AI-Derived Biomarker From Deep Brain Stimulation Tracks Depression Recovery in Real Time
In a clinical trial of subcallosal cingulate deep brain stimulation for treatment-resistant depression, researchers used explainable AI to identify electrophysiological biomarkers from local field potentials that objectively tracked individual recovery states. At 24 weeks, 90% of ten participants showed robust clinical response and 70% achieved remission. The AI-derived biomarker distinguished genuine clinical state changes from transient mood fluctuations, demonstrating the potential for objective, brain-based monitoring to guide personalized neuromodulation therapy.
The original study
Cingulate dynamics track depression recovery with deep brain stimulation.
- Authors
- Alagapan S, Choi KS, Heisig S, Riva-Posse P, Crowell A, Tiruvadi V, et al.
- Journal
- Nature
- Type
- Clinical Trial, Journal Article, Research Support, N.I.H., Extramural, Research Support, Non-U.S. Gov't
- PMID
- 37730990
Original abstract
Deep brain stimulation (DBS) of the subcallosal cingulate (SCC) can provide long-term symptom relief for treatment-resistant depression (TRD)1. However, achieving stable recovery is unpredictable2, typically requiring trial-and-error stimulation adjustments due to individual recovery trajectories and subjective symptom reporting3. We currently lack objective brain-based biomarkers to guide clinical decisions by distinguishing natural transient mood fluctuations from situations requiring intervention. To address this gap, we used a new device enabling electrophysiology recording to deliver SCC DBS to ten TRD participants (ClinicalTrials.gov identifier NCT01984710). At the study endpoint of 24 weeks, 90% of participants demonstrated robust clinical response, and 70% achieved remission. Using SCC local field potentials available from six participants, we deployed an explainable artificial intelligence approach to identify SCC local field potential changes indicating the patient's current clinical state. This biomarker is distinct from transient stimulation effects, sensitive to therapeutic adjustments and accurate at capturing individual recovery states. Variable recovery trajectories are predicted by the degree of preoperative damage to the structural integrity and functional connectivity within the targeted white matter treatment network, and are matched by objective facial expression changes detected using data-driven video analysis. Our results demonstrate the utility of objective biomarkers in the management of personalized SCC DBS and provide new insight into the relationship between multifaceted (functional, anatomical and behavioural) features of TRD pathology, motivating further research into causes of variability in depression treatment.