Biomarker tracks depression recovery after deep brain stimulation

Key takeaways:

• Changes in local field potential correlated with reported recovery from MDD symptoms in a study.
• These findings could take some of the trial-and-error out of deep brain stimulation.

A small study of patients with treatment-resistant depression identified a brain-based biomarker of recovery following deep brain stimulation of the subcallosal cingulate.

The study, published in Nature and supported by the NIH, found that local field potential changes in the subcallosal cingulate correlated with a patient’s clinical state. The finding addresses a lack of objective benchmarks in the path to recovery after deep brain stimulation (DBS), which usually requires trial-and-error therapeutic adjustments, researchers wrote.

“This study also gives us an amazing scientific platform to understand the variation between patients, which is key to treating complex psychiatric disorders like treatment-resistant depression,” Christopher J. Rozell, PhD, chair and professor of electrical and computer engineering at Georgia Tech and co-senior author of the study, said in a press release from the NIH.

The single-site study enrolled 10 patients (mean age, 49.4 years; 60% women) with treatment-resistant major depressive disorder who received weekly stimulation through a DBS device also equipped to record local field potential changes.

At 24 weeks, 90% of patients responded to treatment, and seven out of 10 were deemed to be in remission, as assessed using the Hamilton Depression Rating Scale (HDRS). All were assessed to be in a “sick” state during the first 4 weeks of the study period and in a “stable response” state in the last 4 weeks.

Researchers further analyzed five “typical responder” patients who had sufficient local field potential data, which were compared with HDRS scores. Using a neural network classifier and a generative causal explainer, researchers identified a biomarker of recovery they called the spectral discriminative component (SDC).

The study described SDC as “a low-dimensional latent representation of the spectral features that collectively capture the difference between ‘sick’ and ‘stable response’ states as determined by the neural network classifier.”

Using the area under the curve, clinical states determined by SDC were found to match those determined by HDRS over 90% of the time (AUC = 0.94 ± 0.036). In one patient who relapsed after 4 months in remission, SDC retrospectively indicated a relapse over 1 month prior to clinical relapse measured by HDRS.

Researchers also recorded weekly clinical interviews on video and compared facial expression data with SDC. They found a significant relationship between face classifier output and SDC (P = .01).

A limitation of the study is that the DBS device was a prototype, and four participants’ local field potential data were unusable. Also, the retrospective analysis leaves questions about how exactly SDC can be used to adjust treatment, researchers wrote.

Researchers called for future studies that collect data at higher frequencies, allowing for the modelling of transient mood and anxiety symptoms.

Joshua A. Gordon, MD, PhD, director of the NIMH, said in the release that “the findings mark a major advance in translating a therapy into practice.”

He added: “This biomarker suggests that brain signals can be used to help understand a patient’s response to DBS treatment and adjust the treatment accordingly.”

Reference:

• Researchers discover biomarker for tracking depression recovery. https://www.nih.gov/news-events/news-releases/researchers-discover-biomarker-tracking-depression-recovery. Published Sept. 20, 2023. Accessed Sept. 22, 2023.