Aim: This study aims to develop new approaches to characterize brain networks to potentially contribute to a better understanding of mechanisms involved in depression.
Method and subjects: We recruited 90 subjects: 49 healthy controls (HC) and 41 patients with a major depressive episode (MDE). All subjects underwent clinical evaluation and functional resting-state MRI. The data were processed investigating functional connectivity network measures across the two groups using Brain Connectivity Toolbox. The statistical inferences were developed at a functional network level, using a false discovery rate method. Linear discriminant analysis was used to differentiate between the two groups.
Results and discussion: Significant differences in functional connectivity (FC) between depressed patients vs. healthy controls was demonstrated, with brain regions including the lingual gyrus, cerebellum, midcingulate cortex and thalamus more prominent in healthy subjects as compared to depression where the orbitofrontal cortex emerged as a key node. Linear discriminant analysis demonstrated that full-connectivity matrices were the most precise in differentiating between depression vs. health subjects.
Conclusion: The study provides supportive evidence for impaired functional connectivity networks in MDE patients.
Keywords: classification; functional connectivity; functional magnetic-resonance imaging; mood disorders; resting state.