The Dominance of Anticipatory Prefrontal Activity in Uncued Sensory-Motor Tasks

Bayesian interpretation of the prefrontal P2 ERP component based on stimulus/response mapping uncertainty

The brain can be seen as a predictive system continuously computing prior information to guess posterior probabilities minimizing sources of uncertainty. To test this Bayesian view of the brain, event-related potentials (ERP) methods have been used focusing on the well-known P3 component, traditionally associated with decision-making processes and sources of uncertainty regarding target probability. Another ERP component linked with decision-making is the prefrontal P2 (pP2) component, which has never been considered within the Bayesian framework. To test which source of uncertainty could be associated with the pP2, uncertainty induced by target probability and stimulus-response (S/R) mapping were modulated in three visuomotor tasks. Results showed that the pP2 had the largest amplitude in the task with the largest uncertainty regarding the S/R mapping and degraded as the S/R mapping became more predictable. The P3 was maximal in the tasks with larger uncertainty regarding the target probability. While we confirmed the P3 association with target probability, we extended our knowledge on the pP2 associating it with S/R mapping uncertainty. This component, which has been previously localized within the anterior insular cortex, may minimize S/R mapping uncertainty allowing response-related evidence accumulation and comparing current events with internal representations to extract action-related probabilities.

Application of Machine Learning to Diagnostics of Schizophrenia Patients Based on Event-Related Potentials

Schizophrenia is a major psychiatric disorder that significantly reduces the quality of life. Early treatment is extremely important in order to mitigate the long-term negative effects. In this paper, a machine learning based diagnostics of schizophrenia was designed. Classification models were applied to the event-related potentials (ERPs) of patients and healthy subjects performing the visual cued Go/NoGo task. The sample consisted of 200 adult individuals ranging in age from 18 to 50 years. In order to apply the machine learning models, various features were extracted from the ERPs. The process of feature extraction was parametrized through a special procedure and the parameters of this procedure were selected through a grid-search technique along with the model hyperparameters. Feature extraction was followed by sequential feature selection transformation in order to prevent overfitting and reduce the computational complexity. Various models were trained on the resulting feature set. The best model was support vector machines with a sensitivity and specificity of 91% and 90.8%, respectively.