Many studies on resting-state functional connectivity (FC) in major depressive disorder (MDD) have investigated FC as a biomarker of disease pathogenesis. However, few studies have examined conditional dependencies among FC, clinical status, and demographic variables. Considering such dependencies allows the identification of direct relationships obscured by spurious correlations.

This study aimed to examine the neural mechanisms of MDD and propose a structural relationship between FC and MDD, focusing on sulcal regions.

Using a large dataset of 431 healthy controls and 235 MDD patients with MDD, we combined partial least squares (PLS)-based feature extraction with logistic regression and light gradient boosting machine (LightGBM) models for diagnostic classification, followed by Bayesian network (BN) analysis employing a directed acyclic graph.

The classification models demonstrated moderate accuracy (logistic regression: area under the curve [AUC] = 0.735; LightGBM: AUC = 0.710). Structure learning with the Max–Min Hill-Climbing algorithm revealed direct edges from the MDD diagnosis to variables derived from the BDI and PLS components, but no direct parent nodes of MDD were identified. Intervention simulation showed that the MDD diagnosis significantly reduced FC in the default mode network (DMN), dorsal attention network, and between subcortical structures and cortex.

MDD diagnosis is associated with disease-specific disruptions not only in the DMN but also across multiple networks, underscoring the need to consider widespread network dysfunction in the pathophysiology of MDD. Future longitudinal and interventional research is required to clarify the causal relationships between the diagnosis and brain function.