Prediction of cognitive function in patients with cerebral small vessel disease based on morphological brain network connection model
10.3969/j.issn.1009-0126.2024.11.015
- VernacularTitle:基于形态学脑网络连接模型预测脑小血管病患者的认知功能
- Author:
Cunsheng WEI
1
;
Yuan CHEN
;
Zhenzhen HE
;
Meng CAO
;
Yusheng YU
;
Xuemei CHEN
Author Information
1. 211100 南京医科大学附属江宁医院神经内科
- Keywords:
cerebral small vessel diseases;
cognition;
morphological brain network connection model
- From:
Chinese Journal of Geriatric Heart Brain and Vessel Diseases
2024;26(11):1320-1324
- CountryChina
- Language:Chinese
-
Abstract:
Objective To construct a morphological brain network in patients with cerebral small vessel disease(CSVD)and predict it application for cognitive function.Methods A total of 64 eld-erly CSVD patients admitted in our hospital from January 2020 to February 2024 were retrospec-tively recruited.Cognitive function was assessed with Mini-Mental State Examination(MMSE)and Montreal Cognitive Assessment(MoCA).Their clinical data,and results of cognitive function and multi-modal MRI scanning were collected and analyzed.3D T1-weighted imaging based on Kullback-Leibler divergence similarity was used to construct individual morphological brain net-work,and the connectome-based predictive model was employed to construct a cognitive predic-tion model.Results The network,which is significantly and positively correlated with the MMSE and MoCA scores,was mainly located in the default mode network,and could effectively predict individual MMSE and MoCA scores(r=0.795,P=4.436×10-15;r=0.794,P=4.974×10-15,P＜0.01).The connections,which were significantly negatively correlated with MMSE or MoCA scores,were mainly located between the salience/ventral attention network and other networks,and could also effectively predict individual MMSE and MoCA scores(r=0.766,P=1.679× 10-13;r=0.850,P=6.915×10-19,P＜0.01).Combined positive correlation and negative correla-tion networks,the model showed further improved predictive performance(r=0.849,P=7.603 × 10-19;r=0.888,P=1.445 × 10-22,P＜0.01).Conclusion Individual morphological brain network can effectively predict cognitive function in elderly CSVD patients,and can be used as a convenient tool for early warning of cognitive impairment related to CSVD.
