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Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites
Du,Kai1,2; Chen,Pindong1,2; Zhao,Kun3,4; Qu,Yida1,2; Kang,Xiaopeng1,2; Liu,Yong3; ,
Source PublicationBMC Bioinformatics
2022-07-14
Volume23Issue:Suppl 6
Corresponding AuthorLiu,Yong(yongliu@bupt.edu.cn)
AbstractAbstractBackgroundThe 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?
KeywordTime distance nodal connectivity diversity Dynamic functional connectivity Network reconfiguration Multicenter Alzheimer's disease
DOI10.1186/s12859-022-04776-x
Language英语
WOS IDBMC:10.1186/s12859-022-04776-x
PublisherBioMed Central
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/49214
Collection脑网络组研究
Corresponding AuthorLiu,Yong
Affiliation1.University of Chinese Academy of Sciences; School of Artificial Intelligence
2.Chinese Academy of Sciences; Brainnetome Center and National Laboratory of Pattern Recognition, Institute of Automation
3.Beijing University of Posts and Telecommunications; School of Artificial Intelligence
4.Beihang University; Beijing Advanced Innovation Centre for Biomedical Engineering, School of Biological Science and Medical Engineering
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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).
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