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Alzheimer’s disease clinical variants show distinct regional patterns of neurofibrillary tangle accumulation

Acta Neuropathologica volume 138, pages 597–612 (2019)Cite this article

Abstract

The clinical spectrum of Alzheimer’s disease (AD) extends well beyond the classic amnestic–predominant syndrome. The previous studies have suggested differential neurofibrillary tangle (NFT) burden between amnestic and logopenic primary progressive aphasia presentations of AD. In this study, we explored the regional distribution of NFT pathology and its relationship to AD presentation across five different clinical syndromes. We assessed NFT density throughout six selected neocortical and hippocampal regions using thioflavin-S fluorescent microscopy in a well-characterized clinicopathological cohort of pure AD cases enriched for atypical clinical presentations. Subjects underwent apolipoprotein E genotyping and neuropsychological testing. Main cognitive domains (executive, visuospatial, language, and memory function) were assessed using an established composite z score. Our results showed that NFT regional burden aligns with the clinical presentation and region-specific cognitive scores. Cortical, but not hippocampal, NFT burden was higher among atypical clinical variants relative to the amnestic syndrome. In analyses of specific clinical variants, logopenic primary progressive aphasia showed higher NFT density in the superior temporal gyrus (p = 0.0091), and corticobasal syndrome showed higher NFT density in the primary motor cortex (p = 0.0205) relative to the amnestic syndrome. Higher NFT burden in the angular gyrus and CA1 sector of the hippocampus were independently associated with worsening visuospatial dysfunction. In addition, unbiased hierarchical clustering based on regional NFT densities identified three groups characterized by a low overall NFT burden, high overall burden, and cortical-predominant burden, respectively, which were found to differ in sex ratio, age, disease duration, and clinical presentation. In comparison, the typical, hippocampal sparing, and limbic-predominant subtypes derived from a previously proposed algorithm did not reproduce the same degree of clinical relevance in this sample. Overall, our results suggest domain-specific functional consequences of regional NFT accumulation. Mapping these consequences presents an opportunity to increase understanding of the neuropathological framework underlying atypical clinical manifestations.

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References

  1. Ahmed S, De Jager CA, Haigh AMF, Garrard P (2012) Logopenic aphasia in Alzheimer’s disease: clinical variant or clinical feature? J Neurol Neurosurg Psychiatry 83:1056–1062. https://doi.org/10.1136/jnnp-2012-302798

    Article  PubMed  Google Scholar 

  2. Andrade-Moraes CH, Oliveira-Pinto AV, Castro-Fonseca E, da Silva CG, Guimaraes DM, Szczupak D et al (2013) Cell number changes in Alzheimer’s disease relate to dementia, not to plaques and tangles. Brain 136:3738–3752. https://doi.org/10.1093/brain/awt273

    Article  PubMed  PubMed Central  Google Scholar 

  3. Armstrong MJ, Litvan I, Lang AE, Bak TH, Bhatia KP, Borroni B et al (2013) Criteria for the diagnosis of corticobasal degeneration. Neurology 80:496–503. https://doi.org/10.1212/WNL.0b013e31827f0fd1

    Article  PubMed  PubMed Central  Google Scholar 

  4. Berg L, McKeel DW, Miller JP, Storandt M, Rubin EH, Morris JC et al (1998) Clinicopathologic studies in cognitively healthy aging and Alzheimer disease: relation of histologic markers to dementia severity, age, sex, and apolipoprotein E genotype. Arch Neurol 55:326–335. https://doi.org/10.1001/archneur.55.3.326

    Article  CAS  PubMed  Google Scholar 

  5. Brock G, Pihur V, Datta S, Datta S (2008) clValid: an R package for cluster validation. J Stat Softw 25:1–22. https://doi.org/10.18637/jss.v025.i04

    Article  Google Scholar 

  6. Cairns NJ, Bigio EH, Mackenzie IRA, Neumann M, Lee VM-Y, Hatanpaa KJ et al (2007) Neuropathologic diagnostic and nosologic criteria for frontotemporal lobar degeneration: consensus of the Consortium for Frontotemporal Lobar Degeneration. Acta Neuropathol 114:5–22. https://doi.org/10.1007/s00401-007-0237-2

    Article  PubMed  PubMed Central  Google Scholar 

  7. Cattell RB (1966) The scree test for the number of factors. Multivariate Behav Res 1:245–276. https://doi.org/10.1207/s15327906mbr0102_10

    Article  CAS  PubMed  Google Scholar 

  8. Cooper DC, Klipec WD, Fowler MA, Ozkan ED (2006) A role for the subiculum in the brain motivation/reward circuitry. Behav Brain Res 174:225–231. https://doi.org/10.1016/J.BBR.2006.05.036

    Article  CAS  PubMed  Google Scholar 

  9. Coughlin DG, Xie SX, Liang M, Williams A, Peterson C, Weintraub D et al (2018) Cognitive and pathological influences of tau pathology in Lewy body disorders. Ann Neurol 85:259–271. https://doi.org/10.1002/ana.25392

    Article  CAS  Google Scholar 

  10. Crutch SJ, Lehmann M, Schott JM, Rabinovici GD, Rossor MN, Fox NC (2012) Posterior cortical atrophy. Lancet Neurol 11:170–178. https://doi.org/10.1016/S1474-4422(11)70289-7

    Article  PubMed  PubMed Central  Google Scholar 

  11. Crutch SJ, Schott JM, Rabinovici GD, Murray M, Snowden JS, van der Flier WM et al (2017) Consensus classification of posterior cortical atrophy. Alzheimer’s Dement 13:870–884. https://doi.org/10.1016/j.jalz.2017.01.014

    Article  Google Scholar 

  12. Datta S, Datta S (2006) Methods for evaluating clustering algorithms for gene expression data using a reference set of functional classes. BMC Bioinform 7:397. https://doi.org/10.1186/1471-2105-7-397

    Article  CAS  Google Scholar 

  13. Dickson DW, Bergeron C, Chin SS, Duyckaerts C, Horoupian D, Ikeda K et al (2002) Office of rare diseases neuropathologic criteria for corticobasal degeneration. J Neuropathol Exp Neurol 61:935–946. https://doi.org/10.1093/jnen/61.11.935

    Article  CAS  PubMed  Google Scholar 

  14. Dubois B, Feldman HH, Jacova C, Hampel H, Molinuevo JL, Blennow K et al (2014) Advancing research diagnostic criteria for Alzheimer’s disease: the IWG-2 criteria. Lancet Neurol 13:614–629. https://doi.org/10.1016/S1474-4422(14)70090-0

    Article  PubMed  Google Scholar 

  15. Dunn LM, Dunn DM (2007) Peabody picture vocabulary test, PPVT-4. Eff Br mindfulness interval acute pain. Exp An Exam Individ Differ 10:10. https://doi.org/10.1017/cbo9781107415324.004

    Article  Google Scholar 

  16. van der Flier WM, Pijnenburg YAL, Fox NC, Scheltens P (2011) Early-onset versus late-onset Alzheimer’s disease: the case of the missing APOE ε4 allele. Lancet Neurol 10:280–288. https://doi.org/10.1016/S1474-4422(10)70306-9

    Article  CAS  PubMed  Google Scholar 

  17. Galton CJ, Patterson K, Xuereb JH, Hodges JR (2000) Atypical and typical presentations of Alzheimer’s disease: a clinical, neuropsychological, neuroimaging and pathological study of 13 cases. Brain 123(Pt 3):484–498

    Article  PubMed  Google Scholar 

  18. Gefen T, Gasho K, Rademaker A, Lalehzari M, Weintraub S, Rogalski E et al (2012) Clinically concordant variations of Alzheimer pathology in aphasic versus amnestic dementia. Brain 135:1554–1565. https://doi.org/10.1093/brain/aws076

    Article  PubMed  PubMed Central  Google Scholar 

  19. Gentle JE, Kaufman L, Rousseuw PJ (1991) Finding groups in data: an introduction to cluster analysis. Biometrics 47:788. https://doi.org/10.2307/2532178

    Article  Google Scholar 

  20. Giannakopoulos P, Herrmann FR, Bussière T, Bouras C, Kövari E, Perl DP et al (2003) Tangle and neuron numbers, but not amyloid load, predict cognitive status in Alzheimer’s disease. Neurology 60:1495–1500. https://doi.org/10.1212/01.WNL.0000063311.58879.01

    Article  CAS  PubMed  Google Scholar 

  21. Gorno-Tempini ML, Brambati SM, Ginex V, Ogar J, Dronkers NF, Marcone A et al (2008) The logopenic/phonological variant of primary progressive aphasia. Neurology 71:1227–1234. https://doi.org/10.1212/01.wnl.0000320506.79811.da

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Gorno-Tempini ML, Hillis AE, Weintraub S, Kertesz A, Mendez M, Cappa SF et al (2011) Classification of primary progressive aphasia and its variants. Neurology 76:1006–1014. https://doi.org/10.1212/WNL.0b013e31821103e6

    Article  PubMed  PubMed Central  Google Scholar 

  23. Grijalvo-Perez AM, Litvan I (2014) Corticobasal degeneration. Semin Neurol 34:160–173. https://doi.org/10.1055/s-0034-1381734

    Article  PubMed  Google Scholar 

  24. Hauw JJ, Daniel SE, Dickson D, Horoupian DS, Jellinger K, Lantos PL et al (1994) Preliminary NINDS neuropathologic criteria for Steele–Richardson–Olszewski syndrome (progressive supranuclear palsy). Neurology 44:2015–2019

    Article  CAS  PubMed  Google Scholar 

  25. Herman JP, Mueller NK (2006) Role of the ventral subiculum in stress integration. Behav Brain Res 174:215–224. https://doi.org/10.1016/J.BBR.2006.05.035

    Article  CAS  PubMed  Google Scholar 

  26. Hickok G, Poeppel D (2004) Dorsal and ventral streams: a framework for understanding aspects of the functional anatomy of language. Cognition 92:67–99. https://doi.org/10.1016/j.cognition.2003.10.011

    Article  PubMed  Google Scholar 

  27. Insausti R, Muñoz M (2001) Cortical projections of the non-entorhinal hippocampal formation in the cynomolgus monkey (Macaca fascicularis). Eur J Neurosci 14:435–451. https://doi.org/10.1046/j.0953-816X.2001.01662.x

    Article  CAS  PubMed  Google Scholar 

  28. Janocko NJ, Brodersen KA, Soto-Ortolaza AI, Ross OA, Liesinger AM, Duara R et al (2012) Neuropathologically defined subtypes of Alzheimer’s disease differ significantly from neurofibrillary tangle-predominant dementia. Acta Neuropathol 124:681–692. https://doi.org/10.1007/s00401-012-1044-y

    Article  PubMed  PubMed Central  Google Scholar 

  29. Ji J, Maren S (2008) Differential roles for hippocampal areas CA1 and CA3 in the contextual encoding and retrieval of extinguished fear. Learn Mem 15:244–251. https://doi.org/10.1101/lm.794808

    Article  PubMed  PubMed Central  Google Scholar 

  30. Josephs KA, Dickson DW, Murray ME, Senjem ML, Parisi JE, Petersen RC et al (2013) Quantitative neurofibrillary tangle density and brain volumetric MRI analyses in Alzheimer’s disease presenting as logopenic progressive aphasia. Brain Lang 127:127–134. https://doi.org/10.1016/j.bandl.2013.02.003

    Article  PubMed  Google Scholar 

  31. Kaiser HF (1960) The application of electronic computers to factor analysis. Educ Psychol Meas 20:141–151. https://doi.org/10.1177/001316446002000116

    Article  Google Scholar 

  32. Kaplan E (1983) Boston naming test. Lea & Febiger, Philadelphia

    Google Scholar 

  33. Lam B, Masellis M, Freedman M, Stuss DT, Black SE (2013) Clinical, imaging, and pathological heterogeneity of the Alzheimer’s disease syndrome. Alzheimer’s Res Ther 5:1. https://doi.org/10.1186/alzrt155

    Article  Google Scholar 

  34. Lamy C, Duyckaerts C, Delaere P, Payan C, Fermanian J, Poulain V et al (1989) Comparison of seven staining methods for senile plaques and neurofibrillary tangles in a prospective series of 15 elderly patients. Neuropathol Appl Neurobiol 15:563–578. https://doi.org/10.1111/j.1365-2990.1989.tb01255.x

    Article  CAS  PubMed  Google Scholar 

  35. Lee SE, Rabinovici GD, Mayo MC, Wilson SM, Seeley WW, Dearmond SJ et al (2011) Clinicopathological correlations in corticobasal degeneration. Ann Neurol 70:327–340. https://doi.org/10.1002/ana.22424

    Article  PubMed  PubMed Central  Google Scholar 

  36. Leutgeb S, Leutgeb JK, Barnes CA, Moser EI, McNaughton BL, Moser MB (2005) Neuroscience: independent codes for spatial and episodic memory in hippocampal neuronal ensembles. Science (80-) 309:619–623. https://doi.org/10.1126/science.1114037

    Article  CAS  Google Scholar 

  37. Mackenzie IRA, Neumann M, Baborie A, Sampathu DM, Du Plessis D, Jaros E et al (2011) A harmonized classification system for FTLD-TDP pathology. Acta Neuropathol 122:111–113. https://doi.org/10.1007/s00401-011-0845-8

    Article  PubMed  PubMed Central  Google Scholar 

  38. Mackenzie IRA, Neumann M, Bigio EH, Cairns NJ, Alafuzoff I, Kril J et al (2010) Nomenclature and nosology for neuropathologic subtypes of frontotemporal lobar degeneration: an update. Acta Neuropathol 119:1–4. https://doi.org/10.1007/s00401-009-0612-2

    Article  PubMed  Google Scholar 

  39. Mattsson N, Schott JM, Hardy J, Turner MR, Zetterberg H (2016) Selective vulnerability in neurodegeneration: insights from clinical variants of Alzheimer’s disease. J Neurol Neurosurg Psychiatry 87:1000–1004. https://doi.org/10.1136/jnnp-2015-311321

    Article  PubMed  Google Scholar 

  40. McKeith IG, Dickson DW, Lowe J, Emre M, O’Brien JT, Feldman H et al (2005) Diagnosis and management of dementia with Lewy bodies: third report of the DLB consortium. Neurology 65:1863–1872. https://doi.org/10.1212/01.wnl.0000187889.17253.b1

    Article  CAS  PubMed  Google Scholar 

  41. McKhann G, Drachman D, Folstein M, Katzman R, Price D, Stadlan EM (1984) 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 34:939–944

    Article  CAS  PubMed  Google Scholar 

  42. McKhann GM, Knopman DS, Chertkow H, Hyman BT, Jack CR, Kawas CH et al (2011) The diagnosis of dementia due to Alzheimer’s disease: recommendations from the National Institute on Aging-Alzheimer’s Association workgroups on diagnostic guidelines for Alzheimer’s disease. Alzheimers Dement 7:263–269. https://doi.org/10.1016/j.jalz.2011.03.005

    Article  PubMed  PubMed Central  Google Scholar 

  43. Mesulam MM, Weintraub S, Rogalski EJ, Wieneke C, Geula C, Bigio EH (2014) Asymmetry and heterogeneity of Alzheimer’s and frontotemporal pathology in primary progressive aphasia. Brain 137:1176–1192. https://doi.org/10.1093/brain/awu024

    Article  PubMed  PubMed Central  Google Scholar 

  44. Montine TJ, Phelps CH, Beach TG, Bigio EH, Cairns NJ, Dickson DW et al (2012) National institute on aging-Alzheimer’s association guidelines for the neuropathologic assessment of Alzheimer’s disease: a practical approach. Acta Neuropathol 123:1–11. https://doi.org/10.1007/s00401-011-0910-3

    Article  CAS  PubMed  Google Scholar 

  45. Murray ME, Graff-Radford NR, Ross OA, Petersen RC, Duara R, Dickson DW (2011) Neuropathologically defined subtypes of Alzheimer’s disease with distinct clinical characteristics: a retrospective study. Lancet Neurol 10:785–796. https://doi.org/10.1016/S1474-4422(11)70156-9

    Article  PubMed  PubMed Central  Google Scholar 

  46. Neary D, Snowden JS, Gustafson L, Passant U, Stuss D, Black S et al (1998) Frontotemporal lobar degeneration: a consensus on clinical diagnostic criteria. Neurology 51:1546–1554. https://doi.org/10.1212/WNL.51.6.1546

    Article  CAS  PubMed  Google Scholar 

  47. O’Mara SM, Sanchez-Vives MV, Brotons-Mas JR, O’Hare E (2009) Roles for the subiculum in spatial information processing, memory, motivation and the temporal control of behaviour. Prog Neuro-Psychopharmacol Biol Psychiatry 33:782–790. https://doi.org/10.1016/j.pnpbp.2009.03.040

    Article  Google Scholar 

  48. Ohm TG, Scharnagl H, März W, Bohl J (1999) Apolipoprotein E isoforms and the development of low and high Braak stages of Alzheimer’s disease-related lesions. Acta Neuropathol 98:273–280. https://doi.org/10.1007/s004010051080

    Article  CAS  PubMed  Google Scholar 

  49. Ossenkoppele R, Cohn-Sheehy BI, La Joie R, Vogel JW, Möller C, Lehmann M et al (2015) Atrophy patterns in early clinical stages across distinct phenotypes of Alzheimer’s disease. Hum Brain Mapp 36:4421–4437. https://doi.org/10.1002/hbm.22927

    Article  PubMed  PubMed Central  Google Scholar 

  50. Ossenkoppele R, Mattsson N, Teunissen CE, Barkhof F, Pijnenburg Y, Scheltens P et al (2015) Cerebrospinal fluid biomarkers and cerebral atrophy in distinct clinical variants of probable Alzheimer’s disease. Neurobiol Aging 36:2340–2347. https://doi.org/10.1016/j.neurobiolaging.2015.04.011

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Ossenkoppele R, Pijnenburg YAL, Perry DC, Cohn-Sheehy BI, Scheltens NME, Vogel JW et al (2015) The behavioural/dysexecutive variant of Alzheimer’s disease: clinical, neuroimaging and pathological features. Brain 138:2732–2749. https://doi.org/10.1093/brain/awv191

    Article  PubMed  PubMed Central  Google Scholar 

  52. Ossenkoppele R, Schonhaut DR, Schöll M, Lockhart SN, Ayakta N, Baker SL et al (2016) Tau PET patterns mirror clinical and neuroanatomical variability in Alzheimer’s disease. Brain 139:1551–1567. https://doi.org/10.1093/brain/aww027

    Article  PubMed  PubMed Central  Google Scholar 

  53. Phillips JS, Das SR, McMillan CT, Irwin DJ, Roll EE, Da Re F et al (2018) Tau PET imaging predicts cognition in atypical variants of Alzheimer’s disease. Hum Brain Mapp 39:691–708. https://doi.org/10.1002/hbm.23874

    Article  PubMed  Google Scholar 

  54. Phillips JS, Da Re F, Dratch L, Xie SX, Irwin DJ, McMillan CT et al (2018) Neocortical origin and progression of gray matter atrophy in nonamnestic Alzheimer’s disease. Neurobiol Aging 63:75–87. https://doi.org/10.1016/j.neurobiolaging.2017.11.008

    Article  PubMed  Google Scholar 

  55. Rabinovici GD, Furst AJ, Alkalay A, Racine CA, O’Neil JP, Janabi M et al (2010) Increased metabolic vulnerability in early-onset Alzheimer’s disease is not related to amyloid burden. Brain 133:512–528. https://doi.org/10.1093/brain/awp326

    Article  PubMed  PubMed Central  Google Scholar 

  56. Rascovsky K, Hodges JR, Knopman D, Mendez MF, Kramer JH, Neuhaus J et al (2011) Sensitivity of revised diagnostic criteria for the behavioural variant of frontotemporal dementia. Brain 134:2456–2477. https://doi.org/10.1093/brain/awr179

    Article  PubMed  PubMed Central  Google Scholar 

  57. Rosenbloom MH, Alkalay A, Agarwal N, Baker SL, O’Neil JP, Janabi M et al (2011) Distinct clinical and metabolic deficits in PCA and AD are not related to amyloid distribution. Neurology 76:1789–1796. https://doi.org/10.1212/WNL.0b013e31821cccad

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Saur D, Kreher BW, Schnell S, Kummerer D, Kellmeyer P, Vry M-S et al (2008) Ventral and dorsal pathways for language. Proc Natl Acad Sci 105:18035–18040. https://doi.org/10.1073/pnas.0805234105

    Article  PubMed  PubMed Central  Google Scholar 

  59. Seghier ML (2013) The angular gyrus: multiple functions and multiple subdivisions. Neuroscientist 19:43–61

    Article  PubMed  PubMed Central  Google Scholar 

  60. Spinelli EG, Mandelli ML, Miller ZA, Santos-Santos MA, Wilson SM, Agosta F et al (2017) Typical and atypical pathology in primary progressive aphasia variants. Ann Neurol 81:430–443. https://doi.org/10.1002/ana.24885

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Staffaroni AM, Brown JA, Casaletto KB, Elahi FM, Deng J, Neuhaus J et al (2018) The longitudinal trajectory of default mode network connectivity in healthy older adults varies as a function of age and is associated with changes in episodic memory and processing speed. J Neurosci 38:3067–4017. https://doi.org/10.1523/JNEUROSCI.3067-17.2018

    Article  Google Scholar 

  62. Suemoto CK, Ferretti-Rebustini REL, Rodriguez RD, Leite REP, Soterio L, Brucki SMD et al (2017) Neuropathological diagnoses and clinical correlates in older adults in Brazil: a cross-sectional study. PLoS Med 14:e1002267. https://doi.org/10.1371/journal.pmed.1002267

    Article  PubMed  PubMed Central  Google Scholar 

  63. Suemoto CK, Leite REP, Ferretti-Rebustini REL, Rodriguez RD, Nitrini R, Pasqualucci CA, Jacob-Filho W et al (2019) Neuropathological lesions in the very old: results from a large Brazilian autopsy study. Brain Pathol. https://doi.org/10.1111/bpa.12719

    Article  PubMed  PubMed Central  Google Scholar 

  64. Terry RD, Hansen LA, Deteresa R, Da Vies P, Tobias H, Katzman R (1987) Senile dementia of the alzheimer type without neocortical neurofibrillary tangles. J Neuropathol Exp Neurol 46:262–268. https://doi.org/10.1097/00005072-198705000-00003

    Article  CAS  PubMed  Google Scholar 

  65. Warren JD, Fletcher PD, Golden HL (2012) The paradox of syndromic diversity in Alzheimer disease. Nat Rev Neurol 8:451–464. https://doi.org/10.1038/nrneurol.2012.135

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

The authors thank the patients and their families for their invaluable contribution to brain aging neurodegenerative disease research. ER is an Atlantic Fellow for Equity in Brain Health and thanks the fellowship for supporting her work. This study was supported by the National Institute of Health Grant K24AG053435 and institutional Grants P50AG023501, P01AG019724. MLGT was funded by the National Institute of Health Grants K24DC015544A and R01NS50915.

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Authors and Affiliations

  1. Memory and Aging Center, Weill Institute for Neurosciences, University of California, San Francisco, USA

    Cathrine Petersen, Amber L. Nolan, Elisa de Paula França Resende, Zachary Miller, Alexander J. Ehrenberg, Maria Luisa Gorno-Tempini, Howard J. Rosen, Joel H. Kramer, Salvatore Spina, Gil D. Rabinovici, Bruce L. Miller, William W. Seeley & Lea Tenenholz Grinberg

  2. Global Brain Health Institute Based at University of California, San Francisco and Trinity College, Dublin, Ireland

    Elisa de Paula França Resende & Lea Tenenholz Grinberg

  3. LIM-44, University of Sao Paulo Medical School, Sao Paulo, Brazil

    Helmut Heinsen

  4. Clinic of Psychiatry, University of Würzburg, Wurzburg, Germany

    Helmut Heinsen

  5. LIM-22, Department of Pathology, University of Sao Paulo Medical School, Sao Paulo, Brazil

    Lea Tenenholz Grinberg

  6. Departments of Neurology and Pathology, UCSF, Box 1207, 675 Nelson Rising Lane, San Francisco, CA, 94158, USA

    Lea Tenenholz Grinberg

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  1. Cathrine Petersen
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Petersen, C., Nolan, A.L., de Paula França Resende, E. et al. Alzheimer’s disease clinical variants show distinct regional patterns of neurofibrillary tangle accumulation. Acta Neuropathol 138, 597–612 (2019). https://doi.org/10.1007/s00401-019-02036-6

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Keywords

  • Alzheimer’s disease
  • Neurofibrillary tangles
  • Atypical Alzheimer’s disease
  • Tau pathology
  • Autopsy
  • Human