Advanced Signal Processing Methods for Characterization of Schizophrenia.

Objective: Schizophrenia is a severe mental disorder associated with nerobiological deficits. Auditory oddball P300 have been found to be one of the most consistent markers of schizophrenia. The goal of this study is to find quantitative features that can objectively distinguish patients with schizophrenia (SCZs) from healthy controls (HCs) based on their recorded auditory odd-ball P300 electroencephalogram (EEG) data.

A Machine Learning Approach Using Effective Connectivity to Predict Response to Clozapine Treatment.

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biological markers of clozapine response are extremely valuable to aid on-time initiation of treatment.

Quantitative biomarkers to predict response to clozapine treatment using resting EEG data.

Clozapine is an anti-psychotic drug that is known to be effective in the treatment of patients with chronic treatment-resistant schizophrenia (TRS-SCZ), commonly estimated to be around one third of all cases. However, clinicians sometimes delay the initiation of this drug because of its adverse side-effects. Therefore, identification of predictive biomarkers of clozapine response is extremely valuable to aid on-time initiation of clozapine treatment.