Amygdala volume is associated with risky probability cognition in gambling disorder

Neural cue-reactivity in pathological gambling as evidence for behavioral addiction: a systematic review

Increasing incidence of problem gambling has led to prioritizing the problem from the point of view of public health. Additionally, gambling disorder has been recently classified as a behavioral addiction, with implications for both its diagnosis and treatment. However, the shared neural substrate of addictions, to substances and behavioral, is still discussed. Thus, this systematic review aims to provide up-to-date knowledge from the past five years (2017-2022) concerning the neural correlates of gambling related stimuli (cue-reactivity) on the basis of a previous review (Brevers et al., Cognitive, Affective and Behavioral Neuroscience 18:718-729, 2019). A total of five studies were included in the review. Activation of brain areas related to memory, reward and executive functions could be the underlying mechanism of this behavioral addiction. Specifically, nucleus accumbens and striatum (ventral and dorsal), parahippocampal regions, the right amygdala and several prefrontal cortex regions have systematically been found more active in those subjects exposed to gambling-related cues. Also, the insula could play a pivotal role connecting these three systems in a highly integrated neural network with several implications for reward processing modulation, associative learning and top-down attentional regulation to improve saliency of addiction-related cues. These results are consistent with previous findings on other substance addictions, such as alcohol, tobacco, marijuana or cocaine. The study of neural reactivity to stimuli related to addiction could be useful as a biomarker of the severity of the disorder, the efficacy of the treatment, the risk of relapse, in addition to being an objective criterion to measure the effectiveness of prevention campaigns.

Brain Structural Correlates of Dispositional Insight and the Mediation Role of Neuroticism in Young Adults

Studies on the neural correlates of episodic insight have made significant progress in the past decades. However, the neural mechanisms underlying dispositional insight are largely unknown. In the present study, we recruited forty-four young, healthy adults and performed several analyses to reveal the neural mechanisms of dispositional insight. Firstly, a voxel-based morphometry (VBM) technique was used to explore the structural brain mechanisms of dispositional insight. We found that dispositional insight was significantly and negatively correlated with the regional gray matter volume (rGMV) in the left thalamus (TLM.L), right temporoparietal junction (TPJ.R), and left dorsal medial prefrontal cortex (DMPFC.L). Secondly, we performed a seed-based resting-state functional connectivity (RSFC) analysis to complement the findings of VBM analysis further. The brain regions of TLM.L, DMPFC.L, and TPJ.R were selected as seed regions. We found that dispositional insight was associated with altered RSFC between the DMPFC.L and bilateral TPJ, between the TPJ.R and left dorsolateral prefrontal cortex, left ventrolateral prefrontal cortex, DMPFC.L, TPJ.L, right insula, and right cerebellum. Finally, a mediation analysis found that the personality of neuroticism partially mediated the relationship between the brain region of TLM.L and dispositional insight. These findings imply that dispositional insight has a specific functional and structural neural mechanism. The personality of neuroticism may play a pivotal role in the processes of dispositional insight.

Brain morphometry in adults with gambling disorder

Little is known regarding the brain substrates of Gambling Disorder, including surface brain morphometry, and whether these are linked to the clinical profile. A better understanding of the brain substrates will likely help determine targets to treat patients. Hence, the aim of this study was two-fold, that is to examine surface-based morphometry in 17 patients with gambling disorder as compared to norms of healthy individuals (2713 and 2790 subjects for cortical and subcortical anatomical scans, respectively) and to assess the clinical relevance of morphometry in patients with Gambling Disorder. This study measured brain volume, surface and thickness in Gambling Disorder. We compared these measures to those of a normative database that controlled for factors such as age and sex. We also tested for correlations with gambling-related behaviors, such as gambling severity and duration, impulsivity, and depressive symptoms (assessed using the South Oaks Gambling Screen, years of gambling, Barratt Impulsiveness Scale, and Beck Depression Inventory, respectively). Patients displayed thinner prefrontal and parietal cortices, greater volume and thickness of the occipital and the entorhinal cortices, and greater volume of subcortical regions as compared to the norms of healthy individuals. There were positive correlations between surface area of occipital regions and depressive symptoms. This work contributes to better characterize the brain substrates of Gambling Disorder, which appear to resemble those of substance use disorders and Internet Gaming Disorder.

Neural correlates of cue-induced changes in decision-making distinguish subjects with gambling disorder from healthy controls

In addiction, there are few human studies on the neural basis of cue-induced changes in value-based decision making (Pavlovian-to-instrumental transfer, PIT). It is especially unclear whether neural alterations related to PIT are due to the physiological effects of substance abuse or rather related to learning processes and/or other etiological factors related to addiction. We have thus investigated whether neural activation patterns during a PIT task help to distinguish subjects with gambling disorder (GD), a nonsubstance-based addiction, from healthy controls (HCs). Thirty GD and 30 HC subjects completed an affective decision-making task in a functional magnetic resonance imaging (fMRI) scanner. Gambling-associated and other emotional cues were shown in the background during the task. Data collection and feature modeling focused on a network of nucleus accumbens (NAcc), amygdala, and orbitofrontal cortex (OFC) (derived from PIT and substance use disorder [SUD] studies). We built and tested a linear classifier based on these multivariate neural PIT signatures. GD subjects showed stronger PIT than HC subjects. Classification based on neural PIT signatures yielded a significant area under the receiver operating curve (AUC-ROC) (0.70, p = 0.013). GD subjects showed stronger PIT-related functional connectivity between NAcc and amygdala elicited by gambling cues, as well as between amygdala and OFC elicited by negative and positive cues. HC and GD subjects were thus distinguishable by PIT-related neural signatures including amygdala-NAcc-OFC functional connectivity. Neural PIT alterations in addictive disorders might not depend on the physiological effect of a substance of abuse but on related learning processes or even innate neural traits.

Prediction errors and valence: From single units to multidimensional encoding in the amygdala

The amygdala-one of the primary structures of the limbic system-is comprised of interconnected nuclei situated within the temporal lobe. It has a well-established role in the modulation of negative affective states, as well as in fear processing. However, its vast projections with diverse brain regions-ranging from the cortex to the brainstem-are suggestive of its more complex involvement in affective or motivational aspects of cognitive processing. The amygdala can play an invaluable role in context-dependent associative learning, unsigned prediction error learning, influencing outcome selection, and multidimensional encoding. In this review, we delve into the amygdala's role in associative learning and outcome selection, emphasizing its intrinsic involvement in the appropriate context-dependent modulation of motivated behavior.

The neural basis of gambling disorder: An activation likelihood estimation meta-analysis

Previous imaging studies suggested that impairments of prefrontal-striatal and limbic circuits are correlated to excessive gambling. However, the neural underpinnings of gambling disorder (GD) continue to be the topic of debate. The present study aimed to identify structural changes in GD and differentiate the specific brain activity patterns associated with decision-making and reward-processing. We performed a systematic review complemented by Activation likelihood estimation (ALE) meta-analyses on morphometric and functional studies on neural correlates of GD. The ALE meta-analysis on structural studies revealed that patients with GD showed significant cortical grey-matter thinning in the right ventrolateral and ventromedial prefrontal cortex compared to healthy subjects. The ALE meta-analyses on functional studies revealed that patients with GD showed a significant hyperactivation in the medial prefrontal cortex and in the right ventral striatum during decision-making and gain processing compared to healthy subjects. These findings suggest that GD is related to an alteration of brain mechanisms underlying top-down control and appraisal of gambling-related stimuli and provided indications to develop new interventions in clinical practice.

Framing effects on financial and health problems in gambling disorder

Gambling disorder (GD) patients show excessively risky decision-making in the financial domain. We aimed to clarify whether GD patients show risky decision-making in domain-general or in domain-specific. Furthermore, we also investigated the effect of the well-known cognitive bias, the framing effect on GD's decision-making under risk. Sixty-two male GD patients and 74 age-matched healthy male controls (HC) conducted a risky choice task in which they should choose solutions for difficult situations between a sure and a risky option that had the same expectations. Six situations were prepared for each financial and health domain. For each domain, three situations were presented with options using positive frames, and the other three situations were presented with options using negative frames. The results showed that GD chose more risky options in the financial domain with positive frames than HC, but chose comparably in the financial domain with negative frames, whereas GD and HC chose comparably in the health domain regardless of the frames. Thus, GD showed risky decision-making in domain-specific. In addition, the results indicate the importance of considering the influence of the framing effect for assessment of risky decision-making by GD. Domains and the influence of the framing effect should be considered when decision-making patterns of neuropsychiatric disorders are studied.

Deficient Decision Making in Pathological Gamblers Correlates With Gray Matter Volume in Medial Orbitofrontal Cortex

Individuals suffering from pathological gambling (PG) show impaired decision making, but it is still not clear how this impairment is related to other traits and neuroanatomical characteristics. In this study, we investigated how the influence of PG on decision making (1) is connected to different impulsivity facets and (2) how it is related to gray matter volume (GMV) in various brain regions. Twenty-eight diagnosed PG patients and 23 healthy controls completed the cups task to measure decision making. In this task, participants had to decide between safe and risky options, which varied in expected value (EV) between risk advantageous, equal EV, and risk disadvantageous choices. A delay discounting task and the Barrant Impulsiveness Scale were applied to assess multiple impulsivity facets. In addition, structural magnetic resonance images were acquired. In comparison to the control group PG patients demonstrated more deficits in decision making, indicated by less EV sensitivity, but there was no significant difference in number of overall risky choices. Also, PG patients showed increased impulsivity in nearly every dimension. Results revealed (1) a positive correlation between decision making impairments and non-planning impulsivity but no significant relation to other impulsivity facets. Although we found no GMV differences between PG patients and controls, (2) a regions of interest analysis showed a correlation between medial orbitofrontal GMV and EV sensitivity in PG patients. Our findings showed that (1) the association between decision making and impulsivity can also be found in PG patients, but only for certain impulsivity facets. This suggests that it is essential to consider measuring different dimensions, when investigating impulsivity in a PG sample. Secondly, our findings revealed that (2) dysfunctional decision making-particularly the component of risk evaluation-is related to decreased GMV in the medial orbitofrontal cortex, a brain region concerned with processing of rewards. Interestingly, we did not find more risky choices for PG patients, and thus, we assume that decision making deficits in PG are primarily related to risk evaluation, not risk seeking, which is in line with our GMV findings.

Understanding Addiction Using Animal Models

Drug addiction is a neuropsychiatric disorder with grave personal consequences that has an extraordinary global economic impact. Despite decades of research, the options available to treat addiction are often ineffective because our rudimentary understanding of drug-induced pathology in brain circuits and synaptic physiology inhibits the rational design of successful therapies. This understanding will arise first from animal models of addiction where experimentation at the level of circuits and molecular biology is possible. We will review the most common preclinical models of addictive behavior and discuss the advantages and disadvantages of each. This includes non-contingent models in which animals are passively exposed to rewarding substances, as well as widely used contingent models such as drug self-administration and relapse. For the latter, we elaborate on the different ways of mimicking craving and relapse, which include using acute stress, drug administration or exposure to cues and contexts previously paired with drug self-administration. We further describe paradigms where drug-taking is challenged by alternative rewards, such as appetitive foods or social interaction. In an attempt to better model the individual vulnerability to drug abuse that characterizes human addiction, the field has also established preclinical paradigms in which drug-induced behaviors are ranked by various criteria of drug use in the presence of negative consequences. Separation of more vulnerable animals according to these criteria, along with other innate predispositions including goal- or sign-tracking, sensation-seeking behavior or impulsivity, has established individual genetic susceptibilities to developing drug addiction and relapse vulnerability. We further examine current models of behavioral addictions such as gambling, a disorder included in the DSM-5, and exercise, mentioned in the DSM-5 but not included yet due to insufficient peer-reviewed evidence. Finally, after reviewing the face validity of the aforementioned models, we consider the most common standardized tests used by pharmaceutical companies to assess the addictive potential of a drug during clinical trials.