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1- Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
2- Department of Psychology, University of Tehran, Tehran, Iran.
3- Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran.
Repetitive transcranial magnetic stimulation (rTMS) is considered a non-pharmacological treatment for drug-resistance Major Depressive Disorder (MDD) patients. Since the outcome rate of rTMS treatment is about 50-55 %, it is essential to predict the treatment outcome before starting the treatment based on electroencephalogram (EEG) signals, which can lead to the identification of effective biomarkers and could reduce the burden of health care centers. Pretreatment EEG data with 19-channel in the resting state from 34 drug-resistant MDD patients were recorded. All patients received 20 sessions of rTMS treatment, and a reduction of at least 50% in the total Beck Depression Inventory (BDI-II) score before and after the rTMS treatment is defined as a reference. In current study, effective brain connectivity features are determined by the direct directed transfer function (dDTF) method from pre-treatment EEG data of patients in all frequency bands separately. Then, the brain functional connectivity patterns are modeled as graphs by the dDTF method and examined with the local graph theory indices including degree, out-degree, in-degree, strength, out-strength, in-strength, and betweenness centrality. The results indicated that the Betweenness centrality index in node Fp2 and delta frequency band is the best biomarker and has the highest area under the receiver operating characteristic curve (AUC-ROC) value of 0.85 for prediction of the rTMS treatment outcome in drug-resistance MDD patients. The proposed method investigates the significant biomarkers that can be used to obtain the rTMS treatment outcome in drug-resistance MDD patients to help clinical decisions.
Type of Study: Original | Subject: Cognitive Neuroscience
Received: 2022/12/22 | Accepted: 2023/05/20

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