Browsing by Subject "major depressive disorder; structural connectivity; diffusion tensor imaging; white matter"

White matter (WM) structural connectivity alterations have been linked to depression. This study aimed to identify structural connectivity metrics associated with Major depressive disorder (MDD) and predictive of different symptom phenotypes. The study sample included N=29 control and N=86 subjects with MDD who underwent a clinical interview, Mini-International Neuropsychiatric Interview (MINI), and assessment of depression symptoms severity. Using a 3T MRI scanner, diffusion tensor imaging (DTI) was employed to capture WM connectivity markers at baseline. While no distinct differences between control and MDD groups were observed at the whole-brain network level, significant alterations were evident at the node level. Clinical group demonstrated enhanced connectivity, particularly in the DefaultB and LimbicB subsystems, as evidenced by measures such as eigenvector centrality. Furthermore, notable differences were observed in clustering coefficient and local efficiency, predominantly in DefaultB, LimbicB, and VisPeri networks, with MDD patients showing higher connectivity. Analysis of the association between WM structural connectivity measures, both global (e.g. global efficiency) and local (e.g. clustering coefficient) with MDD symptom scores and related symptoms, revealed no significant correlation at the whole-brain level, both at baseline and post-intervention. Distinct patterns were identified when evaluating node-level metrics averaged across networks, which together with group differences, point to MDD patients exhibiting characteristics consistent with regular networks. Hierarchical clustering based on standardized baseline DTI structural connectivity within the clinical cohort revealed three distinct clusters of MDD patients, with the first cluster exhibiting a higher WHO-5 score, indicating a potential association with better well-being. These findings provide insight into MDD-specific brain regions’ structural alterations and underscore the heterogeneity of depression symptom profiles. Further research is needed, including a higher sample size and control for confounding factors.