S6. MACHINE LEARNING-BASED CLASSIFICATION OF SCHIZOPHRENIA THEIR BIOLOGICAL RELATIVES AND HEALTHY INDIVIDUALS USING FUNCTIONAL CONNECTIVITY EEG FEATURES AT SOURCE-LEVEL

摘要

Machine learning (ML) approaches using functional brain measures have been widely used to develop advanced diagnostic tools of schizophrenia. Although numerous ML studies have improved the accuracy of classification differentiating patients with schizophrenia (PSZ) from healthy individuals (HI), their utilities as a truly diagnostic tool are still limited. Also, to our knowledge, ML has not been used to identify individuals with the genetic liability of schizophrenia (i.e., biological relatives of PSZ [RSZ]). Toward the development of such a reliable and accurate tool to identify endophenotypic biomarkers of schizophrenia, we conducted an ML study using high-density EEG collected with a cognitive task that is known to measure endophenotypic markers of cognitive deficits in schizophrenia and source-level EEG functional connectivity analysis to improve spatial-temporal-frequency features of brain network dynamics.