Get the latest tech news

Study finds 6 biological subtypes of depression w brain imaging/machine learning

A Stanford Medicine study reveals six subtypes of depression, identified through brain imaging and machine learning. These subtypes exhibit unique brain activity patterns, helping predict which patients will benefit from specific antidepressants or behavioral therapies. This approach aims to persona

Williams believes they likely haven’t explored the full range of brain biology underlying this disorder — their study focused on regions known to be involved in depression and anxiety, but there could be other types of dysfunction in this biotype that their imaging didn’t capture. Her colleague Laura Hack, MD, PhD, an assistant professor of psychiatry and behavioral sciences, has begun using the imaging technique in her clinical practice at Stanford Medicine through an experimental protocol. Reference: “Personalized brain circuit scores identify clinically distinct biotypes in depression and anxiety” by Leonardo Tozzi, Xue Zhang, Adam Pines, Alisa M. Olmsted, Emily S. Zhai, Esther T. Anene, Megan Chesnut, Bailey Holt-Gosselin, Sarah Chang, Patrick C. Stetz, Carolina A. Ramirez, Laura M. Hack, Mayuresh S. Korgaonkar, Max Wintermark, Ian H. Gotlib, Jun Ma and Leanne M. Williams, 17 June 2024, Nature Medicine.

Get the Android app