Changes of gray matter volume and structure covariant network in patients with cerebral small vascular disease and cognitive impairment
10.3760/cma.j.cn112149-20231016-00296
- VernacularTitle:脑灰质体积及结构协变网络在脑小血管病伴认知障碍患者中变化特征
- Author:
Lin MA
1
;
Siyuan ZENG
;
Haixia MAO
;
Yachen SHI
;
Feng WANG
;
Xiangming FANG
Author Information
1. 南京医科大学无锡医学中心 南京医科大学附属无锡人民医院放射科，无锡 214023
- Keywords:
Cognition disorders;
Magnetic resonance imaging;
Cerebral small vessel disease;
Gray matter volume;
Graph theoretical analysis
- From:
Chinese Journal of Radiology
2024;58(5):496-502
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To explore the characteristics of gray matter volume (GMV) and structural covariant network (SCN) in patients with cerebral small vessel disease (CSVD) related cognitive impairment.Methods:This was a cross-sectional study. Ninety-eight patients with CSVD who attended Wuxi People′s Hospital of Nanjing Medical University between October 2021 and December 2022 were prospectively included. The patients were evaluated using the cognitive status assessment scale and were categorized into 57 cases in the CSVD with cognitive impairment group and 41 cases in the CSVD without cognitive impairment group according to the presence or absence of cognitive impairment. 3D-T 1WI structural image data were collected, and GMV differences between the two groups were compared by SPM 12 toolbox and CAT12 toolkit. At the same time, Pearson correlation analysis was also performed to analyze the GMV of differences between the 2 groups and cognitive status assessment scale scores. The BCT software package based on MATLAB platform was used to construct the GMV-related structural covariant network (SCN), and the graph theory method was utilized for SCN analysis to calculate the area under the curve (AUC) of the global and local parameters within the set sparsity range, and the permutation test was used to compare the differences in the AUC of the 2 groups. Results:In the CSVD with cognitive impairment group, GMV in bilateral hippocampus, parahippocampal gyrus, fusiform gyrus, and left amygdala was significantly lower than that in the CSVD without cognitive impairment group (family wise error corrected P<0.05), and the GMV in these regions had correlation with cognitive status assessment scale ( P<0.05). At the global network level of the SCN, the area under the curve (AUC) of the characteristic path length was significantly higher in the CSVD with cognitive impairment group than in the CSVD without cognitive impairment group ( P=0.023), while the AUC of global efficiency was significantly lower in CSVD with cognitive impairment group than in the CSVD without cognitive impairment group ( P=0.005). At the local level, the nodal degree and nodal efficiency of the left putamen were significantly decreased in the CSVD with cognitive impairment group compared to the CSVD without cognitive impairment group (false discovery rate corrected P<0.05). Conclusions:GMV reduce in patients of CSVD with cognitive impairment in the bilateral hippocampus, parahippocampal gyrus, fusiform gyrus, and left amygdala. In the structural covariance network, characteristic path length increase while global efficiency reduce, and node degree and nodal efficiency of the left putamen reduce.
