Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI.
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| Title: | Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI. |
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| Authors: | Keyvanfard, Farzaneh1 (AUTHOR) f.keyvanfard@kntu.ac.ir, Nasiraei-Moghaddam, Abbas2,3 (AUTHOR) |
| Source: | Biomedical Engineering & Computational Biology. 5/15/2026, Vol. 17, p1-12. 12p. |
| Subjects: | Alzheimer's disease, Functional connectivity, Functional magnetic resonance imaging, Quantitative research, Brain imaging, Independent component analysis, Graph theory, Cognition disorders |
| Abstract: | Introduction: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to examine functional connectivity (FC) alterations in neurological disorders such as Alzheimer's disease (AD). Traditional studies either employ whole-brain analyses or focus on specific regions, yet the vast number of FCs and their interrelations complicate interpretation. This study adopts a data-driven, hypothesis-free approach to detect altered functional subnetworks in AD. Methods: Independent component analysis (ICA) was applied to FC matrices from 34 AD patients and 49 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After pruning, significant subnetworks distinguishing AD from HC were identified. Graph theoretical parameters were computed for each subnetwork, and their associations with Mini-Mental State Examination (MMSE) scores were assessed. Results: Three subnetworks effectively differentiated AD patients from HCs. One subnetwork showed significant group differences in network strength, clustering coefficient, and local efficiency, despite no whole-brain differences. Abnormal functional lateralization also emerged within subnetworks. Moreover, FC weights in the identified subnetworks positively correlated with MMSE scores, linking cognitive performance to subnetwork connectivity. Conclusion: These results demonstrate the utility of a data-driven approach in detecting AD-specific altered subnetworks. By providing a modular perspective, this method facilitates targeted examination of connectivity changes, improves interpretability, and deepens understanding of functional disruptions in AD. [ABSTRACT FROM AUTHOR] |
| Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Header | DbId: egs DbLabel: Engineering Source An: 193813535 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Keyvanfard%2C+Farzaneh%22">Keyvanfard, Farzaneh</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> f.keyvanfard@kntu.ac.ir</i><br /><searchLink fieldCode="AR" term="%22Nasiraei-Moghaddam%2C+Abbas%22">Nasiraei-Moghaddam, Abbas</searchLink><relatesTo>2,3</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Biomedical+Engineering+%26+Computational+Biology%22">Biomedical Engineering & Computational Biology</searchLink>. 5/15/2026, Vol. 17, p1-12. 12p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Alzheimer's+disease%22">Alzheimer's disease</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+connectivity%22">Functional connectivity</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Quantitative+research%22">Quantitative research</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+imaging%22">Brain imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+component+analysis%22">Independent component analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+theory%22">Graph theory</searchLink><br /><searchLink fieldCode="DE" term="%22Cognition+disorders%22">Cognition disorders</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Introduction: Resting-state functional magnetic resonance imaging (rs-fMRI) is widely used to examine functional connectivity (FC) alterations in neurological disorders such as Alzheimer's disease (AD). Traditional studies either employ whole-brain analyses or focus on specific regions, yet the vast number of FCs and their interrelations complicate interpretation. This study adopts a data-driven, hypothesis-free approach to detect altered functional subnetworks in AD. Methods: Independent component analysis (ICA) was applied to FC matrices from 34 AD patients and 49 healthy controls (HCs) from the Alzheimer's Disease Neuroimaging Initiative (ADNI). After pruning, significant subnetworks distinguishing AD from HC were identified. Graph theoretical parameters were computed for each subnetwork, and their associations with Mini-Mental State Examination (MMSE) scores were assessed. Results: Three subnetworks effectively differentiated AD patients from HCs. One subnetwork showed significant group differences in network strength, clustering coefficient, and local efficiency, despite no whole-brain differences. Abnormal functional lateralization also emerged within subnetworks. Moreover, FC weights in the identified subnetworks positively correlated with MMSE scores, linking cognitive performance to subnetwork connectivity. Conclusion: These results demonstrate the utility of a data-driven approach in detecting AD-specific altered subnetworks. By providing a modular perspective, this method facilitates targeted examination of connectivity changes, improves interpretability, and deepens understanding of functional disruptions in AD. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Biomedical Engineering & Computational Biology is the property of Sage Publications Inc. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1177/11795972251404254 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 12 StartPage: 1 Subjects: – SubjectFull: Alzheimer's disease Type: general – SubjectFull: Functional connectivity Type: general – SubjectFull: Functional magnetic resonance imaging Type: general – SubjectFull: Quantitative research Type: general – SubjectFull: Brain imaging Type: general – SubjectFull: Independent component analysis Type: general – SubjectFull: Graph theory Type: general – SubjectFull: Cognition disorders Type: general Titles: – TitleFull: Blind Identification of Altered Functional Subnetworks in Alzheimer's Disease Using Resting-State fMRI. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Keyvanfard, Farzaneh – PersonEntity: Name: NameFull: Nasiraei-Moghaddam, Abbas IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 05 Text: 5/15/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11795972 Numbering: – Type: volume Value: 17 Titles: – TitleFull: Biomedical Engineering & Computational Biology Type: main |
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