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Lifespan Changes of the Human Brain In Alzheimer's Disease

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Lifespan Changes of the Human Brain In Alzheimer's Disease

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dc.contributor.author Coupé, Pierrick es_ES
dc.contributor.author Manjón Herrera, José Vicente es_ES
dc.contributor.author Lanuza, Enrique es_ES
dc.contributor.author Catheline, Gwenaelle es_ES
dc.date.accessioned 2021-02-04T04:32:24Z
dc.date.available 2021-02-04T04:32:24Z
dc.date.issued 2019-03-08 es_ES
dc.identifier.issn 2045-2322 es_ES
dc.identifier.uri http://hdl.handle.net/10251/160690
dc.description.abstract [EN] Brain imaging studies have shown that slow and progressive cerebral atrophy characterized the development of Alzheimer's Disease (AD). Despite a large number of studies dedicated to AD, key questions about the lifespan evolution of AD biomarkers remain open. When does the AD model diverge from the normal aging model? What is the lifespan trajectory of imaging biomarkers for AD? How do the trajectories of biomarkers in AD differ from normal aging? To answer these questions, we proposed an innovative way by inferring brain structure model across the entire lifespan using a massive number of MRI (N = 4329). We compared the normal model based on 2944 control subjects with the pathological model based on 3262 patients (AD + Mild cognitive Impaired subjects) older than 55 years and controls younger than 55 years. Our study provides evidences of early divergence of the AD models from the normal aging trajectory before 40 years for the hippocampus, followed by the lateral ventricles and the amygdala around 40 years. Moreover, our lifespan model reveals the evolution of these biomarkers and suggests close abnormality evolution for the hippocampus and the amygdala, whereas trajectory of ventricular enlargement appears to follow an inverted U-shape. Finally, our models indicate that medial temporal lobe atrophy and ventricular enlargement are two mid-life physiopathological events characterizing AD brain. es_ES
dc.description.sponsorship This work benefited from the support of the project DeepVolBrain of the French National Research Agency (ANR-18-CE45-0013). This study was achieved within the context of the Laboratory of Excellence TRAIL ANR-10-LABX-57 for the BigDataBrain project. Moreover, we thank the Investments for the future Program IdEx Bordeaux (ANR-10-IDEX- 03-02, HL-MRI Project), Cluster of excellence CPU and the CNRS. This study has been also supported by the DPI2017-87743-R grant from the Spanish Ministerio de Economia, Industria y Competitividad. Moreover, this work is based on multiple samples. We wish to thank all investigators of these projects who collected these datasets and made them freely accessible. The C-MIND data used in the preparation of this article were obtained from the C-MIND Data Repository (accessed in Feb 2015) created by the C-MIND study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by Cincinnati Children's Hospital Medical Center and UCLA and supported by the National Institute of Child Health and Human Development (Contract #s HHSN275200900018C). A listing of the participating sites and a complete listing of the study investigators can be found at https://research.cchmc.org/c-mind. The NDAR data used in the preparation of this manuscript were obtained from the NIH-supported National Database for Autism Research (NDAR). NDAR is a collaborative informatics system created by the National Institutes of Health to provide a national resource to support and accelerate research in autism. The NDAR dataset includes data from the NIH Pediatric MRI Data Repository created by the NIH MRI Study of Normal Brain Development. This is a multisite, longitudinal study of typically developing children from ages newborn through young adulthood conducted by the Brain Development Cooperative Group and supported by the National Institute of Child Health and Human Development, the National Institute on Drug Abuse, the National Institute of Mental Health, and the National Institute of Neurological Disorders and Stroke (Contract #s N01- HD02-3343, N01-MH9-0002, and N01-NS-9-2314, -2315, -2316, -2317, -2319 and -2320). A listing of the participating sites and a complete listing of the study investigators can be found at http://pediatricmri.nih.gov/nihpd/info/participating_centers.html. The ADNI data used in the preparation of this manuscript were obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904). The ADNI is funded by the National Institute on Aging and the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: Abbott, AstraZeneca AB, Bayer Schering Pharma AG, Bristol-Myers Squibb, Eisai Global Clinical Development, Elan Corporation, Genentech, GE Healthcare, GlaxoSmithKline, Innogenetics NV, Johnson & Johnson, Eli Lilly and Co., Medpace, Inc., Merck and Co., Inc., Novartis AG, Pfizer Inc., F. Hoffmann-La Roche, Schering-Plough, Synarc Inc., as well as nonprofit partners, the Alzheimer's Association and Alzheimer's Drug Discovery Foundation, with participation from the U.S. Food and Drug Administration. Private sector contributions to the ADNI are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). es_ES
dc.language Inglés es_ES
dc.publisher Nature Publishing Group es_ES
dc.relation.ispartof Scientific Reports es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject.classification FISICA APLICADA es_ES
dc.title Lifespan Changes of the Human Brain In Alzheimer's Disease es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1038/s41598-019-39809-8 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/Human Brain Project//PO1MHO5217611/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CIHR//MOP-34996/CA/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NHMRC/NHMRC Project Grants/1011689/AU/Neuroimaging Stream/
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-IDEX-0003/FR/Initiative d’excellence de l’Université de Bordeaux/IDEX BORDEAUX/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-18-CE45-0013/FR/Deep Learning for Volumetric Brain Analysis: Towards BigData in Neuroscience/DeepVolBrain/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/UKRI//GR%2FS21533%2F02/GB/Information eXtraction from Images (IXI)/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//U24RR021382/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//U01AG024904/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//R03MH096321/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//R01MH56584/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//R01AG021910/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P50MH071616/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P50AG05681/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P30AG010129/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P01AG03991/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//P01AG026276/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92320/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92319/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92317/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92316/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92315/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01NS92314/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01MH90002/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//N01HD023343/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//K23MH087770/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//K01 AG030514/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/NIH//275200900018C/US/PEDIATRIC FUNCTIONAL NEUROIMAGING RESEARCH NETWORK/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2017-87743-R/ES/DESARROLLO DE UNA PLATAFORMA ONLINE PARA EL ANALISIS ANATOMICO DEL CEREBRO TOLERANTE A LA PRESENCIA DE ALTERACIONES PATOLOGICAS/ es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada es_ES
dc.description.bibliographicCitation Coupé, P.; Manjón Herrera, JV.; Lanuza, E.; Catheline, G. (2019). Lifespan Changes of the Human Brain In Alzheimer's Disease. Scientific Reports. 9:1-12. https://doi.org/10.1038/s41598-019-39809-8 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1038/s41598-019-39809-8 es_ES
dc.description.upvformatpinicio 1 es_ES
dc.description.upvformatpfin 12 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 9 es_ES
dc.identifier.pmid 30850617 es_ES
dc.identifier.pmcid PMC6408544 es_ES
dc.relation.pasarela S\403809 es_ES
dc.contributor.funder Pfizer es_ES
dc.contributor.funder UK Research and Innovation es_ES
dc.contributor.funder AstraZeneca es_ES
dc.contributor.funder Dana Foundation es_ES
dc.contributor.funder Human Brain Project es_ES
dc.contributor.funder Leon Levy Foundation es_ES
dc.contributor.funder Agencia Estatal de Investigación es_ES
dc.contributor.funder National Institute on Aging, EEUU es_ES
dc.contributor.funder National Institutes of Health, EEUU es_ES
dc.contributor.funder Canadian Institutes of Health Research es_ES
dc.contributor.funder National Institute on Drug Abuse, EEUU es_ES
dc.contributor.funder Agence Nationale de la Recherche, Francia es_ES
dc.contributor.funder National Institute of Mental Health, EEUU es_ES
dc.contributor.funder Science and Industry Endowment Fund, Australia es_ES
dc.contributor.funder National Health and Medical Research Council, Australia es_ES
dc.contributor.funder National Institute of Neurological Disorders and Stroke, EEUU es_ES
dc.contributor.funder National Institute of Child Health and Human Development, EEUU es_ES
dc.contributor.funder Engineering and Physical Sciences Research Council, Reino Unido es_ES
dc.contributor.funder National Institute of Biomedical Imaging and Bioengineering, EEUU es_ES
dc.description.references Lobo, A. et al. Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurology 54, S4 (2000). es_ES
dc.description.references Barnes, J. et al. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimer’s & dementia 11, 1349–1357 (2015). es_ES
dc.description.references Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. The Lancet Neurology 12, 207–216 (2013). es_ES
dc.description.references Nestor, P. J., Scheltens, P. & Hodges, J. R. Advances in the early detection of Alzheimer’s disease. Nature medicine 10 (2004). es_ES
dc.description.references Davatzikos, C., Fan, Y., Wu, X., Shen, D. & Resnick, S. M. Detection of prodromal Alzheimer’s disease via pattern classification of magnetic resonance imaging. Neurobiology of aging 29, 514–523 (2008). es_ES
dc.description.references Bakkour, A., Morris, J. C. & Dickerson, B. C. The cortical signature of prodromal AD Regional thinning predicts mild AD dementia. Neurology 72, 1048–1055 (2009). es_ES
dc.description.references Chan, D. et al. Change in rates of cerebral atrophy over time in early-onset Alzheimer’s disease: longitudinal MRI study. The Lancet 362, 1121–1122 (2003). es_ES
dc.description.references Ridha, B. H. et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. The Lancet Neurology 5, 828–834 (2006). es_ES
dc.description.references Sala-Llonch, R., Bartrés-Faz, D. & Junqué, C. Reorganization of brain networks in aging: a review of functional connectivity studies. Frontiers in psychology 6 (2015). es_ES
dc.description.references Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine 367, 795–804 (2012). es_ES
dc.description.references Dickerson, B. et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76, 1395–1402 (2011). es_ES
dc.description.references Miller, M. I. et al. The diffeomorphometry of temporal lobe structures in preclinical Alzheimer’s disease. NeuroImage: Clinical 3, 352–360 (2013). es_ES
dc.description.references Bernard, C. et al. Time course of brain volume changes in the preclinical phase of Alzheimer’s disease. Alzheimer’s & Dementia 10, 143–151. e141 (2014). es_ES
dc.description.references den Heijer, T. et al. A 10-year follow-up of hippocampal volume on magnetic resonance imaging in early dementia and cognitive decline. Brain 133, 1163–1172 (2010). es_ES
dc.description.references Coupé, P. et al. Detection of Alzheimer’s disease signature in MR images seven years before conversion to dementia: Toward an early individual prognosis. Hum Brain Mapp 36, 4758–4770, https://doi.org/10.1002/hbm.22926 (2015). es_ES
dc.description.references Albert, M. et al. Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years. Brain (2018). es_ES
dc.description.references Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nature neuroscience 17, 1510–1517 (2014). es_ES
dc.description.references Solomon, A. et al. Serum cholesterol changes after midlife and late-life cognition twenty-one-year follow-up study. Neurology 68, 751–756 (2007). es_ES
dc.description.references Debette, S. et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77, 461–468 (2011). es_ES
dc.description.references Tolppanen, A.-M. et al. Midlife and late-life body mass index and late-life dementia: results from a prospective population-based cohort. Journal of Alzheimer’s Disease 38, 201–209 (2014). es_ES
dc.description.references Coupe, P., Catheline, G., Lanuza, E. & Manjon, J. V. & Alzheimer’s Disease Neuroimaging, I. Towards a unified analysis of brain maturation and aging across the entire lifespan: A MRI analysis. Hum Brain Mapp 38, 5501–5518, https://doi.org/10.1002/hbm.23743 (2017). es_ES
dc.description.references Villemagne, V. L. et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. The Lancet Neurology 12, 357–367 %@1474–4422 (2013). es_ES
dc.description.references Villemagne, V. L. et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Annals of neurology 69, 181–192 (2011). es_ES
dc.description.references Poulin, S. P. et al. Amygdala atrophy is prominent in early Alzheimer’s disease and relates to symptom severity. Psychiatry Research: Neuroimaging 194, 7–13 (2011). es_ES
dc.description.references Jack, C. R. et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology 49, 786–794 (1997). es_ES
dc.description.references Apostolova, L. G. et al. Hippocampal atrophy and ventricular enlargement in normal aging, mild cognitive impairment and Alzheimer’s disease. Alzheimer disease and associated disorders 26, 17 (2012). es_ES
dc.description.references Nestor, S. M. et al. Ventricular enlargement as a possible measure of Alzheimer’s disease progression validated using the Alzheimer’s disease neuroimaging initiative database. Brain 131, 2443–2454 (2008). es_ES
dc.description.references Petersen, R. C. et al. Alzheimer’s disease Neuroimaging Initiative (ADNI) clinical characterization. Neurology 74, 201–209 (2010). es_ES
dc.description.references Marcus, D. S. et al. Open Access Series of Imaging Studies (OASIS): cross-sectional MRI data in young, middle aged, nondemented, and demented older adults. Journal of cognitive neuroscience 19, 1498–1507 (2007). es_ES
dc.description.references Manjon, J. V. & Coupe, P. volBrain: An Online MRI Brain Volumetry System. Front Neuroinform 10, 30, https://doi.org/10.3389/fninf.2016.00030 (2016). es_ES
dc.description.references Manjon, J. V., Coupe, P., Marti-Bonmati, L., Collins, D. L. & Robles, M. Adaptive non-local means denoising of MR images with spatially varying noise levels. J Magn Reson Imaging 31, 192–203, https://doi.org/10.1002/jmri.22003 (2010). es_ES
dc.description.references Tustison, N. J. et al. N4ITK: improved N3 bias correction. IEEE Trans Med Imaging 29, 1310–1320, https://doi.org/10.1109/TMI.2010.2046908 (2010). es_ES
dc.description.references Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011). es_ES
dc.description.references Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018 (2005). es_ES
dc.description.references Manjón, J. V., Tohka, J. & Robles, M. Improved estimates of partial volume coefficients from noisy brain MRI using spatial context. Neuroimage 53, 480–490 (2010). es_ES
dc.description.references Manjon, J. V. et al. Nonlocal intracranial cavity extraction. Int J Biomed Imaging 2014, 820205, https://doi.org/10.1155/2014/820205 (2014). es_ES
dc.description.references Coupe, P. et al. Patch-based segmentation using expert priors: application to hippocampus and ventricle segmentation. Neuroimage 54, 940–954, https://doi.org/10.1016/j.neuroimage.2010.09.018 (2011). es_ES
dc.description.references Frisoni, G. B. et al. The EADC-ADNI Harmonized Protocol for manual hippocampal segmentation on magnetic resonance: evidence of validity. Alzheimer’s & Dementia 11, 111–125 (2015). es_ES
dc.description.references Solow, R. M. A contribution to the theory of economic growth. The quarterly journal of economics 70, 65–94 %@1531–4650 (1956). es_ES
dc.description.references Coupe, P. et al. Scoring by nonlocal image patch estimator for early detection of Alzheimer’s disease. Neuroimage Clin 1, 141–152, https://doi.org/10.1016/j.nicl.2012.10.002 (2012). es_ES
dc.description.references Cuingnet, R. et al. Automatic classification of patients with Alzheimer’s disease from structural MRI: a comparison of ten methods using the ADNI database. Neuroimage 56, 766–781, https://doi.org/10.1016/j.neuroimage.2010.06.013 (2011). es_ES
dc.description.references Eskildsen, S. F. et al. Prediction of Alzheimer’s disease in subjects with mild cognitive impairment from the ADNI cohort using patterns of cortical thinning. Neuroimage 65, 511–521 (2013). es_ES
dc.description.references Eskildsen, S. F. et al. Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiology of aging 36, S23–S31 (2015). es_ES
dc.description.references Tong, T. et al. A Novel Grading Biomarker for the Prediction of Conversion From Mild Cognitive Impairment to Alzheimer’s Disease. IEEE Transactions on Biomedical Engineering 64, 155–165 (2017). es_ES
dc.description.references Wolz, R. et al. Multi-method analysis of MRI images in early diagnostics of Alzheimer’s disease. PLoS One 6, e25446, https://doi.org/10.1371/journal.pone.0025446 (2011). es_ES
dc.description.references Bron, E. E. et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111, 562–579, https://doi.org/10.1016/j.neuroimage.2015.01.048 (2015). es_ES
dc.description.references Chaddad, A., Desrosiers, C., Hassan, L. & Tanougast, C. Hippocampus and amygdala radiomic biomarkers for the study of autism spectrum disorder. BMC Neurosci 18, 52, https://doi.org/10.1186/s12868-017-0373-0 (2017). es_ES
dc.description.references Chaddad, A., Desrosiers, C. & Toews, M. Multi-scale radiomic analysis of sub-cortical regions in MRI related to autism, gender and age. Sci Rep 7, 45639, https://doi.org/10.1038/srep45639 (2017). es_ES
dc.description.references Apostolova, L. G. et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiology of aging 31, 1077–1088 (2010). es_ES
dc.description.references Younes, L., Albert, M., Miller, M. I. & Team, B. R. Inferring changepoint times of medial temporal lobe morphometric change in preclinical Alzheimer’s disease. NeuroImage: Clinical 5, 178–187 (2014). es_ES
dc.description.references Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta neuropathologica 82, 239–259 (1991). es_ES
dc.description.references Badea, A. et al. The fornix provides multiple biomarkers to characterize circuit disruption in a mouse model of Alzheimer’s disease. NeuroImage 142, 498–511 (2016). es_ES
dc.description.references Micotti, E. et al. Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis. Neurobiology of aging 36, 776–788 (2015). es_ES
dc.description.references Whitwell, J. L. et al. MRI correlates of neurofibrillary tangle pathology at autopsy A voxel-based morphometry study. Neurology 71, 743–749 (2008). es_ES
dc.description.references Iaccarino, L. et al. Local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer’s Disease. NeuroImage: Clinical 17, 452–464 (2018). es_ES
dc.description.references Das, S. R. et al. Longitudinal and cross-sectional structural magnetic resonance imaging correlates of AV-1451 uptake. Neurobiology of aging 66, 49–58 (2018). es_ES
dc.description.references Knopman, D. S. et al. Joint associations of β-amyloidosis and cortical thickness with cognition. Neurobiology of aging 65, 121–131 (2018). es_ES
dc.description.references Doré, V. et al. Cross-sectional and longitudinal analysis of the relationship between Aβ deposition, cortical thickness, and memory in cognitively unimpaired individuals and in Alzheimer disease. JAMA neurology 70, 903–911 (2013). es_ES
dc.description.references Jack, C. R. et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016). es_ES
dc.description.references Cavedo, E. et al. Local amygdala structural differences with 3T MRI in patients with Alzheimer disease. Neurology 76, 727–733 (2011). es_ES
dc.description.references Qiu, A., Fennema-Notestine, C., Dale, A. M., Miller, M. I. & Alzheimer’s Disease Neuroimaging, I. Regional shape abnormalities in mild cognitive impairment and Alzheimer’s disease. Neuroimage 45, 656–661 (2009). es_ES
dc.description.references Lin, T.-W. et al. Neurodegeneration in amygdala precedes hippocampus in the APPswe/PS1dE9 mouse model of Alzheimer’s disease. Current Alzheimer Research 12, 951–963 (2015). es_ES
dc.description.references Phelps, E. A. Human emotion and memory: interactions of the amygdala and hippocampal complex. Current opinion in neurobiology 14, 198–202 (2004). es_ES
dc.description.references Kumfor, F. et al. Degradation of emotion processing ability in corticobasal syndrome and Alzheimer’s disease. Brain 137, 3061–3072 (2014). es_ES
dc.description.references De Olmos, J. S. In The Human Nervous System (Second Edition) Ch. 22, 739–868 (2004). es_ES
dc.description.references Tabert, M. H. et al. A 10‐item smell identification scale related to risk for Alzheimer’s disease. Annals of neurology 58, 155–160 (2005). es_ES
dc.description.references Serby, M., Larson, P. & Kalkstein, D. The nature and course of olfactory deficits in Alzheimer’s disease. The American journal of psychiatry 148, 357 (1991). es_ES
dc.description.references Djordjevic, J., Jones-Gotman, M., De Sousa, K. & Chertkow, H. Olfaction in patients with mild cognitive impairment and Alzheimer’s disease. Neurobiology of aging 29, 693–706 (2008). es_ES
dc.description.references Price, J. L., Davis, P., Morris, J. & White, D. The distribution of tangles, plaques and related immunohistochemical markers in healthy aging and Alzheimer’s disease. Neurobiology of aging 12, 295–312 (1991). es_ES
dc.description.references Ohm, T. & Braak, H. Olfactory bulb changes in Alzheimer’s disease. Acta neuropathologica 73, 365–369 (1987). es_ES
dc.description.references Carmichael, O. T. et al. Cerebral ventricular changes associated with transitions between normal cognitive function, mild cognitive impairment, and dementia. Alzheimer disease and associated disorders 21, 14 (2007). es_ES
dc.description.references Prince, M., Bryce, R. & Ferri, C. World Alzheimer Report 2011: The benefits of early diagnosis and intervention. (Alzheimer’s Disease International, 2011). es_ES
dc.description.references De Jong, L. W. et al. Strongly reduced volumes of putamen and thalamus in Alzheimer’s disease: an MRI study. Brain 131, 3277–3285 (2008). es_ES
dc.description.references Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta neuropathologica 81, 261–268 (1991). es_ES
dc.description.references Fjell, A. M. et al. Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiol Aging 34, 2239–2247, https://doi.org/10.1016/j.neurobiolaging.2013.04.006 (2013). es_ES
dc.description.references Fotenos, A. F., Snyder, A. Z., Girton, L. E., Morris, J. C. & Buckner, R. L. Normative estimates of cross-sectional and longitudinal brain volume decline in aging and AD. Neurology 64, 1032–1039 (2005). es_ES
dc.description.references Fjell, A. M. et al. One-year brain atrophy evident in healthy aging. Journal of Neuroscience 29, 15223–15231 (2009). es_ES
dc.description.references Jack, C. R. et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600 (2004). es_ES
dc.description.references Barnes, J. et al. A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiology of aging 30, 1711–1723 (2009). es_ES
dc.description.references McDonald, C. R. et al. Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 73, 457–465 (2009). es_ES
dc.description.references Sankar, T. et al. Your algorithm might think the hippocampus grows in Alzheimer’s disease: Caveats of longitudinal automated hippocampal volumetry. Human Brain Mapping 38, 2875–2896 (2017). es_ES
dc.description.references Small, B. J., Fratiglioni, L., Viitanen, M., Winblad, B. & Bäckman, L. The course of cognitive impairment in preclinical Alzheimer disease: three-and 6-year follow-up of a population-based sample. Archives of neurology 57, 839–844 (2000). es_ES
dc.description.references La Rue, A. & Jarvik, L. F. Cognitive function and prediction of dementia in old age. The International Journal of Aging and Human Development 25, 79–89 (1987). es_ES
dc.description.references Elias, M. F. et al. The preclinical phase of Alzheimer disease: a 22-year prospective study of the Framingham Cohort. Archives of neurology 57, 808–813 (2000). es_ES
dc.description.references Snowdon, D. A. et al. Linguistic ability in early life and cognitive function and Alzheimer’s disease in late life: Findings from the Nun Study. Jama 275, 528–532 (1996). es_ES
dc.description.references Dubois, B. et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia 12, 292–323 (2016). es_ES
dc.description.references Krell-Roesch, J. et al. Leisure-Time Physical Activity and the Risk of IncidentDementia: The Mayo Clinic Study of Aging. Journal of Alzheimer’s Disease, 1–7 (2018). es_ES
dc.description.references Rusanen, M., Kivipelto, M., Quesenberry, C. P., Zhou, J. & Whitmer, R. A. Heavy smoking in midlife and long-term risk of Alzheimer disease and vascular dementia. Archives of internal medicine 171, 333–339 (2011). es_ES


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