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An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals

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Abstract

One of the most effective treatments for drug-resistant Major depressive disorder (MDD) patients is repetitive transcranial magnetic stimulation (rTMS). To improve treatment efficacy and reduce health care costs, it is necessary to predict the treatment response. In this study, we intend to predict the rTMS treatment response in MDD patients from electroencephalogram (EEG) signals before starting the treatment using machine learning approaches. Effective brain connectivity of 19-channel EEG data of MDD patients was calculated by the direct directed transfer function (dDTF) method. Then, using three feature selection methods, the best features were selected and patients were classified as responders or non-responders to rTMS treatment by using the support vector machine (SVM). Results on the 34 MDD patients indicated that the Fp2 region in the delta and theta frequency bands has a significant difference between the two groups and can be used as a significant brain biomarker to assess the rTMS treatment response. Also, the highest accuracy (89.6%) using the SVM classifier for the best features of the dDTF method based on the area under the receiver operating characteristic curve (AUC-ROC) criteria was obtained by combining the delta and theta frequency bands. Consequently, the proposed method can accurately detect the rTMS treatment response in MDD patients before starting treatment on the EEG signal to avoid financial and time costs to patients and medical centers.

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Acknowledgements

This research is financially supported by "Research Department of School of Medicine Shahid Beheshti University of Medical Sciences" (Grant No 13731).

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Authors and Affiliations

  1. Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Behrouz Nobakhsh, Ahmad Shalbaf & Erfan Rezaei

  2. Department of Psychology, University of Tehran, Tehran, Iran

    Reza Rostami

  3. Department of Cognitive Psychology, Institute for Cognitive Science Studies, Tehran, Iran

    Reza Kazemi

  4. Institute for Cognitive Science Studies, Tehran, Iran

    Reza Shalbaf

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  1. Behrouz Nobakhsh
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Correspondence to Ahmad Shalbaf.

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Nobakhsh, B., Shalbaf, A., Rostami, R. et al. An effective brain connectivity technique to predict repetitive transcranial magnetic stimulation outcome for major depressive disorder patients using EEG signals. Phys Eng Sci Med 46, 67–81 (2023). https://doi.org/10.1007/s13246-022-01198-0

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