Abnormal negative feedback processing in individuals with autistic traits in the Iowa gambling task: Evidence from behavior and event-related potentials

Autistic traits and event-related potentials in the general population: A scoping review and meta-analysis

BACKGROUND

Differences in short and long-latency Event-Related Potentials (ERPs) can help us infer abnormalities in brain processing, considering early and later stages of stimuli processing across tasks and conditions. In autism research, the adult population remains largely understudied compared to samples at early stages of development. In this context, this scoping review briefly summarises what has been described in community and subclinical adult samples of autism.

METHOD

The current scoping review and meta-analysis includes 50 records (N = 1652) and comprehensively explores short and long-latency ERP amplitudes and their relationship with autistic traits in adult community samples.

RESULTS

This meta-analysis identified, with small to medium effect sizes, distinctive patterns in late ERP amplitudes, indicating enhanced responses to visual stimuli and the opposite patterns to auditory tasks in the included sample. Additionally, a pattern of higher amplitudes was also found for the component P3b in autistic traits.

DISCUSSION

Differential effects in visual and auditory domains are explored in light of the predictive processing framework for Autism. It remains possible that different brain mechanisms operate to explain symptoms related with different sensory modalities. P3b is discussed as a possible component of interest in future studies as it revealed a more robust effect for differentiating severity in the expression of autistic traits in adulthood.

High-Definition Transcranial Direct Current Stimulation Improves Decision-Making Ability: A Study Based on EEG

High-definition transcranial direct current stimulation (HD-tDCS) has been shown to modulate decision-making; however, the neurophysiological mechanisms underlying this effect remain unclear. To further explore the neurophysiological processes of decision-making modulated by HD-tDCS, health participants underwent ten anodal (n = 16)/sham (n = 17) HD-tDCS sessions targeting the left DLPFC. Iowa gambling task was performed simultaneously with electroencephalography (EEG) before and after HD-tDCS. Iowa gambling task performance, the P300 amplitude, and the power of theta oscillation as an index of decision-making were compared. Behavioral changes were found that showed anodal HD-tDCS could improve the decision-making function, in which participants could make more advantageous choices. The electrophysiological results showed that the P300 amplitude significantly increased in CZ, CPZ electrode placement site and theta oscillation power significantly activated in FCZ, CZ electrode placement site after anodal HD-tDCS. Significant positive correlations were observed between the changes in the percent use of negative feedback and the changes in theta oscillation power before and after anodal HD-tDCS. This study showed that HD-tDCS is a promising technology in improving decision-making and theta oscillation induced by may be a predictor of improved decision-making.

Autism and gambling: A systematic review, focusing on neurocognition

Autism spectrum disorders (hereafter autism) are prevalent and often associated with elevated rates of substance use disorders. A subset of people who gamble develop gambling disorder, which is functionally impairing. Characterization of relationships between autism and gambling, particularly as relates to cognition, may have important implications. We conducted a systematic review of the literature. Nine out of 343 publications were found eligible for inclusion. Most studies examined decision-making using cognitive tasks, showing mixed results (less, equivalent or superior performance in autistic people compared to non-autistic people). The most consistent cognitive finding was relatively slower responses in autistic people on gambling tasks, compared to non-autistic people. One study reported a link between problem gambling and autism scores, in people who gamble at least occasionally. This systematic review highlights a profound lack of research on the potential neurocognitive overlap between autism and gambling. Future work should address the link between autism and behavioral addictions in adequately powered samples, using validated tools.

Decision-making in autism: A narrative review

LAY SUMMARY

Many autistic people report difficulties with real-life decision-making. However, when doing decision-making tests in laboratory experiments, autistic people often perform as well or better than non-autistic people. We review previously published studies on autistic people's decision-making, across different types of tests, to understand what type of decision-making is more challenging. To do this, we searched four databases of research papers. We found 104 studies that tested, in total, 2712 autistic and 3189 comparison participants on different decision-making tasks. We found that there were four categories of decision-making tests that were used in these experiments: perceptual (e.g. deciding which image has the most dots); reward learning (e.g. learning which deck of cards gives the best reward); metacognition (e.g. knowing how well you perform or what you want); and value-based (e.g. making a decision based on a choice between two outcomes that differ in value to you). Overall, these studies suggest that autistic and comparison participants tend to perform similarly well at perceptual and reward-learning decisions. However, autistic participants tended to decide differently from comparison participants on metacognition and value-based paradigms. This suggests that autistic people might differ from typically developing controls in how they evaluate their own performance and in how they make decisions based on weighing up the subjective value of two different options. We suggest these reflect more general differences in metacognition, thinking about thinking, in autism.

Can we rewire criminal mind via non-invasive brain stimulation of prefrontal cortex? Insights from clinical, forensic and social cognition studies

Non-compliance with social and legal norms and regulations represents a high burden for society. Social cognition deficits are frequently called into question to explain criminal violence and rule violations in individuals diagnosed with antisocial personality disorder (APD), borderline personality disorder (BPD), and psychopathy. In this article, we proposed to consider the potential benefits of non-invasive brain stimulation (NIBS) to rehabilitate forensic population. We focused on the effects of NIBS of the prefrontal cortex, which is central in social cognition, in modulating aggression and impulsivity in clinical disorders, as well as in forensic population. We also addressed the effect of NIBS on empathy, and theory of mind in non-clinical and/or prison population. The reviewed data provide promising evidence on the beneficial effect of NIBS on aggression/impulsivity dyscontrol and social cognitive functions, suggesting its relevance in promoting reintegration of criminals into society.

Application of a FL Time Series Building Model in Mobile Network Interaction Anomaly Detection in the Internet of Things Environment

With the continuous development of the social economy, mobile network is becoming more and more popular. However, it should be noted that it is vulnerable to different security risks, so it is extremely important to detect abnormal behaviors in mobile network interaction. This paper mainly introduces how to detect the characteristic data of mobile Internet interaction behavior based on IOT FL time series component model, set the corresponding threshold to screen the abnormal data, and then use K-means++ clustering algorithm to obtain the abnormal set of multiple interactive data, and conduct intersection operation on all abnormal sets, so as to obtain the final abnormal detection object set. The simulation results show that the FL time series component model of the Internet of Things is effective and can support abnormal detection of mobile network interaction behavior.