BIOINFORMATICS SEMINAR SERIES
https://bioinformatics.udel.edu/seminar
Uncovering Key Predictive Channels and Clinical Variables in the Gamma Band Auditory Steady-State Response in Early Stage Psychosis – a Longitudinal Study
Kristina Holton
Ph.D. candidate in Bioinformatics Data Science, University of Delaware
Abstract: Psychotic disorders are characterized by abnormalities insynchronization of neuronal responses. The 40 Hz gamma band deficit measured by auditory steady-state response (ASSR) electroencephalogram (EEG) is a robust observation in psychosis, and is associated with symptoms and functional deficits. However, most scalp EEG studies report only one or a few electrodes to characterize cases and controls, nor do they investigate if clinical and/or functional variables are associated with the ASSR signals in patients with early stage psychosis (ESP), and there is a paucity of longitudinal ASSR ESP studies. In this study we predict ESP status using machine learning with all channels, correlate individual EEG channels to find what clinical/demographic/functioning variables correspond to the EEG signature, and assess whether baseline ASSR deficits predictshort-term functional outcome.
Bio: Kristina Holton is a Ph.D. candidate in Bioinformatics Data Science. She has an BS in biology from MIT and an MS in bioinformatics from Brandeis University. Her work in the Computational Neural Information Engineering Lab at the University of Delaware focuses on multimodal imaging and signal analysis of a first episode of psychosis longitudinal cohort, looking for prognostic biomarkers of functional outcome to guide precision medicine.
