Alcohol-related stimuli modulate functional connectivity during response inhibition in young binge drinkers.

Binge drinking is a pattern of intermittent excessive alcohol consumption that is highly prevalent in young people. Neurocognitive dual-process models have described substance abuse and adolescence risk behaviours as the result of an imbalance between an overactivated affective-automatic system (related to motivational processing) and damaged and/or immature reflective system (related to cognitive control abilities). Previous studies have evaluated the reflective system of binge drinkers (BDs) through neutral response inhibition tasks and have reported anomalies in theta (4-8 Hz) and beta (12-30 Hz) bands. The present study aimed to investigate the influence of the motivational value of alcohol-related stimuli on brain functional networks devoted to response inhibition in young BDs. Sixty eight BDs and 78 control participants performed a beverage Go/NoGo task while undergoing electrophysiological recording. Whole cortical brain functional connectivity (FC) was evaluated during successful response inhibition trials (NoGo). BDs exhibited fast-beta and theta hyperconnectivity in regions related to cognitive control. These responses were modulated differently depending on the motivational content of the stimuli. The increased salience of alcohol-related stimuli may lead to overactivation of the affective-automatic system in BDs, and compensatory neural resources of the reflective system will thus be required during response inhibition. In BDs, inhibition of the response to alcohol stimuli may require higher theta FC to facilitate integration of information related to the task goal (withholding a response), while during inhibition of the response to no-alcoholic stimuli, higher fast-beta FC would allow to apply top-down inhibitory control of the information related to the prepotent response.

Artificial neural network detects human uncertainty.

Artificial neural networks (ANNs) are known to be a powerful tool for data analysis. They are used in social science, robotics, and neurophysiology for solving tasks of classification, forecasting, pattern recognition, etc. In neuroscience, ANNs allow the recognition of specific forms of brain activity from multichannel EEG or MEG data. This makes the ANN an efficient computational core for brain-machine systems. However, despite significant achievements of artificial intelligence in recognition and classification of well-reproducible patterns of neural activity, the use of ANNs for recognition and classification of patterns in neural networks still requires additional attention, especially in ambiguous situations. According to this, in this research, we demonstrate the efficiency of application of the ANN for classification of human MEG trials corresponding to the perception of bistable visual stimuli with different degrees of ambiguity. We show that along with classification of brain states associated with multistable image interpretations, in the case of significant ambiguity, the ANN can detect an uncertain state when the observer doubts about the image interpretation. With the obtained results, we describe the possible application of ANNs for detection of bistable brain activity associated with difficulties in the decision-making process.