Loss, gain and choice difficulty in gambling patients: Neural and behavioural processes.

Impaired decision-making is often displayed by individuals suffering from gambling disorder (GD). Since there are a variety of different phenomena influencing decision-making, we focused in this study on the effects of GD on neural and behavioural processes related to loss aversion and choice difficulty. Behavioural responses as well as brain images of 23 patients with GD and 20 controls were recorded while they completed a mixed gambles task, where they had to decide to either accept or reject gambles with different amounts of potential gain and loss. We found no behavioural loss aversion in either group and no group differences regarding loss and gain-related choice behaviour, but there was a weaker relation between choice difficulty and decision time in patients with GD. Similarly, we observed no group differences in processing of losses or gains, but choice difficulty was weaker associated with brain activity in the right anterior insula and anterior cingulate cortex in patients with GD. Our results showed for the first time the effects of GD on neural processes related to choice difficulty. In addition, our findings on choice difficulty give new insights on the psychopathology of GD and on neural processes related to impaired decision-making in GD.

Abnormal frontostriatal connectivity and serotonin function in gambling disorder: A preliminary exploratory study.

Background: The neurobiological mechanisms of gambling disorder are not yet fully characterized, limiting the development of treatments. Defects in frontostriatal connections have been shown to play a major role in substance use disorders, but data on behavioral addictions, such as gambling disorder, are scarce. The aim of this study was to 1) investigate whether gambling disorder is associated with abnormal frontostriatal connectivity and 2) characterize the key neurotransmitter systems underlying the connectivity abnormalities. Methods: Fifteen individuals with gambling disorder and 17 matched healthy controls were studied with resting-state functional connectivity MRI and three brain positron emission tomography scans, investigating dopamine (18F-FDOPA), opioid (11C-carfentanil) and serotonin (11C-MADAM) function. Frontostriatal connectivity was investigated using striatal seed-to-voxel connectivity and compared between the groups. Neurotransmitter systems underlying the identified connectivity differences were investigated using region-of-interest and voxelwise approaches. Results: Individuals with gambling disorder showed loss of functional connectivity between the right nucleus accumbens (NAcc) and a region in the right dorsolateral prefrontal cortex (DLPFC) (PFWE <0.05). Similarly, there was a significant Group x right NAcc interaction in right DLPFC 11C-MADAM binding (p = 0.03) but not in 18F-FDOPA uptake or 11C-carfentanil binding. This was confirmed in voxelwise analyses showing a widespread Group x right NAcc interaction in the prefrontal cortex 11C-MADAM binding (PFWE <0.05). Right NAcc 11C-MADAM binding potential correlated with attentional impulsivity in individuals with gambling disorder (r = -0.73, p = 0.005). Discussion: Gambling disorder is associated with right hemisphere abnormal frontostriatal connectivity and serotonergic function. These findings will contribute to understanding the neurobiological mechanism and may help identify potential treatment targets for gambling disorder.

Neural fingerprints of gambling disorder using diffusion tensor imaging.

Gambling disorder (GD) is a behavioral addiction associated with personal, social and occupational consequences. Thus, examining GD's clinical relationship with its neural substrates is critical. We compared neural fingerprints using diffusion tensor imaging (DTI) in GD subjects undergoing treatment relative to healthy volunteers (HV). Fifty-three (25 GD, 28 age-matched HV) males were scanned with structural magnetic resonance imaging (MRI) and DTI. We applied probabilistic tractography based on DTI scanning data, preprocessed and analyzed using permutation testing of individual connectivity weights between regions for group comparison. Permutation-based comparisons between group-averaged connectomes highlighted significant structural differences. The GD group demonstrated increased connectivity, and striatal network reorganisation, contrasted by reduced connectivity within and to frontal lobe nodes. Modularity analysis revealed that the GD group had fewer hubs integrating information across the brain. We highlight GD neural changes involved in controlling risk-seeking behaviors. The observed striatal restructuring converges with previous research, and the increased connectivity affects subnetworks highly active in gambling situations, although these findings are not significant when correcting for multiple comparisons. Modularity analysis underlines that, despite connectivity increases, the GD connectome loses hubs, impeding its neuronal network coherence. Together, these results demonstrate the feasibility of using whole-brain computational modeling in assessing GD.

Connectome-based prediction of craving in gambling disorder and cocaine use disorder.

INTRODUCTION: Craving, involving intense and urgent desires to engage in specific behaviours, is a feature of addictions. Multiple studies implicate regions of salience/limbic networks and basal ganglia, fronto-parietal, medial frontal regions in craving in addictions. However, prior studies have not identified common neural networks that reliably predict craving across substance and behavioural addictions. METHODS: Functional magnetic resonance imaging during an audiovisual cue-reactivity task and connectome-based predictive modelling (CPM), a data-driven method for generating brain-behavioural models, were used to study individuals with cocaine-use disorder and gambling disorder. Functions of nodes and networks relevant to craving were identified and interpreted based on meta-analytic data. RESULTS: Craving was predicted by neural connectivity across disorders. The highest degree nodes were mostly located in the prefrontal cortex. Overall, the prediction model included complex networks including motor/sensory, fronto-parietal, and default-mode networks. The decoding revealed high functional associations with components of memory, valence ratings, physiological responses, and finger movement/motor imagery. CONCLUSIONS: Craving could be predicted across substance and behavioural addictions. The model may reflect general neural mechanisms of craving despite specificities of individual disorders. Prefrontal regions associated with working memory and autobiographical memory seem important in predicting craving. For further validation, the model should be tested in diverse samples and contexts.

Striatal ups or downs? Neural correlates of monetary reward anticipation, cue reactivity and their interaction in alcohol use disorder and gambling disorder.

Background and aims: Dysfunction of the striatum, a brain region part of the mesolimbic reward system, is a key characteristic of addictive disorders, but neuroimaging studies have reported conflicting findings. An integrative model of addiction points to the presence or absence of addiction-related cues as an explanation for hyper- or hypoactivation, respectively, of the striatum. Methods: To test this model directly, we investigated striatal activation during monetary reward anticipation in the presence versus absence of addiction-related cues using functional MRI. Across two studies, we compared 46 alcohol use disorder (AUD) patients with 30 matched healthy controls; and 24 gambling disorder (GD) patients with 22 matched healthy controls. Results: During monetary reward anticipation, hypoactivation of the reward system was seen in AUD individuals compared to HCs. Additionally, a behavioral interaction was seen where gambling cues made participants, across groups, respond faster for bigger, but slower for smaller rewards. However, no striatal differences were seen in response to addiction-related cues between AUD or GD patients and their matched controls. Finally, despite substantial individual differences in neural activity to cue-reactivity and reward anticipation, these measures did not correlate, suggesting that they contribute independently to addiction aetiology. Discussion and Conclusions: Our findings replicate previous findings of blunted striatal activity during monetary reward anticipation in alcohol use disorder but do not support the idea that addiction-related cues explain striatal dysfunction as suggested by the model.

Goal-directed and habitual decision making under stress in gambling disorder: An fMRI study.

The development of addictive behaviors has been suggested to be related to a transition from goal-directed to habitual decision making. Stress is a factor known to prompt habitual behavior and to increase the risk for addiction and relapse. In the current study, we therefore used functional MRI to investigate the balance between goal-directed 'model-based' and habitual 'model-free' control systems and whether acute stress would differentially shift this balance in gambling disorder (GD) patients compared to healthy controls (HCs). Using a within-subject design, 22 patients with GD and 20 HCs underwent stress induction or a control condition before performing a multistep decision-making task during fMRI. Salivary cortisol levels showed that the stress induction was successful. Contrary to our hypothesis, GD patients did not show impaired goal-directed 'model-based' decision making, which remained similar to HCs after stress induction. Bayes factors provided three times more evidence against a difference between the groups or a group-by-stress interaction on the balance between model-based and model-free decision making. Similarly, no differences were found between groups and conditions on the neural estimates of model-based or model-free decision making. These results challenge the notion that GD is related to an increased reliance on habitual (or decreased goal-directed) control, even during stress.

Relationship between motor learning and gambling propensity in Parkinson's disease.

INTRODUCTION: The basal ganglia and related dopaminergic cortical areas are important neural systems underlying motor learning and are also implicated in impulse control disorders (ICDs). Motor learning impairments and ICDs are frequently observed in Parkinson's disease (PD). Nevertheless, the relationship between motor learning ability and ICDs has not been elucidated. METHODS: We examined the relationship between motor learning ability and gambling propensity, a possible symptom for prodromal ICDs, in PD patients. Fifty-nine PD patients without clinical ICDs and 43 normal controls (NC) were administered a visuomotor rotation perturbation task and the Iowa Gambling Task (IGT) to evaluate motor learning ability and gambling propensity, respectively. Participants also performed additional cognitive assessments and underwent brain perfusion SPECT imaging. RESULTS: Better motor learning ability was significantly correlated with lower IGT scores, i.e., higher gambling propensity, in PD patients but not in NC. The higher scores on assessments reflecting prefrontal lobe function and well-preserved blood perfusion in prefrontal areas were correlated with lower IGT scores along with better motor learning ability. CONCLUSIONS: Our findings suggest that better motor learning ability and higher gambling propensity are based on better prefrontal functions, which are in accordance with the theory that the prefrontal cortex is one of the common essential regions for both motor learning and ICDs.

Gaming behavior and brain activation using functional near-infrared spectroscopy, Iowa gambling task, and machine learning techniques.

INTRODUCTION: The current study investigates the utilization and performance of machine learning (ML) algorithms in the cognitive task of finding the correlation between numerical parameters of the human brain activation during gaming. We hypothesize that our integrated feature extraction platform is able to distinguish between different psychosomatic conditions in the gaming process as measured by the functional near-infrared brain imaging technique. METHODS: For demonstration, the decision-making process was constructed in the experiment environment that combined gaming simulator, such as the Iowa Gaming Task (IGT), with functional near-infrared spectroscopy (fNIRS) as the neuroimaging technique. Features of fNIRS levels were extracted, averaged, and synchronized by time with the IGT dataset to predict the task score inside ML algorithms, such as multiple regression, classification and regression trees, support vector machine, artificial neural network, and random forest. For findings validation, the experiment data were resampled by training and testing sets. Further, a training dataset was used to train the ML algorithms, and prediction accuracy was estimated by repeated cross-validation methods and compared by R squared and root mean square error (RMSE). The model with the best accuracy was used with the testing dataset and finalized the experiment. RESULTS: During the experiment, the highest correlation was identified in the fourth block between the oxy-hemoglobin signal and IGT score in average value (0.24) and signal feature (0.57). Such relationship is due to block 4 characterization as "conceptual" period when participants task experience reaches the maximum, and rewards raise accordingly. Simultaneously, ML algorithms, constructed based on training data set, demonstrate acceptable performance, and RMSE as the primary performance metric dynamically increases from block 1 to block 5, from the state of uncertainty and unknown to the certainty and risky. In contrast, R squared decreases during the same transition. In most IGT blocks, the best fitted model was determined as support vector machine with radial bases function kernel, and predictions were made with the highest accuracy (lowest RMSE) than in training models. CONCLUSION: Obtained findings showed the applicability and capability of ML models as a powerful technique to evaluate the cognitive neuroimaging task result. Moreover, in terms of features it was identified that the hemodynamic response reacts to the acceleration decision-making process and raises more significance than it was observed before.

Development of a classifier for gambling disorder based on functional connections between brain regions.

AIM: Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data. METHODS: As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. RESULTS: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. CONCLUSION: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

The PRO model accounts for the anterior cingulate cortex role in risky decision-making and monitoring.

The anterior cingulate cortex (ACC) has been implicated in a number of functions, including performance monitoring and decision-making involving effort. The prediction of responses and outcomes (PRO) model has provided a unified account of much human and monkey ACC data involving anatomy, neurophysiology, EEG, fMRI, and behavior. We explored the computational nature of ACC with the PRO model, extending it to account specifically for both human and macaque monkey decision-making under risk, including both behavioral and neural data. We show that the PRO model can account for a number of additional effects related to outcome prediction, decision-making under risk, gambling behavior. In particular, we show that the ACC represents the variance of uncertain outcomes, suggesting a link between ACC function and mean-variance theories of decision making. The PRO model provides a unified account of a large set of data regarding the ACC.

Functional connectivity in reward-related networks is associated with individual differences in gambling strategies in male Lister hooded rats.

Individuals with gambling disorder display deficits in decision-making in the Iowa Gambling Task. The rat Gambling Task (rGT) is a rodent analogue that can be used to investigate the neurobiological mechanisms underlying gambling behaviour. The aim of this explorative study was to examine individual strategies in the rGT and investigate possible behavioural and neural correlates associated with gambling strategies. Thirty-two adult male Lister hooded rats underwent behavioural testing in the multivariate concentric square field™ (MCSF) and the novel cage tests, were trained on and performed the rGT and subsequently underwent resting-state functional magnetic resonance imaging (R-fMRI). In the rGT, stable gambling strategies were found with subgroups of rats that preferred the suboptimal safest choice as well as the disadvantageous choice, that is, the riskiest gambling strategy. R-fMRI results revealed associations between gambling strategies and brain regions central for reward networks. Moreover, rats with risky gambling strategies differed from those with strategic and intermediate strategies in brain functional connectivity. No differences in behavioural profiles, as assessed with the MCSF and novel cage tests, were observed between the gambling strategy groups. In conclusion, stable individual differences in gambling strategies were found. Intrinsic functional connectivity using R-fMRI provides novel evidence to support the notion that individual differences in gambling strategies are associated with functional connectivity in brain regions important for reward networks.

Simultaneous EEG-fMRI reveals theta network alterations during reward feedback processing in borderline personality disorder.

Previous studies using imaging techniques such as electroencephalography (EEG) or functional magnetic resonance imaging (fMRI) have identified neurophysiological markers of impaired feedback processing in patients with Borderline Personality Disorder (BPD). These mainly include reduced oscillatory activity in the theta frequency range in the EEG and altered activations in frontal and striatal regions in fMRI studies. The aim of the present study is to integrate these results using a coupling of simultaneously recorded EEG and fMRI. Simultaneous EEG (64-channel) and fMRI (3-Tesla Siemens Prisma) was recorded whilst participants (19 BPD patients and 18 controls) performed a gambling task. Data was analysed for the two imaging techniques separately as well as in a single-trial coupling of both modalities. Evoked theta oscillatory power as a response to loss feedback was reduced in BPD patients. EEG-fMRI coupling revealed an interaction between feedback valence and group in prefrontal regions centering in the dorsolateral prefrontal cortex (dlPFC), with healthy controls showing stronger modulation by theta responses during loss when compared to gain feedback and the opposite effect in BPD patients. Our results show multiple alterations in the processing of feedback in BPD, which were partly linked to impulsivity. The dlPFC was identified as the seed of theta-associated activation differences.

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.

Persistent dependent behaviour is accompanied by dynamic switching between the ventral and dorsal striatal connections in internet gaming disorder.

Cross-sectional studies have suggested that functional heterogeneity within the striatum in individuals with addictive behaviours may involve the transition from ventral to dorsal partitions; however, due to limitations of the cross-sectional design, whether the contribution of this transition to addiction was confused by individual differences remains unclear, especially for internet gaming disorder (IGD). Longitudinal functional magnetic resonance imaging (fMRI) data from 22 IGD subjects and 18 healthy controls were collected at baseline and more than 6 months later. We examined the connectivity features of subregions within the striatum between these two scans. Based on the results, we further performed dynamic causal modelling to explore the directional effect between regions and used these key features for data classification in machine learning to test the replicability of the results. Compared with controls, IGD subjects exhibited decreased functional connectivity between the left dorsal striatum (putamen) and the left insula, whereas connectivity between the right ventral striatum (nucleus accumbens [Nacc]) and the left insula was relatively stable over time. An inhibitory effective connectivity from the left putamen to the right Nacc was found in IGD subjects during the follow-up scan. Using the above features, the classification accuracy of the training model developed with the follow-up was better than that of the model based on the initial scan. Persistent IGD status was accompanied by a switch in the locus of control within the striatum, which provided new insights into association between IGD and drug addiction.

Increased brain reactivity to gambling unavailability as a marker of problem gambling.

The unprecedented development and ubiquity of sports betting constitute an emerging public health concern. It is crucial to provide markers that could help to better identify people experiencing sports betting-related harms. The current study investigated whether problem gambling status, sports betting passion, and trait-self-control modulate brain reactivity to sports betting cues. Sixty-five frequent sports bettors (35 "nonproblem bettors" and 30 "problem bettors") were exposed to cues representing real upcoming sport events (with varying levels of winning confidence) that were made available or blocked for betting, during functional magnetic resonance imaging (fMRI) recording. Sports betting passion and trait-self-control were assessed using self-report scales. Sport events nonavailable for betting elicited higher insular and striatal activation in problem bettors, as compared with nonproblem bettors. Within a large cluster encompassing the ventral striatum, hippocampus, and amygdala, lower trait-self-control was associated with increased brain reactivity to sport events with high levels of winning confidence that were nonavailable for betting. No significant effect of sports betting passion was observed. These findings suggest that sports bettors' brain reactivity to gambling unavailability might be a relevant marker of sports betting-related harms, as well as of blunted trait-self-control.

A voxel-wise meta-analysis of task-based functional MRI studies on impaired gain and loss processing in adults with addiction.

BACKGROUND: Disturbances in gain and loss processing have been extensively reported in adults with addiction, a brain disorder characterized by obsession with addictive substances or behaviours. Previous studies have provided conflicting results with respect to neural abnormalities in gain processing in addiction, and few investigations into loss processing. METHODS: We conducted voxel-wise meta-analyses of abnormal task-evoked regional activities in adults with substance dependence and gambling addiction during the processing of gains and losses not related to their addiction (mainly monetary). We identified 24 studies, including 465 participants with substance dependence, 81 with gambling addiction and 490 healthy controls. RESULTS: Compared with healthy controls, all participants with addictions showed hypoactivations in the prefrontal cortex, striatum and insula and hyperactivations in the default mode network during gain anticipation; hyperactivations in the prefrontal cortex and both hyper- and hypoactivations in the striatum during loss anticipation; and hyperactivations in the occipital lobe during gain outcome. In the substance dependence subgroup, activity in the occipital lobe was increased during gain anticipation but decreased during loss anticipation. LIMITATIONS: We were unable to conduct meta-analyses in the gambling addiction subgroup because of a limited data set. We did not investigate the effects of clinical variables because of limited information. CONCLUSION: The current study identified altered brain activity associated with higher- and lower-level function during gain and loss processing for non-addiction (mainly monetary) stimuli in adults with substance dependence and gambling addiction. Adults with addiction were more sensitive to anticipatory gains than losses at higher- and lower-level brain areas. These results may help us to better understand the pathology of gain and loss processing in addiction.

Higher Risk Taking and Impaired Probability Judgment in Behavioral Addiction.

BACKGROUND: Accumulating evidence suggests that deficits in decision-making and judgment may be involved in several psychiatric disorders, including addiction. Behavioral addiction is a conceptually new psychiatric condition, raising a debate of what criteria define behavioral addiction, and several impulse control disorders are equivalently considered as types of behavioral addiction. In this preliminary study with a relatively small sample size, we investigated how decision-making and judgment were compromised in behavioral addiction to further characterize this psychiatric condition. METHOD: Healthy control subjects (n = 31) and patients with kleptomania and paraphilia as behavioral addictions (n = 16) were recruited. A battery of questionnaires for assessments of cognitive biases and economic decision-making were conducted, as was a psychological test for the assessment of the jumping-to-conclusions bias, using functional near-infrared spectroscopy recordings of prefrontal cortical (PFC) activity. RESULTS: Although behavioral addicts exhibited stronger cognitive biases than controls in the questionnaire, the difference was primarily due to lower intelligence in the patients. Behavioral addicts also exhibited higher risk taking and worse performance in economic decision-making, indicating compromised probability judgment, along with diminished PFC activity in the right hemisphere. CONCLUSION: Our study suggests that behavioral addiction may involve impairments of probability judgment associated with attenuated PFC activity, which consequently leads to higher risk taking in decision-making.

Amygdala-prefrontal connectivity modulates loss aversion bias in anxious individuals.

Anxious individuals tend to make pessimistic judgments in decision making under uncertainty. While this phenomenon is commonly attributed to risk aversion, loss aversion is a critical but often overlooked factor. In this study, we simultaneously examined risk aversion and loss aversion during decision making in high and low trait anxious individuals in a variable gain/loss gambling task during functional magnetic resonance imaging. Although high relative to low anxious individuals showed significant increased risk aversive behavior reflected by decreased overall gamble decisions, there was no group difference in subjective aversion to risk. Instead, loss aversion rather than risk aversion dominantly contributed to predict behavioral decisions, which was associated with attenuated functional connectivity between the amygdala-based emotional system and the prefrontal control regions. Our findings suggest a dominant role of loss aversion in maladaptive risk assessment of anxious individuals, underpinned by disorganization of emotion-related and cognitive-control-related brain networks.

Increased functional connectivity in gambling disorder correlates with behavioural and emotional dysregulation: Evidence of a role for the cerebellum.

Gambling disorder (GD) is a psychiatric disease that has been recently classified as a behavioural addiction. So far, a very few studies have investigated the alteration of functional connectivity in GD patients, thus the concrete interplay between relevant function-dependent circuitries in such disease has not been comprehensively assessed. The aim of this research was to investigate resting-state functional connectivity in GD patients, searching for a correlation with GD symptoms severity. GD patients were assessed for gambling behaviour, impulsivity, cognitive distortions, anxiety and depression, in comparison with healthy controls (HC). Afterwards, they were assessed for resting-state functional magnetic resonance imaging; functional connectivity was assessed through a data-driven approach, by using independent component analysis. The correlation between gambling severity and the strength of specific resting-state networks was also investigated. Our results show that GD patients displayed higher emotional and behavioural impairment than HC, together with an increased resting state functional connectivity in the network including anterior cingulate cortex, the caudate nucleus and nucleus accumbens, and within the cerebellum, in comparison with the control group. Moreover, a significant correlation between behavioural parameters and the strength of the resting-state cerebellar network was found. Overall, the functional alterations in brain connectivity involving the cerebellum observed in this study underpin the emotional and behavioural impairment recorded in GD patients. This evidence suggests the employment of novel neuromodulatory therapeutic approaches involving specific and salient targets such as the cerebellum in addictive disorders.