A multidisciplinary team of leading professionals in clinical, engineering, and neuroscience fields has achieved a groundbreaking milestone in the realm of treatment-resistant depression. Through an examination of the brain activity in patients undergoing deep brain stimulation (DBS), a promising therapy involving the implantation of electrodes to stimulate the brain, these researchers have pinpointed a distinctive brain activity pattern that mirrors the recovery process in individuals grappling with treatment-resistant depression. This pattern, identified as a biomarker, offers a quantifiable gauge of disease recovery and signifies a significant leap forward in treating the most severe and unresponsive forms of depression.
The team’s findings, published in the journal Nature on September 20, provide the initial insights into the intricate mechanisms and effects of DBS on the brain during severe depression treatment.
DBS entails the placement of thin electrodes in a specific region of the brain to administer gentle electrical impulses, akin to a pacemaker. While DBS has been established and utilized for movement disorders such as Parkinson’s disease for an extended period, it remains an experimental approach for depression. This study marks a pivotal stride toward utilizing objective data acquired directly from the brain via the DBS apparatus to inform clinicians about the patient’s treatment response. This insight can aid in fine-tuning DBS therapy to suit each patient’s individual response, thus optimizing their treatment outcomes.
The research now demonstrates the feasibility of monitoring the antidepressant effect throughout the treatment process, presenting clinicians with a tool somewhat akin to a blood glucose test for diabetes or blood pressure monitoring for heart disease—a real-time status report of the disease. Critically, it distinguishes between typical day-to-day mood variations and the potential onset of a depressive episode recurrence.
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, leveraged artificial intelligence (AI) to identify shifts in brain activity that correlated with patients’ recovery.
Supported by funding from the National Institutes of Health Brain Research Through Advancing Innovative Neurotechnologies (BRAIN) Initiative, the study encompassed 10 patients grappling with severe treatment-resistant depression, all of whom underwent the DBS procedure at Emory University. The utilization of a new DBS device that facilitated brain activity recording culminated in the identification of a shared biomarker that evolved as each patient recuperated from their depression. After six months of DBS therapy, a significant improvement in depression symptoms was observed in 90 percent of the subjects, and 70 percent no longer met the criteria for depression.
The notable response rates within this cohort enabled the researchers to devise algorithms termed “explainable artificial intelligence,” which grant humans insight into the decision-making process of AI systems. This methodology proved instrumental in identifying and comprehending the unique brain patterns distinguishing a “depressed” brain from a “recovered” one.
Sankar Alagapan, PhD, a research scientist at Georgia Tech and the study’s lead author, elucidated, “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… 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.”
Helen S. Mayberg, MD, co-senior author of the study, spearheaded the initial experimental trial of subcallosal cingulate cortex (SCC) DBS for treatment-resistant depression patients in 2003, demonstrating its potential clinical benefit. In 2019, she and the Emory team reported the technique’s sustained and robust antidepressant effect with ongoing treatment over numerous years for previously treatment-resistant patients.
“This study adds an important new 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 in 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 appears that this signal can also provide an early warning signal that the patient may require a DBS adjustment in advance of clinical symptoms. This is a game changer for how we might adjust DBS in the future.”
“Understanding and treating disorders of the brain are some of our most pressing grand challenges, but the complexity of the problem means it’s beyond the scope of any one discipline to solve,” said Christopher Rozell, PhD, Julian T. Hightower Chair and Professor of Electrical and Computer Engineering at Georgia Tech and co-senior author 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 significant advance toward translating this much-needed therapy into practice, as well as an increased fundamental understanding that can help guide the development of future therapies.”
The team’s research also confirmed a longstanding subjective observation 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.
In addition, the team used two types of magnetic resonance imaging to identify both structural and functional abnormalities in 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 recover, with more pronounced deficits in the targeted brain network correlated to a longer time for the treatment to show maximum effectiveness. These observed facial changes and structural deficits provide behavioral and anatomical evidence supporting the relevance of the electrical activity signature or biomarker.
“When we treat patients with depression, we rely on their reports, a clinical interview, and psychiatric rating scales to monitor symptoms. Direct biological signals from our patients’ brains will provide a 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 in 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 another completed cohort of patients at Mount Sinai. They are using the next generation of the dual stimulation/sensing DBS system with the aim of translating these findings into the use of a commercially available version of this technology.
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