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Updated: Dec 11, 2025

Individualized rTMS Treatment for Depression using an fMRI-Based Targeting Method
Published on: August 2, 2021
Disrupted rich-club network organization and individualized identification of patients with major depressive disorder
Xinyi Liu1, Cancan He1, Dandan Fan1
1Department of Neurology, Affiliated ZhongDa Hospital, School of Medicine, Southeast University, Nanjing, Jiangsu 210009, China.
Background:
Altered structural and functional brain networks have been extensively studied in major depressive disorder (MDD) patients. However, whether the differential connectivity patterns in the rich-club organization, assessed from structural brain network analyses, and the associated connections of these regions are particularly susceptible to depression remain unclear.
Methods:
We acquired resting-state functional magnetic resonance imaging (R-fMRI) and diffusion tensor imaging (DTI) from 31 unmedicated MDD patients and 32 cognitively normal (CN) subjects and completed a series of neuropsychological tests. Rich-club organization, network properties, and coupling between structural and functional connectivity (SC-FC) were explored. Furthermore, whether these indices could potentially deliver effective clinical predictive value for MDD patients were examined.
Results:
The MDD patients showed disrupted structural rich-club organization and modularity, as well as a distinct correlation pattern between global efficiency and rich-club organization. Importantly, reduced SC-FC coupling, reflecting a decreased agreement in the integrity of the networks, was significantly associated with the strength of structural rich-club connections in the MDD patients. Furthermore, the disrupted structural rich-club organization, which was primarily located in the default mode network (DMN) and executive control network (ECN), emerged as a valuable indicator to distinguish between MDD and CN.
Conclusions:
Findings of this study identified that the disrupted rich-club structural organization significantly influenced brain structural network modularity and integrity and could serve as a promising biological marker for the identification of MDD patients.
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