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Alterations in dynamic connectivity in Alzheimer's disease: Network changes and improved multi-stage classification.

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Τίτλος: Alterations in dynamic connectivity in Alzheimer's disease: Network changes and improved multi-stage classification.
Συγγραφείς: Guo R; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China., Li D; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; State Key Laboratory of Software Development Environment, State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing, China., Zhang X; Shenzhen University General Hospital, Shenzhen University, Shenzhen, China., Liu S; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China., Wang D; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China. Electronic address: daifa.wang@buaa.edu.cn., Du Y; School of Automation Science and Electrical Engineering, Beihang University, China. Electronic address: duyaoo@buaa.edu.cn.
Συλλογικό Έργο: Alzheimer's Disease Neuroimaging Initiative
Πηγή: Experimental gerontology [Exp Gerontol] 2026 Oct 15; Vol. 224, pp. 113285. Date of Electronic Publication: 2026 Aug 13.
Τύπος έκδοσης: Journal Article
Γλώσσα: English
Στοιχεία περιοδικού: Publisher: Elsevier Science Country of Publication: England NLM ID: 0047061 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1873-6815 (Electronic) Linking ISSN: 05315565 NLM ISO Abbreviation: Exp Gerontol Subsets: MEDLINE
Imprint Name(s): Publication: Tarrytown Ny : Elsevier Science
Original Publication: Oxford.
Ιατρικοί όροι (MeSH): Alzheimer Disease*/physiopathology , Alzheimer Disease*/diagnostic imaging , Alzheimer Disease*/classification , Alzheimer Disease*/psychology , Cognitive Dysfunction*/physiopathology , Cognitive Dysfunction*/diagnostic imaging , Brain*/physiopathology , Brain*/diagnostic imaging , Nerve Net*/physiopathology , Nerve Net*/diagnostic imaging, Magnetic Resonance Imaging/methods ; Brain Mapping/methods ; Humans ; Female ; Aged ; Male ; Aged, 80 and over ; Disease Progression
Περίληψη: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by large-scale network disruption. While static functional connectivity (sFC) has been extensively studied, dynamic functional connectivity (dFC) and its discriminative value across the AD spectrum remain insufficiently understood. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data from 174 participants in the Alzheimer's Disease Neuroimaging Initiative, including cognitively normal (CN, n = 44), subjective memory concern (SMC, n = 24), early mild cognitive impairment (EMCI, n = 46), late MCI (LMCI, n = 30), and AD (n = 30), were analyzed to assess group differences in sFC, dFC, and graph-theoretical metrics, as well as their associations with cognition. A BrainNetCNN model was further employed to evaluate the classification performance of sFC, dFC, and their combined features. The results revealed that sFC decreased across MCI stages but increased in AD, whereas dFC variability was predominantly reduced in the pre-dementia groups and increased in AD, particularly in frontal and temporal regions. Several static graph-theoretical metrics were significantly correlated with Mini-Mental State Examination (MMSE) scores, while dFC provided complementary information. In classification tasks, dFC showed higher accuracy than sFC in binary and five-class tasks, and their integration achieved the highest accuracy (CN vs. SMC: 89.6%; five-class: 82.7%). These findings suggest that dFC may provide complementary imaging information for characterizing stage-related network alterations and differentiating diagnostic groups across the AD spectrum.
(Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.)
Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Contributed Indexing: Keywords: Alzheimer's disease; Biomarkers; Classification; Functional connectivity; MRI
Entry Date(s): Date Created: 20260813 Date Completed: 20260924 Latest Revision: 20260924
Update Code: 20260925
DOI: 10.1016/j.exger.2026.113285
PMID: 42595023
Βάση Δεδομένων: MEDLINE
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  Data: Alterations in dynamic connectivity in Alzheimer's disease: Network changes and improved multi-stage classification.
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  Data: <searchLink fieldCode="AU" term="%22Guo+R%22">Guo R</searchLink>; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Li+D%22">Li D</searchLink>; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China; State Key Laboratory of Software Development Environment, State Key Laboratory of Virtual Reality Technology and System, Beihang University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Zhang+X%22">Zhang X</searchLink>; Shenzhen University General Hospital, Shenzhen University, Shenzhen, China.<br /><searchLink fieldCode="AU" term="%22Liu+S%22">Liu S</searchLink>; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China.<br /><searchLink fieldCode="AU" term="%22Wang+D%22">Wang D</searchLink>; School of Biological Science and Medical Engineering, Ministry of Education, Beijing Advanced Innovation Center for Biomedical Engineering, Beihang University, Beijing, China. Electronic address: daifa.wang@buaa.edu.cn.<br /><searchLink fieldCode="AU" term="%22Du+Y%22">Du Y</searchLink>; School of Automation Science and Electrical Engineering, Beihang University, China. Electronic address: duyaoo@buaa.edu.cn.
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  Data: Alzheimer's disease (AD) is a progressive neurodegenerative disorder characterized by large-scale network disruption. While static functional connectivity (sFC) has been extensively studied, dynamic functional connectivity (dFC) and its discriminative value across the AD spectrum remain insufficiently understood. In this study, resting-state functional magnetic resonance imaging (rs-fMRI) data from 174 participants in the Alzheimer's Disease Neuroimaging Initiative, including cognitively normal (CN, n = 44), subjective memory concern (SMC, n = 24), early mild cognitive impairment (EMCI, n = 46), late MCI (LMCI, n = 30), and AD (n = 30), were analyzed to assess group differences in sFC, dFC, and graph-theoretical metrics, as well as their associations with cognition. A BrainNetCNN model was further employed to evaluate the classification performance of sFC, dFC, and their combined features. The results revealed that sFC decreased across MCI stages but increased in AD, whereas dFC variability was predominantly reduced in the pre-dementia groups and increased in AD, particularly in frontal and temporal regions. Several static graph-theoretical metrics were significantly correlated with Mini-Mental State Examination (MMSE) scores, while dFC provided complementary information. In classification tasks, dFC showed higher accuracy than sFC in binary and five-class tasks, and their integration achieved the highest accuracy (CN vs. SMC: 89.6%; five-class: 82.7%). These findings suggest that dFC may provide complementary imaging information for characterizing stage-related network alterations and differentiating diagnostic groups across the AD spectrum.<br /> (Copyright © 2026 The Authors. Published by Elsevier Inc. All rights reserved.)
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  Data: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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  Data: <i>Keywords: </i>Alzheimer's disease; Biomarkers; Classification; Functional connectivity; MRI
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