AI-based analysis helps identify 6 distinct forms of depression

A breakthrough in depression research has revealed the existence of six distinct biologically different forms of the psychiatric condition. This finding holds promise for explaining why traditional treatment methods, such as antidepressants and talk therapy, may not be effective for everyone suffering from depression. This is reported by SSP.

In this latest study, a team of scientists conducted an analysis on brain scans obtained from over 800 patients diagnosed with depression and anxiety. These scans were taken while the patients were in a resting state and also during specific tasks designed to assess brain function.

The researchers focused their investigation on identifying variations in brain activity among different regions and the connections between them. They specifically honed in on known brain circuits involved in depression, including the frontoparietal network associated with goal-driven behavior, and the default mode network linked to daydreaming.

Utilizing machine learning, a type of artificial intelligence (AI), the scientists were able to categorize patients into distinct groups based solely on their brain scans. Notably, each group exhibited unique symptom profiles as well as differences in their performance on cognitive tasks. This significant development was detailed in a report published in the journal Nature Medicine on June 17th.

For example, individuals with heightened activity in brain regions responsible for processing emotions were more likely to experience anhedonia - the inability to experience pleasure - compared to their counterparts. Moreover, these individuals also faced challenges in tasks assessing executive function and task management.

Despite commonly falling within the umbrella term of major depressive disorder (MDD), it is clear that not all individuals experience depression in the same manner. As Leanne Williams, co-senior study author and professor of psychiatry and behavioral sciences at Stanford University highlighted, this breakthrough carries considerable implications for understanding the diverse manifestations of depression.