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 Marmar, Amit Etkin
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...
April 2021: Nature Biomedical Engineering