Beyond Gray Matter: White Matter Functional Network Signatures of Major Depressive Disorder Track Symptom Severity
Year: 2026
Type: preprint
Abstract: Resting-state neuroimaging studies in major depressive disorder (MDD) have consistently identified abnormalities across large-scale gray matter (GM) networks, including the default mode, salience, and sensorimotor systems. Although prior work has primarily focused on GM, evidence suggests that white matter (WM) hemodynamic signals are meaningful and reliable, supporting an active role for WM in brain function. A key gap remains in understanding WM functional network connectivity (FNC) and spectral characteristics in MDD. We used resting-state fMRI scans from the MDD-DIRECT phase II study, comprising 1,944 participants across 13 sites, to examine WM FNC and spectral characteristics in individuals with MDD and healthy controls using a spatially constrained independent component analysis (ICA) framework. This hybrid framework leverages ICA’s ability to identify spatially robust, functionally meaningful brain networks guided by replicable spatial priors to examine WM (1) FNC differences and (2) spectral power alterations between MDD and healthy participants. MDD was characterized by reduced long-range integrative connectivity alongside increased localized and limbic–sensorimotor coupling within WM networks, highlighting a systems-level imbalance in neural communication that may underlie core cognitive, affective, and somatic symptoms of depression. We also observed reduced low-frequency spectral power within frontal and subcortical WM systems involved in cognitive control, emotional regulation, and decision-making. Notably, both functional and spectral alterations were associated with clinical measures of depression and anxiety, with opposing associations observed for frontal connectivity patterns. Studying WM function alongside GM may provide a more comprehensive understanding of functional pathology in depression.
Source: Research Square
Authors: Vince Calhoun, Malvika Sridhar, Bradley Baker
Institutions: Georgia State University, Stanford University
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: G1533216475, Collaborative Research:CISE-ANR:CIF:Small:Learning from Large Datasets - Application to Multi-Subject fMRI Analysis, G6283993744, Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivity, Multivariate methods for identifying multitask/multimodal brain imaging biomarkers
OpenAlex ID: W7221776240