MDD ATR dataset

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 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 entire algorithm achieved a better performance through the Gaussian support vector machine (SVM), with 82.70% accuracy, 78.44% sensitivity, 86.15% precision, and 91.02% specificity, respectively, when compared with conventional approaches. The actual effect in feature space of the mentioned proposal is also visualized via 2-dimensional t-distributed stochastic embeddings (t-SNE) to support the claim that the fusion of fNIRS and miRNA with custom inter-subject variability removal helps differentiate various categories of MDD ATRs.