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Article: Disrupted Structural White Matter Network in Alzheimer’s Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study

TitleDisrupted Structural White Matter Network in Alzheimer’s Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study
Authors
KeywordsAlzheimer's disease
brain network
diffusion tensor imaging
mixed dementia
vascular dementia
Issue Date15-Aug-2023
PublisherIOS Press
Citation
Journal of Alzheimer's Disease, 2023, v. 94, n. 4, p. 1487-1502 How to Cite?
Abstract

Abstract: Background:Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types. Objective:This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network. Methods:A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer’s disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network. Results:Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately. Conclusion:These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.


Persistent Identifierhttp://hdl.handle.net/10722/338207
ISSN
2023 Impact Factor: 3.4
2023 SCImago Journal Rankings: 1.172
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorZhang, HQ-
dc.contributor.authorChau, ACM-
dc.contributor.authorShea, YF-
dc.contributor.authorChiu, PK-
dc.contributor.authorBao, YW-
dc.contributor.authorCao, P-
dc.contributor.authorMak, HK-
dc.date.accessioned2024-03-11T10:27:04Z-
dc.date.available2024-03-11T10:27:04Z-
dc.date.issued2023-08-15-
dc.identifier.citationJournal of Alzheimer's Disease, 2023, v. 94, n. 4, p. 1487-1502-
dc.identifier.issn1387-2877-
dc.identifier.urihttp://hdl.handle.net/10722/338207-
dc.description.abstract<p>Abstract: Background:Dementia presents a significant burden to patients and healthcare systems worldwide. Early and accurate diagnosis, as well as differential diagnosis of various types of dementia, are crucial for timely intervention and management. However, there is currently a lack of clinical tools for accurately distinguishing between these types. Objective:This study aimed to investigate the differences in the structural white matter (WM) network among different types of cognitive impairment/dementia using diffusion tensor imaging, and to explore the clinical relevance of the structural network. Methods:A total of 21 normal control, 13 subjective cognitive decline (SCD), 40 mild cognitive impairment (MCI), 22 Alzheimer’s disease (AD), 13 mixed dementia (MixD), and 17 vascular dementia (VaD) participants were recruited. Graph theory was utilized to construct the brain network. Results:Our findings revealed a monotonic trend of disruption in the brain WM network (VaD > MixD > AD > MCI > SCD) in terms of decreased global efficiency, local efficiency, and average clustering coefficient, as well as increased characteristic path length. These network measurements were significantly associated with the clinical cognition index in each disease group separately. Conclusion:These findings suggest that structural WM network measurements can be utilized to differentiate between different types of cognitive impairment/dementia, and these measurements can provide valuable cognition-related information.</p>-
dc.languageeng-
dc.publisherIOS Press-
dc.relation.ispartofJournal of Alzheimer's Disease-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectAlzheimer's disease-
dc.subjectbrain network-
dc.subjectdiffusion tensor imaging-
dc.subjectmixed dementia-
dc.subjectvascular dementia-
dc.titleDisrupted Structural White Matter Network in Alzheimer’s Disease Continuum, Vascular Dementia, and Mixed Dementia: A Diffusion Tensor Imaging Study-
dc.typeArticle-
dc.identifier.doi10.3233/JAD-230341-
dc.identifier.scopuseid_2-s2.0-85168428203-
dc.identifier.volume94-
dc.identifier.issue4-
dc.identifier.spage1487-
dc.identifier.epage1502-
dc.identifier.eissn1875-8908-
dc.identifier.isiWOS:001055512700018-
dc.identifier.issnl1387-2877-

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