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Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites

文献类型:期刊论文

作者Du,Kai3,4; Chen,Pindong3,4; Zhao,Kun1,2; Qu,Yida3,4; Kang,Xiaopeng3,4; Liu,Yong2; ,
刊名BMC Bioinformatics
出版日期2022-07-14
卷号23期号:Suppl 6
关键词Time distance nodal connectivity diversity Dynamic functional connectivity Network reconfiguration Multicenter Alzheimer's disease
DOI10.1186/s12859-022-04776-x
通讯作者Liu,Yong(yongliu@bupt.edu.cn)
英文摘要AbstractBackgroundThe dynamic functional connectivity (dFC) has been used successfully to investigate the dysfunction of Alzheimer's disease (AD) patients. The reconfiguration intensity of nodal dFC, which means the degree of alteration between FCs at different time scales, could provide additional information for understanding the reconfiguration of brain connectivity.ResultsIn this paper, we introduced a feature named time distance nodal connectivity diversity (tdNCD), and then evaluated the network reconfiguration intensity in every specific brain region in AD using a large multicenter dataset (N?=?809 from 7 independent sites). Our results showed that the dysfunction involved in three subnetworks in AD, including the default mode network (DMN), the subcortical network (SCN), and the cerebellum network (CBN). The nodal tdNCD inside the DMN increased in AD compared to normal controls, and the nodal dynamic FC of the SCN and the CBN decreased in AD. Additionally, the classification analysis showed that the classification performance was better when combined tdNCD and FC to classify AD from normal control (ACC?=?81%, SEN?=?83.4%, SPE?=?80.6%, and F1-score?=?79.4%) than that only using FC (ACC?=?78.2%, SEN?=?76.2%, SPE?=?76.5%, and F1-score?=?77.5%) with a leave-one-site-out cross-validation. Besides, the performance of the three classes classification was improved from 50% (only using FC) to 53.3% (combined FC and tdNCD) (macro F1-score accuracy from 46.8 to 48.9%). More importantly, the classification model showed significant clinically predictive correlations (two classes classification: R?=??0.38, P?
语种英语
出版者BioMed Central
WOS记录号BMC:10.1186/S12859-022-04776-X
源URL[http://ir.ia.ac.cn/handle/173211/49213]  
专题自动化研究所_脑网络组研究中心
通讯作者Liu,Yong
作者单位1.Beihang University; Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering
2.Beijing University of Posts and Telecommunications; School of Artificial Intelligence
3.Chinese Academy of Sciences; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation
4.University of Chinese Academy of Sciences; School of Artificial Intelligence
推荐引用方式
GB/T 7714
Du,Kai,Chen,Pindong,Zhao,Kun,et al. Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites[J]. BMC Bioinformatics,2022,23(Suppl 6).
APA Du,Kai.,Chen,Pindong.,Zhao,Kun.,Qu,Yida.,Kang,Xiaopeng.,...&,.(2022).Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites.BMC Bioinformatics,23(Suppl 6).
MLA Du,Kai,et al."Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites".BMC Bioinformatics 23.Suppl 6(2022).

入库方式: OAI收割

来源:自动化研究所

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