Machine learning links dimensions of mental illness to abnormalities of brain networks

A new study using machine learning has identified brain-based dimensions of mental health disorders, an advance towards much-needed biomarkers to more accurately diagnose and treat patients. 

Currently, psychiatry relies on patient reporting and physician observations alone for clinical decision making, while other branches of medicine have incorporated biomarkers to aid in diagnosis, determination of prognosis, and selection of treatment for patients. 

To uncover the brain networks associated with psychiatric disorders, the team studied a large sample of adolescents and young adults (999 participants, ages 8 to 22). 

The researchers found that each brain-guided dimension contained symptoms from several different clinical diagnostic categories. Source.

Journal Reference:

Cedric Huchuan Xia, Zongming Ma, Rastko Ciric, Shi Gu, Richard F. Betzel, Antonia N. Kaczkurkin, Monica E. Calkins, Philip A. Cook, Angel García de la Garza, Simon N. Vandekar, Zaixu Cui, Tyler M. Moore, David R. Roalf, Kosha Ruparel, Daniel H. Wolf, Christos Davatzikos, Ruben C. Gur, Raquel E. Gur, Russell T. Shinohara, Danielle S. Bassett & Theodore D. Satterthwaite. Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communicationsvolume 9, Article number: 3003 (2018) DOI: 10.1038/s41467-018-05317-y