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Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease

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dc.contributor.author Vermunt, Lisa
dc.contributor.author Dicks, Ellen
dc.contributor.author Wang, Guoqiao
dc.contributor.author Dincer, Aylin
dc.contributor.author Flores, Shaney
dc.contributor.author Keefe, Sarah J.
dc.contributor.author Berman, Sarah B.
dc.contributor.author Cash, David M.
dc.contributor.author Chhatwal, Jasmeer P.
dc.contributor.author Cruchaga, Carlos
dc.contributor.author Fox, Nick C.
dc.contributor.author Ghetti, Bernardino
dc.contributor.author Graff-Radford, Neill R.
dc.contributor.author Hassenstab, Jason
dc.contributor.author Karch, Celeste M.
dc.contributor.author Laske, Christoph
dc.contributor.author Levin, Johannes
dc.contributor.author Masters, Colin L.
dc.contributor.author Dominantly Inherited Alzheimer Network
dc.contributor.author Allegri, Ricardo Francisco
dc.date.accessioned 2021-09-29T12:49:05Z
dc.date.available 2021-09-29T12:49:05Z
dc.date.issued 2020-06-15
dc.identifier.citation Vermunt L, Dicks E, Wang G, Dincer A, Flores S, Keefe SJ, Berman SB, Cash DM, Chhatwal JP, Cruchaga C, Fox NC, Ghetti B, Graff-Radford NR, Hassenstab J, Karch CM, Laske C, Levin J, Masters CL, McDade E, Mori H, Morris JC, Noble JM, Perrin RJ, Schofield PR, Xiong C, Scheltens P, Visser PJ, Bateman RJ, Benzinger TLS, Tijms BM, Gordon BA; Dominantly Inherited Alzheimer Network (DIAN). Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's disease. Brain Commun. 2020 Jul 15;2(2):fcaa102. doi: 10.1093/braincomms/fcaa102. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/593
dc.identifier.uri https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7475695/
dc.description.abstract Structural grey matter covariance networks provide an individual quantification of morphological patterns in the brain. The network integrity is disrupted in sporadic Alzheimer's disease, and network properties show associations with the level of amyloid pathology and cognitive decline. Therefore, these network properties might be disease progression markers. However, it remains unclear when and how grey matter network integrity changes with disease progression. We investigated these questions in autosomal dominant Alzheimer's disease mutation carriers, whose conserved age at dementia onset allows individual staging based upon their estimated years to symptom onset. From the Dominantly Inherited Alzheimer Network observational cohort, we selected T1-weighted MRI scans from 269 mutation carriers and 170 non-carriers (mean age 38 ± 15 years, mean estimated years to symptom onset -9 ± 11), of whom 237 had longitudinal scans with a mean follow-up of 3.0 years. Single-subject grey matter networks were extracted, and we calculated for each individual the network properties which describe the network topology, including the size, clustering, path length and small worldness. We determined at which time point mutation carriers and non-carriers diverged for global and regional grey matter network metrics, both cross-sectionally and for rate of change over time. Based on cross-sectional data, the earliest difference was observed in normalized path length, which was decreased for mutation carriers in the precuneus area at 13 years and on a global level 12 years before estimated symptom onset. Based on longitudinal data, we found the earliest difference between groups on a global level 6 years before symptom onset, with a greater rate of decline of network size for mutation carriers. We further compared grey matter network small worldness with established biomarkers for Alzheimer disease (i.e. amyloid accumulation, cortical thickness, brain metabolism and cognitive function). We found that greater amyloid accumulation at baseline was associated with faster decline of small worldness over time, and decline in grey matter network measures over time was accompanied by decline in brain metabolism, cortical thinning and cognitive decline. In summary, network measures decline in autosomal dominant Alzheimer's disease, which is alike sporadic Alzheimer's disease, and the properties show decline over time prior to estimated symptom onset. These data suggest that single-subject networks properties obtained from structural MRI scans form an additional non-invasive tool for understanding the substrate of cognitive decline and measuring progression from preclinical to severe clinical stages of Alzheimer's disease. es_ES
dc.language.iso eng es_ES
dc.publisher Oxford University 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.title Single-subject grey matter network trajectories over the disease course of autosomal dominant Alzheimer's 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: Vermunt, Lisa. VU University. Alzheimer Center Amsterdam. Amsterdam Neuroscience. Department of Neurology; Países Bajos.
dc.description.fil Fil: Dicks, Ellen. VU University. Alzheimer Center Amsterdam. Amsterdam Neuroscience. Department of Neurology; Países Bajos.
dc.description.fil Fil: Wang, Guoqiao. Washington University in St. Louis. Division of Biostatistics; Estados Unidos.
dc.description.fil Fil: Dincer, Aylin. Washington University in St. Louis. Mallinckrodt Institute of Radiology; Estados Unidos.
dc.description.fil Fil: Flores, Shaney. Washington University in St. Louis. Mallinckrodt Institute of Radiology; Estados Unidos.
dc.description.fil Fil: Keefe, Sarah J. Washington University in St. Louis. Mallinckrodt Institute of Radiology; Estados Unidos.
dc.description.fil Fil: Berman, Sarah B. Alzheimer's Disease Research Center. Department of Neurology; Estados Unidos. University of Pittsburgh. Pittsburgh Institute for Neurodegenerative Diseases; Estados Unidos.
dc.description.fil Fil: Cash, David M. UCL Queen Square Institute of Neurology; Reino Unido.
dc.description.fil Fil: Chhatwal, Jasmeer P. Massachusetts General Hospital. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Cruchaga, Carlos. Washington University in St. Louis. Department of Psychiatry; Estados Unidos. Washington University in St. Louis. Hope Center for Neurological Disorders; Estados Unidos. Washington University in St. Louis. NeuroGenomics and Informatics; Estados Unidos.
dc.description.fil Fil: Fox, Nick C. Department of Neurodegenerative Disease. Dementia Research Centre; Reino Unidos. UCL Institute of Neurology. Dementia Research Institute at UCL; Reino Unidos.
dc.description.fil Fil: Ghetti, Bernardino. Indiana University. Department of Pathology and Laboratory Medicine; Estados Unidos.
dc.description.fil Fil: Graff-Radford, Neill R. Mayo Clinic Florida; Estados Unidos.
dc.description.fil Fil: Hassenstab, Jason. Washington University in St. Louis. Knight Alzheimer's Disease Research Center; Estados Unidos. Washington University in St. Louis. Department of Neurology; Estados Unidos. Washington University in St. Louis. Department of Psychological & Brain Sciences; Estados Unidos.
dc.description.fil Fil: Karch, Celeste M. Washington University in St. Louis. Department of Psychiatry; Estados Unidos.
dc.description.fil Fil: Laske, Christoph. German Center for Neurodegenerative Diseases (DZNE); Alemania. University of Tübingen. Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy. Section for Dementia Research; Alemania.
dc.description.fil Fil: Levin, Johannes. Ludwig-Maximilians-Universität München; Alemania.
dc.description.fil Fil: Masters, Colin L. Florey Institute; Autralia. The University of Melbourne; Autralia.
dc.relation.ispartofVOLUME 2
dc.relation.ispartofNUMBER 2
dc.relation.ispartofPAGINATION fcaa102
dc.relation.ispartofCOUNTRY Inglaterra
dc.relation.ispartofCOUNTRY Reino Unido
dc.relation.ispartofISSN 2632-1297
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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