Group Sparse Representation Enhances Brain Network Classification of Major Depressive Disorder in Two Chinese Cohorts.

Abstract

Background: Major depressive disorder (MDD) is associated with altered organization of functional brain networks. This study aims to evaluate the classification efficacy of three brain networks constructed by Pearson correlation (PC), sparse representation (SR), and group sparse representation (GSR) in distinguishing patients with MDD from healthy controls (HCs).

Methods: The present study involved the recruitment of 117 Chinese participants, comprising 61 individuals diagnosed with MDD and 56 HCs, all of whom underwent functional magnetic resonance imaging (fMRI). Brain time-series signals were extracted from 116 regions to construct whole-brain networks utilizing PC, SR, and GSR. A linear support vector machine (SVM) classifier with least absolute shrinkage and selection operator (LASSO) feature selection was trained using leave-one-out cross-validation (LOOCV) to optimize generalizability. An independent dataset of Chinese (124 first-episode drug-naïve MDD and 105 HCs) was utilized for additional validation.

Results: Compared to the PC and SR, the GSR network yielded superior classification results, with an area under the receiver operating characteristic curve of 0.85, an accuracy of 0.81, and a sensitivity of 0.95. Similar results were observed in the independent MDD dataset. We identified 17 brain connections and 27 brain regions within the GSR network.

Conclusions: Our findings support the adoption of GSR-based brain networks as a robust tool for MDD diagnosis, challenging the conventional reliance on PC in neuroimaging research.