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Toward biophysical markers of depression vulnerability

Pinotsis, D. A. ORCID: 0000-0002-6865-8103, Fitzgerald, S., See, C. , Sementsova, A. & Widge, A. S. (2022). Toward biophysical markers of depression vulnerability. Frontiers in Psychiatry, 13, 938694. doi: 10.3389/fpsyt.2022.938694

Abstract

A major difficulty with treating psychiatric disorders is their heterogeneity: different neural causes can lead to the same phenotype. To address this, we propose describing the underlying pathophysiology in terms of interpretable, biophysical parameters of a neural model derived from the electroencephalogram. We analyzed data from a small patient cohort of patients with depression and controls. Using DCM, we constructed biophysical models that describe neural dynamics in a cortical network activated during a task that is used to assess depression state. We show that biophysical model parameters are biomarkers, that is, variables that allow subtyping of depression at a biological level. They yield a low dimensional, interpretable feature space that allowed description of differences between individual patients with depressive symptoms. They could capture internal heterogeneity/variance of depression state and achieve significantly better classification than commonly used EEG features. Our work is a proof of concept that a combination of biophysical models and machine learning may outperform earlier approaches based on classical statistics and raw brain data.

Publication Type: Article
Additional Information: Copyright © 2022 Pinotsis, Fitzgerald, See, Sementsova and Widge. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Publisher Keywords: depression, dynamic causal modeling (DCM), biomarkers, event-related potentials (ERPs), machine learning
Subjects: R Medicine > RA Public aspects of medicine > RA0421 Public health. Hygiene. Preventive Medicine
R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Departments: School of Health & Psychological Sciences > Psychology
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