The bivariate distribution of amyloid-β and tau: relationship with established neurocognitive clinical syndromes

Clifford R Jack, Heather J Wiste, Hugo Botha, Stephen D Weigand, Terry M Therneau, David S Knopman, Jonathan Graff-Radford, David T Jones, Tanis J Ferman, Bradley F Boeve, Kejal Kantarci, Val J Lowe, Prashanthi Vemuri, Michelle M Mielke, Julie A Fields, Mary M Machulda, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Ronald C Petersen, Clifford R Jack, Heather J Wiste, Hugo Botha, Stephen D Weigand, Terry M Therneau, David S Knopman, Jonathan Graff-Radford, David T Jones, Tanis J Ferman, Bradley F Boeve, Kejal Kantarci, Val J Lowe, Prashanthi Vemuri, Michelle M Mielke, Julie A Fields, Mary M Machulda, Christopher G Schwarz, Matthew L Senjem, Jeffrey L Gunter, Ronald C Petersen

Abstract

Large phenotypically diverse research cohorts with both amyloid and tau PET have only recently come into existence. Our objective was to determine relationships between the bivariate distribution of amyloid-β and tau on PET and established clinical syndromes that are relevant to cognitive ageing and dementia. All individuals in this study were enrolled in the Mayo Clinic Study of Aging, a longitudinal population-based study of cognitive ageing, or the Mayo Alzheimer Disease Research Center, a longitudinal study of individuals recruited from clinical practice. We studied 1343 participants who had amyloid PET and tau PET from 2 April 2015 to 3 May 2019, and met criteria for membership in one of five clinical diagnostic groups: cognitively unimpaired, mild cognitive impairment, frontotemporal dementia, probable dementia with Lewy bodies, and Alzheimer clinical syndrome. We examined these clinical groups in relation to the bivariate distribution of amyloid and tau PET values. Individuals were grouped into amyloid (A)/tau (T) quadrants based on previously established abnormality cut points of standardized uptake value ratio 1.48 (A) and 1.33 (T). Individual participants largely fell into one of three amyloid/tau quadrants: low amyloid and low tau (A-T-), high amyloid and low tau (A+T-), or high amyloid and high tau (A+T+). Seventy per cent of cognitively unimpaired and 74% of FTD participants fell into the A-T- quadrant. Participants with mild cognitive impairment spanned the A-T- (42%), A+T- (28%), and A+T+ (27%) quadrants. Probable dementia with Lewy body participants spanned the A-T- (38%) and A+T- (44%) quadrants. Most (89%) participants with Alzheimer clinical syndrome fell into the A+T+ quadrant. These data support several conclusions. First, among 1343 participants, abnormal tau PET rarely occurred in the absence of abnormal amyloid PET, but the reverse was common. Thus, with rare exceptions, amyloidosis appears to be required for high levels of 3R/4R tau deposition. Second, abnormal amyloid PET is compatible with normal cognition but highly abnormal tau PET is not. These two conclusions support a dynamic biomarker model in which Alzheimer's disease is characterized first by the appearance of amyloidosis and later by tauopathy, with tauopathy being the proteinopathy associated with clinical symptoms. Third, bivariate amyloid and tau PET relationships differed across clinical groups and thus have a role for clarifying the aetiologies underlying neurocognitive clinical syndromes.

Keywords: Alzheimer’s disease; amyloid PET; dementia with Lewy bodies; frontotemporal dementia; tau PET.

© The Author(s) (2019). Published by Oxford University Press on behalf of the Guarantors of Brain.

Figures

Figure 1
Figure 1
Amyloid and tau PET distributions by clinical group overall and within study. Scatter plots of tau PET SUVR versus amyloid PET SUVR among all individuals combined (A) and separately among individuals in the MCSA (B) and ADRC (C). Tau PET and amyloid PET values are in SUVR units but the data is plotted on log scale, which accounts for the uneven spacing. Points are coloured by clinical diagnosis. Histograms in the margins show the distributions of tau PET SUVR (right) and amyloid PET SUVR (top). Axis labels on the top represent amyloid PET values on a centiloid scale.
Figure 2
Figure 2
Amyloid and tau PET clusters. Scatterplot of tau PET SUVR versus amyloid PET SUVR with points coloured according to the three-cluster classification from a bivariate mixture model (A). Points shown in black represent individuals who were inconsistent with one of the three clusters. The vertical and horizontal lines represent the cut points of 1.48 SUVR for amyloid PET and 1.33 SUVR for tau PET. The ellipses show the centre 50% of the data for the three cluster distributions with a black star indicating the bivariate mean from the clustering. In B, these ellipses are shown along with a square for each clinical diagnosis group representing the bivariate median (centroid) of the tau and amyloid distributions. Tau PET and amyloid PET values are in SUVR units but the data is plotted on log scale, which accounts for the uneven spacing. Axis labels on the top represent amyloid PET values on a centiloid scale.
Figure 3
Figure 3
Amyloid and tau PET groups within clinical diagnosis. Per cent of individuals in each quadrant (A) or cluster (B) within each clinical diagnostic group. (A) Percentages according to amyloid and tau PET groupings based on the established cut points of 1.48 SUVR for amyloid PET and 1.33 SUVR for tau PET. (B) Percentages according to the bivariate mixture model clusters. These are labelled according to amyloid (low or high) and tau (low or high). Those individuals whose values were inconsistent with one of the three clusters were labelled as other.
Figure 4
Figure 4
Scatter plots of tau PET SUVR versus amyloid PET SUVR by age groups among cognitively unimpaired (CU), MCI, and AlzCS individuals. MCSA individuals are shown in the left column and ADRC individuals in the right column. The vertical and horizontal lines represent the cut points of 1.48 SUVR for amyloid PET and 1.33 SUVR for tau PET. Points are coloured by clinical diagnosis. Tau PET and amyloid PET values are in SUVR units but the data is plotted on a log scale, which accounts for the uneven spacing. Axis labels on the top of the columns represent amyloid PET values on a centiloid scale.
Figure 5
Figure 5
Scatter plots of tau PET SUVR versus amyloid PET SUVR among all individuals combined and separately among individuals in the MCSA and ADRC for three alternative tau PET regions of interest. Points are coloured by clinical diagnosis. Histograms in the margins show the distributions of tau PET SUVR (right) and amyloid PET SUVR (top). Axis labels on the top of each plot represent amyloid PET values on a centiloid scale. Tau PET and amyloid PET values are in SUVR units but the data is plotted on a log scale, which accounts for the uneven spacing.

References

    1. American Psychiatric Association. Diagnostic and statistical manual of mental disorders, DSM-IV. 4th edn. Washington, DC: American Psychiatric Association; 1994.
    1. Arriagada PV, Growdon JH, Hedley-Whyte ET, Hyman BT. Neurofibrillary tangles but not senile plaques parallel duration and severity of Alzheimer’s disease. Neurology 1992; 42 (3 Pt 1): 631–9.
    1. Banfield JD, Raftery AE. Model-based Gaussian and non-Gaussian clustering. Biometrics 1993; 49: 803–21.
    1. Bateman RJ, Xiong C, Benzinger TL, Fagan AM, Goate A, Fox NC et al. . Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N Engl J Med 2012; 367: 795–804.
    1. Beach TG, Monsell SE, Phillips LE, Kukull W. Accuracy of the clinical diagnosis of Alzheimer disease at National Institute on Aging Alzheimer Disease Centers, 2005–2010. J Neuropathol Exp Neurol 2012; 71: 266–73.
    1. Bennett DA, Schneider JA, Wilson RS, Bienias JL, Arnold SE. Neurofibrillary tangles mediate the association of amyloid load with clinical Alzheimer disease and level of cognitive function. Arch Neurol 2004; 61: 378–84.
    1. Benzinger TL, Blazey T, Jack CR Jr, Koeppe RA, Su Y, Xiong C et al. . Regional variability of imaging biomarkers in autosomal dominant Alzheimer’s disease. Proc Natl Acad Sci USA 2013; 110: E4502–9.
    1. Bilgel M, An Y, Helphrey J, Elkins W, Gomez G, Wong DF et al. . Effects of amyloid pathology and neurodegeneration on cognitive change in cognitively normal adults. Brain 2018; 141: 2475–85.
    1. Botha H, Mantyh WG, Graff-Radford J, Machulda MM, Przybelski SA, Wiste HJ et al. . Tau-negative amnestic dementia masquerading as Alzheimer disease dementia. Neurology 2018a; 90: e940–6.
    1. Botha H, Mantyh WG, Murray ME, Knopman DS, Przybelski SA, Wiste HJ et al. . FDG-PET in tau-negative amnestic dementia resembles that of autopsy-proven hippocampal sclerosis. Brain 2018b; 141: 1201–17.
    1. Braak H, Thal DR, Ghebremedhin E, Del Tredici K. Stages of the pathologic process in Alzheimer disease: age categories from 1 to 100 years. J Neuropathol Exp Neurol 2011; 70: 960–9.
    1. Buckley RF, Mormino EC, Amariglio RE, Properzi MJ, Rabin JS, Lim YY et al. . Sex, amyloid, and APOE ɛ4 and risk of cognitive decline in preclinical Alzheimer’s disease: findings from three well-characterized cohorts. Alzheimer’s Dement 2018; 14: 1193–203.
    1. Chien DT, Bahri S, Szardenings AK, Walsh JC, Mu F, Su MY et al. . Early clinical PET imaging results with the novel PHF-tau radioligand [F-18]-T807. J Alzheimers Dis 2013; 34: 457–68.
    1. Chiotis K, Saint-Aubert L, Rodriguez-Vieitez E, Leuzy A, Almkvist O, Savitcheva I et al. . Longitudinal changes of tau PET imaging in relation to hypometabolism in prodromal and Alzheimer’s disease dementia. Mol Psychiatry 2018; 23: 1666–73.
    1. Cho H, Choi JY, Hwang MS, Kim YJ, Lee HM, Lee HS et al. . In vivo cortical spreading pattern of tau and amyloid in the Alzheimer disease spectrum. Ann Neurol 2016a; 80: 247–58.
    1. Cho H, Choi JY, Hwang MS, Lee JH, Kim YJ, Lee HM et al. . Tau PET in Alzheimer disease and mild cognitive impairment. Neurology 2016b; 87: 375–83.
    1. Coughlin D, Xie SX, Liang M, Williams A, Peterson C, Weintraub D et al. . Cognitive and pathological influences of tau pathology in Lewy body disorders. Ann Neurol 2019; 85: 259–71.
    1. Crary JF, Trojanowski JQ, Schneider JA, Abisambra JF, Abner EL, Alafuzoff I et al. . Primary age-related tauopathy (PART): a common pathology associated with human aging. Acta Neuropathol 2014; 128: 755–66.
    1. de Wilde A, van der Flier WM, Pelkmans W, Bouwman F, Verwer J, Groot C et al. . Association of amyloid positron emission tomography with changes in diagnosis and patient treatment in an unselected memory clinic cohort: the ABIDE project. JAMA Neurol 2018; 75: 1062–70.
    1. Forsberg A, Almkvist O, Engler H, Wall A, Langstrom B, Nordberg A. High PIB retention in Alzheimer’s disease is an early event with complex relationship with CSF biomarkers and functional parameters. Curr Alzheimer Res 2010; 7: 56–66.
    1. Gomez-Isla T, Hollister R, West H, Mui S, Growdon JH, Petersen RC et al. . Neuronal loss correlates with but exceeds neurofibrillary tangles in Alzheimer’s disease. Ann Neurol 1997; 41: 17–24.
    1. Gomperts SN, Locascio JJ, Makaretz SJ, Schultz A, Caso C, Vasdev N et al. . Tau positron emission tomographic imaging in the Lewy body diseases. JAMA Neurol 2016; 73: 1334–41.
    1. Gordon BA, Blazey TM, Christensen J, Dincer A, Flores S, Keefe S et al. . Tau PET in autosomal dominant Alzheimer’s disease: relationship with cognition, dementia and other biomarkers. Brain 2019; 142: 1063–76.
    1. Gordon BA, Blazey TM, Su Y, Hari-Raj A, Dincer A, Flores S et al. . Spatial patterns of neuroimaging biomarker change in individuals from families with autosomal dominant Alzheimer’s disease: a longitudinal study. Lancet Neurol 2018; 17: 241–50.
    1. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF et al. . Classification of primary progressive aphasia and its variants. Neurology 2011; 76: 1006–14.
    1. Hanseeuw BJ, Betensky RA, Jacobs HIL, Schultz AP, Sepulcre J, Becker JA et al. . Association of amyloid and tau with cognition in preclinical Alzheimer disease: a longitudinal study. JAMA Neurol 2019; 76: 915–24.
    1. Hyman BT, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Carrillo MC et al. . National Institute on Aging-Alzheimer’s Association guidelines for the neuropathologic assessment of Alzheimer’s disease. Alzheimers Dement 2012; 8: 1–13.
    1. Ingelsson M, Fukumoto H, Newell KL, Growdon JH, Hedley-Whyte ET, Frosch MP et al. . Early Abeta accumulation and progressive synaptic loss, gliosis, and tangle formation in AD brain. Neurology 2004; 62: 925–31.
    1. Irwin DJ, Grossman M, Weintraub D, Hurtig HI, Duda JE, Xie SX et al. . Neuropathological and genetic correlates of survival and dementia onset in synucleinopathies: a retrospective analysis. Lancet Neurol 2017; 16: 55–65.
    1. Jack CR Jr, Bennett DA, Blennow K, Carrillo MC, Dunn B, Haeberlein SB et al. . NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimers Dement 2018; 14: 535–62.
    1. Jack CR Jr, Knopman DS, Jagust WJ, Petersen RC, Weiner MW, Aisen PS et al. . Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol 2013; 12: 207–16.
    1. Jack CR Jr, Knopman DS, Jagust WJ, Shaw LM, Aisen PS, Weiner MW et al. . Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol 2010; 9: 119–28.
    1. Jack CR, Wiste HJ, Weigand SD, Therneau TM, Lowe VJ, Knopman DS et al. . Defining imaging biomarker cut points for brain aging and Alzheimer’s disease. Alzheimers Dement 2017; 13: 205–16.
    1. Jagust W, Jack CR Jr, Bennett DA, Blennow K, Haeberlein SB, Holtzman DM et al. . “Alzheimer’s disease” is neither “Alzheimer’s clinical syndrome” nor “dementia”. Alzheimer’s Dement 2019; 15: 153–7.
    1. Jansen WJ, Ossenkoppele R, Tijms BM, Fagan AM, Hansson O, Klunk WE et al. . Association of cerebral amyloid-beta aggregation with cognitive functioning in persons without dementia. JAMA Psychiatry 2018; 75: 84–95.
    1. Johnson KA, Shultz A, Betensky RA, Becker JA, Sepulcre J, Rentz DM et al. . Tau positron emission tomographic imaging in aging and early Alzheimer’s disease. Ann Neurol 2016; 79: 110–9.
    1. Jones DT, Graff-Radford J, Lowe VJ, Wiste HJ, Gunter JL, Senjem ML et al. . Tau, amyloid, and cascading network failure across the Alzheimer’s disease spectrum. Cortex 2017; 97: 143–59.
    1. Kantarci K, Lowe VJ, Boeve BF, Senjem ML, Tosakulwong N, Lesnick TG et al. . AV-1451 tau and beta-amyloid positron emission tomography imaging in dementia with Lewy bodies. Annu Neurol 2017; 81: 58–67.
    1. Kawas CH, Kim RC, Sonnen JA, Bullain SS, Trieu T, Corrada MM. Multiple pathologies are common and related to dementia in the oldest-old: the 90+ study. Neurology 2015; 85: 535–42.
    1. Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP et al. . Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B. Ann Neurol 2004; 55: 306–19.
    1. Klunk WE, Koeppe RA, Price JC, Benzinger T, Devous M, Jagust W et al. . The centiloid project: standardizing quantitative amyloid plaque estimation by PET. Alzheimer’s Dement 2015; 11: 1–15.
    1. Knopman DS, Lundt ES, Therneau TM, Vemuri P, Lowe VJ, Kantarci K et al. . Entorhinal cortex tau, amyloid-beta, cortical thickness and memory performance in non-demented subjects. Brain 2019.
    1. La Joie R, Ayakta N, Seeley WW, Borys E, Boxer AL, DeCarli C et al. . Multisite study of the relationships between antemortem [(11)C]PIB-PET Centiloid values and postmortem measures of Alzheimer’s disease neuropathology. Alzheimers Dement 2019; 15: 205–16.
    1. Landau SM, Horng A, Fero A, Jagust WJ. Amyloid negativity in patients with clinically diagnosed Alzheimer disease and MCI. Neurology 2016; 86: 1377–85.
    1. Leal SL, Lockhart SN, Maass A, Bell RK, Jagust WJ. Subthreshold amyloid predicts tau deposition in aging. J Neurosci 2018; 38: 4482–9.
    1. Lemoine L, Gillberg PG, Svedberg M, Stepanov V, Jia Z, Huang J et al. . Comparative binding properties of the tau PET tracers THK5117, THK5351, PBB3, and T807 in postmortem Alzheimer brains. Alzheimers Res Ther 2017; 9: 96.
    1. Leuzy A, Savitcheva I, Chiotis K, Lilja J, Andersen P, Bogdanovic N et al. . Clinical impact of [(18)F]flutemetamol PET among memory clinic patients with an unclear diagnosis. Eur J Nucl Med Mol Imaging 2019; 46: 1276–86.
    1. Lim YY, Kalinowski P, Pietrzak RH, Laws SM, Burnham SC, Ames D et al. . Association of beta-amyloid and apolipoprotein E epsilon4 with memory decline in preclinical Alzheimer disease. JAMA Neurol 2018; 75: 488–94.
    1. Lopez OL, Becker JT, Chang Y, Klunk WE, Mathis C, Price J et al. . Amyloid deposition and brain structure as long-term predictors of MCI, dementia, and mortality. Neurology 2018; 90: e1920–8.
    1. Lowe VJ, Curran G, Fang P, Liesinger AM, Josephs KA, Parisi JE et al. . An autoradiographic evaluation of AV-1451 tau PET in dementia. Acta Neuropathol Commun 2016; 4: 58.
    1. Lowe VJ, Wiste HJ, Senjem ML, Weigand SD, Therneau TM, Boeve BF et al. . Widespread brain tau and its association with ageing, Braak stage and Alzheimer’s dementia. Brain 2018; 141: 271–87.
    1. Maass A, Landau S, Baker SL, Horng A, Lockhart SN, La Joie R et al. . Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease. Neuroimage 2017; 157: 448–63.
    1. Maass A, Lockhart SN, Harrison TM, Bell RK, Mellinger T, Swinnerton K et al. . Entorhinal tau pathology, episodic memory decline, and neurodegeneration in aging. J Neurosci 2018; 38: 530–43.
    1. Markesbery WR, Schmitt FA, Kryscio RJ, Davis DG, Smith CD, Wekstein DR. Neuropathologic substrate of mild cognitive impairment. Arch Neurol 2006; 63: 38–46.
    1. Marquie M, Siao Tick Chong M, Anton-Fernandez A, Verwer EE, Saez-Calveras N, Meltzer AC et al. . [F-18]-AV-1451 binding correlates with postmortem neurofibrillary tangle Braak staging. Acta Neuropathol 2017; 134: 619–28.
    1. McKeith IG, Boeve BF, Dickson DW, Halliday G, Taylor JP, Weintraub D et al. . Diagnosis and management of dementia with Lewy bodies: Fourth consensus report of the DLB Consortium. Neurology 2017; 89: 88–100.
    1. 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: 939–44.
    1. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR Jr, Kawas CH et al. . The diagnosis of dementia due to Alzheimer’s disease: Recommendations from the National Institute on Aging and the Alzheimer’s Assocation Workgroup. Alzheimers Dement 2011; 7: 263–9.
    1. Mishra S, Gordon BA, Su Y, Christensen J, Friedrichsen K, Jackson K et al. . AV-1451 PET imaging of tau pathology in preclinical Alzheimer disease: defining a summary measure. Neuroimage 2017; 161: 171–8.
    1. Murray ME, Lowe VJ, Graff-Radford NR, Liesinger AM, Cannon A, Przybelski SA et al. . Clinicopathologic and 11C-Pittsburgh compound B implications of Thal amyloid phase across the Alzheimer’s disease spectrum. Brain 2015; 138 (Pt 5): 1370–81.
    1. Nelson PT, Alafuzoff I, Bigio EH, Bouras C, Braak H, Cairns NJ et al. . Correlation of Alzheimer disease neuropathologic changes with cognitive status: a review of the literature. J Neuropathol Exp Neurol 2012; 71: 362–81.
    1. Nelson PT, Dickson DW, Trojanowski JQ, Jack CR, Boyle PA, Arfanakis K et al. . Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report. Brain 2019; 142: 1503–27.
    1. Nelson PT, Head E, Schmitt FA, Davis PR, Neltner JH, Jicha GA et al. . Alzheimer’s disease is not “brain aging”: neuropathological, genetic, and epidemiological human studies. Acta Neuropathol 2011; 121: 571–87.
    1. Nordberg A, Carter SF, Rinne J, Drzezga A, Brooks DJ, Vandenberghe R et al. . A European multicentre PET study of fibrillar amyloid in Alzheimer’s disease. Eur J Nucl Med Mol Imaging 2013; 40: 104–14.
    1. Ossenkoppele R, Jansen WJ, Rabinovici GD, Knol DL, van der Flier WM, van Berckel BN, et al. . Prevalence of amyloid pet positivity in dementia syndromes: a meta-analysis. JAMA 2015; 313: 1939–49.
    1. Ossenkoppele R, Rabinovici GD, Smith R, Cho H, Schöll M, Strandberg O. et al.Discriminative accuracy of F18 flortaucipir positron emission tomography for Alzheimer disease vs other neurodegenerative disorders. JAMA 2018; 320: 1151–62.
    1. Ossenkoppele R, Schonhaut DR, Scholl M, Lockhart SN, Ayakta N, Baker SL et al. . Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 2016; 139 (Pt 5): 1551–67.
    1. Petersen RC. Mild cognitive impairment as a diagnostic entity. J Intern Med 2004; 256: 183–94.
    1. Petersen RC. How early can we diagnose Alzheimer disease (and is it sufficient)? The 2017 Wartenberg lecture. Neurology 2018; 91: 395–402.
    1. Pontecorvo MJ, Devous MD Sr, Navitsky M, Lu M, Salloway S, Schaerf FW et al. . Relationships between flortaucipir PET tau binding and amyloid burden, clinical diagnosis, age and cognition. Brain 2017a; 140: 748–63.
    1. Pontecorvo MJ, Siderowf A, Dubois B, Doraiswamy PM, Frisoni GB, Grundman M et al. . Effectiveness of florbetapir PET imaging in changing patient management. Dement Geriatr Cogn Disord 2017b; 44: 129–43.
    1. Rabinovici GD, Gatsonis C, Apgar C, Chaudhary K, Gareen I, Hanna L et al. . Association of amyloid positron emission tomography with subsequent change in clinical management among medicare beneficiaries with mild cognitive impairment or dementia. JAMA 2019; 321: 1286–94.
    1. Rabinovici GD, Jagust WJ, Furst AJ, Ogar JM, Racine CA, Mormino EC et al. . Abeta amyloid and glucose metabolism in three variants of primary progressive aphasia. Ann Neurol 2008; 64: 388–401.
    1. Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J et al. . Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 2011; 134 (Pt 9): 2456–77.
    1. Roberts RO, Geda YE, Knopman DS, Cha RH, Pankratz VS, Boeve BF et al. . The Mayo Clinic Study of Aging: design and sampling, participation, baseline measures and sample characteristics. Neuroepidemiology 2008; 30: 58–69.
    1. Schneider JA, Arvanitakis Z, Bang W, Bennett DA. Mixed brain pathologies account for most dementia cases in community-dwelling older persons. Neurology 2007; 69: 2197–204.
    1. Scholl M, Lockhart SN, Schonhaut DR, O’Neil JP, Janabi M, Ossenkoppele R et al. . PET imaging of tau deposition in the aging human brain. Neuron 2016; 89: 971–82.
    1. Schwarz CG, Gunter JL, Lowe VJ, Weigand S, Vemuri P, Senjem ML et al. . A comparison of partial volume correction techniques for measuring change in serial amyloid PET SUVR. J Alzheimers Dis 2019; 67: 181–95.
    1. Scrucca L, Fop M, Murphy TB, Raftery AE. mclust 5: clustering, classification and density estimation using gaussian finite mixture models. R J 2016; 8: 289–317.
    1. Sonnen JA, Larson EB, Crane PK, Haneuse S, Li G, Schellenberg GD et al. . Pathological correlates of dementia in a longitudinal, population-based sample of aging. Ann Neurol 2007; 62: 406–13.
    1. Sperling RA, Mormino EC, Schultz AP, Betensky RA, Papp KV, Amariglio RE et al. . The impact of amyloid-beta and tau on prospective cognitive decline in older individuals. Ann Neurol 2019; 85: 181–93.
    1. St Sauver JL, Grossardt BR, Leibson CL, Yawn BP, Melton LJ, Rocca WA. Generalizability of epidemiological findings and public health decisions: an illustration from the Rochester Epidemiology Project. Mayo Clin Proc 2012; 87: 151–60.
    1. Timmers T, Ossenkoppele R, Verfaillie SCJ, van der Weijden CWJ, Slot RER, Wesselman LMP et al. . Amyloid PET and cognitive decline in cognitively normal individuals: the SCIENCe project. Neurobiol Aging 2019; 79: 50–8.
    1. Villemagne VL, Burnham S, Bourgeat P, Brown B, Ellis KA, Salvado O et al. . Amyloid beta deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol 2013; 12: 357–67.
    1. Villemagne VL, Dore V, Bourgeat P, Cummins TL, Pejoska S, Mulligan RS et al. . The tau MeTeR scale for the generation of continuous and categorical measures of tau deposits in the brain: results from 18F-AV1451 and 18F-THK5351 tau imaging studies. Alzheimer’s Dement 2016; 12: 244.
    1. Walker L, McAleese KE, Thomas AJ, Johnson M, Martin-Ruiz C, Parker C et al. . Neuropathologically mixed Alzheimer’s and Lewy body disease: burden of pathological protein aggregates differs between clinical phenotypes. Acta Neuropathol 2015; 129: 729–48.
    1. Wisse LE, Butala N, Das SR, Davatzikos C, Dickerson BC, Vaishnavi SN et al. . Suspected non-AD pathology in mild cognitive impairment. Neurobiol Aging 2015; 36: 3152–62.
    1. Wolk DA, Price JC, Madeira C, Saxton JA, Snitz BE, Lopez OL et al. . Amyloid imaging in dementias with atypical presentation. Alzheimers Dement 2012; 8: 389–98.
    1. Xiong C, Jasielec MS, Weng H, Fagan AM, Benzinger TL, Head D et al. . Longitudinal relationships among biomarkers for Alzheimer disease in the Adult Children Study. Neurology 2016; 86: 1499–506.

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