Impulsivity is related to overhasty risk learning in attention-deficit/hyperactivity disorder: A computational psychiatric approach

Adaptations of the balloon analog risk task for neuroimaging settings: a systematic review

Introduction

The Balloon Analog Risk Task (BART), a computerized behavioral paradigm, is one of the most common tools used to assess the risk-taking propensity of an individual. Since its initial behavioral version, the BART has been adapted to neuroimaging technique to explore brain networks of risk-taking behavior. However, while there are a variety of paradigms adapted to neuroimaging to date, no consensus has been reached on the best paradigm with the appropriate parameters to study the brain during risk-taking assessed by the BART. In this review of the literature, we aimed to identify the most appropriate BART parameters to adapt the initial paradigm to neuroimaging and increase the reliability of this tool.

Methods

A systematic review focused on the BART versions adapted to neuroimaging was performed in accordance with PRISMA guidelines.

Results

A total of 105 articles with 6,879 subjects identified from the PubMed database met the inclusion criteria. The BART was adapted in four neuroimaging techniques, mostly in functional magnetic resonance imaging or electroencephalography settings.

Discussion

First, to adapt the BART to neuroimaging, a delay was included between each trial, the total number of inflations was reduced between 12 and 30 pumps, and the number of trials was increased between 80 and 100 balloons, enabling us to respect the recording constraints of neuroimaging. Second, explicit feedback about the balloon burst limited the decisions under ambiguity associated with the first trials. Third, employing an outcome index that provides more informative measures than the standard average pump score, along with a model incorporating an exponential monotonic increase in explosion probability and a maximum explosion probability between 50 and 75%, can yield a reliable estimation of risk profile. Additionally, enhancing participant motivation can be achieved by increasing the reward in line with the risk level and implementing payment based on their performance in the BART. Although there is no universal adaptation of the BART to neuroimaging, and depending on the objectives of a study, an adjustment of parameters optimizes its evaluation and clinical utility in assessing risk-taking.

Bidirectional connectivity alterations in schizophrenia: a multivariate, machine-learning approach

Objective

It is well known that altered functional connectivity is a robust neuroimaging marker of schizophrenia. However, there is inconsistency in the direction of alterations, i.e., increased or decreased connectivity. In this study, we aimed to determine the direction of the connectivity alteration associated with schizophrenia using a multivariate, data-driven approach.

Methods

Resting-state functional magnetic resonance imaging data were acquired from 109 individuals with schizophrenia and 120 controls across two openly available datasets. A whole-brain resting-state functional connectivity (rsFC) matrix was computed for each individual. A modified connectome-based predictive model (CPM) with a support vector machine (SVM) was used to classify patients and controls. We conducted a series of multivariate classification analyses using three different feature sets, increased, decreased, and both increased and decreased rsFC.

Results

For both datasets, combining information from both increased and decreased rsFC substantially improved prediction accuracy (Dataset 1: accuracy = 70.2%, permutation p = 0.001; Dataset 2: accuracy = 64.4%, permutation p = 0.003). When tested across datasets, the prediction model using decreased rsFC performed best. The identified predictive features of decreased rsFC were distributed mostly in the motor network for both datasets.

Conclusion

These findings suggest that bidirectional alterations in rsFC are distributed in schizophrenia patients, with the pattern of decreased rsFC being more similar across different populations.

Computational approaches to modeling gambling behaviour: Opportunities for understanding disordered gambling

Computational modeling has become an important tool in neuroscience and psychiatry research to provide insight into the cognitive processes underlying normal and pathological behavior. There are two modeling frameworks, reinforcement learning (RL) and drift diffusion modeling (DDM), that are well-developed in cognitive science, and have begun to be applied to Gambling Disorder. RL models focus on explaining how an agent uses reward to learn about the environment and make decisions based on outcomes. The DDM is a binary choice framework that breaks down decision making into psychologically meaningful components based on choice reaction time analyses. Both approaches have begun to yield insight into aspects of cognition that are important for, but not unique to, gambling, and thus relevant to the development of Gambling Disorder. However, these approaches also oversimplify or neglect various aspects of decision making seen in real-world gambling behavior. Gambling Disorder presents an opportunity for 'bespoke' modeling approaches to consider these neglected components. In this review, we discuss studies that have used RL and DDM frameworks to investigate some of the key cognitive components in gambling and Gambling Disorder. We also include an overview of Bayesian models, a methodology that could be useful for more tailored modeling approaches. We highlight areas in which computational modeling could enable progression in the investigation of the cognitive mechanisms relevant to gambling.

Connectome-based predictive modeling predicts paranoid ideation in young men with paranoid personality disorder: a resting-state functional magnetic resonance imaging study

Paranoid personality disorder (PPD), a mental disorder that affects interpersonal relationships and work, is frequently neglected during diagnosis and evaluation at the individual-level. This preliminary study aimed to investigate whether connectome-based predictive modeling (CPM) can predict paranoia scores of young men with PPD using whole-brain resting-state functional connectivity (rs-FC). College students with paranoid tendencies were screened using paranoia scores ≥60 derived from the Minnesota Multiphasic Personality Inventory; 18 participants were ultimately diagnosed with PPD according to the Diagnostic and Statistical Manual of Mental Disorders and subsequently underwent resting-state functional magnetic resonance imaging. Whole-brain rs-FC was constructed, and the ability of this rs-FC to predict paranoia scores was evaluated using CPM. The significance of the models was assessed using permutation tests. The model constructed based on the negative prediction network involving the limbic system-temporal lobe was observed to have significant predictive ability for paranoia scores, whereas the model constructed using the positive and combined prediction network had no significant predictive ability. In conclusion, using CPM, whole-brain rs-FC predicted the paranoia score of patients with PPD. The limbic system-temporal lobe FC pattern is expected to become an important neurological marker for evaluating paranoid ideation.