RT Journal Article
SR Electronic
T1 Towards Biophysical Markers of Depression Vulnerability
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.12.08.471836
DO 10.1101/2021.12.08.471836
A1 Pinotsis, D.A.
A1 Fitzgerald, S.
A1 See, C.
A1 Sementsova, A.
A1 Widge, A. S.
YR 2021
UL http://biorxiv.org/content/early/2021/12/10/2021.12.08.471836.abstract
AB 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. 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 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.Competing Interest StatementThe authors have declared no competing interest.