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Published in final edited form as: Addict Behav. 2024 Apr 4;155:108027. doi: 10.1016/j.addbeh.2024.108027

Intrinsic Connectivity Demonstrates a Shared Role of the Posterior Cingulate for Cue Reactivity in Both Gambling and Cocaine Use Disorders

Anthony G Vaccaro 1, Cheryl M Lacadie 2, Marc N Potenza 3,4,5,6,7,8
PMCID: PMC11273263  NIHMSID: NIHMS1987311  PMID: 38581751

Abstract

Cue reactivity is relevant across addictive disorders as a process relevant to maintenance, relapse, and craving. Understanding the neurobiological foundations of cue reactivity across substance and behavioral addictions has important implications for intervention development. The present study used intrinsic connectivity distribution methods to examine functional connectivity during a cue-exposure fMRI task involving gambling, cocaine and sad videos in 22 subjects with gambling disorder, 24 with cocaine use disorder, and 40 healthy comparison subjects. Intrinsic connectivity distribution implicated the posterior cingulate cortex (PCC) at a stringent whole-brain threshold. Post-hoc analyses investigating the nature of the findings indicated that individuals with gambling disorder and cocaine use disorder exhibited decreased connectivity in the posterior cingulate during gambling and cocaine cues, respectively, as compared to other cues and compared to other groups. Brain-related cue reactivity in substance and behavioral addictions involve PCC connectivity in a content-to-disorder specific fashion. The findings suggesting that PCC-related circuitry underlies cue reactivity across substance and behavioral addictions suggests a potential biomarker for targeting in intervention development.

Keywords: Addictive behavior, impulsive behavior, compulsive behavior, gambling disorder, cocaine use disorder, craving, posterior cingulate cortex, functional magnetic resonance imaging

INTRODUCTION

Cue reactivity and craving are important aspects of addictions that involve affect, conditional learning, and attentional biases (Elton et al., 2021; Franken, 2003; O’Brien et al., 1998; Robinson & Berridge, 1993; Welberg, 2013). Cue reactivity and craving are of clinical relevance across drug and behavioral addictions, relating prospectively to recovery and relapse (Allenby et al., 2020; Antons et al., 2020; Epstein et al., 2010; Hormes, 2017).

Gambling disorder (GD), classified as a behavioral addiction in the DSM-5 (American_Psychiatric_Association, 2013) and ICD-11 (World_Health_Organization, 2019), is a significant public health concern that shares similarities with and exhibits differences from drug addictions like cocaine use disorder (CUD) (Potenza et al., 2019). Preliminary data from video-tape-based cue-elicitation fMRI tasks comparing neural correlates of gambling urges in GD and cocaine cravings in CUD have suggested shared neural contributions in the posterior cingulate cortex (PCC), precuneus, thalamus, ventral striatum, lingual gyrus, and insula, and group-specific activity in the inferior parietal and anterior cingulate cortices (ACC) (Potenza, 2008). A subsequent larger study suggested shared neural correlates of cue reactivity and craving in the medial prefrontal cortex (mPFC) and ACC for relevant cues in a disorder-specific fashion (Kober et al., 2016). However, diagnostic-group-related neural correlates in the ventromedial and dorsomedial prefrontal cortices were also suggested, as were gender-related differences in insula activation to gambling cues in individuals with GD (Kober et al., 2016). While differences exist in the neurobiologies of GD and CUD, it has been suggested that features such as reduced frontostriatal activation in response to reward anticipation may be shared between GD and substance use disorders (SUDs) (Balodis & Potenza, 2020), as well as related conditions (Mestre-Bach and Potenza, 2023).

To date, most studies of the neural correlates of cue reactivity and urges/cravings for gambling and cocaine have largely focused on activation. However, functional connectivity analyses may provide complementary and unique insights into the neurobiological nature of cue reactivity and craving processes. Activation-related circuit-based findings have suggested that individuals with GD and CUD may differentially engage circuits during processes such as loss-chasing (Qiu & Wang, 2021; Worhunsky et al., 2017), emotion regulation (Picó-Pérez et al., 2022), and processing of near-miss events (Worhunsky et al., 2014). Additionally, resting-state functional connectivity studies have found increases in anterior default mode network (DMN) connectivity, decreases in posterior DMN connectivity, and overall less functional interaction for DMN with limbic and executive networks (Zhang & Volkow, 2019). These findings suggest that disorder-related similarities and differences in functional connectivity during cue responsiveness may provide additional insight into neurocognitive features of addictions beyond what region-specific activation can reveal, and help inform treatment-development-related efforts (Antons et al., 2020; García-Castro et al., 2022; Sutherland et al., 2012).

Connectivity approaches complement regional activity analyses, with the latter providing particular insight into functional networks (or connections within networks) that may more closely align with how the brain operates and links to motivations and behaviors. Traditional seed-based approaches to functional connectivity rely on a priori hypotheses for seed selection, informed by theories arising from the previous literature. This approach is potentially biased by activation-related results, thus limiting the extent to which novel insights may be derived. Data-driven approaches (that may also involve hypotheses but may have fewer restrictions) have the potential to identify novel results not constrained to a priori hypotheses. The intrinsic connectivity distribution (ICD) is a data-driven approach that can assess the degree to which each voxel in the brain is correlated with every other voxel (Scheinost et al., 2012). From a functional standpoint, this metric may be especially sensitive to state-related changes in regional dynamics, reflecting shifts in the broader functional networks in which regions are interacting, and within which they are situated (Salehi et al., 2020). For example, a recent study on smoking and non-smoking compared activation approaches, traditional functional connectivity approaches, and ICD methods. The ICD analyses found unique differences in corticolimbic connectivity that were specific to the current state of nicotine satiety (Yip et al., 2022) Additionally, in a study of youth prenatally exposed to cocaine, a group-by-condition interaction implicated the PCC, with post-hoc analyses showing that the drug-exposed versus non-drug-exposed group showed less PCC connectivity specifically during a stress condition (Zakiniaeiz et al., 2017). These results additionally highlight the link between the ICD approach and inferences about the neural dynamics behind the neurocognitive states important for understanding addictive disorders. Insights into cue reactivity and craving, and how the states that facilitate them are similar and different across diagnoses, could shed valuable insights into addictions.

Although prior ICD studies have been used to investigate functional connectivity in SUDs related to cocaine (Mitchell et al., 2013), tobacco (Garrison et al., 2016; Yip et al., 2022) and alcohol (Zakiniaeiz et al., 2016), as well as with internet gaming disorder (Liu et al., 2021), it has yet to be applied to GD. In this study, we used ICD to examine data from a sample of individuals with GD, CUD and HC subjects as previously reported (Kober et al., 2016). The previous activation-based analyses found increased activity in the ACC and mPFC in response to each group’s disorder-relevant cues. However, these midline nodes have dynamic relationships within broader functional networks, and the regional activity level may not elucidate the entire picture of the neurocognitive processes behind brain-related cue reactivity (BRCR). A recent study using connectome-based predictive modeling on a subset of these data investigated functional canonical networks, such as executive networks and the DMN, implicating regions and network interactions in cocaine cravings and gambling urges (Antons et al., 2023). These whole-brain results suggest that the connectivity of midline DMN nodes may contribute to craving across diagnoses, and our current analyses aim to complement previous work by investigating functional connectivity dynamics using ICD. We hypothesized that the connectivity of midline DMN nodes, such as the PCC and mPFC, would be decreased in BRCR across addictive disorders in a disorder-and-cue-specific fashion. We also hypothesized increased whole-brain functional connectivity in prefrontal regions in healthy versus addicted groups, based on findings relating to top-down control over cue-related reactivity (Albein-Urios et al., 2014; Kober et al., 2010).

METHODS

Ethics

Study procedures were approved by the Yale Human Investigation Committee and followed the Declaration of Helsinki. All participants signed written informed consent form before participation.

Procedure

103 subjects were identified from a recruited sample which initially included 114 participants, as described previously (Kober et al., 2016). Thirteen subjects were excluded from analysis due to meeting diagnostic criteria for both GD and CUD. Twenty-eight had pathological gambling (now termed GD), 30 had cocaine dependence (now termed CUD), as determined by clinical interview for DSM-IV diagnoses (Kübler, 2013) and the structured clinical interview for pathological gambling (Grant et al., 2004). The remaining 45 were healthy control participants (HCs). The behavioral results and full details on participant recruitment, demographic data are described elsewhere (Kober et al., 2016).

After removing runs with greater than 0.4mm framewise displacement due to the sensitivity of ICD to motion, we included 86 participants for ICD analysis (22 with GD, 24 with CUD, and 40 HCs).

Group Differences

The CUD group was older than the other two groups (in years; M=41.63 vs. M=31.54 and M=29.80 for GD and HC groups, respectively; p<.001 for each pair-wise comparison). The CUD group also differed (p=.001) from the other two groups in terms of race, being mostly Black. The CUD and GD groups reported more tobacco use compared to the HC group (Kober, et al., 2016). The GD and CUD groups had more co-occurring diagnoses than the HC group, as expected given the exclusionary criteria for the HC group and the co-occurring disorders often found in CUD and GD (Potenza et al., 2019). The GD and CUD groups did not differ at p<0.05 on measures of any co-occurring diagnosis (Table S1).

Task

The task and video content were based on previous cue-based fMRI craving studies in CUD and GD populations (Kosten et al., 2006; Potenza et al., 2003; Wexler et al., 2001), as detailed in (Kober et al., 2016). Subjects viewed six videos in the scanner depicting gambling-related, cocaine-related, and sad scenarios, each with two videos involving one male actor and one female actress, in a counterbalanced order. In the videos, actors described and portrayed gambling, cocaine, and sad situations/activities, while also describing their motivations for what they were doing and how they were feeling during and after the activity. After each video, subjects rated their cravings to use cocaine and their urges to gamble on a 1–10 scale, with 10 being strongest. A previous study on this dataset, investigating regional brain activation, identified a three-way interaction between type of subjective response, diagnostic group, and video type that implicated cortical and subcortical regions (Kober et al., 2016). Subjective responses also differed by group and condition with the strongest gambling urges reported by the GD group during gambling videos and the strongest cocaine cravings by the CUD group during cocaine videos (Kober et al., 2016). We demonstrate this for the average ratings in our sample of 86 in the Supplementary Materials (Figure S1), though subjective ratings are not available from all trials due to acquisition failures in the initial scanning (Kober, et al., 2016). Given that ICD analyses require several minutes of contiguous fMRI data (Scheinost et al., 2012), we analyzed the entire period of tape viewing, as done previously (Potenza, 2008). Each subject had a total of 6 blocks- two from each condition - with each video block being 3.0 to 4.5 minutes.

Imaging parameters

Subjects were scanned using a 3T Siemens Trio MRI system. Functional images were acquired in the axial plane parallel to the AC-PC line (TR/TE =1500/27 ms, flip angle = 60°, field of view= 220 × 220 mm, and 25 × 5 mm slices). High-resolution 3D Magnetization Prepared Rapid Gradient Echo structural images also were performed, for multi-subject registration (TR/TE =2530/3.34 ms; flip angle = 7°, field of view=256 × 256 mm, and 176 × 1 mm slices).

Pre-processing

For each run, the first 6 images were removed from analyses. SPM8 (Wellcome Trust Imaging Laboratory, UK) was used for slice-time and motion correction. Trials with linear motion greater than 2mm or rotation greater than 3 degrees were removed. Runs with greater than 0.4mm of frame-wise motion were removed. Our final sample did not show motion differences between groups or tasks (Table S2). Signals from cerebrospinal fluid and white matter were removed via regression. Images were smoothed with a 6mm full-width half-maximum Gaussian filter. Bandpass temporal filtering was applied to remove low-frequency drift and high-frequency noise (0.01–0.10 Hz). Functional images were warped into Montreal Neurological Institute space using BioImage Suite (Papademetris et al., 2006). Data were then converted to AFNI format (http://afni.nimh.nih.gov).

Single-subject ICD analyses

For each condition (gambling, cocaine, sad), two ICD analyses were run per subject using BioImage Suite: one mean-adjusted to the subject’s average whole-brain intrinsic connectivity, and one non-mean-adjusted (Scheinost et al., 2012). The ICD analyses were performed by determining the correlation of each voxel’s time course with every other voxel’s time course, within a whole-brain gray-matter mask. Unlike other voxel-based connectivity methods, ICD then models these correlations as a degree distribution. This data-driven approach to connectivity eliminates the need to specify a particular threshold at which correlated voxels are considered functionally connected, as it represents connectivity at all possible thresholds. Finally, this distribution is fit as a survival function where the variance, alpha, is greater for voxels that have larger numbers of high correlations. For non-mean-adjusted ICD, alpha is the variable representing the connectivity of each voxel for between-group analyses. For mean-adjusted ICD, the alpha in each voxel is then divided by the standard deviation of alphas to produce a mean-adjusted version of alpha. This creates a z-score-like metric to represent connectivity in each voxel relative to that individual’s brain. Significance was set at a voxel-level of p<0.001 with a cluster-forming threshold of p<0.05. More information is available in the Supplementary Materials.

Statistical analyses

We first ran four voxel-based linear mixed effect (LME) analyses for the effects of group and task condition. These consisted of two each for mean-adjusted and non-mean-adjusted intrinsic connectivity: one with gender as a covariate and one without. Because the CUD group was older than the other two groups, we also then ran LME analyses on the mean-adjusted and non-mean-adjusted analyses with age as an additional covariate. This gave a total of 6 LME analyses.

We focused on the mean-adjusted LME analyses with age and gender as co-variates to test our stated hypotheses as we considered the others preliminary, although they showed similar results (Figure S2).

In post-hoc analyses, independent t-tests were used to compare between-group differences in alpha for gambling, cocaine, and sad conditions in the region significant for the group-by-task-condition interaction effect. In cases where variances were significantly different, Welch’s t-tests were used instead. Pair-wise t-tests were also performed for this region within groups and between conditions (i.e., comparing alphas for gambling cues and cocaine cues among GD subjects). For both inter-group and intra-group comparisons, significance was determined at alpha of p<.05 after Bonferroni correction, which corresponded to an unthresholded p-value of p<0.0027.

Exploratory seed-based follow-up

We used the PCC cluster as our ROI in a seed-based functional connectivity analysis to try and shed light on what specific network connections with the PCC may explain the significant group-by-task interaction.

RESULTS

Our main result came from the LME analyses of the non-mean-adjusted ICD data with gender and age as covariates (Figure 1). The results of the other mean-adjusted models, and from using the fully sample of 103 subjects, were largely similar (Figure S1). The primary analysis identified a cluster in the PCC for the interaction effect of group–by-task-condition (Figure 1). These analyses did not find any significant clusters for a main effect of group. For the effect of task, clusters were significant in audiovisual regions, as might be expected from a video-taped-based task.

Figure 1: Area of the posterior cingulate implicated in the interaction of group by condition.

Figure 1:

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Significance was set at a voxel-based level of p<.001 with a cluster-forming threshold of p<.05. Mapped in MNI space, slices are displayed from approximately z=25 to z=49, evenly spaced. The PCC cluster is 1046mm3 in volume and centered at (3, −40, 38), with peak coordinate (3, −39, 39). The cluster is presented in MNI space and displayed with right hemisphere on the left. Post-hoc intergroup and intragroup comparisons were performed based on the condition-specific connectivity in this cluster.

Differences in PCC connectivity

To understand the nature of the PCC interaction, we extracted a metric of connectivity from the region by condition for each individual to conduct post-hoc analyses investigating the group and condition differences related to PCC connectivity (Figure 2). Within the three groups, three significant differences related to the interaction were identified. The GD group showed significantly less connectivity during gambling cues compared to cocaine cues (p=.0003; t-statistic= −4.24). The CUD group showed significantly less connectivity during cocaine cues as compared to both gambling cues (p<.0024; t-statistic= −3.41) and sad cues (p<.00002; t-statistic= −5.31). Additional information may be found in supplemental materials (Figure S3).

Figure 2: Intragroup differences in mean connectivity of the PCC by condition and group.

Figure 2:

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PCC= Posterior cingulate cortex. * denotes significance at a Bonferroni corrected alpha of p<.05 (p<0.0027 uncorrected). Welch’s t-test was used to determine significance.

Using the PCC cluster for a seed-based functional connectivity analysis did not result in any significant regions for the effect of group, or the effect of group by task at a threshold of p<0.001. Frontoparietal regions, auditory cortices, and the parieto-occipital sulcal region were significant for the effect of task (Figure S4.1). At p<0.005, a broader swath of the PCC was significantly connected with itself (Figure S4.2).

DISCUSSION

We used a data-driven connectivity approach to investigate intrinsic connectivity differences between CUD, GD and HC groups during a videotape-based cue-exposure fMRI task. A group-by-task interaction implicated the PCC in which diagnostic-group/cue-condition relationships were observed – a region implicated in previous studies of functional connectivity in addiction, but not in the previous activation analyses of these data. These results suggest the importance of this region in the overall functional coordination of BRCR across a behavioral and drug addiction.

PCC connectivity

For the CUD and GD groups, the PCC exhibited the lowest connectivity during processing of disorder-related stimuli in a disorder-related fashion. As a core node in the DMN, the PCC contributes to integrating information from and modulating attention towards internal sources (such as affect and memories) and external sources (like sensory cues), and in “self-related” processing (Damasio, 2010; Fransson & Marrelec, 2008; Johnson et al., 2002). It has been theorized that aberrant PCC function may associate with persistent or intrusive thoughts, or “getting caught up” in feelings or experiences, through imbalanced connections with other functional networks (Brewer et al., 2013b). This theory may be especially related to addictive conditions like GD, where dissociation during gambling has been reported (Cosenza & Nigro, 2015; Murch, Chu, & Clark, 2017; Spada, Giustina, Rolandi, Fernie, & Caselli, 2015).

The PCC contributes to networks that can predict the intensity of cravings transdiagnostically and for both drugs and food, as well as distinguish those who use drugs from those who do not (Garrison et al., 2023; Koban et al., 2023). This suggests that the PCC may relate to affective underpinnings of BRCR. Aside from GD and CUD, the PCC has been implicated in the experience of craving in internet gaming (Zhang et al., 2021), opioid use (Yang et al., 2021) and tobacco use (Haugg et al., 2022) disorders. A recent attempt to create a functional connectome predictive of BRCR across disorders using a distinct audio-tape-based task found that nodes which contributed the most predictive power to the model were in the PCC (Garrison et al., 2023). The functional networks coordinated by these nodes may be related to coordinating the distinct psychobiological networks involved in the development of disordered BRCR across disorders (Verheul et al., 1999). Our findings similarly implicate the PCC in reactivity across addictive disorders. Decreased overall connectivity of the PCC may be a shared neurocognitive feature across behavioral and substance addictions, and may reflect the posterior DMN’s shifting of connectivity away from externally oriented networks and non-craving-related cues (Brewer et al., 2013a; Zhang & Volkow, 2019). Such shifts may in part reflect involuntary motivational processes and have implications for individuals’ abilities/tendencies to voluntarily focus on other tasks, goals, and motivations when in the presence of relevant cues, as has been described in behavioral studies (Hønsi et al., 2013; Sayette et al., 2010). A study investigating the effects of nicotine craving on working memory found that craving states specifically impaired coupling between the DMN and executive and salience networks during working memory tasks, with nodes such as the PCC not exhibiting typical suppression (Lerman et al., 2014). These results were taken to suggest difficulties in volitional orienting towards task-related stimuli due to being “caught up” in craving experiences, and the finding regarding decreased whole-brain PCC connectivity may additionally reflect the DMN’s failure to appropriately task-switch with other networks. Such processes may in part explain difficulties that individuals with addictive disorders experience to voluntarily orient themselves towards important daily tasks. DMN networks may be hampered in their flexibility until homeostasis is restored by satiating craving, and such processes may in part explain why it may be difficult for people with addictions to orient away from motivations towards addictive stimuli in the setting of addiction-related cues..

Understanding the nature of the PCC in addictive disorders may provide clinical utility in certain populations. BRCR in the PCC has been related to relapse, suggesting a relationship with clinically relevant phenomena (Kosten et al., 2006; Li et al., 2012; Zhai et al., 2021). The use of methadone in successfully reducing relapse in people with opioid use disorders has been related to increased functional connectivity between the PCC and mPFC (Wei et al., 2021). Given such findings, neuromodulation of prefrontal-PCC connectivity could be researched as a potential treatment for reducing craving-related relapse (Nardone et al., 2022; Zhai et al., 2021).

Interestingly, neither of the main regions (ACC and mPFC) found to have a group-by task activity-related interaction in Kober, et al. 2016 demonstrated a significant connectivity interaction when using the ICD approach. These differences highlight the importance of using multiple analytic approaches. Both of these regions have been implicated in addictive processes, involved in conflict monitoring and ambivalence during craving and decision-making (Vandaele & Daeppen, 2022; Verdejo-Garcia et al., 2006). However, our study demonstrates that the roles these anterior medial DMN nodes play in BRCR may be more related to region-specific functioning, and the representation of affective information, than they are to broader shifts in network dynamics. Conversely, our data suggest that the PCC’s role during this task is more related to changes in how it interacts within broader networks on a whole-brain scale than by how it may function “in isolation.” Together, multiple approaches provide a more nuanced understanding of the neurobiology of BRCR on both localized and network scales. In this vein, we used the PCC cluster in a seed-based connectivity analysis to investigate what networks may be implicated in the BRCR across disorders. However, our results suggest that our sample may be underpowered to disentangle the specific networks that comprise the group-by-task interaction. Larger studies may be able to further investigate specific regional interactions with the PCC that differ across disorders.

Sadness and PCC-related connectivity

During sad cues, the PCC showed the greatest intrinsic connectivity in the CUD group, and the least connectivity in the GD group. Interestingly, the CUD group exhibited significantly less connectivity during the cocaine condition compared to the sad condition, but the GD group did not significantly differ in connectivity between the gambling and sad conditions. The extent to which the CUD-related differences in PCC connectivity relate to impaired regulation of negative affect and increased motivation to use substances to cope with these feelings warrants additional investigation (Albein-Urios et al., 2014; Worhunsky et al., 2014), though recent work has found that CUD-related decreases in empathic behaviors are associated with changes in regions related to social cognition (Baez et al., 2021). Individuals with GD have been found to exhibit increased distress during empathic scenarios (Rogier et al., 2022; Wu et al., 2023), and recent work has suggested that functional networks associated with empathic processing have more influence over those associated with gambling-like behaviors in individuals with GD compared to HCs (Zhou et al., 2023). These patterns suggest possible general dysregulation of negative affect for the maintenance and development of GD, with disordered gambling behaviors being one possible strategy individuals develop for impaired coping to negative affective scenarios (Blaszczynski & Nower, 2002; Verheul & van den Brink, 1999), and this speculative notion warrants additional examination.

Limitations and future directions

Several limitations warrant mention. The groups differed on sociodemographic measures, future studies should include more balanced groups and investigate further potential impacts of social factors. An important aspect of BRCR is the subjective level of craving. Due to acquisition failures leading to ratings being available for an only limited number of participants, we did not have sufficient data to examine robustly associations between subjective ratings and the ICD metric, although anticipated increases in craving were observed in the existing data – see supplemental materials and (Kober et al., 2016). Another limitation involved the absence of other conditions (e.g., happy) as had been included in prior studies of CUD and GD (Potenza et al., 2003; Wexler et al., 2001). The absence of this condition limits interpretations related to emotional processing. Some research has suggested that the affective experience involved in craving may be emotionally mixed, including both positive and negative feelings and behaviors (Breiner et al., 1999; Chen & Li, 2023; Gunter et al., 2020; Veilleux et al., 2013). Additionally, recent work has suggested that previous neurobiological models of addiction may not adequately account for mixed feelings and ambivalence associated with craving (Vaccaro et al., 2020; Vandaele & Daeppen, 2022). Nonetheless, the current findings distinguished connectivity patterns linked to BRCR in a disorder-/cue-specific fashion across both GD and CUD.

CONCLUSIONS

Reduced PCC-related intrinsic connectivity appears linked to cued responses to addictive stimuli in an addiction-specific fashion. Thus, and consistent with recent network-based investigations, the PCC appears involved in BRCR across different stimuli types. As such, the PCC may constitute a relevant biomarker in addictions that might warrant targeting in intervention development efforts.

Supplementary Material

1

Highlights.

  1. Gambling disorder (GD) and cocaine use disorder (CUD) share features

  2. Craving is relevant to GD and CUD

  3. Intrinsic connectivity distribution (ICD) is a novel data-driven approach

  4. ICD implicated posterior cingulate cortex (PCC) connectivity in GD and CUD craving

  5. PCC connectivity was decreased in a cue-diagnosis-related way in GD and CUD

Declarations of Interests

This work was supported by the National Institute of Health [R01 DA039136, R01 DA019039], the National Center for Responsible Gaming, and the Connecticut Council on Problem Gambling. The funding agencies had no role or influence on the content of this paper or in the decision to submit the paper for publication.

Disclosures:

The authors report no conflicts of interest with respect to the content of this manuscript. Dr. Potenza has consulted for Opiant Therapeutics, Game Day Data, the Addiction Policy Forum, Boehringer Ingelheim and Idorsia Pharmaceuticals; has been involved in a patent application with Yale University and Novartis; received research support from Mohegan Sun Casino, Children and Screens and the Connecticut Council on Problem Gambling; participated in surveys, mailings, or telephone consultations related to drug addiction, impulse-control disorders, or other health topics; consulted for legal and gambling entities on issues related to impulse control, internet use and addictive disorders; performed grant reviews for multiple funding agencies; edited journals and 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. The other authors report no disclosures. The views presented in this manuscript represent those of the authors and not necessarily those of the funding agencies.

Footnotes

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