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

doi:10.1186/s12859-022-04776-x

	Impaired time-distance reconfiguration patterns in Alzheimer's disease: a dynamic functional connectivity study with 809 individuals from 7 sites
	Du,Kai 1,2; Chen,Pindong 1,2; Zhao,Kun3,4 ; Qu,Yida1,2 ; Kang,Xiaopeng 1,2; Liu,Yong3 ; ,
发表期刊	BMC Bioinformatics
	2022-07-14
卷号	23 期号:Suppl 6
通讯作者	Liu,Yong(yongliu@bupt.edu.cn)
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?
关键词	Time distance nodal connectivity diversity Dynamic functional connectivity Network reconfiguration Multicenter Alzheimer's disease
DOI	10.1186/s12859-022-04776-x
语种	英语
WOS记录号	BMC:10.1186/s12859-022-04776-x
出版者	BioMed Central
引用统计
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/49214
专题	脑图谱与类脑智能实验室_脑网络组研究
通讯作者	Liu,Yong
作者单位	1.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
第一作者单位	模式识别国家重点实验室
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).