Diagnosis of Major Depressive Disorder Using Machine Learning Based on Multisequence MRI Neuroimaging Features

Background: Previous studies have found qualitative structural and functional brain changes in major depressive disorder (MDD) patients. However, most studies ignored the complementarity of multisequence MRI neuroimaging features and cannot determine accurate biomarkers.Purpose: To evaluate machine-learning models combined with multisequence MRI neuroimaging features to diagnose patients with MDD.Study Type: Prospective.Subjects: A training cohort including 111 patients and 90 healthy controls (HCs) and a test cohort including 28 patients and 22 HCs.Field Strength/Sequence: A 3.0 T/T1-weighted imaging, resting-state functional MRI with echo-planar sequence, and single-shot echo-planar diffusion tensor imaging.Assessment: Recruitment and integration were used to reflect the dynamic changes of functional networks, while gray matter volume and fractional anisotropy were used to reflect the changes in the morphological and anatomical network. We then fused features with significant differences in functional, morphological, and anatomical networks to evaluate a random forest (RF) classifier to diagnose patients with MDD. Furthermore, a support vector machine (SVM) classifier was used to verify the stability of neuroimaging features. Linear regression analyses were conducted to investigate the relationships among multisequence neuroimaging features and the suicide risk of patients.Statistical Tests: The comparison of functional network attributes between patients and controls by two-sample t-test. Network-based statistical analysis was used to identify structural and anatomical connectivity changes between MDD and HCs. The performance of the model was evaluated by receiver operating characteristic (ROC) curves.Results: The performance of the RF model integrating multisequence neuroimaging features in the diagnosis of depres-sion was significantly improved, with an AUC of 93.6%. In addition, we found that multisequence neuroimaging features could accurately predict suicide risk in patients with MDD (r = 0.691).Data Conclusion: The RF model fusing functional, morphological, and anatomical network features performed well in diagnosing patients with MDD and provided important insights into the pathological mechanisms of MDD.