Related Experiment Video
Updated: Jun 14, 2025
07:12
Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
3.5K
Graph-based Analysis to Predict Repetitive Transcranial Magnetic Stimulation Treatment Response in Patients With
Behrouz Nobakhsh1, Ahmad Shalbaf1, Reza Rostami2
1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
Basic and Clinical Neuroscience
|September 4, 2024
Summary
Predicting repetitive transcranial magnetic stimulation (rTMS) success for major depressive disorder (MDD) is crucial. Brain connectivity analysis of resting-state EEG identified specific biomarkers for effective treatment prediction in drug-resistant patients.
Area of Science:
- Neuroscience
- Biomedical Engineering
- Clinical Psychiatry
Background:
- Repetitive transcranial magnetic stimulation (rTMS) offers a non-pharmacological treatment for drug-resistant major depressive disorder (MDD).
- Current rTMS success rates hover around 50%-55%, necessitating predictive methods to optimize patient selection and healthcare resource allocation.
- Identifying reliable biomarkers from pre-treatment data is essential for personalized MDD treatment strategies.
Purpose of the Study:
- To identify predictive biomarkers from resting-state electroencephalogram (EEG) signals for rTMS treatment outcomes in drug-resistant MDD patients.
- To reduce the burden on healthcare centers by enabling pre-treatment prediction of rTMS efficacy.
- To enhance clinical decision-making for MDD patients undergoing rTMS therapy.
Main Methods:
- Pre-treatment resting-state EEG data from 34 drug-resistant MDD patients were analyzed.
- Brain functional connectivity was modeled using the direct directed transfer function (dDTF) method on EEG data across all frequency bands.
- Local graph theory indices, including betweenness centrality, were computed to examine connectivity patterns.
Main Results:
- The betweenness centrality index in the Fp2 node within the delta (δ) frequency band emerged as the most significant biomarker.
- This biomarker achieved the highest area under the receiver operating characteristic curve (AUC) of 0.85 for predicting rTMS treatment response.
- The findings highlight specific EEG-derived connectivity features as potent predictors of rTMS efficacy.
Conclusions:
- The study successfully identified key biomarkers from pre-treatment EEG data for predicting rTMS outcomes in drug-resistant MDD.
- The proposed method, utilizing graph theory on dDTF connectivity, offers a valuable tool for clinical decision support.
- These findings can aid in selecting appropriate patients for rTMS, improving treatment success rates and optimizing healthcare efficiency.
