Distributed White Matter Abnormalities in Major Depressive Disorder: A Diffusion Tensor Imaging Combined Support Vector Machine Study

Abstract

Objectives: Existing research by machine learning analysis based on neuroimaging in major depressive disorder (MDD) is limited. This study intends to investigate the integrity of white matter in patients with MDD using diffusion tensor imaging (DTI) combining machine learning approaches and to develop a model to differentiate MDD patients from healthy controls (HCs).

Materials and Methods: Clinical and neuroimaging data were collected from 60 MDD patients and 52 HCs. The tract-based spatial statistics (TBSS) and automated fiber quantification (AFQ) techniques were employed to analyze DTI data. Differences in diffusion metrics were then used in a support vector machine (SVM) model to determine the most significant features for distinguishing MDD patients from HC.

Results: No significant differences were observed in the TBSS between two groups. The AFQ analysis revealed that MDD patients exhibited reduced axial diffusivity (AD) and fractional anisotropy (FA) in specific segments of nerve fibers. The combined FA + AD model demonstrated better predictive performance with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.833 and a predictive accuracy of up to 85.00%, surpassing models utilizing single FA or AD metrics.

Conclusion: DTI combined with machine learning distinguished MDD patients through specific white matter alterations, underscoring the role of microstructural connectivity in depression pathology.