Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA

Lee, Lok Hua and Ho, Cyrus Su Hui and Chan, Yee Ling and Tay, Gabrielle Wann Nii and Lu, Cheng-Kai and Tang, Tong Boon (2025) Antidepressant Treatment Response Prediction With Early Assessment of Functional Near-Infrared Spectroscopy and Micro-RNA. IEEE Journal of Translational Engineering in Health and Medicine, 13. 9 – 22. ISSN 21682372

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Abstract

While functional near-infrared spectroscopy (fNIRS) had previously been suggested for major depressive disorder (MDD) diagnosis, the clinical application to predict antidepressant treatment response (ATR) is still unclear. To address this, the aim of the current study is to investigate MDD ATR at three response levels using fNIRS and micro-ribonucleic acids (miRNAs). Our proposed algorithm includes a custom inter-subject variability reduction based on the principal component analysis (PCA). The principal components of extracted features are first identified for non-responders' group. The first few components that sum up to 99 of explained variance are discarded to minimize inter-subject variability while the remaining projection vectors are applied on all response groups (24 non-responders, 15 partial-responders, 13 responders) to obtain their relative projections in feature space. The entire algorithm achieved a better performance through the radial basis function (RBF) support vector machine (SVM), with 82.70 accuracy, 78.44 sensitivity, 86.15 precision, and 91.02 specificity, respectively, when compared with conventional machine learning approaches that combine clinical, sociodemographic and genetic information as the predictor. The performance of the proposed custom algorithm suggests the prediction of ATR can be improved with multiple features sources, provided that the inter-subject variability is properly addressed, and can be an effective tool for clinical decision support system in MDD ATR prediction. Clinical and Translational Impact Statement - The fusion of neuroimaging fNIRS features and miRNA profiles significantly enhances the prediction accuracy of MDD ATR. The minimally required features also make the personalized medicine more practical and realizable. © 2013 IEEE.

Item Type: Article
Impact Factor: Cited by: 1; All Open Access, Gold Open Access
Uncontrolled Keywords: Adult; Algorithms; Antidepressive Agents; Depressive Disorder, Major; Female; Humans; Male; MicroRNAs; Middle Aged; Principal Component Analysis; Spectroscopy, Near-Infrared; Support Vector Machine; Treatment Outcome; Young Adult; Radial basis function networks; Support vector machines; antidepressant agent; microRNA; microRNA 125a 5p; microRNA 374b 3p; microRNA 550b 2 5p; unclassified drug; antidepressant agent; microRNA; 'current; Clinical application; Functional near infrared spectroscopy; Machine-learning; Micro-ribonucleic acid; Performance; Response levels; Response prediction; Treatment response; Treatment response prediction; adult; algorithm; Article; clinical assessment; clinical decision support system; clinical feature; comparative study; controlled study; female; functional connectivity; functional near-infrared spectroscopy; genetic analysis; human; machine learning; major clinical study; male; nerve cell network; performance indicator; predictive value; principal component analysis; radial basis function; sensitivity and specificity; sociodemographics; support vector machine; treatment response; diagnostic imaging; drug therapy; major depression; metabolism; middle aged; near infrared spectroscopy; procedures; support vector machine; treatment outcome; young adult; Near infrared spectroscopy
Depositing User: Mr Ahmad Suhairi Mohamed Lazim
Date Deposited: 08 Jul 2025 16:37
Last Modified: 08 Jul 2025 16:37
URI: http://scholars.utp.edu.my/id/eprint/38912

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