EEG activity represents the correctness of perceptual decisions trial-by-trial

Performance monitoring is an executive function, which we depend on for detecting and evaluating the consequences of our behavior. Although event related potentials (ERPs) have revealed the existence of differences after correct and incorrect decisions, it is not known whether there is a trial-by-trial representation of the accuracy of the decision. We recorded the electroencephalographic activity (EEG) while participants performed a perceptual discrimination task, with two levels of difficulty, in which they received immediate feedback. Receiver Operating Characteristic (ROC) analyses were used to reveal two components that convey trial-by-trial representations of the correctness of the decisions. Firstly, the performance monitoring-related negativity (PM-N), a negative deflection whose amplitude is higher (more negative) after incorrect trials. Secondly, the performance monitoring-related positivity (PM-P), a positive deflection whose amplitude is higher after incorrect trials. During the time periods corresponding to these components, trials can be accurately categorized as correct or incorrect by looking at the EEG activity; this categorization is more accurate when based on the PM-P. We further show that the difficulty of the discrimination task has a different effect on each component: after easy trials the latency of the PM-N is shorter and the amplitude of the PM-P is higher than after difficult trials. Consistent with previous interpretations of performance-related ERPs, these results suggest a functional differentiation between these components. The PM-N could be related to an automatic error detection system, responsible for fast behavioral corrections of ongoing actions, while the PM-P could reflect the difference between expected and actual outcomes and be related to long-term changes in the decision process

Investigation into functional large-scale networks in individuals with schizophrenia using fMRI data and Dynamic Causal Modelling

Schizophrenia is a complex and severe psychiatric disorder with positive symptoms, negative symptoms and cognitive deficits. Preclinical neurobiological studies showed that alterations of dopaminergic and glutamatergic neurotransmitter circuits involving the prefrontal cortex resulted in cognitive impairment such as working memory. Functional activation and functional connectivity findings of functional Magnetic Resonance Imaging (fMRI) data provided support for prefrontal dysfunction during fMRI working memory tasks in individuals with schizophrenia. However, these findings do not offer a neurobiological interpretation of the fMRI data. Biophysical modelling of functional large-scale networks has been designed for the analysis of fMRI data, which can be interpreted in a mechanistic way. This approach may enable the interpretation of fMRI data in terms of altered synaptic plasticity processes found in schizophrenia. One such process is gating mechanism, which has been shown to be altered for the thalamo-cortical and meso-cortical connection in schizophrenia. The primary aim of the thesis was to investigate altered synaptic plasticity and gating mechanisms with Dynamic Causal Modelling (DCM) within functional large-scale networks during two fMRI tasks in individuals with schizophrenia. Applying nonlinear DCM to the verbal fluency fMRI task of the Edinburgh High Risk Study, we showed that the connection strengths with nonlinear modulation for the thalamo-cortical connection was reduced in subjects at high familial risk of schizophrenia when compared to healthy controls. These results suggest that nonlinear DCM enables the investigation of altered synaptic plasticity and gating mechanism from fMRI data. For the Scottish Family Mental Health Study, we reported two different optimal linear models for individuals with established schizophrenia (EST) and healthy controls during working memory function. We suggested that this result may indicate that EST and healthy controls used different functional large-scale networks. The results of nonlinear DCM analyses may suggest that gating mechanism was intact in EST and healthy controls. In conclusion, the results presented in this thesis give evidence for the role of synaptic plasticity processes as assessed in functional large-scale networks during cognitive tasks in individuals with schizophrenia