A machine learning approach using P300 responses to investigate effect of clozapine therapy.
Clozapine (CLZ) is uniquely effective as a treatment for medication resistant schizophrenia. Information regarding its mechanism of action may offer clues to the pathophysiology of the disease and to improved treatment. In this study we employ a machine learning (ML) analysis of P300 evoked potentials obtained from quantitative electroencephalography (QEEG) data to identify changes in the brain induced by CLZ treatment. We employ brain source localization (BSL) on the EEG signals to extract source waveforms from specified regions of the brain. A subset of 8 features is selected from a large set of candidate features (consisting of spectral coherences between all identified source waveforms at multiple frequencies) that discriminate (by means of a classifier) between the pre- and post-treatment data for the schizophrenics (SCZ) most responsive to CLZ. We show these same selected features also discriminate between pre-treatment most responsive SCZ and healthy volunteers (HV), but not after treatment. Of note, these same features discriminate the least responsive SCZ from HV both pre- and post-treatment. This analysis suggests that the net beneficial effects of CLZ in SCZ are reflected in a normalization of P300 brain-source generators.
Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON, L8S 4K1, Canada. mravan@ece.mcmaster.ca
Maryam
M
Ravan
Duncan
D
MacCrimmon
Gary
G
Hasey
James P
JP
Reilly
Ahmad
A
Khodayari-Rostamabad
D014150:Antipsychotic Agents / Q000494:pharmacology / Q000627:therapeutic use*; D001185:Artificial Intelligence; D003024:Clozapine / Q000494:pharmacology / Q000627:therapeutic use*; D005071:Evoked Potentials; D006801:Humans
D014150:Antipsychotic Agents; D003024:Clozapine