The pyramid representation of the functional network using resting-state fMRI  

作　　者：Zhipeng Yang Luying Li Yaxi Peng Yuanyuan Qin Muwei Li 

机构地区：[1]College of Electronic Engineering,Chengdu University of Information Technology,Chengdu,Sichuan 610225,China [2]Department of Radiology,Tongji Hospital,Tongji Medical College,Huazhong University of Science and Technology,Wuhan,Hubei 430000,China [3]Vanderbilt University Institute of Imaging Science,Vanderbilt University,Nashville,TN 37232,USA

出　　处：《Psychoradiology》2022年第3期100-112,共13页精神影像学（英文）

基　　金：This work was funded by National Natural Science Foundation of China(grant numbers 81901828,81873890)。

摘　　要：Background Resting-state functional magnetic resonance imaging(RS-fMRI)has been proved to be a useful tool to study the brain mechanism in the quest to probe the distinct pattern of inter-region interactions in the brain.As an important application of RSfMRI,the graph-based approach characterizes the brain as a complex network.However,the network is susceptible to its scale that determines the trade-off between sensitivity and anatomical variability.Objective To balance sensitivity and anatomical variability,a pyramid representation of the functional network is proposed,which is composed of five individual networks reconstructed at multiple scales.Methods The pyramid representation of the functional network was applied to two groups of participants,including patients with Alzheimer’s disease(AD)and normal elderly(NC)individuals,as a demonstration.Features were extracted from the multi-scale networks andwere evaluated with their inter-group differences between AD andNC,aswell as the discriminative power in recognizing AD.Moreover,the proposed method was also validated by another dataset from people with autism.Results The different features reflect the highest sensitivity to distinguish AD at different scales.In addition,the combined features have higher accuracy than any single scale-based feature.These findings highlight the potential use ofmulti-scale features asmarkers of the disrupted topological organization in AD networks.Conclusion We believe that multi-scale metrics could provide a more comprehensive characterization of the functional network and thus provide a promising solution for representing the underlying functional mechanism in the human brain on a multi-scale basis.

关 键 词：MULTI-SCALE functional network graph theory resting-state fMRI support vector machine Alzheimer’s disease 

分 类 号：R445.2[医药卫生—影像医学与核医学]