Advancing Diagnosis of Major Depressive Disorder: Integrating Transcranial Magnetic Stimulation and Electroencephalography

Major depressive disorder (MDD) continues to be a significant challenge in mental health diagnosis and treatment. However, recent advancements in neuroimaging technology offer promising avenues for improved understanding and diagnosis of this complex condition. A recent study combining transcranial magnetic stimulation (TMS) with electroencephalography (EEG) has shown remarkable potential in discriminating between patients with MDD and healthy controls (HCs), shedding light on the neurophysiological underpinnings of depression. 

Noda et al. (2024) utilised a 70BF Coil with the DuoMAG TMS device in conjunction with a 64 channel TruScan LT EEG device to collect data from 60 patients with MDD and 60 HCs. The selection criteria ensured the homogeneity of the groups while controlling for potential confounding factors. Notably, all patients with MDD were on controlled medication, which standardised the treatment aspect of the study. 

Resting-state EEG and TMS-EEG measurements were performed using a TMS-compatible 64-channel EEG system. The combination of these technologies allowed researchers to investigate neurophysiological markers associated with MDD, such as power spectrum analysis, phase synchronisation, and phase-amplitude coupling. 

The EEG data analysis involved rigorous pre-processing steps to ensure data quality and reliability. Feature extraction and selection were then performed to identify discriminative features between the MDD and HC groups. Machine learning techniques were applied to these features to develop a model capable of distinguishing between individuals with MDD and HCs. 

The results demonstrated that combining features from resting-state EEG and TMS-EEG significantly improved the accuracy of the diagnostic model. Notably, the model achieved a mean area under the curve (AUC) of 0.922, indicating excellent discrimination performance. Further analysis revealed specific neurophysiological findings that may contribute to MDD discrimination, including increased beta power and gamma phase synchronisation in the right prefrontal cortex and decreased alpha and theta phase synchronisation in the left prefrontal area. 

These findings underscore the capabilities of integrated TMS-EEG as a powerful tool for diagnosing MDD and elucidating its neurobiological basis. By leveraging advanced neuroimaging techniques and machine learning algorithms, clinicians may soon have access to more accurate and objective diagnostic tools for mental health disorders. 

Noda, Y., Sakaue, K. and Wada, M. et al. (2024). Development of artificial intelligence for determining major depressive disorder based on resting-state EEG and single-pulse transcranial magnetic stimulation-evoked EEG indices. Journal of Personalised Medicine 14(1): 101. DOI: