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Multiband EEG signatures decoded using machine learning for predicting rTMS treatment response in MDD.

Alexander Arteaga1, Xiaoyu Tong1, Kanhao Zhao1

  • 1Department of Bioengineering, Lehigh University, Bethlehem, PA, USA.

Journal of Affective Disorders
|May 29, 2025
PubMed
Summary

Machine learning analysis of electroencephalograms (EEG) identified multiband signatures that predict treatment outcomes for major depression disorder (MDD) patients undergoing repetitive transcranial magnetic stimulation (rTMS). This offers a pathway for personalized depression treatment.

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Area of Science:

  • Neuroscience
  • Computational Psychiatry
  • Biomedical Engineering

Background:

  • Repetitive transcranial magnetic stimulation (rTMS) shows promise for treating major depression disorder (MDD), especially treatment-resistant cases.
  • Predictive biomarkers for rTMS treatment outcomes in MDD are currently underexplored.
  • Personalized treatment strategies are crucial for optimizing patient outcomes in MDD.

Purpose of the Study:

  • To identify translatable EEG-based biomarkers predictive of rTMS treatment response in MDD patients.
  • To explore the utility of machine learning and multiband EEG signatures for personalized depression treatment.
  • To investigate the relationship between specific oscillatory patterns and treatment outcomes.

Main Methods:

  • Participants with treatment-resistant depression (TRD) from the TDBRAIN dataset received either high-frequency (10 Hz) left DLPFC rTMS or low-frequency (1 Hz) right DLPFC rTMS.
  • Pre-treatment electroencephalograms (EEG) were recorded and analyzed using a machine learning framework.
  • EEG oscillations were decomposed into multiband intrinsic mode functions (IMFs) to identify predictive signatures of treatment response, measured by changes in Beck Depression Inventory scores.

Main Results:

  • Multiband EEG signatures significantly predicted rTMS treatment outcomes in both high-frequency (r=0.40, p<0.01) and low-frequency (r=0.26, p<0.05) protocols.
  • Key predictive oscillations included IMF-Alpha, IMF-Beta, and the residual signal, with distinct spatial patterns identified for each protocol.
  • Specific frontal, parietal, and central brain regions were implicated in treatment response prediction, with correlations to personality measures noted.

Conclusions:

  • Machine learning-driven multiband EEG signatures show significant promise for predicting rTMS treatment outcomes in MDD.
  • These findings offer a translatable pathway towards personalized treatment strategies for major depression disorder.
  • The identified oscillatory patterns may serve as valuable biomarkers for guiding clinical interventions in MDD.