scores, suggesting increased impairment of attentional control. Post-game, however, the active stimulation groups reported less gambling desire, relative to the sham condition. In particular, cTBS to the right dlPFC produced both subjective and physiological alterations: participants reported decreases in self-reported arousing effects of gambling and displayed reduced diastolic blood pressure. Another study (Gay et al. 2017) used a randomized sham-controlled crossover design in a

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larger sample of 22 treatment-seeking GD patients. Relative to the sham session, high-frequency

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rTMS to the left dlPFC was associated with reduced cue-induced craving in GD patients. These

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findings are consistent with those in SUD populations that have reported reduced cue-induced

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craving following left dlPFC stimulation (Jansen et al. 2013, Grall-Bronnec and Sauvaget 2014, Hone-Blanchet et al. 2015). Additionally, the findings are consistent with other rTMS findings of

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reduced gambling desire following slot-machine gambling (Zack et al. 2016), although

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stimulation parameters and brain regions differed between studies. The Gay et al. study only used a single treatment session, and larger studies with repeated sessions are needed to examine

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further the potential efficacy or rTMS. Across all rTMS studies in GD to date, the treatment

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appears well tolerated with few adverse effects. Nevertheless, more research is necessary to determine the number and duration of sessions, the specificity of brain locations and the stimulation patterns.

Combining methodologies to disentangle heterogeneity The combination of multiple methodologies provides intriguing clinical implications for GD. One of the first such studies by Grant and colleagues (Grant et al. 2013) examined a polymorphism in the gene coding for the enzyme catechol-o-methyl-transferase (COMT). This

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Journal Pre-proof polymorphism leads to proteins with differing abilities to catabolize cortical dopamine. Tolcapone, a COMT inhibitor, may influence gambling urges and inhibitory control. Individuals with the COMT val/val polymorphism (one linked to a protein with greater enzymatic activity) receiving tolcapone reported reduced gambling urges and behaviours. This improvement was further related to increased fronto-parietal activity during the Tower of London task in the scanner. These multiple methodologies, combining genetic and neural information, provide

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information on who might best respond to specific treatments as well as the mechanisms by

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which the treatments may operate (Grant et al. 2013), as has been suggested in cognitive

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domains suggested for targeting in GD treatment and the neural correlates of these domains

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(Potenza et al. 2013). As multiple cognitive distortions have been implicated in GD (Potenza 2014, Potenza et al. 2019), they may represent important treatment targets. As genetic

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polymorphisms in other genes have been linked to the behavioral and neural correlates of other

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processes relevant to GD treatment (e.g., emotional processing (Yang et al. 2016)), a wider range of relevant domains warrant consideration in GD treatment, with common genetic

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approaches.

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polymorphisms representing a potential opportunity for developing precision- medicine

While these early neurogenetic studies integrate various research streams, the approaches may be limited by various conceptual and methodological shortcomings. Categorical diagnoses used in psychiatry are not ideal for complex genetic-environmental etiologies; moreover, many of these studies are limited by multiple comparison problems, the absence of replication samples, or inclusion of highly selected samples, all of which increase the likelihood of detecting a genotype association (Barnett et al. 2008, Grabitz et al. 2018).

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Journal Pre-proof More recently, tolcapone effects were examined in a randomized double-blind placebocontrolled, within-subject study including 17 individuals with problem and/or pathological gambling (PPG) (Kayser et al. 2017). The investigators found that increased right inferior frontal cortex (rIFC) activity during placebo was related to larger decreases in impulsivity when on tolcapone. Additionally, investigators found that tolcapone increased functional connectivity between the rIFC and the striatum, suggesting a possible mechanism by which tolcapone might

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improve delay discounting and top-down control in individuals who have greater baseline rIFC

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activity. While it is unclear whether this increased rIFC activity may represent a compensatory

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response by which individuals require greater activity to recruit inhibitory control, the results

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provide preliminary evidence that regulating prefrontal-to-subcortical connectivity may provide

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Limitations and Future Directions

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a means of adjusting impulsive behaviour in GD (Kayser et al. 2017).

A current limitation in GD research relates to the use of varied populations in

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neuropsychological and neuroimaging studies. For example, some studies use clinically defined

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populations based on DSM-IV or DSM-5 criteria, whereas others use self-report measures such as scores on the South Oaks Gambling Screen (SOGS) thresholded at varying levels. Several studies demonstrate group differences in problem gamblers versus those with full GD; therefore, it will be important to investigate whether “at-risk” groups show categorical or continuous differences on gambling-related cognitive constructs. Additionally, there is a dearth of studies with recreational-gambling comparison groups, which could provide insight into how some individuals may gamble without experiencing significant difficulties.

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Journal Pre-proof GD research indicates substantial heterogeneity; an important future direction may involve assessment of motivations (e.g. arousal versus coping) as well as cognitions as these may vary considerably between individuals in manners relevant to GD (Potenza 2014). There are also sex-/gender-related differences noted in the motivations for gambling as well as in GD prevalence rates; however, to date most neuroimaging studies in GD have been underpowered to investigate potential sex- or gender-related differences. Identifying neural correlates of gambling

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subtypes is also a potentially important future direction, with possible implications for guiding

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treatment. The heterogeneity of GD, combined with the varied and often multidimensional tasks

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employed in the scanner, may produce a range of seemingly ambiguous results; therefore,

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replication of neuroimaging findings is important, as are findings from meta-analyses. As reviewed, more recent neuroimaging studies in GD have begun including clinical

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reference groups, in particular those with SUDs. This represents an important effort in

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characterizing the degree of overlap in specific addiction features. This early work suggests shared alterations in inhibitory control across SUD and GD groups, characterized by reduced

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mPFC/dACC recruitment. Additionally, several studies now report heightened cue reactivity in

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mPFC/dACC areas in these groups to addiction-specific stimuli. Nevertheless, other clinical comparison groups accounting for other comorbid conditions (e.g. mood disorders with shared features of poor emotional regulation) may also reveal specificity of findings to addiction. Also often overlooked is the inclusion of personality disorders, which are particularly prevalent in GD (Petry et al. 2005, Black et al. 2014) and to problem gambling at sub-diagnostic levels (Desai and Potenza 2008). Finally, most neuropsychological and neuroimaging studies in GD to date use cross-sectional designs. There is a great need for longitudinal studies in GD to examine task variability as well as changes in cognitive and neural measures over disorder progression or with

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Journal Pre-proof treatment. Additionally, to date, no neuroimaging study has examined problematic gambling in other populations at seemingly high risk for GD, including but not limited to adolescents and traumatized and socially disadvantaged groups.

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Conflicts and Disclosures No funding agencies had input into the content of this manuscript, and none of the authors have any relevant conflicts of interests. Dr. Potenza has consulted for Opiant/Lakelight Therapeutics, Addiction Policy Forum, AXA, Game Day Data and Jazz Pharmaceuticals; has received research support (to Yale) from Pfizer, Mohegan Sun Casino and the National Center for Responsible Gaming; has participated in surveys, mailings or telephone consultations related to drug addiction, impulse-control disorders or other health topics; has consulted for gambling and legal entities on issues related to impulse-control/addictive disorders; provides clinical care in a problem gambling services program; has performed grant reviews for the National Institutes of Health and other agencies; has edited journals and journal sections; has given academic lectures in grand rounds, CME events and other clinical or scientific venues; and has generated books or book chapters for publishers of mental health texts. The other authors have no disclosures.

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Funding and Acknowledgments This work was supported in part by the Gambling Research Exchange of Ontario (GREO); the Peter Boris Centre for Addictions Research; R01 DA019039 from the National Institutes of Health; the Connecticut Mental Health Center; and a Center of Excellence in Gambling Research Award from the National Center for Responsible Gaming.

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Zack, M., S. S. Cho, J. Parlee, M. Jacobs, C. Li, I. Boileau and A. Strafella (2016). Effects of High Frequency Repeated Transcranial Magnetic Stimulation and Continuous Theta Burst Stimulation on Gambling Reinforcement, Delay Discounting, and Stroop Interference in Men with Pathological Gambling. Brain Stimul 9(6): 867-875.

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Zeeb, F. D., S. B. Floresco and C. A. Winstanley (2010). Contributions of the orbitofrontal cortex to impulsive choice: interactions with basal levels of impulsivity, dopamine signalling, and rewardrelated cues. Psychopharmacology (Berl) 211(1): 87-98.

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Zeeb, F. D. and C. A. Winstanley (2011). Lesions of the basolateral amygdala and orbitofrontal cortex differentially affect acquisition and performance of a rodent gambling task. J Neurosci 31(6): 2197-2204.

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Zeeb, F. D. and C. A. Winstanley (2013). Functional disconnection of the orbitofrontal cortex and basolateral amygdala impairs acquisition of a rat gambling task and disrupts animals' ability to alter decision-making behavior after reinforcer devaluation. J Neurosci 33(15): 6434-6443. Zois, E., F. Kiefer, T. Lemenager, S. Vollstadt-Klein, K. Mann and M. Fauth-Buhler (2017). Frontal cortex gray matter volume alterations in pathological gambling occur independently from substance use disorder. Addict Biol 22(3): 864-872.

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Journal Pre-proof Clinical Reference Group Studies Populations GD & OCD

Construct/Task/Methodology Reward Processing/ MIDT

De Ruiter et al., 2012

GD & TUD

Inhibitory Control/SST

Worhunsky et al., 2014

GD & CUD

Near-Miss/Slot-machine task

Worhunsky et al., 2017

GD & CUD

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Loss Chasing

Cue reactivity/ watching videos

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Kober et al., 2016

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fMRI Studies

Table 1

PET

Majuri et al., 2017

GD and BED

Opioid and dopamine function

Main Findings  anticipatory striatal processing in GD relative to OCD group  decreased activity in dACC areas in both GD & TUD groups during successful and failed inhibition trials; hypoactivity in the dACC correlated with problem-gambling severity  fronto-striatal signaling in both CUD and GD groups during anticipation;  medial frontal-ACC activity during nearmiss loss outcomes in GD group only. GD group shows  recruitment of medial frontal circuitry during choice to quit chasing losses. GD group shows  ACC/mPFC activity while watching gambling videos; CUD group shows  ACC/mPFC activity while watching cocaine videos BED group shows  [11C]carfentanil BP ND and Ki values with [18F]fluorodopa, relative to the GD group.

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Journal Pre-proof Van Holst et al., 2012

GD & AUD

Voxel-based morphometry

Yip et al., 2018

GD & CUD

Voxel-based morphometry

Grey matter volume

GD & CUD

White-matter

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GD-only; GD+AUD; GD+polysubstance

GD-only group shows  grey matter in superior medial and orbitofrontal cortices relative to groups with SUDs Both GD and CD groups have  anisotropy in corticolimbic tracts.

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Structural

Zois et al., 2017

 grey matter volume in AUD group in frontal, insular, striatal and parietal areas relative to the GD group.  ventral and dorsal PFC in CUD relative to GD group.

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Abbreviations: fMRI= functional magnetic resonance imaging; PET= Positron Emission Tomography; GD= Gambling Disorder; OCD= Obsessive-Compulsive Disorder; TUD= TobaccoUse Disorder; MIDT= Monetary Incentive Delay Task; SST = Stop-Signal Task; dACC= dorsal anterior cingulate cortex; CUD= Cocaine-Use Disorder; AUD= Alcohol-Use Disorder; SUDs= Substance-Use Disorders.

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Journal Pre-proof Conflict of Interest and Disclosures

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The authors have no pertinent disclosures or conflict of interest. Over the past three years, Dr. Potenza has received financial support (to Yale or personally) for the following. He has consulted for and advised Game Day Data, Addiction Policy Forum, and Opiant Therapeutics; received research support from the Mohegan Sun Casino and the National Center for Responsible Gaming; consulted for or advised legal and gambling entities on issues related to impulse control and addictive behaviors; provided clinical care related to impulse-control and addictive behaviors; performed grant reviews; edited journals/journal sections; given academic lectures in grand rounds, CME events and other clinical/scientific venues; and generated books or chapters for publishers of mental health texts. Dr. Balodis reports no disclosures.

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Journal Pre-proof Highlights

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Gambling disorder (GD) and substance-use disorders (SUDs) often co-occur. Transdiagnostic features (e.g., impulsivity) are shared by GD and SUDs. Blunted striatal activation during reward anticipation is seen in GD and SUDs. Unique features of GD and SUDs may relate to substance-related toxicities. More research should translate biological understanding to treatment advances.

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