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MRI-guided dmPFC-rTMS as a Treatment for Treatment-resistant Major Depressive Disorder
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Non-linear processing and reinforcement learning to predict rTMS treatment response in depression.

Elias Ebrahimzadeh1, Amin Dehghani2, Mostafa Asgarinejad3

  • 1School of Electrical and Computer Engineering, College of Engineering, University of Tehran, Tehran, Iran; School of Cognitive Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

Psychiatry Research. Neuroimaging
|December 3, 2023
PubMed
Summary

Machine learning accurately predicts repetitive transcranial magnetic stimulation (rTMS) therapy response in major depressive disorder (MDD) patients using pre-treatment EEG data. This approach saves time and costs by identifying likely responders before treatment begins.

Keywords:
Electroencephalography (EEG)Independent component analysis (ICA)Machine learningMajor depressive disorder (MDD)Repetitive transcranial magnetic stimulation (rTMS)

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Area of Science:

  • Neuroscience
  • Machine Learning
  • Medical Diagnostics

Background:

  • Repetitive transcranial magnetic stimulation (rTMS) is a treatment for major depressive disorder (MDD).
  • Predicting rTMS efficacy can save significant time and healthcare costs by avoiding non-beneficial treatments.
  • Machine learning models can potentially categorize patients into responders (R) and non-responders (NR) to rTMS.

Purpose of the Study:

  • To develop and validate a machine learning approach for predicting rTMS treatment outcomes in MDD patients.
  • To identify reliable neurophysiological markers from pre-treatment EEG data that distinguish between rTMS responders and non-responders.
  • To enhance the accuracy of treatment selection for MDD patients undergoing rTMS therapy.

Main Methods:

  • Resting-state EEG data were collected from 106 MDD patients using 32 electrodes prior to a 7-week rTMS treatment course.
  • Independent Component Analysis (ICA) was applied to EEG data to identify neural activity markers in the dorsolateral prefrontal cortex (DLPFC).
  • Features derived from independent component time-series, including power and bispectral measures, were analyzed using Reinforcement Learning (RL) and classified with K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Multilayer Perceptron (MLP) via ten-fold cross-validation.

Main Results:

  • Key EEG features, including beta power and delta/beta band bispectrum elements, were identified as robust predictors of rTMS response.
  • A combined feature vector achieved high classification performance: 95.28% accuracy, 94.23% specificity, 96.29% sensitivity, and 94.54% precision using SVM.
  • Statistical analysis confirmed the discriminative capacity of the selected features between responders and non-responders.

Conclusions:

  • The proposed machine learning method, using power, non-linear, and bispectral features from pre-treatment EEG, can effectively forecast rTMS outcomes for MDD patients.
  • This approach demonstrates superior performance compared to existing methods for predicting treatment response.
  • The findings support the use of a single pre-treatment EEG session for personalized rTMS treatment selection in MDD.