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

Yu Zhang, Wei Wu, Russell T. Toll, Sharon Naparstek, Adi Maron-Katz, Mallissa Watts, Joseph Gordon, Jisoo Jeong, Laura Astolfi, Emmanuel Shpigel, Parker Longwell, Kamron Sarhadi, Dawlat El-Said, Yuanqing Li, Crystal Cooper, Cherise Chin-Fatt, Martijn Arns, Madeleine S. Goodkind, Madhukar H. Trivedi, Charles R. MarmarAmit Etkin

Research output: Contribution to journalArticlepeer-review

75 Scopus citations


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 transferable across independent datasets recorded under different conditions. The subtype whose functional connectivity differed most from those of healthy controls was less responsive to psychotherapy treatment for PTSD and failed to respond to an antidepressant medication for MDD. By contrast, both subtypes responded equally well to two different forms of repetitive transcranial magnetic stimulation therapy for MDD. Our data-driven approach may constitute a generalizable solution for connectome-based diagnosis.

Original languageEnglish
Pages (from-to)309-323
Number of pages15
JournalNature Biomedical Engineering
Issue number4
StatePublished - Apr 2021
Externally publishedYes

Bibliographical note

Funding Information:
This study was supported by grants from the Steven and Alexandra Cohen Foundation, Cohen Veterans Bioscience grant no. CVB034 and NIH grant nos. U01MH092221 and U01MH092250. A.E. was additionally funded by NIH grant no. DP1MH116506, and was previously supported by the Sierra-Pacific Mental Illness Research, Education and Clinical Center at the Veterans Affairs Palo Alto Healthcare System. Y.L. was supported by the Key R&D Program of Guangdong Province, China under grant no. 2018B030339001, National Key Research and Development Plan of China (no. 2017YFB1002505), and National Natural Science Foundation of China (no. 61633010). The support from N. Krepel in coordinating the TMS is acknowledged.

Publisher Copyright:
© 2020, The Author(s), under exclusive licence to Springer Nature Limited.


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