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A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease

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dc.contributor.author Demirtaş, Murat
dc.contributor.author Falcón, Carles
dc.contributor.author Tucholka, Alan
dc.contributor.author Domingo Gispert, Juan
dc.contributor.author Molinuevo, José Luis
dc.contributor.author Deco, Gustavo
dc.date.accessioned 2017-10-19T11:27:17Z
dc.date.available 2017-10-19T11:27:17Z
dc.date.issued 2017
dc.identifier.citation Demirtaş M, Falcond C, Tucholka A, Domingo Gispert J, Molinuevo JL, Deco G. A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease. Neuroimage Clin. 2017;16:343-54. DOI: 10.1016/j.nicl.2017.08.006
dc.identifier.issn 2213-1582
dc.identifier.uri http://hdl.handle.net/10230/33048
dc.description.abstract Alzheimer's disease (AD) is the most common dementia with dramatic consequences. The research in structural and functional neuroimaging showed altered brain connectivity in AD. In this study, we investigated the whole-brain resting state functional connectivity (FC) of the subjects with preclinical Alzheimer's disease (PAD), mild cognitive impairment due to AD (MCI) and mild dementia due to Alzheimer's disease (AD), the impact of APOE4 carriership, as well as in relation to variations in core AD CSF biomarkers. The synchronization in the whole-brain was monotonously decreasing during the course of the disease progression. Furthermore, in AD patients we found widespread significant decreases in functional connectivity (FC) strengths particularly in the brain regions with high global connectivity. We employed a whole-brain computational modeling approach to study the mechanisms underlying these alterations. To characterize the causal interactions between brain regions, we estimated the effective connectivity (EC) in the model. We found that the significant EC differences in AD were primarily located in left temporal lobe. Then, we systematically manipulated the underlying dynamics of the model to investigate simulated changes in FC based on the healthy control subjects. Furthermore, we found distinct patterns involving CSF biomarkers of amyloid-beta (Aβ1 − 42) total tau (t-tau) and phosphorylated tau (p-tau). CSF Aβ1 − 42 was associated to the contrast between healthy control subjects and clinical groups. Nevertheless, tau CSF biomarkers were associated to the variability in whole-brain synchronization and sensory integration regions. These associations were robust across clinical groups, unlike the associations that were found for CSF Aβ1 − 42. APOE4 carriership showed no significant correlations with the connectivity measures.
dc.description.sponsorship GD is supported by the ERC Advanced Grant: DYSTRUCTURE (no. 295129), by the Spanish Research ProjectPSI2016-75688-P (AEI/FEDER) and by the European Union's Horizon 2020 research and innovation programme under grant agreement no. 720270 (HBP SGA1).
dc.format.mimetype application/pdf
dc.language.iso eng
dc.publisher Elsevier
dc.relation.ispartof Neuroimage: Clinical. 2017;16:343-54.
dc.rights © 2017 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license
dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/4.0/
dc.title A whole-brain computational modeling approach to explain the alterations in resting-state functional connectivity during progression of Alzheimer's disease
dc.type info:eu-repo/semantics/article
dc.subject.keyword Resting state fMRI
dc.subject.keyword Dynamic functional connectivity
dc.subject.keyword Computational modeling
dc.subject.keyword Alzheimer's disease
dc.subject.keyword Biomarkers
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/295129
dc.relation.projectID info:eu-repo/grantAgreement/EC/H2020/720270
dc.rights.accessRights info:eu-repo/semantics/openAccess
dc.type.version info:eu-repo/semantics/publishedVersion


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