Examining the relationship between electrophysiological biomarkers and treatment response in patients with major depressive disorder receiving accelerated transcranial magnetic stimulation

Aim: Transcranial magnetic stimulation (TMS) is a neuromodulation technique that allows for non-invasive brain stimulation. Approximately one-third of patients with treatment-resistant depression respond to TMS. This study aims to investigate electrophysiological biomarkers associated with TMS response. Materials and Methods: Thirty-one patients underwent an accelerated TMS protocol, receiving four daily sessions targeting the dorsomedial prefrontal cortex (DMPFC). Clinical assessments were conducted using the Hamilton Depression Rating Scale (HAMD), Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Beck Scale for Suicide Ideation (BSS), Quick Inventory of Depressive Symptomatology-Self Report (QIDS-SR16), Clinical Global Impressions (CGI), and the Maudsley Staging Method (MSM) scale. These assessments were performed before treatment and repeated post-treatment. Heart rate was continuously monitored throughout the treatment period. EEG recordings were obtained before and after treatment. Average heart rate reduction was calculated, and EEG data were analyzed using spectral power, cordance, and microstate analysis Results: Among the patients, 64.5% responded to treatment, while 51.6% achieved remission. No severe side effects were observed. A 2% reduction in average heart rate differentiated responders from non-responders with 90% sensitivity and 82% specificity. A post-treatment cordance reduction greater than -0.33 distinguished responders with 84% sensitivity and 70% specificity. Conclusion: Our findings suggest that an accelerated TMS protocol targeting the DMPFC with four daily sessions is a safe and effective treatment option. Furthermore, a reduction in average heart rate appears to be a potential biomarker for predicting treatment response. If validated in further studies, this biomarker could facilitate the identification of patients most likely to benefit from TMS, thereby contributing to the development of personalized TMS protocols.