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Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling

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dc.contributor.author Cobigo, Yann
dc.contributor.author Goh, Matthew S.
dc.contributor.author Wolf, Amy
dc.contributor.author Staffaroni, Adam M.
dc.contributor.author Kornak, John
dc.contributor.author Miller, Bruce L.
dc.contributor.author Rabinovici, Gil D.
dc.contributor.author Seeley, William W.
dc.contributor.author Spina, Salvatore
dc.contributor.author Boxer, Adam L.
dc.contributor.author Boeve, Bradley F.
dc.contributor.author Wang, Lei
dc.contributor.author Allegri, Ricardo Francisco
dc.contributor.author Farlow, Marty
dc.contributor.author Mori, Hiroshi
dc.contributor.author Perrin, Richard J.
dc.contributor.author Kramer, Joel
dc.contributor.author Rosen, Howard J.
dc.contributor.author Alzheimer’s Disease Neuroimaging Initiative
dc.contributor.author Dominantly Inherited Alzheimer Network
dc.date.accessioned 2022-10-18T10:44:05Z
dc.date.available 2022-10-18T10:44:05Z
dc.date.issued 2022-07-06
dc.identifier.citation Cobigo Y, Goh MS, Wolf A, Staffaroni AM, Kornak J, Miller BL, Rabinovici GD, Seeley WW, Spina S, Boxer AL, Boeve BF, Wang L, Allegri R, Farlow M, Mori H, Perrin RJ, Kramer J, Rosen HJ; Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the Dominantly Inherited Alzheimer’s Network (DIAN). Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling. Neuroimage Clin. 2022 Aug 6;36:103144. doi: 10.1016/j.nicl.2022.103144. es_ES
dc.identifier.uri https://repositorio.fleni.org.ar/xmlui/handle/123456789/687
dc.identifier.uri https://doi.org/10.1016/j.nicl.2022.103144
dc.description.abstract Early detection of neurodegeneration, and prediction of when neurodegenerative diseases will lead to symptoms, are critical for developing and initiating disease modifying treatments for these disorders. While each neurodegenerative disease has a typical pattern of early changes in the brain, these disorders are heterogeneous, and early manifestations can vary greatly across people. Methods for detecting emerging neurodegeneration in any part of the brain are therefore needed. Prior publications have described the use of Bayesian linear mixed-effects (BLME) modeling for characterizing the trajectory of change across the brain in healthy controls and patients with neurodegenerative disease. Here, we use an extension of such a model to detect emerging neurodegeneration in cognitively healthy individuals at risk for dementia. We use BLME to quantify individualized rates of volume loss across the cerebral cortex from the first two MRIs in each person and then extend the BLME model to predict future values for each voxel. We then compare observed values at subsequent time points with the values that were expected from the initial rates of change and identify voxels that are lower than the expected values, indicating accelerated volume loss and neurodegeneration. We apply the model to longitudinal imaging data from cognitively normal participants in the Alzheimer's Disease Neuroimaging Initiative (ADNI), some of whom subsequently developed dementia, and two cognitively normal cases who developed pathology-proven frontotemporal lobar degeneration (FTLD). These analyses identified regions of accelerated volume loss prior to or accompanying the earliest symptoms, and expanding across the brain over time, in all cases. The changes were detected in regions that are typical for the likely diseases affecting each patient, including medial temporal regions in patients at risk for Alzheimer's disease, and insular, frontal, and/or anterior/inferior temporal regions in patients with likely or proven FTLD. In the cases where detailed histories were available, the first regions identified were consistent with early symptoms. Furthermore, survival analysis in the ADNI cases demonstrated that the rate of spread of accelerated volume loss across the brain was a statistically significant predictor of time to conversion to dementia. This method for detection of neurodegeneration is a potentially promising approach for identifying early changes due to a variety of diseases, without prior assumptions about what regions are most likely to be affected first in an individual. es_ES
dc.language.iso eng es_ES
dc.publisher Elsevier 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 Frontotemporal Lobar Degeneration es_ES
dc.subject Degeneración Lobar Frontotemporal es_ES
dc.subject Diagnóstico por Imagen
dc.subject Diagnostic Imaging
dc.title Detection of emerging neurodegeneration using Bayesian linear mixed-effect modeling 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. Centro de Memoria y Envejecimiento; Argentina.
dc.description.fil Fil: Cobigo, Yann. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Goh, Matthew S. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Wolf, Amy. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Staffaroni, Adam M. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Kornak, John. University of California. Department of Epidemiology and Biostatistics; Estados Unidos.
dc.description.fil Fil: Miller, Bruce L. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Rabinovici, Gil D. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Seeley, William W. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Spina, Salvatore. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Boxer, Adam L. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.description.fil Fil: Boeve, Bradley F. Mayo Clinic. Department of Neurology; Estados Unidos.
dc.description.fil Fil: Wang, Lei. Northwestern University Feinberg School of Medicine. Department of Psychiatry and Behavioral Sciences and Department Radiolog; Estados Unidos.
dc.description.fil Fil: Farlow, Marty. Indiana University; Estados Unidos.
dc.description.fil Fil: Mori, Hiroshi. Osaka City University Medical School. Department of Neurosciences; Japón.
dc.description.fil Fil: Perrin, Richard J. Washington University School of Medicine; Estados Unidos.
dc.description.fil Fil: Kramer, Joel. Washington University School of Medicine; Estados Unidos.
dc.description.fil Fil: Rosen, Howard J. University of California. Department of Neurology. Memory and Aging Center; Estados Unidos.
dc.relation.ispartofVOLUME 36
dc.relation.ispartofPAGINATION 103144
dc.relation.ispartofCOUNTRY Países Bajos
dc.relation.ispartofCITY Ámsterdam
dc.relation.ispartofTITLE NeuroImage. Clinical
dc.relation.ispartofISSN 2213-1582
dc.type.snrd info:ar-repo/semantics/artículo es_ES


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