Major depressive disorder diagnosis from electroencephalogram data and potential treatment with dimethyltryptamine

Year 2023, Volume: 7 Issue: 2, 90 - 96, 15.08.2023

Sushmit Jahan

https://doi.org/10.35860/iarej.1231288

Cited By: 1

https://izlik.org/JA93KY62JN

Abstract

In recent times, there has been increasing interest in utilizing EEG-based techniques for studying Major Depressive Disorder as a dynamic method. Although it is frequently used for identifying depression, the method is still difficult to interpret. The conventional treatment of MDD involves medications such as Selective Serotonin Reuptake Inhibitors, which often have adverse effects. On the other hand, the use of dimethyltryptamine to stimulate brain activity in regions where MDD patients show lower activity has demonstrated promising results. This study analyzed resting-state EEG signals from MDD patients, DMT users, and healthy controls to evaluate and validated a computer-aided approach. The brain activity of DMT users was recorded and compared with MDD individuals and healthy controls. Using Welch's method, the power of several frequency bands was analyzed from the EEG dataset for comparison and diagnosis. The extracted EEG data underwent noise removal and feature extraction. The features from all controls were concatenated to form a data matrix. Furthermore, the data matrix was standardized using the Z-score standardization method. The classifier model logistic regression was employed to train and test the extracted features. The results of the investigations have demonstrated the most important features, such as signal power of the EEG data from the frontal, temporal, parietal, and occipital brain areas, to be significant.

Keywords

Dimethyltryptamine , Electroencephalogram(EEG) , Hanning window , Logistic regression (LR) , Major depressive disorder , Signal processing , Welch Method

References

• 1. Depressive disorder (depression). [2023 March 31]; Available from https://www.who.int/news-room/fact-sheets/detail/depression.
• 2. Park K, Jaekal E, Yoon S, Lee S-H and Choi K-H, Diagnostic Utility and Psychometric Properties of the Beck Depression Inventory-II Among Korean Adults. Frontiers in Psychology, 2020. 10: 2934.
• 3. Gu S, Wang F, Cao C, Wu E, Tang Y-Y and Huang JH, An Integrative Way for Studying Neural Basis of Basic Emotions With fMRI. Frontiers in Neuroscience, 2019. 13: 628.
• 4. Mayberg, H. S., Frontal lobe dysfunction in secondary depression. The Journal of Neuropsychiatry and Clinical Neurosciences, 1994. 6(4): p. 428–442.
• 5. Timmermann, C., Roseman, L., Schartner, M., Milliere, R., Williams, L. T. J., Erritzoe, D., Muthukumaraswamy, S., Ashton, M., Bendrioua, A., Kaur, O., Turton, S., Nour, M. M., Day, C. M., Leech, R., Nutt, D. J., and Carhart-Harris, R. L., Neural correlates of the DMT experience assessed with multivariate EEG. Scientific Reports, 2019. 9(1): 16324.
• 6. Duan L, Duan H, Qiao Y, Sha S, Qi S, Zhang X, Huang J, Huang X and Wang C, Machine Learning Approaches for MDD Detection and Emotion Decoding Using EEG Signals. Frontiers in Human Neuroscience, 2020. 14: 284.
• 7. Sperandei, S., Understanding logistic regression analysis. Biochemia Medica, 2014. 24(1): p. 12–18.
• 8. Tariq, M., Trivailo, P. M., & Simic, M., EEG-based BCI control schemes for lower-limb assistive-robots. Frontiers in Human Neuroscience, 2018. 12: 312.
• 9. Liu, J., Meng, H., Nandi, A., & Li, M., Emotion detection from EEG recordings in 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery 2016. ICNC-FSKD: p. 1722–1727.
• 10. Mumtaz, W., Xia, L., Ali, S. S. A., Yasin, M. A. M., Hussain, M., and Malik, A. S., Electroencephalogram (EEG)-based computer-aided technique to diagnose major depressive disorder (MDD). Biomedical Signal Processing and Control, 2017. 31: p. 108–115.
• 11. Pallavicini, C., Cavanna, F., Zambelan, F., de la Fuente, L. A., Arias, M., Romero, C., Carhart-Harris, R., Timmermann, C., and Tagliazucchi, E., Neural and subjective effects of inhaled DMT in natural settings [Data set]. Zenodo 2020. Available from https://doi.org/10.5281/zenodo.3992359
• 12. Ribas, V. R., Ribas, R. G., Nóbrega, J. de A., da Nóbrega, M. V., Espécie, J. A. de A., Calafange, M. T., Calafange, C. de O. M., and Martins, H. A. de L., Pattern of anxiety, insecurity, fear, panic and/or phobia observed by quantitative electroencephalography (QEEG). Dementia & Neuropsychologia, 2018. 12(3): p. 264–271.
• 13. Zhao, Li, and Yang He., Power Spectrum Estimation of the Welch Method Based on Imagery EEG. Applied Mechanics and Materials, 2013. 278-280: p. 1260–1264.
• 14. Pallavicini, C., Cavanna, F., Zamberlan, F., de la Fuente, L. A., Perl, Y. S., Arias, M., Romero, C., Carhart-Harris, R., Timmermann, C., and Tagliazucchi, E., Neural and subjective effects of inhaled DMT in natural settings. bioRxiv, 2020. Available from https://doi.org/10.1101/2020.08.19.258145
• 15. Pandya M, Altinay M, Malone DA Jr, and Anand A., Where in the brain is depression?. Current Psychiatry Reports, 2012. 14(6): p. 634-642.
• 16. Xiong, Q., Zhang, X., Wang, W.-F., and Gu, Y., A Parallel Algorithm Framework for Feature Extraction of EEG Signals on MPI. Computational and Mathematical Methods in Medicine, 2020: 9812019.