Identification of psychiatric disorder subtypes from functional connectivity patterns in resting-state electroencephalography.

ABSTRACT: The understanding and treatment of psychiatric disorders, which are known to be neurobiologically and clinically heterogeneous, could benefit from the data-driven identification of disease subtypes. Here, we report the identification of two clinically relevant subtypes of post-traumatic stress disorder (PTSD) and major depressive disorder (MDD) on the basis of robust and distinct functional connectivity patterns, prominently within the frontoparietal control network and the default mode network. We identified the disease subtypes by analysing, via unsupervised and supervised machine learning, the power-envelope-based connectivity of signals reconstructed from high-density resting-state electroencephalography in four datasets of patients with PTSD and MDD, and show that the subtypes are tra

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC8053667 | biostudies-literature | 2021 Apr

REPOSITORIES: biostudies-literature