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Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease

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dc.contributor.author Millar, Peter R.
dc.contributor.author Luckett, Patrick H.
dc.contributor.author Gordon, Brian A.
dc.contributor.author Benzinger, Tammie L.S.
dc.contributor.author Schindler, Suzanne E.
dc.contributor.author Fagan, Anne M.
dc.contributor.author Cruchaga, Carlos
dc.contributor.author Bateman, Randall J.
dc.contributor.author Allegri, Ricardo Francisco
dc.contributor.author Jucker, Mathias
dc.contributor.author Lee, Jae-Hong
dc.contributor.author Mori, Hiroshi
dc.contributor.author Salloway, Stephen
dc.contributor.author Igor, Yakushev
dc.contributor.author Morris, John C.
dc.contributor.author Ances, Beau M.
dc.contributor.author Dominantly Inherited Alzheimer Network
dc.date.accessioned 2022-07-06T15:50:57Z
dc.date.available 2022-07-06T15:50:57Z
dc.date.issued 2022-08-01
dc.identifier.citation Millar PR, Luckett PH, Gordon BA, Benzinger TLS, Schindler SE, Fagan AM, Cruchaga C, Bateman RJ, Allegri R, Jucker M, Lee JH, Mori H, Salloway SP, Yakushev I, Morris JC, Ances BM; Dominantly Inherited Alzheimer Network. Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease. Neuroimage. 2022 Aug 1;256:119228. doi: 10.1016/j.neuroimage.2022.119228. Epub 2022 Apr 20. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/621
dc.identifier.uri https://doi.org/10.1016/j.neuroimage.2022.119228
dc.description.abstract "Brain-predicted age" quantifies apparent brain age compared to normative neuroimaging trajectories. Advanced brain-predicted age has been well established in symptomatic Alzheimer disease (AD), but is underexplored in preclinical AD. Prior brain-predicted age studies have typically used structural MRI, but resting-state functional connectivity (FC) remains underexplored. Our model predicted age from FC in 391 cognitively normal, amyloid-negative controls (ages 18-89). We applied the trained model to 145 amyloid-negative, 151 preclinical AD, and 156 symptomatic AD participants to test group differences. The model accurately predicted age in the training set. FC-predicted brain age gaps (FC-BAG) were significantly older in symptomatic AD and significantly younger in preclinical AD compared to controls. There was minimal correspondence between networks predictive of age and AD. Elevated FC-BAG may reflect network disruption during symptomatic AD. Reduced FC-BAG in preclinical AD was opposite to the expected direction, and may reflect a biphasic response to preclinical AD pathology or may be driven by inconsistency between age-related vs. AD-related networks. Overall, FC-predicted brain age may be a sensitive AD biomarker. es_ES
dc.language.iso eng es_ES
dc.publisher Academic Press es_ES
dc.rights info:eu-repo/semantics/openAccess
dc.rights.uri https://creativecommons.org/licenses/by/2.5/ar/
dc.subject Alzheimer Disease es_ES
dc.subject Enfermedad de Alzheimer es_ES
dc.subject Biomarkers es_ES
dc.subject Biomarcadores es_ES
dc.subject Imagen por Resonancia Magnética es_ES
dc.subject Magnetic Resonance Imaging es_ES
dc.subject Neuroimagen es_ES
dc.subject Neuroimaging es_ES
dc.title Predicting brain age from functional connectivity in symptomatic and preclinical Alzheimer disease es_ES
dc.type info:eu-repo/semantics/article es_ES
dc.type info:eu-repo/semantics/publishedVersion
dc.description.fil Fil: Allegri, Ricardo Francisco. Fleni. Departamento de Neurología. Servicio de Neurología Cognitiva, Neuropsicología y Neuropsiquiatría; Argentina.
dc.description.fil Fil: Millar, Peter R. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Luckett, Patrick H. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Gordon, Brian A. Washington University. Department of Radiology; Estados Unidos.
dc.description.fil Fil: Benzinger, Tammie L.S. Washington University. Department of Radiology; Estados Unidos.
dc.description.fil Fil: Schindler, Suzanne E. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Fagan, Anne M. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Cruchaga, Carlos. Washington University. Department of Psychiatry; Estados Unidos.
dc.description.fil Fil: Bateman, Randall J. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Jucker, Mathias. German Center for Neurodegenerative Diseases (DZNE); Alemania. University of Tübingen. Hertie Institute for Clinical Brain Research; Alemania.
dc.description.fil Fil: Lee, Jae-Hong. University of Ulsan College of Medicine. Department of Neurology, Asan Medical Center; Corea.
dc.description.fil Fil: Mori, Hiroshi. Nagaoka Sutoku University; Japón. Osaka City University Medical School. Department of Clinical Neuroscience; Japón.
dc.description.fil Fil: Ances, Beau M. Washington University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Salloway, Stephen. Brown University. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Igor, Yakushev. Technical University of Munich. Department of Nuclear Medicine; Alemania.
dc.description.fil Fil: Morris, John C. Washington University. Department of Neurology; Estados Unidos.
dc.relation.ispartofVOLUME 256
dc.relation.ispartofPAGINATION 119228
dc.relation.ispartofCOUNTRY Estados Unidos
dc.relation.ispartofCITY Orlando
dc.relation.ispartofTITLE NeuroImage
dc.relation.ispartofISSN 1095-9572
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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