[en] [en] BACKGROUND: The NIA-AA proposed amyloid-tau-neurodegeneration (ATN) as a classification system for AD biomarkers. The amyloid cascade hypothesis (ACH) implies a sequence across ATN groups that patients might undergo during transition from healthy towards AD: A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+. Here we assess the evidence for monotonic brain volume decline for this particular (amyloid-conversion first, tau-conversion second, N-conversion last) and alternative progressions using voxel-based morphometry (VBM) in a large cross-sectional MRI cohort.
METHODS: We used baseline data of the DELCODE cohort of 437 subjects (127 controls, 168 SCD, 87 MCI, 55 AD patients) which underwent lumbar puncture, MRI scanning, and neuropsychological assessment. ATN classification was performed using CSF-Aβ42/Aβ40 (A+/-), CSF phospho-tau (T+/-), and adjusted hippocampal volume or CSF total-tau (N+/-). We compared voxel-wise model evidence for monotonic decline of gray matter volume across various sequences over ATN groups using the Bayesian Information Criterion (including also ROIs of Braak stages). First, face validity of the ACH transition sequence A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was compared against biologically less plausible (permuted) sequences among AD continuum ATN groups. Second, we evaluated evidence for 6 monotonic brain volume progressions from A-T-N- towards A+T+N+ including also non-AD continuum ATN groups.
RESULTS: The ACH-based progression A-T-N-➔A+T-N-➔A+T+N-➔A+T+N+ was consistent with cognitive decline and clinical diagnosis. Using hippocampal volume for operationalization of neurodegeneration (N), ACH was most evident in 9% of gray matter predominantly in the medial temporal lobe. Many cortical regions suggested alternative non-monotonic volume progressions over ACH progression groups, which is compatible with an early amyloid-related tissue expansion or sampling effects, e.g., due to brain reserve. Volume decline in 65% of gray matter was consistent with a progression where A status converts before T or N status (i.e., ACH/ANT) when compared to alternative sequences (TAN/TNA/NAT/NTA). Brain regions earlier affected by tau tangle deposition (Braak stage I-IV, MTL, limbic system) present stronger evidence for volume decline than late Braak stage ROIs (V/VI, cortical regions). Similar findings were observed when using CSF total-tau for N instead.
CONCLUSION: Using the ATN classification system, early amyloid status conversion (before tau and neurodegeneration) is associated with brain volume loss observed during AD progression. The ATN system and the ACH are compatible with monotonic progression of MTL atrophy.
TRIAL REGISTRATION: DRKS00007966, 04/05/2015, retrospectively registered.
Disciplines :
Neurology
Author, co-author :
Heinzinger, Nils; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. nils.heinzinger@st.ovgu.de ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany. nils.heinzinger@st.ovgu.de
Maass, Anne; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
Berron, David; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
Yakupov, Renat; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
Peters, Oliver; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ; Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
Fiebach, Jochen; Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
Villringer, Kersten; Center for Stroke Research Berlin, Charité-Universitätsmedizin, Berlin, Germany
Preis, Lukas; Department of Psychiatry, Charité-Universitätsmedizin Berlin, Campus Benjamin Franklin, Berlin, Germany
Priller, Josef; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ; Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany ; Department of Psychiatry and Psychotherapy, School of Medicine, Technical University of Munich, Munich, Germany ; University of Edinburgh and UK DRI, Edinburgh, UK
Spruth, Eike Jacob; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ; Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
Altenstein, Slawek; German Center for Neurodegenerative Diseases (DZNE), Berlin, Germany ; Department of Psychiatry and Psychotherapy, Charité, Berlin, Germany
Schneider, Anja; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
Fliessbach, Klaus; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
Wiltfang, Jens; German Center for Neurodegenerative Diseases (DZNE), Göttingen, Germany ; Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany ; Department of Medical Sciences, Neurosciences and Signaling Group, Institute of Biomedicine (iBiMED), University of Aveiro, Aveiro, Portugal
Bartels, Claudia; Department of Psychiatry and Psychotherapy, University Medical Center Göttingen, University of Göttingen, Göttingen, Germany
Jessen, Frank; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany ; Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Cologne, Germany
Maier, Franziska; Department of Psychiatry, Medical Faculty, University of Cologne, Cologne, Germany
Glanz, Wenzel; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Buerger, Katharina; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
Janowitz, Daniel; Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Munich, Germany
Perneczky, Robert; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany ; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany ; Munich Cluster for Systems Neurology (SyNergy), Munich, Germany ; Ageing Epidemiology Research Unit (AGE), School of Public Health, Imperial College London, London, UK
Rauchmann, Boris-Stephan; Department of Psychiatry and Psychotherapy, University Hospital, LMU Munich, Munich, Germany
Teipel, Stefan; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany ; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
Killimann, Ingo; German Center for Neurodegenerative Diseases (DZNE), Rostock, Germany ; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
Göerß, Doreen; Department of Psychosomatic Medicine, Rostock University Medical Center, Rostock, Germany
Laske, Christoph; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany ; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
Munk, Matthias H; German Center for Neurodegenerative Diseases (DZNE), Tübingen, Germany ; Section for Dementia Research, Hertie Institute for Clinical Brain Research and Department of Psychiatry and Psychotherapy, University of Tübingen, Tübingen, Germany
Spottke, Annika; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurology, University of Bonn, Bonn, Germany
Roy, Nina; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
HENEKA, Michael ; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
Brosseron, Frederic; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
Dobisch, Laura; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany
Ewers, Michael; German Center for Neurodegenerative Diseases (DZNE), Munich, Germany
Dechent, Peter; MR-Research in Neurosciences, Department of Cognitive Neurology, Georg-August-University Göttingen, Göttingen, Germany
Haynes, John Dylan; Bernstein Center for Computational Neuroscience, Charité-Universitätsmedizin, Berlin, Germany
Scheffler, Klaus; Department for Biomedical Magnetic Resonance, University of Tübingen, Tübingen, Germany
Wolfsgruber, Steffen; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Department of Neurodegenerative Diseases and Geriatric Psychiatry/Psychiatry, University of Bonn Medical Center, Bonn, Germany
Kleineidam, Luca; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
Schmid, Matthias; German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany ; Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
Berger, Moritz; Institute for Medical Biometry, University Hospital Bonn, Bonn, Germany
Düzel, Emrah; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
Ziegler, Gabriel; German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany ; Institute of Cognitive Neurology and Dementia Research (IKND), University Hospital Magdeburg, Otto-von-Guericke University, Leipziger Str. 44, 39120, Magdeburg, Germany
Open Access funding enabled and organized by Projekt DEAL. The study was funded by the German Center for Neurodegenerative Diseases (Deutsches Zentrum für Neurodegenerative Erkrankungen (DZNE)), reference number BN012.Data collection and sharing for the ADNI dataset in this project was funded by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12-2-0012). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California. ADNI is further funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and by contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health.
Jack CR, Knopman DS, Jagust WJ, et al. Tracking pathophysiological processes in Alzheimer's disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 2013;12(2):207–16. 10.1016/S1474-4422(12)70291-0. DOI: 10.1016/S1474-4422(12)70291-0
Villemagne VL, Burnham S, Bourgeat P, et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer's disease: a prospective cohort study. Lancet Neurol. 2013;12(4):357–67. 10.1016/S1474-4422(13)70044-9. DOI: 10.1016/S1474-4422(13)70044-9
Dean DC, Jerskey BA, Chen K, et al. Brain differences in infants at differential genetic risk for late-onset Alzheimer disease: a cross-sectional imaging study. JAMA Neurol. 2014;71(1):11–22. 10.1001/jamaneurol.2013.4544. DOI: 10.1001/jamaneurol.2013.4544
Thal DR, Capetillo-Zarate E, Del Tredici K, Braak H. The development of amyloid beta protein deposits in the aged brain. Sci Aging Knowl Environ. 2006;2006(6):re1. 10.1126/sageke.2006.6.re1. DOI: 10.1126/sageke.2006.6.re1
Braak H, Braak E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 1991;82(4):239–59. 10.1007/bf00308809. DOI: 10.1007/bf00308809
Janelidze S, Zetterberg H, Mattsson N, et al. CSF Aβ42/Aβ40 and Aβ42/Aβ38 ratios: better diagnostic markers of Alzheimer disease. Ann Clin Transl Neurol. 2016;3(3):154–65. 10.1002/acn3.274. DOI: 10.1002/acn3.274
Ritchie C, Smailagic N, Noel-Storr AH, Ukoumunne O, Ladds EC, Martin S. CSF tau and the CSF tau/ABeta ratio for the diagnosis of Alzheimer's disease dementia and other dementias in people with mild cognitive impairment (MCI). Cochrane Database Syst Rev. 2017;3:CD010803. 10.1002/14651858.CD010803.pub2. DOI: 10.1002/14651858.CD010803.pub2
Blennow K, Hampel H. CSF markers for incipient Alzheimer's disease. Lancet Neurol. 2003;2(10):605–13. 10.1016/s1474-4422(03)00530-1. DOI: 10.1016/s1474-4422(03)00530-1
Hampel H, Bürger K, Teipel SJ, Bokde ALW, Zetterberg H, Blennow K. Core candidate neurochemical and imaging biomarkers of Alzheimer's disease. Alzheimers Dement. 2008;4(1):38–48. 10.1016/j.jalz.2007.08.006. DOI: 10.1016/j.jalz.2007.08.006
Nathan PJ, Lim YY, Abbott R, et al. Association between CSF biomarkers, hippocampal volume and cognitive function in patients with amnestic mild cognitive impairment (MCI). Neurobiol Aging. 2017;53:1–10. 10.1016/j.neurobiolaging.2017.01.013. DOI: 10.1016/j.neurobiolaging.2017.01.013
Selkoe DJ. The molecular pathology of Alzheimer's disease. Neuron. 1991;6(4):487–98. 10.1016/0896-6273(91)90052-2. DOI: 10.1016/0896-6273(91)90052-2
Reitz C. Alzheimer's disease and the amyloid cascade hypothesis: a critical review. Int J Alzheimers Dis. 2012;2012:369808. 10.1155/2012/369808. DOI: 10.1155/2012/369808
Hou Y, Dan X, Babbar M, et al. Ageing as a risk factor for neurodegenerative disease. Nat Rev Neurol. 2019;15(10):565–81. 10.1038/s41582-019-0244-7. DOI: 10.1038/s41582-019-0244-7
Yamazaki Y, Zhao N, Caulfield TR, Liu C-C, Bu G. Apolipoprotein E and Alzheimer disease: pathobiology and targeting strategies. Nat Rev Neurol. 2019;15(9):501–18. 10.1038/s41582-019-0228-7. DOI: 10.1038/s41582-019-0228-7
Prins ND, Scheltens P. White matter hyperintensities, cognitive impairment and dementia: an update. Nat Rev Neurol. 2015;11(3):157–65. 10.1038/nrneurol.2015.10. DOI: 10.1038/nrneurol.2015.10
Yang J, Pan P, Song W, et al. Voxelwise meta-analysis of gray matter anomalies in Alzheimer's disease and mild cognitive impairment using anatomic likelihood estimation. J Neurol Sci. 2012;316(1-2):21–9. 10.1016/j.jns.2012.02.010. DOI: 10.1016/j.jns.2012.02.010
Matsuda H. Voxel-based Morphometry of Brain MRI in normal aging and Alzheimer's disease. Aging Dis. 2013;4(1):29–37 https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3570139/ Accessed 2 Dec 2020.
Armstrong RA. A critical analysis of the ‘amyloid cascade hypothesis’. Folia Neuropathol. 2014;3:211–25. 10.5114/fn.2014.45562. DOI: 10.5114/fn.2014.45562
Busche MA, Hyman BT. Synergy between amyloid-β and tau in Alzheimer's disease. Nat Neurosci. 2020;23(10):1183–93. 10.1038/s41593-020-0687-6. DOI: 10.1038/s41593-020-0687-6
Schöll M, Lockhart SN, Schonhaut DR, et al. PET imaging of tau deposition in the aging human brain. Neuron. 2016;89(5):971–82. 10.1016/j.neuron.2016.01.028. DOI: 10.1016/j.neuron.2016.01.028
Maass A, Landau S, Baker SL, et al. Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer's disease. Neuroimage. 2017;157:448–63. 10.1016/j.neuroimage.2017.05.058. DOI: 10.1016/j.neuroimage.2017.05.058
Matsuda H. MRI morphometry in Alzheimer's disease. Ageing Res Rev. 2016;30:17–24. 10.1016/j.arr.2016.01.003. DOI: 10.1016/j.arr.2016.01.003
Jack CR, Bennett DA, Blennow K, et al. A/T/N: An unbiased descriptive classification scheme for Alzheimer disease biomarkers. Neurology. 2016;87(5):539–47. 10.1212/WNL.0000000000002923. DOI: 10.1212/WNL.0000000000002923
Jack CR, Bennett DA, Blennow K, et al. NIA-AA research framework: toward a biological definition of Alzheimer's disease. Alzheimers Dement. 2018;14(4):535–62. 10.1016/j.jalz.2018.02.018. DOI: 10.1016/j.jalz.2018.02.018
Soldan A, Pettigrew C, Fagan AM, et al. ATN profiles among cognitively normal individuals and longitudinal cognitive outcomes. Neurology. 2019;92(14):e1567–79. 10.1212/WNL.0000000000007248. DOI: 10.1212/WNL.0000000000007248
Altomare D, de Wilde A, Ossenkoppele R, et al. Applying the ATN scheme in a memory clinic population: the ABIDE project. Neurology. 2019;93(17):e1635–46. 10.1212/WNL.0000000000008361. DOI: 10.1212/WNL.0000000000008361
van Maurik IS, Vos SJ, Bos I, et al. Biomarker-based prognosis for people with mild cognitive impairment (ABIDE): a modelling study. Lancet Neurol. 2019;18(11):1034–44. 10.1016/S1474-4422(19)30283-2. DOI: 10.1016/S1474-4422(19)30283-2
Jack CR, Wiste HJ, Therneau TM, et al. Associations of amyloid, tau, and neurodegeneration biomarker profiles with rates of memory decline among individuals without dementia. JAMA. 2019;321(23):2316–25. 10.1001/jama.2019.7437. DOI: 10.1001/jama.2019.7437
Yu J-T, Li J-Q, Suckling J, et al. Frequency and longitudinal clinical outcomes of Alzheimer's AT(N) biomarker profiles: a longitudinal study. Alzheimers Dement. 2019;15(9):1208–17. 10.1016/j.jalz.2019.05.006. DOI: 10.1016/j.jalz.2019.05.006
Guo T, Korman D, Baker SL, Landau SM, Jagust WJ. Longitudinal cognitive and biomarker measurements support a unidirectional pathway in Alzheimer's disease pathophysiology. Biol Psychiatry. 2020. https://doi.org/10.1016/j.biopsych.2020.06.029.
Tan M-S, Ji X, Li J-Q, et al. Longitudinal trajectories of Alzheimer's ATN biomarkers in elderly persons without dementia. Alzheimers Res Ther. 2020;12(1):55. 10.1186/s13195-020-00621-6. DOI: 10.1186/s13195-020-00621-6
Ekman U, Ferreira D, Westman E. The A/T/N biomarker scheme and patterns of brain atrophy assessed in mild cognitive impairment. Sci Rep. 2018;8(1):8431. 10.1038/s41598-018-26151-8. DOI: 10.1038/s41598-018-26151-8
Jessen F, Spottke A, Boecker H, et al. Design and first baseline data of the DZNE multicenter observational study on predementia Alzheimer's disease (DELCODE). Alzheimers Res Ther. 2018;10(1):15. 10.1186/s13195-017-0314-2. DOI: 10.1186/s13195-017-0314-2
McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM. Clinical diagnosis of Alzheimer's disease: report of the NINCDS-ADRDA Work Group under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease. Neurology. 1984;34(7):939–44. 10.1212/wnl.34.7.939. DOI: 10.1212/wnl.34.7.939
Folstein MF, Folstein SE, McHugh PR. Mini-mental state: a practical method for grading the state of patients for the clinician. J Psychiatr Res. 1975;12(3):189–98. 10.1016/0022-3956(75)90026-6. DOI: 10.1016/0022-3956(75)90026-6
Wolfsgruber S, Kleineidam L, Guski J, et al. Minor neuropsychological deficits in patients with subjective cognitive decline. Neurology. 2020;95(9):e1134–43. 10.1212/WNL.0000000000010142. DOI: 10.1212/WNL.0000000000010142
Wellcome Trust Centre for Human Neuroimaging, University College London. Statistical Parametric Mapping software https://www.fil.ion.ucl.ac.uk/spm/. Accessed 8 Feb 2022.
Structural Brain Mapping group, Jena University Hospital. CAT-Toolbox http://www.neuro.uni-jena.de/cat/. Accessed 8 Feb 2022.
Rajapakse JC, Giedd JN, Rapoport JL. Statistical approach to segmentation of single-channel cerebral MR images. IEEE Trans Med Imaging. 1997;16(2):176–86. 10.1109/42.563663. DOI: 10.1109/42.563663
Ashburner J, Friston KJ. Diffeomorphic registration using geodesic shooting and Gauss-Newton optimisation. Neuroimage. 2011;55(3):954–67. 10.1016/j.neuroimage.2010.12.049. DOI: 10.1016/j.neuroimage.2010.12.049
Laboratory for Computational Neuroimaging, Massachusetts General Hospital. Freesurfer. http://surfer.nmr.mgh.harvard.edu/. Accessed 8 Feb 2022.
Fischl B, van der Kouwe A, Destrieux C, et al. Automatically parcellating the human cerebral cortex. Cereb Cortex. 2004;14(1):11–22. 10.1093/cercor/bhg087. DOI: 10.1093/cercor/bhg087
Fischl B, Salat DH, Busa E, et al. Whole brain segmentation. Neuron. 2002;33(3):341–55. 10.1016/s0896-6273(02)00569-x. DOI: 10.1016/s0896-6273(02)00569-x
Teipel SJ, Meindl T, Grinberg L, Heinsen H, Hampel H. Novel MRI techniques in the assessment of dementia. Eur J Nucl Med Mol Imaging. 2008;35(Suppl 1):S58–69. 10.1007/s00259-007-0703-z. DOI: 10.1007/s00259-007-0703-z
Baker SL, Maass A, Jagust WJ. Considerations and code for partial volume correcting 18F-AV-1451 tau PET data. Data Brief. 2017;15:648–57. 10.1016/j.dib.2017.10.024. DOI: 10.1016/j.dib.2017.10.024
Iglesias JE, Augustinack JC, Nguyen K, et al. A computational atlas of the hippocampal formation using ex vivo, ultra-high resolution MRI: application to adaptive segmentation of in vivo MRI. Neuroimage. 2015;115:117–37. 10.1016/j.neuroimage.2015.04.042. DOI: 10.1016/j.neuroimage.2015.04.042
Koncz R, Sachdev PS. Are the brain's vascular and Alzheimer pathologies additive or interactive? Curr Opin Psychiatry. 2018;31(2):147–52. 10.1097/YCO.0000000000000395. DOI: 10.1097/YCO.0000000000000395
Roseborough A, Ramirez J, Black SE, Edwards JD. Associations between amyloid β and white matter hyperintensities: a systematic review. Alzheimers Dement. 2017;13(10):1154–67. 10.1016/j.jalz.2017.01.026. DOI: 10.1016/j.jalz.2017.01.026
Toledo JB, Arnold SE, Raible K, et al. Contribution of cerebrovascular disease in autopsy confirmed neurodegenerative disease cases in the National Alzheimer's Coordinating Centre. Brain. 2013;136(Pt 9):2697–706. 10.1093/brain/awt188. DOI: 10.1093/brain/awt188
Mortamais M, Artero S, Ritchie K. Cerebral white matter hyperintensities in the prediction of cognitive decline and incident dementia. Int Rev Psychiatry. 2013;25(6):686–98. 10.3109/09540261.2013.838151. DOI: 10.3109/09540261.2013.838151
Schmidt P. Bayesian Inference for Structured Additive Regression Models for Large-Scale Problems with Applications Tomedical Imaging: Dissertation an Der Fakultät Für Mathematik, Informatik Und Statistik Der Ludwig-Maximilians-Universität München. [Dissertation]: Ludwig-Maximilians-Universität München; 2017. http://nbn-resolving.de/urn:nbn:de:bvb:19-203731
Schmidt P. LST. A lesion segmentation tool for SPM. http://www.statistical-modelling.de/lst.html. Accessed 7 March 2022.
Bertens D, Tijms BM, Scheltens P, Teunissen CE, Visser PJ. Unbiased estimates of cerebrospinal fluid β-amyloid 1-42 cutoffs in a large memory clinic population. Alzheimers Res Ther. 2017;9(1):8. 10.1186/s13195-016-0233-7. DOI: 10.1186/s13195-016-0233-7
Schwarz G. Estimating the dimension of a model. Ann Stat. 1978;6(2). 10.1214/aos/1176344136.
Laboratory of Neuro Imaging, University of Southern California. ADNI: Alzheimer's Disease Neuroimaging Initiative. https://adni.loni.usc.edu/. Accessed 18 Dec 2022.
Hwang J, Jeong JH, Yoon SJ, et al. Clinical and biomarker characteristics according to clinical spectrum of Alzheimer's disease (AD) in the Validation Cohort of Korean Brain Aging Study for the Early Diagnosis and Prediction of AD. J Clin Med. 2019;8(3). 10.3390/jcm8030341.
Burnham SC, Coloma PM, Li Q-X, et al. Application of the NIA-AA Research Framework: towards a biological definition of Alzheimer's disease using cerebrospinal fluid biomarkers in the AIBL study. J Prev Alzheimers Dis. 2019;6(4):248–55. 10.14283/jpad.2019.25. DOI: 10.14283/jpad.2019.25
Grøntvedt GR, Lauridsen C, Berge G, et al. The amyloid, tau, and neurodegeneration (A/T/N) classification applied to a clinical research cohort with long-term follow-up. J Alzheimers Dis. 2020;74(3):829–37. 10.3233/JAD-191227. DOI: 10.3233/JAD-191227
Kakeda S, Korogi Y. The efficacy of a voxel-based morphometry on the analysis of imaging in schizophrenia, temporal lobe epilepsy, and Alzheimer's disease/mild cognitive impairment: a review. Neuroradiology. 2010;52(8):711–21. 10.1007/s00234-010-0717-2. DOI: 10.1007/s00234-010-0717-2
Karas GB, Scheltens P, Rombouts SA, et al. Global and local gray matter loss in mild cognitive impairment and Alzheimer's disease. Neuroimage. 2004;23(2):708–16. 10.1016/j.neuroimage.2004.07.006. DOI: 10.1016/j.neuroimage.2004.07.006
Chételat G, Landeau B, Eustache F, et al. Using voxel-based morphometry to map the structural changes associated with rapid conversion in MCI: a longitudinal MRI study. Neuroimage. 2005;27(4):934–46. 10.1016/j.neuroimage.2005.05.015. DOI: 10.1016/j.neuroimage.2005.05.015
Pini L, Pievani M, Bocchetta M, et al. Brain atrophy in Alzheimer's disease and aging. Ageing Res Rev. 2016;30:25–48. 10.1016/j.arr.2016.01.002. DOI: 10.1016/j.arr.2016.01.002
Baron JC, Chételat G, Desgranges B, et al. In vivo mapping of gray matter loss with voxel-based morphometry in mild Alzheimer's disease. Neuroimage. 2001;14(2):298–309. 10.1006/nimg.2001.0848. DOI: 10.1006/nimg.2001.0848
Bernardes R, da Silva FS, Oliveira Barbosa JH, Rondinoni C, et al. Neuro-degeneration profile of Alzheimer's patients: a brain morphometry study. Neuroimage Clin. 2017;15:15–24. 10.1016/j.nicl.2017.04.001. DOI: 10.1016/j.nicl.2017.04.001
Zanchi D, Giannakopoulos P, Borgwardt S, Rodriguez C, Haller S. Hippocampal and amygdala gray matter loss in elderly controls with subtle cognitive decline. Front Aging Neurosci. 2017;9:50. 10.3389/fnagi.2017.00050. DOI: 10.3389/fnagi.2017.00050
Jones BF, Barnes J, Uylings HBM, et al. Differential regional atrophy of the cingulate gyrus in Alzheimer disease: a volumetric MRI study. Cereb Cortex. 2006;16(12):1701–8. 10.1093/cercor/bhj105. DOI: 10.1093/cercor/bhj105
Kumar D, Ganeshpurkar A, Kumar D, Modi G, Gupta SK, Singh SK. Secretase inhibitors for the treatment of Alzheimer's disease: Long road ahead. Eur J Med Chem. 2018;148:436–52. 10.1016/j.ejmech.2018.02.035. DOI: 10.1016/j.ejmech.2018.02.035
van Dyck CH. Anti-amyloid-β monoclonal antibodies for Alzheimer's disease: pitfalls and promise. Biol Psychiatry. 2018;83(4):311–9. 10.1016/j.biopsych.2017.08.010. DOI: 10.1016/j.biopsych.2017.08.010
Masliah E, Mallory M, Hansen L, Richard D, Alford M, Terry R. Synaptic and neuritic alterations during the progression of Alzheimer's disease. Neurosci Lett. 1994;174(1):67–72. 10.1016/0304-3940(94)90121-X. DOI: 10.1016/0304-3940(94)90121-X
Knowles RB, Gomez-Isla T, Hyman BT. Abeta associated neuropil changes: correlation with neuronal loss and dementia. J Neuropathol Exp Neurol. 1998;57(12):1122–30. 10.1097/00005072-199812000-00003. DOI: 10.1097/00005072-199812000-00003
Nation DA, Sweeney MD, Montagne A, et al. Blood-brain barrier breakdown is an early biomarker of human cognitive dysfunction. Nat Med. 2019;25(2):270–6. 10.1038/s41591-018-0297-y. DOI: 10.1038/s41591-018-0297-y
Buccellato FR, D'Anca M, Serpente M, Arighi A, Galimberti D. The role of glymphatic system in Alzheimer's and Parkinson's disease pathogenesis. Biomedicines. 2022;10(9). 10.3390/biomedicines10092261.
Fortea J, Vilaplana E, Alcolea D, et al. Cerebrospinal fluid β-amyloid and phospho-tau biomarker interactions affecting brain structure in preclinical Alzheimer disease. Ann Neurol. 2014;76(2):223–30. 10.1002/ana.24186. DOI: 10.1002/ana.24186
Montal V, Vilaplana E, Alcolea D, et al. Cortical microstructural changes along the Alzheimer's disease continuum. Alzheimers Dementia. 2018;14(3):340–51. 10.1016/j.jalz.2017.09.013. DOI: 10.1016/j.jalz.2017.09.013
Stern Y, Arenaza-Urquijo EM, Bartrés-Faz D, et al. Whitepaper: Defining and investigating cognitive reserve, brain reserve, and brain maintenance. Alzheimers Dementia. 2020;16(9):1305–11. 10.1016/j.jalz.2018.07.219. DOI: 10.1016/j.jalz.2018.07.219
Ingala S, de Boer C, Masselink LA, et al. Application of the ATN classification scheme in a population without dementia: findings from the EPAD cohort. Alzheimers Dementia. 2021;17(7):1189–204. 10.1002/alz.12292. DOI: 10.1002/alz.12292
Young AL, Oxtoby NP, Daga P, et al. A data-driven model of biomarker changes in sporadic Alzheimer's disease. Brain. 2014;137(Pt 9):2564–77. 10.1093/brain/awu176. DOI: 10.1093/brain/awu176
Firth NC, Primativo S, Brotherhood E, et al. Sequences of cognitive decline in typical Alzheimer's disease and posterior cortical atrophy estimated using a novel event-based model of disease progression. Alzheimers Dementia. 2020;16(7):965–73. 10.1002/alz.12083. DOI: 10.1002/alz.12083
Nelson PT, Abner EL, Patel E, et al. The amygdala as a locus of pathologic misfolding in neurodegenerative diseases. J Neuropathol Exp Neurol. 2018;77(1):2–20. 10.1093/jnen/nlx099. DOI: 10.1093/jnen/nlx099
Mattsson-Carlgren N, Leuzy A, Janelidze S, et al. The implications of different approaches to define AT(N) in Alzheimer disease. Neurology. 2020;94(21):e2233–44. 10.1212/WNL.0000000000009485. DOI: 10.1212/WNL.0000000000009485
Illán-Gala I, Pegueroles J, Montal V, et al. Challenges associated with biomarker-based classification systems for Alzheimer's disease. Alzheimers Dement (Amst). 2018;10:346–57. 10.1016/j.dadm.2018.03.004. DOI: 10.1016/j.dadm.2018.03.004