  Diagnosis of major depressive disorder (MDD) primarily relies on the
patient's self-reported symptoms and a clinical evaluation. Effective
connectivity (EC) from resting-state functional magnetic resonance imaging
(rs-fMRI) analysis can reflect the directionality of connections between brain
regions, making it a candidate method to classify MDD. This study used Granger
causality analysis to extract EC features from a large multi-site MDD dataset.
The ComBat algorithm and multivariate linear regression were used to harmonize
site difference and to remove age and sex covariates, respectively. Two-sample
t-tests and model-based feature selection methods were used to screen for
highly discriminative EC features for MDD, and LightGBM was used to classify
MDD. In this large-scale multi-site rs-fMRI dataset, 97 EC features deemed
highly discriminative for MDD were screened. In the nested five-fold
cross-validation, the best classification model with the 97 EC features
achieved accuracy, sensitivity, and specificity of 94.35%, 93.52%, and 95.25%,
respectively. In another independent large dataset, which tested the
generalization performance of the 97 EC features, the best classification
models achieved 94.74%, 90.59%, and 96.75% for accuracy, sensitivity, and
specificity, respectively. This work demonstrated that EC had a reasonable
discriminative ability and supported the notion for using EC to potentially
assist clinical diagnosis of MDD.
