# Unveiling Major Depressive Disorder Through TMS-EEG: From Traditional to Emerging Approaches

## TL;DR

This paper reviews how TMS-EEG is advancing the understanding of major depressive disorder by moving from traditional to more advanced neurophysiological analysis methods.

## Contribution

The paper highlights the shift from traditional TEP analysis to multidimensional TMS-EEG approaches for better MDD biomarker identification.

## Key findings

- Traditional TEP measures are limited in capturing network dysfunction in MDD.
- Emerging TMS-EEG methods reveal oscillatory activity and connectivity patterns.
- Advanced computational techniques improve biomarker sensitivity and predictive value.

## Abstract

Major depressive disorder (MDD) is one of the most prevalent psychiatric conditions and is characterized by alterations in cortical excitability, network connectivity, and neuroplasticity. Despite significant progress in neuroimaging and neurophysiology, the identification of objective and reliable biomarkers remains a major challenge, limiting diagnostic accuracy and treatment optimization. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) has emerged as a powerful methodology to probe causal brain dynamics with high temporal resolution. This review aims to summarize recent advances in the application of TMS-EEG to MDD, highlighting the transition from traditional TMS-evoked potential (TEP) analyses to more advanced, multidimensional approaches. We reviewed original research articles published between 2020 and 2025 that investigated neurophysiological markers and approaches to MDD using TMS-EEG. Traditional TEP measures provide markers of local cortical responses but are limited in capturing distributed network dysfunction. Emerging approaches expand the scope of TMS-EEG, allowing for the characterization of oscillatory activity, connectivity patterns, and large-scale network dynamics. Recent contributions also demonstrate the potential of computational and multivariate techniques to enhance biomarker sensitivity and predictive value. Taken together, recent evidence highlights TMS-EEG as a uniquely positioned methodology to investigate the neurophysiological substrates of MDD. By linking conventional TEP-based indices with innovative analytic strategies, TMS-EEG enables a multidimensional assessment of cortical function and dysfunction that transcends traditional descriptive markers. This integrative perspective not only refines mechanistic models of MDD but also opens new avenues for biomarker discovery, patient stratification, and treatment monitoring. Ultimately, the convergence of advanced TMS-EEG approaches with clinical applications holds promise for translating neurophysiological insights into precision psychiatry interventions aimed at improving outcomes in MDD.