Classification of Major Depressive Disorder Based on Integrated Temporal and Spatial Functional MRI Variability Features of Dynamic Brain Network

Background: Characterization of the dynamics of functional brain network has gained increased attention in the study of depression. However, most studies have focused on single temporal dimension, while ignoring spatial dimensional information, hampering the discovery of validated biomarkers for depression. Purpose: To integrate temporal and spatial functional MRI variability features of dynamic brain network in machine-learning techniques to distinguish patients with major depressive disorder (MDD) from healthy controls (HCs). Study Type: Prospective. Population: A discovery cohort including 119 patients and 106 HCs and an external validation cohort including 126 patients and 124 HCs from Rest-meta-MDD consortium. Field Strength/Sequence: A 3.0 T/resting-state functional MRI using the gradient echo sequence. Assessment: A random forest (RF) model integrating temporal and spatial variability features of dynamic brain networks with separate feature selection method (M-SFS) was implemented for MDD classification. Its performance was compared with three RF models that used: temporal variability features (M-TVF), spatial variability features (M-SVF), and integrated temporal and spatial variability features with hybrid feature selection method (M-HFS). A linear regression model based on M-SFS was further established to assess MDD symptom severity, with prediction performance evaluated by the correlations between true and predicted scores. Statistical Tests: Receiver operating characteristic analyses with the area under the curve (AUC) were used to evaluate models' performance. Pearson's correlation was used to assess relationship of predicted scores and true scores. P < 0.05 was considered statistically significant. Results: The model with M-SFS achieved the best performance, with AUCs of 0.946 and 0.834 in the discovery and validation cohort, respectively. Additionally, altered temporal and spatial variability could significantly predict the severity of depression (r = 0.640) and anxiety (r = 0.616) in MDD. Data Conclusion: Integration of temporal and spatial variability features provides potential assistance for clinical diagnosis and symptom prediction of MDD