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

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