A team of leading clinicians, engineers, and neuroscientists has made a groundbreaking discovery in the sector 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 singular brain activity pattern reflecting the recovery process in patients with treatment-resistant depression. This pattern, generally known as a biomarker, serves as a measurable indicator of disease recovery and represents a major advance in treatment for probably the most severe and untreatable types of depression. 

The team’s findings, published online within the journal Nature on September 20, offer the primary window into the intricate workings and mechanistic effects of DBS on the brain during treatment for severe depression.

Study: Cingulate dynamics track depression recovery with deep brain stimulation. Image Credit: Alphavector / Shutterstock

DBS involves implanting thin electrodes in a selected brain area to deliver small electrical pulses, much like a pacemaker. Although DBS has been approved and used for movement disorders equivalent to Parkinson’s disease for a few years, it stays experimental for depression. This study is a vital step toward using objective data collected directly from the brain via the DBS device to tell clinicians concerning the patient’s response to treatment. This information might help guide adjustments to DBS therapy, tailoring it to every patient’s unique response and optimizing their treatment outcomes. 

Now, the researchers have shown it’s possible to observe that antidepressant effect throughout the course of treatment, offering clinicians a tool somewhat analogous to a blood glucose test for diabetes or blood pressure monitoring for heart disease: a readout of the disease state at any given time. Importantly, it distinguishes between typical day-to-day mood fluctuations and the opportunity of an impending relapse of the depressive episode. 

The research team, which incorporates experts from the Georgia Institute of Technology, the Icahn School of Medicine at Mount Sinai, and Emory University School of Medicine, used artificial intelligence (AI) to detect shifts in brain activity that coincided with patients’ recovery.

The study, funded by the National Institutes of Health Brain Research Through Advancing Revolutionary Neurotechnologies ®, or the BRAIN Initiative ®, involved 10 patients with severe treatment-resistant depression, all of whom underwent the DBS procedure at Emory University. The study team used a brand new DBS device that allowed brain activity to be recorded. Evaluation of those brain recordings over six months led to the identification of a standard biomarker that modified as each patient recovered from their depression. After six months of DBS therapy, 90 percent of the topics exhibited a major improvement of their depression symptoms, and 70 percent now not met the standards for depression.

The high response rates on this study cohort enabled the researchers to develop “explainable artificial intelligence” algorithms that allow humans to grasp the decision-making means of AI systems. This method helped the team discover and understand the unique brain patterns that differentiated a “depressed” brain from a “recovered” brain.

“Using explainable AI allowed us to discover 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, Ph.D., a Georgia Tech research scientist and lead creator of the study. “This approach enabled us to trace the brain’s recovery in a way that was interpretable by the clinical team, making a serious advance within the potential for these methods to pioneer recent therapies in psychiatry.”

Helen S. Mayberg, MD, co-senior creator of the study, led the primary experimental trial of subcallosal cingulate cortex (SCC) DBS for treatment-resistant depression patients in 2003, demonstrating that it could have clinical profit. In 2019, she and the Emory team reported the technique had a sustained and robust antidepressant effect with ongoing treatment over a few years for previously treatment-resistant patients.

“This study adds a very important recent layer to our previous work, providing measurable changes underlying the predictable and sustained antidepressant response seen when patients with treatment-resistant depression are precisely implanted within the SCC region and receive chronic DBS therapy,” said Dr. Mayberg, now Founding Director of the Nash Family Center for Advanced Circuit Therapeutics at Icahn Mount Sinai. “Beyond giving us a neural signal that the treatment has been effective, it seems that this signal also can provide an early warning signal that the patient may require a DBS adjustment prematurely of clinical symptoms. It is a game changer for the way we’d adjust DBS in the longer term.” 

“Understanding and treating disorders of the brain are a few of our most pressing grand challenges, however the complexity of the issue means it’s beyond the scope of anybody discipline to resolve,” said Christopher Rozell, Ph.D., Julian T. Hightower Chair and Professor of Electrical and Computer Engineering at Georgia Tech and co-senior creator of the paper. “This research demonstrates the immense power of interdisciplinary collaboration. By bringing together expertise in engineering, neuroscience, and clinical care, we achieved a major advance toward translating this much-needed therapy into practice, in addition to an increased fundamental understanding that might help guide the event of future therapies.”

The team’s research also confirmed a longstanding subjective remark by psychiatrists: as patients’ brains change and their depression eases, their facial expressions also change. The team’s AI tools identified patterns in individual facial expressions that corresponded with the transition from a state of illness to stable recovery. These patterns proved more reliable than current clinical rating scales.

As well as, the team used two kinds of magnetic resonance imaging to discover each structural and functional abnormalities within the brain’s white matter and interconnected regions that form the network targeted by the treatment. They found these irregularities correlate with the time required for patients to get well, with more pronounced deficits within the targeted brain network correlated to an extended time for the treatment to indicate maximum effectiveness. These observed facial changes and structural deficits provide behavioral and anatomical evidence supporting the relevance of the electrical activity signature or biomarker.

“After we treat patients with depression, we depend on their reports, a clinical interview, and psychiatric rating scales to observe symptoms. Direct biological signals from our patients’ brains will provide a brand new level of precision and evidence to guide our treatment decisions,” said Patricio Riva-Posse, MD, Associate Professor and Director of the Interventional Psychiatry Service within the Department of Psychiatry and Behavioral Sciences at Emory University School of Medicine, and lead psychiatrist for the study.

Given these initial promising results, the team is now confirming their findings in one other accomplished cohort of patients at Mount Sinai. They’re using the following generation of the twin stimulation/sensing DBS system with the aim of translating these findings into using a commercially available version of this technology.

Research reported on this press release was supported by the National Institutes of Health BRAIN Initiative under award number UH3NS103550; the National Science Foundation, grant No. CCF-1350954; the Hope for Depression Research Foundation; and the Julian T. Hightower Chair at Georgia Tech. Any opinions, findings, conclusions, or recommendations expressed on this material are those of the authors and don’t necessarily reflect the views of any funding agency.