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.