Edge-centric brain connectomes reveal two molecularly distinct neurobiological subtypes of adolescent major depressive disorder.

Altered Cortical Morphological Brain Networks and Their Diagnostic Classification Utility in Major Depressive Disorder

Major depressive disorder (MDD) has been increasingly characterized as a network dysconnectivity syndrome. Although single‐subject morphological networks are advantageous in studying the brain connectome, extant research on MDD is limited by either small samples or a lack of integration of multi‐feature across different morphological features. We used the largest structural MRI data from 1442 MDD patients and 1277 controls to construct individual‐level cortical morphological networks based on cortical thickness (CT), cortical volume (CV), surface area (SA), and sulcal depth (SD). Group comparisons in interregional morphological connectivity (MC) and graph‐theoretical nodal properties were performed. Furthermore, support vector machine (SVM) was applied to evaluate whether the network alterations could distinguish patients from controls. As a result, MDD patients presented widespread alterations in MC, with distinct alteration patterns observed across four morphological networks. Specifically, CT‐based networks exhibited reduced MC primarily within and between higher‐order networks involving the default mode and frontoparietal networks, whereas CV‐based networks showed increased MC predominantly within the default mode network. By contrast, both SA‐ and SD‐based networks demonstrated enhanced MC mainly within and between lower‐order networks implicating the somatomotor and visual networks. Similar patterns of MC alterations were observed in first‐episode, drug‐naive MDD patients. Concurrently, nodal property analysis revealed increased betweenness centrality in multiple cortical regions in MDD. Moreover, SVM models based on the altered MC achieved moderate‐to‐good classification performance in distinguishing patients from controls. Overall, our findings of individual‐level morphological network alterations in depressed patients may corroborate the dysconnectivity hypothesis of MDD and could further inform its more accurate diagnosis.

Mapping brain connections to inflammatory networks in depression identifies sex-specific neuroimmune networks and neurobiological subtypes

White matter functional connectome topology and its clinical correlations in adolescent major depressive disorder

Abstract Background Adolescence is a critical period for brain network remodeling and the onset of major depressive disorder (MDD); however, white matter (WM) functional topology in adolescent MDD remains underexplored. Given that WM functional signals reflect meaningful neural activity and are disrupted in psychiatric disorders, this study aimed to characterize WM functional connectome alterations in adolescents with MDD and examine their clinical associations. Methods Resting-state fMRI data were obtained from a cohort of adolescents with MDD (n = 320) and healthy controls (HCs, n = 144), as well as from an independent replication cohort. Following the construction of thresholded WM functional networks, graph-theoretical analyses were used to calculate global topological properties. Canonical correlation analysis (CCA) was used to examine associations between topology and clinical symptoms, while exploratory classification assessed their discriminative information and generalizability. Furthermore, subgroup analyses were conducted to evaluate the effects of a history of suicide attempt, non-suicidal self-injury, childhood trauma, and sex. Results Compared with HCs, adolescent MDD exhibited significant reductions in the clustering coefficient, characteristic path length, and local efficiency. CCA identified distinct covariation patterns: reduced global integration was linked to severe suicidal ideation and depressed mood, while impaired local segregation was associated with vegetative symptoms such as weight loss and insomnia. Subgroup analyses revealed significant sexual dimorphism, with male patients demonstrating more severe topological impairments than females. A similar pattern was observed in the independent replication cohort. The classification analysis achieved above-chance accuracy (69.6 and 60% in the two cohorts). Conclusions Our results reveal a topologically shifted WM functional connectome structure in adolescent MDD, providing new clues to aid in understanding the pathophysiology of its pathophysiology.

Electroconvulsive Therapy Drives Sensorimotor Network Segregation in Depression: A Multiscale Edge-Centric Connectomic Study

Background: Electroconvulsive therapy (ECT) induces widespread brain effects and remains the most effective intervention for severe major depressive disorder (MDD). However, how ECT reshapes the global organization of functional connectomes remains poorly understood. Edge-centric connectomics offers a framework for characterizing large-scale reconfiguration beyond conventional node-based analyses. Methods: Longitudinal resting-state fMRI data from a primary cohort (80 MDD patients, 75 healthy controls) and an independent validation cohort (30 MDD patients) were analyzed. Edge-centric normalized entropy was utilized to quantify connectomic topology at baseline and post-ECT. These topological changes were evaluated for clinical associations and multiscale spatial correlations encompassing cognitive dimensions, neurotransmitter maps, and transcriptomic profiles. Additionally, baseline edge-centric features were leveraged in a machine learning framework to predict treatment response. Results: At baseline, MDD patients showed increased entropy in the subcortical network and decreased entropy in the dorsal attention and sensorimotor networks. Following ECT, a further reduction in sensorimotor network (SMN) entropy was observed, which was replicated in the independent cohort. SMN reorganization was significantly associated with improvements in specific depressive symptoms. Multiscale decoding revealed that these topological shifts spatially aligned with broad monoaminergic receptor distributions and transcriptomic signatures governing neuroplasticity and specific cell types. Furthermore, baseline edge-centric features outperformed conventional fMRI metrics in predicting treatment response and maintained partial cross-site generalizability. Conclusions: ECT is associated with selective reorganization of the sensorimotor network rather than normalization of baseline abnormalities. Edge-centric connectomics combined with multiscale biological annotations provides a robust framework for characterizing therapeutic mechanisms and developing predictive biomarkers in MDD.

Analysis of individualized brain–gut association networks and transcriptional patterns in major depressive disorder

The results suggested that associations between proinflammatory gut microbiota and the herpes simplex virus type 1 infection pathway may contribute to the onset of MDD through inflammatory processes.

Mapping the Heterogeneity of Major Depressive Disorder: A Systematic Review of Multi-Omics Integration Studies

Results indicate that multi-omics integration, in addition to explaining the molecular architecture of MDD, also characterizes patient subgroups with pathophysiological mechanisms, dimensions of symptoms, and disease treatment, which demonstrates that there is a shift in psychiatry toward a more mechanistic approach.