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000910707 0247_ $$2doi$$a10.1016/j.neuroscience.2021.12.031
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000910707 1001_ $$0P:(DE-HGF)0$$aHan, Hongfang$$b0
000910707 245__ $$aBiomarkers Derived from Alterations in Overlapping Community Structure of Resting-state Brain Functional Networks for Detecting Alzheimer’s Disease
000910707 260__ $$aAmsterdam [u.a.]$$bElsevier Science$$c2022
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000910707 520__ $$aRecent studies show that overlapping community structure is an important feature of the brain functional network. However, alterations in such overlapping community structure in Alzheimer’s disease (AD) patients have not been examined yet. In this study, we investigate the overlapping community structure in AD by using resting-state functional magnetic resonance imaging (rs-fMRI) data. The collective sparse symmetric non-negative matrix factorization (cssNMF) is adopted to detect the overlapping community structure. Experimental results on 28 AD patients and 32 normal controls (NCs) from the ADNI2 dataset show that the two groups have remarkable differences in terms of the optimal number of communities, the hierarchy of communities detected at different scales, network functional segregation, and nodal functional diversity. In particular, the frontal-parietal and basal ganglia networks exhibit significant differences between the two groups. A machine learning framework proposed in this paper for AD detection achieved an accuracy of 76.7% when using the detected community strengths of the frontal-parietal and basal ganglia networks only as input features. These findings provide novel insights into the understanding of pathological changes in the brain functional network organization of AD and show the potential of the community structure-related features for AD detection.
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000910707 7001_ $$0P:(DE-Juel1)184969$$aLi, Xuan$$b1$$ufzj
000910707 7001_ $$0P:(DE-HGF)0$$aGan, John Q.$$b2
000910707 7001_ $$0P:(DE-HGF)0$$aYu, Hua$$b3
000910707 7001_ $$0P:(DE-HGF)0$$aWang, Haixian$$b4$$eCorresponding author
000910707 773__ $$0PERI:(DE-600)1498423-4$$a10.1016/j.neuroscience.2021.12.031$$gVol. 484, p. 38 - 52$$p38 - 52$$tNeuroscience$$v484$$x0306-4522$$y2022
000910707 8564_ $$uhttps://juser.fz-juelich.de/record/910707/files/Biomarkers%20Derived%20from%20Alterations%20in%20Overlapping%20Community%20Structure%20of%20Resting-state%20Brain%20Functional%20Networks%20for%20Detecting%20Alzheimers%20Disease.pdf$$yOpenAccess
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000910707 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)184969$$aForschungszentrum Jülich$$b1$$kFZJ
000910707 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)184969$$a School of Computer Science and Electronic Engineering, University of Essex, Colchester$$b1
000910707 9101_ $$0I:(DE-HGF)0$$6P:(DE-Juel1)184969$$a Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing $$b1
000910707 9101_ $$0I:(DE-HGF)0$$6P:(DE-HGF)0$$a Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science & Medical Engineering, Southeast University, Nanjing $$b4
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