A comparative study of gray matter structural and functional network topological properties in bipolar depression patients with and without comorbid obsessive-compulsive symptoms
10.3760/cma.j.cn113661-20241016-00333
- VernacularTitle:伴与不伴强迫症状双相抑郁患者的脑灰质结构和功能网络拓扑属性比较分析
- Author:
Xinyue TANG
1
;
Zibin YANG
;
Guanmao CHEN
;
Pan CHEN
;
Zixuan GUO
;
Shilin SUN
;
Yanbin JIA
;
Shuming ZHONG
;
Li HUANG
;
Ying WANG
Author Information
1. 暨南大学附属第一医院医学影像中心，广州510630
- Publication Type:Journal Article
- Keywords:
Bipolar disorder;
Bipolar depression;
Obsessive-compulsive symptoms;
Structural covariant networks;
Resting-state functional networks;
Support vector machi
- From:
Chinese Journal of Psychiatry
2025;58(2):113-124
- CountryChina
- Language:Chinese
-
Abstract:
Objective:Using graph theory analysis, this study compares the topological and node attributes of the brain network to explore the differences in gray matter structural and functional network topological properties between bipolar depression (BD) patients with and without obsessive-compulsive symptoms (OCS).Methods:A total of 90 BD patients (27 males, 63 females; median age 19.0(22.0, 25.0) years) were recruited from the psychiatric outpatient and inpatient departments of the First Affiliated Hospital of Jinan University between March 2018 and December 2022. Fifty healthy controls (19 males, 31 females; median age: 23.0 (20.0, 27.0) years) were also enrolled. The BD patients were divided into two groups based on the presence of OCS: 53 with OCS (OCS group) and 37 without OCS (NOCS group). Resting-state structural and functional MRI data were collected for all participants to construct gray matter structural and functional networks. Graph therory analysis was applied to calculate network topological metrics such as small-world properties. The structural and functional network topological properties were compared among the BD-OCS, BD-nOCS, and control groups. Partial correlation analysis was conducted to examine the association between network topological metrics with significant group differences and Yale-Brown Obsessive-Compulsive Scale (Y-BOCS) scores. Support vector machines (SVM) were used with these metrics as classification feature values to improve diagnostic accuracy through pairwise group classification.Results:Structural network analysis of gray matter: compared to HC group, both OCS group and NOCS group showed increased shortest path length and standardized characteristic path length (shortest path length: 0.78 and 0.80 vs. 0.69; normalized characteristic path length: 0.48 and 0.49 vs. 0.43), and decreased global efficiency (0.21 and 0.21 vs. 0.24) compared to the HC group (permutation test, all P<0.05). Compared to NOCS and HC groups, the OCS group showed increased nodal centrality and betweenness centrality in the right rolandic operculum and left superior occipital gyrus (permutation test, all P<0.05). Functional network analysis of gray matter: compared to the NOCS group, the OCS group showed increased node efficiency and decreased betweenness centrality in the cerebellum ( t=2.15, -3.04; all P<0.05); compared to HC groups, the OCS group showed decreased betweenness centrality in the cerebellum and left inferior frontal gyrus, along with increased node centrality and nodal efficiency in the right transverse temporal gyrus ( t=-2.99, -3.61, 3.06, 3.10; all P<0.05). In the OCS group, betweenness centrality in the left inferior frontal gyrus positively correlated with Y-BOCS scale obsessive thinking score ( r=0.303, P=0.034). Nodal centrality and node efficiency of the right transverse temporal gyrus negatively correlated with Y-BOCS total score ( r=-0.301, -0.311) and Y-BOCS obsessional thinking scores ( r=-0.385, -0.380) separately(all P<0.05). SVM classification: the combined network features achieved an area under the curve of 0.80 in distinguising OCS from NOCS patients. Conclusion:BD-OCS and BD-nOCS patients both exhibit consistent changes in gray matter structural network topology, with the OCS group displaying more pronounced nodal topological abnormalities. Multi-network feature integration demostrates potential for diagnostic classfication.
