Brain synchronization as a predictive factor in depression treatment given EEG-synchronized rTMS

Repetitive Transcranial Magnetic Stimulation (rTMS) is an FDA-approved therapy recognized for its effectiveness in treating major depressive disorder, particularly in cases of treatment-resistant depression (TRD). Although substantial clinical data supports its efficacy, the precise mechanism of action and the optimal combination of stimulation parameters remain elusive. This lack of clarity results in a significant portion of patients (around 50%) not achieving desired treatment outcomes. This variability in treatment response may be attributed to the absence of robust biomarkers or measures of target engagement.

Since recent evidence emphasizes the crucial role of alpha oscillations in network connectivity, especially in mediating top-down influences within the human brain, including responses to neurostimulation methods like rTMS, in response to the challenge, we introduce an innovative approach—a closed-loop, phase-locked repetitive TMS (rTMS) treatment. This approach synchronizes the delivery of rTMS pulses with an individual patient's electroencephalographic (EEG) prefrontal alpha oscillations, as validated by functional magnetic resonance imaging (fMRI).

Our results reveal that, in responders, synchronized rTMS significantly enhances brain synchronization within the target region (near the dorsolateral prefrontal cortex). Notably, it emerges as a strong predictor of clinical responses within the synchronized treatment group (N=12) but not in an active-treatment unsynchronized control group (N=12). More importantly, in addition to a retrospective assessment of the correlation between measurements and clinical improvement in the end, we further demonstrate the potential utility of this biomarker in predicting the likelihood of a favorable treatment outcome before the treatment concludes. Our study underscores the potential of weekly tracking of brain synchronization changes, which enables the prediction of treatment efficacy and the opportunity for dynamic adjustments throughout the treatment course. This innovative approach substantially improves overall response rates, offering a promising direction for personalized and adaptive depression treatment strategies.