Mount Sinai Researchers Identify Biomarker That Tracks Recovery From Treatment-Resistant Depression –

Mount Sinai Researchers Identify Biomarker That Tracks Recovery From Treatment-Resistant Depression -


What You Should Know:

  • A team of leading clinicians, engineers, and neuroscientists has made a groundbreaking discovery in the field of treatment-resistant depression. By analyzing the brain activity of patients undergoing deep brain stimulation (DBS), a promising therapy involving implanted electrodes that stimulate the brain, the researchers identified a unique pattern in brain activity that reflects the recovery process in patients with treatment-resistant depression.
  • This pattern, known as a biomarker, serves as a measurable indicator of disease recovery and represents a significant advance in treatment for the most severe and untreatable forms of depression. The team’s findings, published online in the journal Nature on September 20, offer the first window into the intricate workings and mechanistic effects of DBS on the brain during treatment for severe depression.

Modifying Brain Stimulation Techniques to Combat Depression

Deep Brain Stimulation (DBS) entails the placement of slender electrodes in a specific region of the brain to administer gentle electrical impulses, akin to a pacemaker. Although DBS has obtained approval and has been utilized for movement disorders like Parkinson’s disease for an extended period, its application in treating depression remains experimental. This research represents a critical advancement towards harnessing objective data acquired directly from the brain through the DBS device to inform healthcare professionals about a patient’s response to treatment. This valuable information can facilitate the adjustment of DBS therapy, customizing it to match each patient’s individual response and enhancing the efficacy of their treatment.

The researchers have now demonstrated the feasibility of monitoring the antidepressant effect continuously throughout the treatment process, offering healthcare providers a tool somewhat analogous to blood glucose testing for diabetes or blood pressure monitoring for heart disease. It provides real-time insight into the patient’s condition, distinguishing between normal day-to-day mood fluctuations and the potential onset of a depressive episode relapse. The research team, comprising experts from the Georgia Institute of Technology, the Icahn School of Medicine at Mount Sinai, and Emory University School of Medicine, employed artificial intelligence (AI) to detect changes in brain activity that coincided with patients’ recovery.

This study, funded by the National Institutes of Health through the Brain Research Advancing Innovative Neurotechnologies (BRAIN) Initiative, involved ten patients grappling with severe treatment-resistant depression, all of whom underwent the DBS procedure at Emory University. A novel DBS device was employed to record brain activity. Analysis of these brain recordings over a span of six months led to the identification of a shared biomarker that exhibited changes as each patient recovered from depression. Following six months of DBS therapy, 90 percent of the subjects experienced a substantial improvement in their depression symptoms, with 70 percent no longer meeting the criteria for depression.

“The use of explainable AI allowed us to identify complex and usable patterns of brain activity that correspond to a depression recovery despite the complex differences in a patient’s recovery,” explained Sankar Alagapan PhD, a Georgia Tech research scientist and lead author of the study. ”This approach enabled us to track the brain’s recovery in a way that was interpretable by the clinical team, making a major advance in the potential for these methods to pioneer new therapies in psychiatry.”



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