Spatiotemporal discoordination of brain spontaneous activity in major depressive disorder.

Major depressive disorder (MDD) is a widespread mental health issue, impacting spatial and temporal aspects of brain activity. The neural mechanisms behind MDD remain unclear. To address this gap, we introduce a novel measure, spatiotemporal topology (SPT), capturing both the hierarchy and dynamic attributes of brain activity in depressive disorder patients. We analyzed fMRI data from 285 MDD inpatients and 141 healthy controls (HC). SPT was assessed by coupling brain gradient measurement and time delay estimation. A nested machine learning process distinguished between MDD and HC using SPT. Person's correlation tested the link between SPT's and symptom severity, and another machine learning method predicted the gap between patients' chronological and brain age. SPT demonstrated significant differences between patients and healthy controls (F = 2.944, p < 0.001). Machine learning approaches revealed SPT's ability to discriminate between patients and healthy controls (Accuracy = 0.65, Sensitivity = 0.67, Specificity = 0.64). Moreover, SPT correlated with the severity of depression symptom (r = 0.32. p FDR = 0.045) and predicted the gap between patients' chronological age and brain age (r = 0.756, p < 0.001). Evaluation of brain dynamics was constrained by MRI temporal resolution. Our study introduces SPT as a promising metric to characterize the spatiotemporal signature of brain function, providing insights into deviant brain activity associated with depressive disorders and advancing our understanding of their psychopathological mechanisms. • Spatial and temporal brain aberrations linked to Major Depressive Disorder (MDD). • SPT integrates spatial and temporal features as a potential MDD biomarker. • SPT effectively discriminates between MDD and healthy populations via machine learning. • SPT correlates with depression severity and predicts differences in brain-age predictions. [ABSTRACT FROM AUTHOR]