Brain State Analysis of rs-fMRI Dynamic Functional Connection in Mild Cognitive Impairment
YAN Jie
WU Haifeng
BAO Han
Abstract：Due to the complexity of brain function connectivity and the high dimensionality expression of brain dynamic attri-butions,unsupervised clustering analysis is commonly used to analyze the time-varying network characteristics.However,the tradi-tional unsupervised clustering method is difficult to separate clusters of different sizes and densities and is highly sensitive to outli-ers.To solve this problem,a supervised clustering method is proposed to analyze the changes of brain state.The dynamic state of brain network is obtained by combining minimum intra-class distance and least-square methods.Then,the significant difference of dynamic state between mild cognitive impairment and normal control group is analyzed.The results show that patients with MCI have lower dynamic mobility and range of motion,that is,their non-stationarity is weaker and supervised clustering yields superior clas-sification accuracy to unsupervised clustering,which maximizes the state difference.
Keywords：brain stateresting-state functional magnetic resonance imagingclusteringdynamic functional brain connec-tivity
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：7( 3120-3126 )