Objective Depression Diagnosis derived from Effective Connectivity through Dynamic Causal Modelling

Motivation: The diagnosis of major depressive disorder (MDD) currently involves subjectivity, but an objective test based on a measurement is desired. Goal(s): To obtain effective connections between brain networks from functional MRI that both allow MDD to be diagnosed and offer clinically relevant insight. Approach: Stochastic Dynamic Causal Modelling is applied to the time series of resting-state networks. The most discriminative connections are found through Bayesian Model Reduction and Chi-Square feature selection. These connections are used for classification using machine learning. Results: Eight clinically relevant effective connections result in 94% leave-one-out cross-validation accuracy, which resulted in 100% accuracy on a separate test set. Impact: The discriminative ability of the eight resulting effective connections aid understanding of MDD's pathophysiology. Furthermore, the results may inspire researchers to investigate the eight most discriminative connections on other datasets, which can lead to an objective diagnostic biomarker for MDD.