Dynamic Functional Connectivity in Adolescence-Onset Major Depression: Relationships With Severity and Symptom Dimensions

BACKGROUND: The spatial functional chronnectome is an innovative mathematical model designed to capture dynamic features in the organization of brain function derived from resting-state functional magnetic resonance imaging data. Measurements of dynamic functional connectivity have been developed from this model to quantify the brain dynamical self-reconfigurations at different spatial and temporal scales. This study examined whether two spatiotemporal dynamic functional connectivity quantifications were linked to late adolescence-onset major depressive disorder (AO-MDD), and scaled with depression and symptom severity measured with the Montgomery-Asberg Depression Rating Scale.METHODS: Thirty-five patients with AO-MDD (21 +/- 6 years of age) and 53 age-and sex-matched healthy young participants (20 +/- 3 years of age) underwent 3T magnetic resonance imaging structural and resting-state functional magnetic resonance imaging acquisitions. The chronnectome here comprised seven individualized functional networks portrayed along 132 temporal overlapping windows, each framing 110 seconds of resting brain activity.RESULTS: Based on voxelwise analyses, patients with AO-MDD demonstrated significantly reduced temporal variability within the bilateral prefrontal cortex in five functional networks including the limbic network, default mode network, and frontoparietal network. Furthermore, the limbic network appeared to be particularly involved in this sample and was associated with Montgomery-Asberg Depression Rating Scale scores, and its progressive dynamic inflexibility was linked to sadness. Default mode network and frontoparietal network dynamics scaled with negative thoughts and neurovegetative symptoms, respectively.CONCLUSIONS: This triple-network imbalance could delay spatiotemporal integration, while across-subject symptom variability would be network specific. Therefore, the present approach supports that brain network dynamics underlie patients' symptom heterogeneity in AO-MDD.