Cited by 612Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatmentsMedical Imaging Techniques and Applications

Prospective longitudinal atrophy in Alzheimer’s disease correlates with the intensity and topography of baseline tau-PET

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La Joie, Renaud, Visani, Adrienne V., Baker, Suzanne L., Brown, Jesse A., Bourakova, Viktoriya, Cha, Jungho, Chaudhary, Kiran, Edwards, Lauren +14 more·2020·Science Translational Medicine

Abstract

β-Amyloid plaques and tau-containing neurofibrillary tangles are the two neuropathological hallmarks of Alzheimer's disease (AD) and are thought to play crucial roles in a neurodegenerative cascade leading to dementia. Both lesions can now be visualized in vivo using positron emission tomography (PET) radiotracers, opening new opportunities to study disease mechanisms and improve patients' diagnostic and prognostic evaluation. In a group of 32 patients at early symptomatic AD stages, we tested whether β-amyloid and tau-PET could predict subsequent brain atrophy measured using longitudinal magnetic resonance imaging acquired at the time of PET and 15 months later. Quantitative analyses showed that the global intensity of tau-PET, but not β-amyloid-PET, signal predicted the rate of subsequent atrophy, independent of baseline cortical thickness. Additional investigations demonstrated that the specific distribution of tau-PET signal was a strong indicator of the topography of future atrophy at the single patient level and that the relationship between baseline tau-PET and subsequent atrophy was particularly strong in younger patients. These data support disease models in which tau pathology is a major driver of local neurodegeneration and highlight the relevance of tau-PET as a precision medicine tool to help predict individual patient's progression and design future clinical trials.

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  • The distinguishing feature between Braak NFT stages V and VI is not just the presence of NFTs in V1, motor and sensory cortical areas, but more severe neurofibrillary degeneration and atrophy throughout the cortical mantle.

    “the distinguishing feature between Braak NFT stages V and VI is not just the presence of NFTs in the primary visual (V1), motor and sensory cortical areas, but more severe neurofibrillary degeneration21 and atrophy throughout the cortical mantle51”

    From Astrocyte transcriptomic changes along the spatiotemporal progression of Alzheimer’s disease (2024) · cites this paper as [51]

    The Results report cortical atrophy in patients with early-stage Alzheimer’s disease, and the Discussion reports an association between global cortical tau-PET signal and global cortical atrophy (pages unavailable). The paper does not compare Braak NFT stages V and VI or establish that more severe atrophy throughout the cortical mantle distinguishes them. Citation 51 is attached to the atrophy portion of the sentence, not the separate claim about neurofibrillary degeneration. (LLM, conf=0.79)

    Experimental — accuracy not yet measured
  • In 32 early AD participants, tau PET but not amyloid PET predicted the rate of subsequent brain atrophy.

    “Another study in 32 early AD participants showed that tau PET but not amyloid PET predicted the rate of subsequent brain atrophy showing that tau pathology is likely a major driver of neurodegeneration and has a role in precision medicine and future trials [197].”
  • Tau pathology is likely a major driver of neurodegeneration.

    “Another study in 32 early AD participants showed that tau PET but not amyloid PET predicted the rate of subsequent brain atrophy showing that tau pathology is likely a major driver of neurodegeneration and has a role in precision medicine and future trials [197].”
  • Jack et al. studied 59 cognitively unimpaired amyloid-negative, 37 cognitively unimpaired amyloid-positive and 30 cognitively impaired amyloid-positive participants over 12-15 months; tau PET signal did not increase in cognitively unimpaired amyloid-negative participants but did in the other groups.

    “Jack et al. 130 studied 59 cognitively unimpaired, amyloid-negative participants; 37 cognitively unimpaired amyloid-positive participants and 30 cognitively impaired amyloid-positive participants in a 12-15 month study.This study showed that in cognitively unimpaired amyloid-negative participants, tau PET signal did not increase, while it did in cognitively unimpaired amyloid-positive and cognitIV”

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Research works printed but not linked: 60

  1. [2]Duyckaerts C, Delatour B, Potier M-C, Classification and basic pathology of Alzheimer disease, Acta Neuropathol. (Berl.) 118, 5– 36 (2009). 19381658
  2. [3]Klunk WE, Engler H, Nordberg A, Wang Y, Blomqvist G, Holt DP, Bergström M, Savitcheva I, Huang G, Estrada S, Ausén B, Debnath ML, Barletta J, Price JC, Sandell J, Lopresti BJ, Wall A, Koivisto P, Antoni G, Mathis CA, Långström B, Imaging brain amyloid in Alzheimer’s disease with Pittsburgh Compound-B, Ann. Neurol 55, 306– 319 (2004). 14991808
  3. [5]Cho H, Choi JY, Hwang MS, Lee JH, Kim YJ, Lee HM, Lyoo CH, Ryu YH, Lee MS, Tau PET in Alzheimer disease and mild cognitive impairment, Neurology 87, 375– 383 (2016). 27358341
  4. [7]Nasrallah IM, Chen YJ, Hsieh M-K, Phillips JS, Ternes K, Stockbower GE, Sheline Y, McMillan CT, Grossman M, Wolk DA, 18F-Flortaucipir PET/MRI Correlations in Nonamnestic and Amnestic Variants of Alzheimer Disease, J. Nucl. Med. Off. Publ. Soc. Nucl. Med 59, 299– 306 (2018). PMC6348438 28747523
  5. [8]Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL, O’Neil JP, Janabi M, Lazaris A, Cantwell A, Vogel J, Santos M, Miller ZA, Bettcher BM, Vossel KA, Kramer JH, Gorno-Tempini ML, Miller BL, Jagust WJ, Rabinovici GD, Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease, Brain J. Neurol 139, 1551– 1567 (2016). PMC5006248 26962052
  6. [9]Dronse J, Fliessbach K, Bischof GN, von Reutern B, Faber J, Hammes J, Kuhnert G, Neumaier B, Onur OA, Kukolja J, van Eimeren T, Jessen F, Fink GR, Klockgether T, Drzezga A, In vivo Patterns of Tau Pathology, Amyloid-β Burden, and Neuronal Dysfunction in Clinical Variants of Alzheimer’s Disease, J. Alzheimers Dis. JAD 55, 465– 471 (2017). 27802224
  7. [10]Bejanin A, Schonhaut DR, La Joie R, Kramer JH, Baker SL, Sosa N, Ayakta N, Cantwell A, Janabi M, Lauriola M, O’Neil JP, Gorno-Tempini ML, Miller ZA, Rosen HJ, Miller BL, Jagust WJ, Rabinovici GD, Tau pathology and neurodegeneration contribute to cognitive impairment in Alzheimer’s disease, Brain J. Neurol 140, 3286– 3300 (2017). PMC5841139 29053874
  8. [11]Pontecorvo MJ, Devous MD, Navitsky M, Lu M, Salloway S, Schaerf FW, Jennings D, Arora AK, McGeehan A, Lim NC, Xiong H, Joshi AD, Siderowf A, Mintun MA, 18F-AV-1451-A05 investigators, Relationships between flortaucipir PET tau binding and amyloid burden, clinical diagnosis, age and cognition, Brain J. Neurol 140, 748– 763 (2017). PMC5382945 28077397
  9. [12]Fiford CM, Ridgway GR, Cash DM, Modat M, Nicholas J, Manning EN, Malone IB, Biessels GJ, Ourselin S, Carmichael OT, Cardoso MJ, Barnes J, Alzheimer’s Disease Neuroimaging Initiative, Patterns of progressive atrophy vary with age in Alzheimer’s disease patients, Neurobiol. Aging 63, 22– 32 (2018). 29220823 PMC5805840
  10. [13]Cho H, Jeon S, Kang SJ, Lee J-M, Lee J-H, Kim GH, Shin JS, Kim CH, Noh Y, Im K, Kim ST, Chin J, Seo SW, Na DL, Longitudinal changes of cortical thickness in early- versus late-onset Alzheimer’s disease, Neurobiol. Aging 34, 1921.e9– 1921.e15 (2013). 23391426
  11. [14]Holland D, Desikan RS, Dale AM, McEvoy LK, Alzheimer’s Disease Neuroimaging Initiative, Rates of decline in Alzheimer disease decrease with age, PloS One 7, e42325 (2012). 22876315 PMC3410919
  12. [15]Wisse LEM, Das SR, Davatzikos C, Dickerson BC, Xie SX, Yushkevich PA, Wolk DA, Defining SNAP by cross-sectional and longitudinal definitions of neurodegeneration, NeuroImage Clin 18, 407– 412 (2018). 29487798 PMC5816023
  13. [16]LaPoint MR, Chhatwal JP, Sepulcre J, Johnson KA, Sperling RA, Schultz AP, The association between tau PET and retrospective cortical thinning in clinically normal elderly, NeuroImage 157, 612– 622 (2017). 28545932 PMC5772972
  14. [17]Das SR, Xie L, Wisse LEM, Ittyerah R, Tustison NJ, Dickerson BC, Yushkevich PA, Wolk DA, Alzheimer’s Disease Neuroimaging Initiative, Longitudinal and cross-sectional structural magnetic resonance imaging correlates of AV-1451 uptake, Neurobiol. Aging 66, 49– 58 (2018). 29518752 PMC5924615
  15. [18]Leung KK, Bartlett JW, Barnes J, Manning EN, Ourselin S, Fox NC, Cerebral atrophy in mild cognitive impairment and Alzheimer disease, Neurology 80, 648– 654 (2013). 23303849 PMC3590059
  16. [19]Sabuncu MR, Desikan RS, Sepulcre J, Yeo BTT, Liu H, Schmansky NJ, Reuter M, Weiner MW, Buckner RL, Sperling RA, Fischl B, for the A. D. N. Initiative, The Dynamics of Cortical and Hippocampal Atrophy in Alzheimer Disease, Arch. Neurol 68, 1040– 1048 (2011). 21825241 PMC3248949
  17. [20]Cedarbaum JM, Jaros M, Hernandez C, Coley N, Andrieu S, Grundman M, Vellas B, Alzheimer’s Disease Neuroimaging Initiative, Rationale for use of the Clinical Dementia Rating Sum of Boxes as a primary outcome measure for Alzheimer’s disease clinical trials, Alzheimers Dement. J. Alzheimers Assoc 9, S45– 55 (2013). 22658286
  18. [21]Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF, Ogar JM, Rohrer JD, Black S, Boeve BF, Manes F, Dronkers NF, Vandenberghe R, Rascovsky K, Patterson K, Miller BL, Knopman DS, Hodges JR, Mesulam MM, Grossman M, Classification of primary progressive aphasia and its variants, Neurology 76, 1006– 1014 (2011). 21325651 PMC3059138
  19. [22]Crutch SJ, Schott JM, Rabinovici GD, Murray M, Snowden JS, van der Flier WM, Dickerson BC, Vandenberghe R, Ahmed S, Bak TH, Boeve BF, Butler C, Cappa SF, Ceccaldi M, de Souza LC, Dubois B, Felician O, Galasko D, Graff-Radford J, Graff-Radford NR, Hof PR, Krolak-Salmon P, Lehmann M, Magnin E, Mendez MF, Nestor PJ, Onyike CU, Pelak VS, Pijnenburg Y, Primativo S, Rossor MN, Ryan NS, Scheltens P, Shakespeare TJ, Suárez González A, Tang-Wai DF, Yong KXX, Carrillo M, Fox NC, Alzheimer’s Association ISTAART Atypical Alzheimer’s Disease and Associated Syndromes Professional Interest Area, Consensus classification of posterior cortical atrophy, Alzheimers Dement. J. Alzheimers Assoc 13, 870– 884 (2017). PMC5788455 28259709
  20. [23]La Joie R, Bejanin A, Fagan AM, Ayakta N, Baker SL, Bourakova V, Boxer AL, Cha J, Karydas A, Jerome G, Maass A, Mensing A, Miller ZA, O’Neil JP, Pham J, Rosen HJ, Tsai R, Visani AV, Miller BL, Jagust WJ, Rabinovici GD, Associations between [18F]AV1451 tau PET and CSF measures of tau pathology in a clinical sample, Neurology 90, e282– e290 (2018). 29282337 PMC5798657
  21. [24]Ashburner J, Ridgway GR, Symmetric Diffeomorphic Modeling of Longitudinal Structural MRI, Front. Neurosci 6 (2013), doi: 10.3389/fnins.2012.00197. PMC3564017 23386806 · DOI
  22. [25]Potvin O, Dieumegarde L, Duchesne S, Alzheimer’s Disease Neuroimaging Initiative, Normative morphometric data for cerebral cortical areas over the lifetime of the adult human brain, NeuroImage 156, 315– 339 (2017). 28512057
  23. [26]Bejanin A, La Joie R, Landeau B, Belliard S, de La Sayette V, Eustache F, Desgranges B, Chételat G, Distinct Interplay Between Atrophy and Hypometabolism in Alzheimer’s Versus Semantic Dementia, Cereb. Cortex N. Y. N 1991 (2018), doi: 10.1093/cercor/bhy069. 29668866 · DOI
  24. [27]Harrison TM, La Joie R, Maass A, Baker SL, Swinnerton K, Fenton L, Mellinger TJ, Edwards L, Pham J, Miller BL, Rabinovici GD, Jagust WJ, Longitudinal tau accumulation and atrophy in aging and alzheimer disease, Ann. Neurol 85, 229– 240 (2019). 30597624 PMC6579738
  25. [28]Wahlund L-O, Blennow K, Cerebrospinal fluid biomarkers for disease stage and intensity in cognitively impaired patients, Neurosci. Lett 339, 99– 102 (2003). 12614904
  26. [29]Henneman WJP, Vrenken H, Barnes J, Sluimer IC, Verwey NA, Blankenstein MA, Klein M, Fox NC, Scheltens P, Barkhof F, van der Flier WM, Baseline CSF p-tau levels independently predict progression of hippocampal atrophy in Alzheimer disease, Neurology 73, 935– 940 (2009). 19770469 PMC2839552
  27. [30]Tarawneh R, Head D, Allison S, Buckles V, Fagan AM, Ladenson JH, Morris JC, Holtzman DM, Cerebrospinal Fluid Markers of Neurodegeneration and Rates of Brain Atrophy in Early Alzheimer Disease, JAMA Neurol 72, 656– 665 (2015). 25867677 PMC4551490
  28. [31]Fjell AM, Walhovd KB, Fennema-Notestine C, McEvoy LK, Hagler DJ, Holland D, Brewer JB, Dale AM, CSF Biomarkers in Prediction of Cerebral and Clinical Change in Mild Cognitive Impairment and Alzheimer’s Disease, J. Neurosci 30, 2088– 2101 (2010). 20147537 PMC2828879
  29. [32]Schuff N, Woerner N, Boreta L, Kornfield T, Shaw LM, Trojanowski JQ, Thompson PM, Jack CR, Weiner MW, Alzheimer’s Disease Neuroimaging Initiative, MRI of hippocampal volume loss in early Alzheimer’s disease in relation to ApoE genotype and biomarkers, Brain J. Neurol 132, 1067– 1077 (2009). PMC2668943 19251758
  30. [33]Sluimer JD, Bouwman FH, Vrenken H, Blankenstein MA, Barkhof F, van der Flier WM, Scheltens P, Whole-brain atrophy rate and CSF biomarker levels in MCI and AD: a longitudinal study, Neurobiol. Aging 31, 758– 764 (2010). 18692273
  31. [34]Mattsson N, Schöll M, Strandberg O, Smith R, Palmqvist S, Insel PS, Hägerström D, Ohlsson T, Zetterberg H, Jögi J, Blennow K, Hansson O, 18F-AV-1451 and CSF T-tau and P-tau as biomarkers in Alzheimer’s disease, EMBO Mol. Med 9, 1212– 1223 (2017). 28743782 PMC5582410
  32. [35]Chhatwal JP, Schultz AP, Marshall GA, Boot B, Gomez-Isla T, Dumurgier J, LaPoint M, Scherzer C, Roe AD, Hyman BT, Sperling RA, Johnson KA, Temporal T807 binding correlates with CSF tau and phospho-tau in normal elderly, Neurology 87, 920– 926 (2016). 27473132 PMC5035159
  33. [36]Schott JM, Bartlett JW, Barnes J, Leung KK, Ourselin S, Fox NC, Reduced sample sizes for atrophy outcomes in Alzheimer’s disease trials: baseline adjustment, Neurobiol. Aging 31, 1452– 1462. e2 (2010). 20620665 PMC2947486
  34. [37]Cash DM, Rohrer JD, Ryan NS, Ourselin S, Fox NC, Imaging endpoints for clinical trials in Alzheimer’s disease, Alzheimers Res. Ther 6, 87 (2014). 25621018 PMC4304258
  35. [38]Whitwell JL, Dickson DW, Murray ME, Weigand SD, Tosakulwong N, Senjem ML, Knopman DS, Boeve BF, Parisi JE, Petersen RC, Jack CR, Josephs KA, Neuroimaging correlates of pathologically defined subtypes of Alzheimer’s disease: a case-control study, Lancet Neurol 11, 868– 877 (2012). 22951070 PMC3490201
  36. [39]Zhang X, Mormino EC, Sun N, Sperling RA, Sabuncu MR, Yeo BTT, Alzheimer’s Disease Neuroimaging Initiative, Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease, Proc. Natl. Acad. Sci. U. S. A 113, E6535– E6544 (2016). 27702899 PMC5081632
  37. [41]Gutman BA, Wang Y, Yanovsky I, Hua X, Toga AW, Jack CR, Weiner MW, Thompson PM, Empowering Imaging Biomarkers of Alzheimer’s Disease, Neurobiol. Aging 36 Suppl 1, S69– S80 (2015). 25260848 PMC4268333
  38. [42]Edland SD, Ard MC, Sridhar J, Cobia D, Martersteck A, Mesulam MM, Rogalski EJ, Proof of concept demonstration of optimal composite MRI endpoints for clinical trials, Alzheimers Dement. N. Y. N 2, 177– 181 (2016). PMC5363955 28345017
  39. [43]La Joie R, Perrotin A, Barré L, Hommet C, Mézenge F, Ibazizene M, Camus V, Abbas A, Landeau B, Guilloteau D, de La Sayette V, Eustache F, Desgranges B, Chételat G, Region-specific hierarchy between atrophy, hypometabolism, and β-amyloid (Aβ) load in Alzheimer’s disease dementia, J. Neurosci. Off. J. Soc. Neurosci 32, 16265– 16273 (2012). PMC6794030 23152610
  40. [45]Falke E, Nissanov J, Mitchell TW, Bennett DA, Trojanowski JQ, Arnold SE, Subicular dendritic arborization in Alzheimer’s disease correlates with neurofibrillary tangle density, Am. J. Pathol 163, 1615– 1621 (2003). 14507668 PMC1868320
  41. [48]Bourgeat P, Chételat G, Villemagne VL, Fripp J, Raniga P, Pike K, Acosta O, Szoeke C, Ourselin S, Ames D, Ellis KA, Martins RN, Masters CL, Rowe CC, Salvado O, Beta-amyloid burden in the temporal neocortex is related to hippocampal atrophy in elderly subjects without dementia, Neurology 74, 121– 127 (2010). 20065247
  42. [49]Klupp E, Grimmer T, Tahmasian M, Sorg C, Yakushev I, Yousefi BH, Drzezga A, Förster S, Prefrontal hypometabolism in Alzheimer disease is related to longitudinal amyloid accumulation in remote brain regions, J. Nucl. Med. Off. Publ. Soc. Nucl. Med 56, 399– 404 (2015). 25678488
  43. [50]Josephs KA, Dickson DW, Tosakulwong N, Weigand SD, Murray ME, Petrucelli L, Liesinger AM, Senjem ML, Spychalla AJ, Knopman DS, Parisi JE, Petersen RC, Jack CR, Whitwell JL, Rates of hippocampal atrophy and presence of post-mortem TDP-43 in patients with Alzheimer’s disease: a longitudinal retrospective study, Lancet Neurol 16, 917– 924 (2017). 28919059 PMC5646369
  44. [51]Whitwell JL, Graff-Radford J, Tosakulwong N, Weigand SD, Machulda MM, Senjem ML, Spychalla AJ, Vemuri P, Jones DT, Drubach DA, Knopman DS, Boeve BF, Ertekin-Taner N, Petersen RC, Lowe VJ, Jack CR, Josephs KA, Imaging correlations of tau, amyloid, metabolism, and atrophy in typical and atypical Alzheimer’s disease, Alzheimers Dement. J. Alzheimers Assoc 14, 1005– 1014 (2018). PMC6097955 29605222
  45. [52]Gerritsen AAJ, Bakker C, Verhey FRJ, de Vugt ME, Melis RJF, Koopmans RTCM, 4C study team, Prevalence of Comorbidity in Patients With Young-Onset Alzheimer Disease Compared With Late-Onset: A Comparative Cohort Study, J. Am. Med. Dir. Assoc 17, 318– 323 (2016). 26778489
  46. [53]Rabinovici GD, Carrillo MC, Forman M, DeSanti S, Miller DS, Kozauer N, Petersen RC, Randolph C, Knopman DS, Smith EE, Isaac M, Mattsson N, Bain LJ, Hendrix JA, Sims JR, Multiple comorbid neuropathologies in the setting of Alzheimer’s disease neuropathology and implications for drug development, Alzheimers Dement. Transl. Res. Clin. Interv 3, 83– 91 (2017). PMC5651346 29067320
  47. [55]Marquié M, Verwer EE, Meltzer AC, Kim SJW, Agüero C, Gonzalez J, Makaretz SJ, Siao Tick Chong M, Ramanan P, Amaral AC, Normandin MD, Vanderburg CR, Gomperts SN, Johnson KA, Frosch MP, Gómez-Isla T, Lessons learned about [F-18]-AV-1451 off-target binding from an autopsy-confirmed Parkinson’s case, Acta Neuropathol. Commun 5, 75 (2017). 29047416 PMC5648451
  48. [56]Sander K, Lashley T, Gami P, Gendron T, Lythgoe MF, Rohrer JD, Schott JM, Revesz T, Fox NC, Årstad E, Characterization of tau positron emission tomography tracer [18F]AV-1451 binding to postmortem tissue in Alzheimer’s disease, primary tauopathies, and other dementias, Alzheimers Dement. J. Alzheimers Assoc 12, 1116– 1124 (2016). 26892233
  49. [57]Choi JY, Cho H, Ahn SJ, Lee JH, Ryu YH, Lee MS, Lyoo CH, Off-Target 18F-AV-1451 Binding in the Basal Ganglia Correlates with Age-Related Iron Accumulation, J. Nucl. Med. Off. Publ. Soc. Nucl. Med 59, 117– 120 (2018). 28775201
  50. [58]Baker SL, Maass A, Jagust WJ, Considerations and code for partial volume correcting [18F]-AV-1451 tau PET data, Data Brief 15, 648– 657 (2017). 29124088 PMC5671473
  51. [59]Lockhart SN, Ayakta N, Winer JR, La Joie R, Rabinovici GD, Jagust WJ, Elevated (18)F-AV-1451 PET tracer uptake detected in incidental imaging findings, Neurology 88, 1095– 1097 (2017). 28188303 PMC5384840
  52. [60]Tsai RM, Bejanin A, Lesman-Segev O, LaJoie R, Visani A, Bourakova V, O’Neil JP, Janabi M, Baker S, Lee SE, Perry DC, Bajorek L, Karydas A, Spina S, Grinberg LT, Seeley WW, Ramos EM, Coppola G, Gorno-Tempini ML, Miller BL, Rosen HJ, Jagust W, Boxer AL, Rabinovici GD, 18F-flortaucipir (AV-1451) tau PET in frontotemporal dementia syndromes, Alzheimers Res. Ther 11, 13 (2019). 30704514 PMC6357510
  53. [61]Bakota L, Brandt R, Tau Biology and Tau-Directed Therapies for Alzheimer’s Disease, Drugs 76, 301– 313 (2016). 26729186 PMC4757605
  54. [64]Rabinovici GD, Rosen HJ, Alkalay A, Kornak J, Furst AJ, Agarwal N, Mormino EC, O’Neil JP, Janabi M, Karydas A, Growdon ME, Jang JY, Huang EJ, Dearmond SJ, Trojanowski JQ, Grinberg LT, Gorno-Tempini ML, Seeley WW, Miller BL, Jagust WJ, Amyloid vs FDG-PET in the differential diagnosis of AD and FTLD, Neurology 77, 2034– 2042 (2011). 22131541 PMC3236517
  55. [65]Diedrichsen J, Balsters JH, Flavell J, Cussans E, Ramnani N, A probabilistic MR atlas of the human cerebellum, NeuroImage 46, 39– 46 (2009). 19457380
  56. [66]Maass A, Landau S, Baker SL, Horng A, Lockhart SN, La Joie R, Rabinovici GD, Jagust WJ, Alzheimer’s Disease Neuroimaging Initiative, Comparison of multiple tau-PET measures as biomarkers in aging and Alzheimer’s disease, NeuroImage 157, 448– 463 (2017). 28587897 PMC5814575
  57. [67]Hammers A, Allom R, Koepp MJ, Free SL, Myers R, Lemieux L, Mitchell TN, Brooks DJ, Duncan JS, Three-dimensional maximum probability atlas of the human brain, with particular reference to the temporal lobe, Hum. Brain Mapp 19, 224– 247 (2003). 12874777 PMC6871794
  58. [68]Girard H, Potvin O, Nugent S, Dallaire-Théroux C, Cunnane S, Duchesne S, Alzheimer’s Disease Neuroimaging Initiative, Faster progression from MCI to probable AD for carriers of a single-nucleotide polymorphism associated with type 2 diabetes, Neurobiol. Aging 64, 157.e11– 157.e17 (2018). 29338921
  59. [69]Pernet CR, Wilcox R, Rousselet GA, Robust correlation analyses: false positive and power validation using a new open source matlab toolbox, Front. Psychol 3, 606 (2012). 23335907 PMC3541537
  60. [70]Xia M, Wang J, He Y, BrainNet Viewer: A Network Visualization Tool for Human Brain Connectomics, PLOS ONE 8, e68910 (2013). 23861951 PMC3701683

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Authors
La Joie, R., Visani, A. V., Baker, S. L., Brown, J. A., Bourakova, V., Cha, J., Chaudhary, K., Edwards, L., Iaccarino, L., Janabi, M., Lesman-Segev, O. H., Miller, Z. A., Perry, D. C., O’Neil, J. P., Pham, J., Rojas, J. C., Rosen, H. J., Seeley, W. W., Tsai, R. M., Miller, B. L., Jagust, W. J., & Rabinovici, G. D.
Affiliations
  • Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA.
  • University of California, San Francisco (US)
  • Lawrence Berkeley National Laboratory (US)
Venue
Science Translational Medicine
Published
2020-01
Volume
12
Issue
524
ISSN
  • 1946-6234 (ISSN-L)
  • 1946-6242
OpenAlex
W2998707549
Cited by
612
References
10
Citations by year
2026: 73 · 2025: 88 · 2024: 83 · 2023: 82 · 2022: 105 · 2021: 109 · 2020: 69
FWCI
66.56
Type
journal article
Language
en
Open access
Locations
  • Science Translational Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
  • eScholarship (California Digital Library) (submittedVersion)
Funders
  • National Institute on Aging (R01-AG045611, R01-AG034570, P50-AG023501)
  • Alzheimer’s Association (AARF-16-443577)
  • Tau Consortium
Topics
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
  • Health Sciences › Medicine › Physiology › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Medical Imaging Techniques and Applications
Keywords
Alzheimer's disease, tau PET, brain atrophy, amyloid PET, neurofibrillary tangles, tau pathology, cortical thickness, disease progression, precision medicine
MeSH terms
Aged, Aged, 80 and over, Alzheimer Disease, Atrophy, Carbolines, Cerebral Cortex, Female, Follow-Up Studies, Humans, Longitudinal Studies, Male, Middle Aged, tau Proteins, Positron-Emission Tomography

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