Toward biophysical markers of depression vulnerability

D. A. Pinotsis, S. Fitzgerald, C. See, A. Sementsova, A. S. Widge

Research output: Contribution to journalArticlepeer-review


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.

Original languageEnglish (US)
Article number938694
JournalFrontiers in Psychiatry
StatePublished - Oct 18 2022

Bibliographical note

Funding Information:
DAP acknowledges financial support from UKRI ES/T01279X/1. ASW acknowledges financial support from the National Institutes of Health (R21MH120785, R01MH123634, and R01EB026938), the MnDRIVE Brain Conditions initiative, and the Minnesota Medical Discovery Team on Addictions.

Publisher Copyright:
Copyright © 2022 Pinotsis, Fitzgerald, See, Sementsova and Widge.


  • biomarkers
  • depression
  • dynamic causal modeling (DCM)
  • event-related potentials (ERPs)
  • machine learning

PubMed: MeSH publication types

  • Journal Article


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