Altered topology in cortical morphometric similarity network in recurrent major depressive disorder

Ziyun Xu; Zhifeng Zhou; Weiqun Tao; Wentao Lai; Long Qian; Wei Cui; Bo Peng; Yingli Zhang; Gangqiang Hou

doi:10.1016/j.jpsychires.2024.11.038

Altered topology in cortical morphometric similarity network in recurrent major depressive disorder

J Psychiatr Res. 2024 Nov 26:181:206-213. doi: 10.1016/j.jpsychires.2024.11.038. Online ahead of print.

Authors

Ziyun Xu¹, Zhifeng Zhou¹, Weiqun Tao², Wentao Lai¹, Long Qian³, Wei Cui⁴, Bo Peng⁵, Yingli Zhang⁶, Gangqiang Hou⁷

Affiliations

¹ Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, 518020, China.
² Department of Psychiatry, Acute Intervention Female Ward 1, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518000, China.
³ Department of Biomedical Engineering, College of Engineering, Peking University, Beijing, 100871, China.
⁴ MR Research, GE Healthcare, Beijing, 100176, China.
⁵ Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518000, China.
⁶ Department of Depressive Disorder, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, 518000, China. Electronic address: yinglizhang2000@163.com.
⁷ Neuropsychiatry Imaging Center, Department of Radiology, Shenzhen Kangning Hospital, Shenzhen Mental Health Center, Shenzhen, Guangdong, 518020, China. Electronic address: nihaohgq@163.com.

PMID: 39616867
DOI: 10.1016/j.jpsychires.2024.11.038

Abstract

Background: Recurrent major depressive disorder (RDD) is increasingly understood to be associated with a 'disconnection' within the brain areas. But, the true understanding of cortical connectivities remains challenging. Morphometric similarity network (MSN) with multi-modal magnetic resonance imaging (MRI) could provide more information about cortical micro-architecture changes in individuals with RDD.

Methods: Here, we integrated multi-modal features from T1-weighted imaging, diffusion tensor imaging (DTI), and inhomogeneous magnetization transfer imaging (ihMT) to construct MSN. We used graph theory to calculate topological changes in MSN and explore their relationship with the severity and recurrence. The topological properties of 42 RDD patients were compared with 56 age, sex, and education-matched healthy controls.

Results: RDD subjects showed significantly decreased global efficiency, increased characteristic path length, reduced nodal efficiencies in the parietal lobe, subcortical area, and temporal lobe, increased betweenness centrality in the left supplementary motor area (SMA), decreased intra-modular connections in the parietal module and decreased inter-modular connections between the parietal and prefrontal modules. Notably, the global efficiency, characteristic path length, local efficiency of the right superior parietal gyrus, and inter-modular connections between the parietal and prefrontal modules were significantly associated with the number of depressive episodes. The betweenness centrality in SMA and the intra-modular connections in the parietal module showed a positive relationship with 17-item Hamilton Rating Scale for Depression (HAMD) scores.

Conclusions: The altered topology of MSN may serve as potential underlying pathological mechanisms of RDD. The impaired information integration of the network, particularly the disconnection within the fronto-parietal network, may be associated with the recurrence of depression. The SMA and the fronto-parietal network may be related to the severity of depression.

Keywords: Diffusion tensor imaging; Graph theory; Inhomogeneous magnetization transfer imaging; Morphometric similarity network; Recurrent depressive disorder.