Prediction of cognitive function in patients with cerebral small vessel disease based on morphological brain network connection model
Wei Cunsheng
Chen Yuan
He Zhenzhen
Cao Meng
Yu Yusheng
Chen Xuemei
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.
Keywords：cerebral small vessel diseasescognitionmorphological brain network connection model
Publication Date：2024-11-15
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1320-1324 )