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.

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Publication Details

Journal
Human Brain Mapping
Published
2026-07-24
DOI
https://doi.org/10.1002/hbm.70612
Primary Topic
Functional Brain Connectivity Studies
Type
article
Field-Weighted Citation Impact
0.00

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article

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

Shi‐Xian Cui, Ce Wang, Zhaosong Chu, Xinlei Ji et al.
Human Brain Mapping
Functional Brain Connectivity Studies
article

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

Shi‐Xian Cui, Ce Wang, Zhaosong Chu, Xinlei Ji, Minlan Yuan, Yonggui Yuan, Zhao‐Yu Deng, Zi-Han Wang, Yuhao Shen, Chunming Xie, J ZHU, Jin Zhao, Xinran Wu, Zheng-Jia-Yi Hu, Zhenpeng Xue, Qiyong Gong, Feng-Nan Jia, Lian Ts, Jiang Hua Qiu, Huaning Wang, Wu Xl, Xuetian Sun, Jiangshan Chen, T Donna Chen, Guan-Mao Chen, Ying Wang, Xiang Wang, Wei Zhang, Li Kuang, Li T, Ying Liu, Tian-Mei Si, Xu Xf, Guangrong Xie, Y N Wu, Q-Y Huang, Can-Can He, Tao-Lin Chen, Xiao-Xiao Shan, Y Y Yu, B Li, Y M Cheng, Xiao‐Yun Liu, Ke‐Rang Zhang, Xue‐Ying Li, Hong Yang, Yi‐Cheng Long, Zhe‐Ning Liu, Chao‐Gan Yan, Peng Xie, Zi‐Jing Zhang, Hui‐Xian Li, Yu‐Wei Wang, Xiao Chen, Bin Lu, Yi‐Fan Liao, Xi‐Long Cui, Hua Yu, Ai‐Xia Zhang, Li‐Ping Cao, Jian‐Ping Lu, Wen‐Bin Guo, Peng‐Feng Sun, Feng Li
article en

Abstract

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.

Human Brain MappingVol. 47(11)
Central South University (CN), Southwest University (CN), Shanxi Medical University (CN), Capital Medical University (CN), Anhui Medical University (CN), Chinese Academy of Sciences (CN), Chinese Academy of Medical Sciences & Peking Union Medical College (CN), Peking University (CN), Sichuan University (CN), Kunming Medical University (CN), Soochow University (CN), Xian Central Hospital (CN), West China Hospital of Sichuan University (CN), First Affiliated Hospital of Guangzhou Medical University (CN), Zhongda Hospital Southeast University (CN), Suzhou Guangji Hospital (CN), Beijing Anding Hospital (CN), Sino-Danish Centre for Education and Research (CN), Shenzhen KangNing Hospital (CN), First Affiliated Hospital of Kunming Medical University (CN), First Hospital of Shanxi Medical University (CN), The Affiliated Yongchuan Hospital of Chongqing Medical University (CN), Institute of Psychology, Chinese Academy of Sciences (CN), Hangzhou Seventh Peoples Hospital (CN), First Affiliated Hospital of Anhui Medical University (CN), Guangzhou Psychiatric Hospital (CN), Second Xiangya Hospital of Central South University (CN), Xijing Hospital (CN), Children's Hospital of Chongqing Medical University (CN), Peking University Sixth Hospital (CN), First Affiliated Hospital of Jinan University (CN), Guangdong 999 Brain Hospital (CN), First Hospital of Xi'an (CN), University of Chinese Academy of Sciences (CN), First Affiliated Hospital Zhejiang University (CN), China Construction Bank (CN), Chongqing Medical University (CN), Guangzhou Medical University (CN), Tsinghua University (CN)
National Natural Science Foundation of China, Natural Science Foundation of Anhui Province
Good health and well-being
Openalex Percentile: Top 7%
Functional Brain Connectivity Studies
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