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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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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

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/160690

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Title: Lifespan Changes of the Human Brain In Alzheimer's Disease
Author: Coupé, Pierrick Manjón Herrera, José Vicente Lanuza, Enrique Catheline, Gwenaelle
UPV Unit: Universitat Politècnica de València. Departamento de Física Aplicada - Departament de Física Aplicada
Issued date:
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 ...[+]
Copyrigths: Reconocimiento (by)
Source:
Scientific Reports. (issn: 2045-2322 )
DOI: 10.1038/s41598-019-39809-8
Publisher:
Nature Publishing Group
Publisher version: https://doi.org/10.1038/s41598-019-39809-8
Project ID:
info:eu-repo/grantAgreement/Human Brain Project//PO1MHO5217611/
...[+]
info:eu-repo/grantAgreement/Human Brain Project//PO1MHO5217611/
info:eu-repo/grantAgreement/CIHR//MOP-34996/CA/
info:eu-repo/grantAgreement/NHMRC/NHMRC Project Grants/1011689/AU/Neuroimaging Stream/
info:eu-repo/grantAgreement/ANR//ANR-10-IDEX-0003/FR/Initiative d’excellence de l’Université de Bordeaux/IDEX BORDEAUX/
info:eu-repo/grantAgreement/ANR//ANR-18-CE45-0013/FR/Deep Learning for Volumetric Brain Analysis: Towards BigData in Neuroscience/DeepVolBrain/
info:eu-repo/grantAgreement/UKRI//GR%2FS21533%2F02/GB/Information eXtraction from Images (IXI)/
info:eu-repo/grantAgreement/NIH//U24RR021382/
info:eu-repo/grantAgreement/NIH//U01AG024904/
info:eu-repo/grantAgreement/NIH//R03MH096321/
info:eu-repo/grantAgreement/NIH//R01MH56584/
info:eu-repo/grantAgreement/NIH//R01AG021910/
info:eu-repo/grantAgreement/NIH//P50MH071616/
info:eu-repo/grantAgreement/NIH//P50AG05681/
info:eu-repo/grantAgreement/NIH//P30AG010129/
info:eu-repo/grantAgreement/NIH//P01AG03991/
info:eu-repo/grantAgreement/NIH//P01AG026276/
info:eu-repo/grantAgreement/NIH//N01NS92320/
info:eu-repo/grantAgreement/NIH//N01NS92319/
info:eu-repo/grantAgreement/NIH//N01NS92317/
info:eu-repo/grantAgreement/NIH//N01NS92316/
info:eu-repo/grantAgreement/NIH//N01NS92315/
info:eu-repo/grantAgreement/NIH//N01NS92314/
info:eu-repo/grantAgreement/NIH//N01MH90002/
info:eu-repo/grantAgreement/NIH//N01HD023343/
info:eu-repo/grantAgreement/NIH//K23MH087770/
info:eu-repo/grantAgreement/NIH//K01 AG030514/
info:eu-repo/grantAgreement/NIH//275200900018C/US/PEDIATRIC FUNCTIONAL NEUROIMAGING RESEARCH NETWORK/
info:eu-repo/grantAgreement/ANR//ANR-10-LABX-0057/FR/Translational Research and Advanced Imaging Laboratory/TRAIL/
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/
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Thanks:
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 ...[+]
Type: Artículo

References

Lobo, A. et al. Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurology 54, S4 (2000).

Barnes, J. et al. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimer’s & dementia 11, 1349–1357 (2015).

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). [+]
Lobo, A. et al. Prevalence of dementia and major subtypes in Europe: a collaborative study of population-based cohorts. Neurology 54, S4 (2000).

Barnes, J. et al. Alzheimer’s disease first symptoms are age dependent: evidence from the NACC dataset. Alzheimer’s & dementia 11, 1349–1357 (2015).

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).

Nestor, P. J., Scheltens, P. & Hodges, J. R. Advances in the early detection of Alzheimer’s disease. Nature medicine 10 (2004).

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).

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).

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).

Ridha, B. H. et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. The Lancet Neurology 5, 828–834 (2006).

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).

Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. New England Journal of Medicine 367, 795–804 (2012).

Dickerson, B. et al. Alzheimer-signature MRI biomarker predicts AD dementia in cognitively normal adults. Neurology 76, 1395–1402 (2011).

Miller, M. I. et al. The diffeomorphometry of temporal lobe structures in preclinical Alzheimer’s disease. NeuroImage: Clinical 3, 352–360 (2013).

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).

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).

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).

Albert, M. et al. Predicting progression from normal cognition to mild cognitive impairment for individuals at 5 years. Brain (2018).

Poldrack, R. A. & Gorgolewski, K. J. Making big data open: data sharing in neuroimaging. Nature neuroscience 17, 1510–1517 (2014).

Solomon, A. et al. Serum cholesterol changes after midlife and late-life cognition twenty-one-year follow-up study. Neurology 68, 751–756 (2007).

Debette, S. et al. Midlife vascular risk factor exposure accelerates structural brain aging and cognitive decline. Neurology 77, 461–468 (2011).

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).

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).

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).

Villemagne, V. L. et al. Longitudinal assessment of Aβ and cognition in aging and Alzheimer disease. Annals of neurology 69, 181–192 (2011).

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).

Jack, C. R. et al. Medial temporal atrophy on MRI in normal aging and very mild Alzheimer’s disease. Neurology 49, 786–794 (1997).

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).

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).

Petersen, R. C. et al. Alzheimer’s disease Neuroimaging Initiative (ADNI) clinical characterization. Neurology 74, 201–209 (2010).

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).

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).

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).

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).

Avants, B. B. et al. A reproducible evaluation of ANTs similarity metric performance in brain image registration. Neuroimage 54, 2033–2044 (2011).

Ashburner, J. & Friston, K. J. Unified segmentation. Neuroimage 26, 839–851, https://doi.org/10.1016/j.neuroimage.2005.02.018 (2005).

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).

Manjon, J. V. et al. Nonlocal intracranial cavity extraction. Int J Biomed Imaging 2014, 820205, https://doi.org/10.1155/2014/820205 (2014).

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).

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).

Solow, R. M. A contribution to the theory of economic growth. The quarterly journal of economics 70, 65–94 %@1531–4650 (1956).

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).

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).

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).

Eskildsen, S. F. et al. Structural imaging biomarkers of Alzheimer’s disease: predicting disease progression. Neurobiology of aging 36, S23–S31 (2015).

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).

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).

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).

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).

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).

Apostolova, L. G. et al. Subregional hippocampal atrophy predicts Alzheimer’s dementia in the cognitively normal. Neurobiology of aging 31, 1077–1088 (2010).

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).

Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta neuropathologica 82, 239–259 (1991).

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).

Micotti, E. et al. Striatum and entorhinal cortex atrophy in AD mouse models: MRI comprehensive analysis. Neurobiology of aging 36, 776–788 (2015).

Whitwell, J. L. et al. MRI correlates of neurofibrillary tangle pathology at autopsy A voxel-based morphometry study. Neurology 71, 743–749 (2008).

Iaccarino, L. et al. Local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer’s Disease. NeuroImage: Clinical 17, 452–464 (2018).

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).

Knopman, D. S. et al. Joint associations of β-amyloidosis and cortical thickness with cognition. Neurobiology of aging 65, 121–131 (2018).

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).

Jack, C. R. et al. A/T/N: an unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology 87, 539–547 (2016).

Cavedo, E. et al. Local amygdala structural differences with 3T MRI in patients with Alzheimer disease. Neurology 76, 727–733 (2011).

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).

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).

Phelps, E. A. Human emotion and memory: interactions of the amygdala and hippocampal complex. Current opinion in neurobiology 14, 198–202 (2004).

Kumfor, F. et al. Degradation of emotion processing ability in corticobasal syndrome and Alzheimer’s disease. Brain 137, 3061–3072 (2014).

De Olmos, J. S. In The Human Nervous System (Second Edition) Ch. 22, 739–868 (2004).

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).

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).

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).

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).

Ohm, T. & Braak, H. Olfactory bulb changes in Alzheimer’s disease. Acta neuropathologica 73, 365–369 (1987).

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).

Prince, M., Bryce, R. & Ferri, C. World Alzheimer Report 2011: The benefits of early diagnosis and intervention. (Alzheimer’s Disease International, 2011).

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).

Braak, H. & Braak, E. Alzheimer’s disease affects limbic nuclei of the thalamus. Acta neuropathologica 81, 261–268 (1991).

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).

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).

Fjell, A. M. et al. One-year brain atrophy evident in healthy aging. Journal of Neuroscience 29, 15223–15231 (2009).

Jack, C. R. et al. Comparison of different MRI brain atrophy rate measures with clinical disease progression in AD. Neurology 62, 591–600 (2004).

Barnes, J. et al. A meta-analysis of hippocampal atrophy rates in Alzheimer’s disease. Neurobiology of aging 30, 1711–1723 (2009).

McDonald, C. R. et al. Regional rates of neocortical atrophy from normal aging to early Alzheimer disease. Neurology 73, 457–465 (2009).

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).

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).

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).

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).

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).

Dubois, B. et al. Preclinical Alzheimer’s disease: definition, natural history, and diagnostic criteria. Alzheimer’s & Dementia 12, 292–323 (2016).

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).

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).

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