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Four distinct trajectories of tau deposition identified in Alzheimer’s disease

Nature Medicine volume 27, pages 871–881 (2021)Cite this article

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Abstract

Alzheimer’s disease (AD) is characterized by the spread of tau pathology throughout the cerebral cortex. This spreading pattern was thought to be fairly consistent across individuals, although recent work has demonstrated substantial variability in the population with AD. Using tau-positron emission tomography scans from 1,612 individuals, we identified 4 distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%. We replicated previously described limbic-predominant and medial temporal lobe-sparing patterns, while also discovering posterior and lateral temporal patterns resembling atypical clinical variants of AD. These ‘subtypes’ were stable during longitudinal follow-up and were replicated in a separate sample using a different radiotracer. The subtypes presented with distinct demographic and cognitive profiles and differing longitudinal outcomes. Additionally, network diffusion models implied that pathology originates and spreads through distinct corticolimbic networks in the different subtypes. Together, our results suggest that variation in tau pathology is common and systematic, perhaps warranting a re-examination of the notion of ‘typical AD’ and a revisiting of tau pathological staging.

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Fig. 1: Spatiotemporal subtypes of tau progression.
Fig. 2: Subtype stability: AD spatiotemporal subtypes were replicated in another cohort using a different PET tracer.
Fig. 3: Progression of AD subtypes.
Fig. 4: Application of epidemic spreading model to determine subtype-specific corticolimbic circuit vulnerability.
Fig. 5: A theoretical model summarizing variation in the spread of tau pathology in AD. Tau pathology varies along an axis of severity (vertical in the diagram), which is inversely associated with age at onset.

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Data availability

The tau PET data contributing to this study were sourced from six different cohorts. One of them, ADNI, is a public access dataset and can be obtained by application from http://adni.loni.usc.edu/. Data from the other datasets are not publicly available for download, but access requests can be made to the respective study investigators: BioFINDER 1,2—O.H.; UCSF Memory and Aging Center—G.D.R.; Gangnam Severance Hospital—C.H.L.; AVID Radiopharmaceuticals—M.J.P. and M.D.D. Additionally, the CMU-60 diffusion tensor imaging (DTI) data used to create the template DTI connectomes are publicly available and can be accessed at https://www.cmu.edu/dietrich/psychology/cognitiveaxon/data.html.

Code availability

Python and MATLAB implementations of the SuStaIn algorithm are available on the UCL-POND GitHub page: https://github.com/ucl-pond. The ESM algorithm is available for academics as part of open-access, user-friendly software (for further details, visit https://www.neuropm-lab.com/).

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Acknowledgements

The authors thank M. Chakravarty, B. Misic, P. Bellec, P. Rosa-Neto, A. Dagher, E. Hamel and W. Seeley for feedback during the composition of this manuscript. J.W.V. acknowledges support from the government of Canada through a tri-council Vanier Canada Graduate Doctoral fellowship from the McGill Centre for Integrative Neuroscience and the Healthy Brains, Healthy Lives initiative, and from the National Institutes of Health (NIH) (no. T32MH019112). A.L.Y. is supported by a Medical Research Council Skills Development Fellowship (MR/T027800/1). N.P.O. is a UK Research and Innovation Future Leaders Fellow (no. MR/S03546X/1). N.P.O. and D.C.A. acknowledge support from the UK National Institute for Health Research University College London Hospitals Biomedical Research Centre, and D.C.A. acknowledges support from the Engineering and Physical Sciences Research Council grant no. EP/M020533/1. M.J.G. is supported by the Miguel Servet program (no. CP19/00031) and a research grant (no. PI20/00613) of the Instituto de Salud Carlos III-Fondo Europeo de Desarrollo Regional. R.L.J. acknowledges support from the NIH (no. K99AG065501). This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant no. 666992. The BioFINDER studies are supported by the Swedish Research Council (no. 2016-00906), the Knut and Alice Wallenberg Foundation (no. 2017-0383), the Marianne and Marcus Wallenberg Foundation (no. 2015.0125), the Strategic Research Area MultiPark (Multidisciplinary Research in Parkinson’s disease) at Lund University, the Swedish Alzheimer’s Foundation (no. AF-939932), the Swedish Brain Foundation (no. FO2019-0326), the Swedish Parkinson Foundation (no. 1280/20), the Skåne University Hospital Foundation (no. 2020-O000028), Regionalt Forskningsstöd (no. 2020-0314) and the Swedish Federal Government under the ALF agreement (no. 2018-Projekt0279). The Tau PET study in Gangnam Severance Hospital was supported by a grant from the Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Education (nos. NRF2018R1D1A1B07049386 and NRF2020R1F1A1076154) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Ministry of Health and Welfare, Republic of Korea (grant no. HI18C1159). We also thank B. L. Miller, H. J. Rosen, M. Gorno Tempini and W. Jagust for supporting the UCSF tau-PET studies, which were funded through the following sources: National Institute on Aging (NIA) no. R01 AG045611 (G.D.R.), no. P50 AG23501 (B.L.M., H.J.R., G.D.R.), no. P01 AG019724 (B.L.M., H.J.R., G.D.R.). The precursor of 18F-flortaucipir was provided by AVID Radiopharmaceuticals. The precursor of 18F-flutemetamol was sponsored by GE Healthcare. The precursor of 18F-RO948 was provided by Roche. Data collection and sharing for this project were funded by ADNI (NIH grant no. U01 AG024904) and Department of Defense ADNI (award no. W81XWH-12-2-0012). ADNI is funded by the NIA, the National Institute of Biomedical Imaging and Bioengineering and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; Bioclinica; Biogen; Bristol Myers Squibb; CereSpir; Cogstate; Eisai; Elan Pharmaceuticals; Eli Lilly and Company; EUROIMMUN; F. Hoffmann-La Roche and its affiliated company Genentech; Fujirebio; GE Healthcare; IXICO; Janssen Alzheimer Immunotherapy Research Development; Johnson & Johnson Pharmaceutical Research Development; Lumosity; Lundbeck; Merck; Meso Scale Diagnostics; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer; 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 (www.fnih.org). 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. Finally, we thank all participants of this study and the families and caregivers of the patients included for their support in volunteering data for this study. Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu/). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report.

Author information

Authors and Affiliations

  1. Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada

    Jacob W. Vogel, Yasser Iturria-Medina & Alan C. Evans

  2. Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK

    Alexandra L. Young

  3. Centre for Medical Image Computing, University College London, London, UK

    Neil P. Oxtoby, Leon M. Aksman & Daniel C. Alexander

  4. Department of Computer Science, University College London, London, UK

    Neil P. Oxtoby & Daniel C. Alexander

  5. Clinical Memory Research Unit, Lund University, Lund, Sweden

    Ruben Smith, Rik Ossenkoppele, Olof T. Strandberg & Oskar Hansson

  6. Department of Neurology, Skåne University Hospital, Lund, Sweden

    Ruben Smith

  7. Alzheimer Center Amsterdam, Department of Neurology, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam University Medical Center, Amsterdam, the Netherlands

    Rik Ossenkoppele

  8. Memory and Aging Center, Department of Neurology, Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA

    Renaud La Joie & Gil D. Rabinovici

  9. Department of Medical Physics and Biomedical Engineering, University College London, London, UK

    Leon M. Aksman

  10. Wallenberg Centre for Molecular and Translational Medicine, University of Gothenburg, Gothenburg, Sweden

    Michel J. Grothe

  11. Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío/Consejo Superior de Investigaciones Científicas/Universidad de Sevilla, Seville, Spain

    Michel J. Grothe

  12. AVID Radiopharmaceuticals, Philadelphia, PA, USA

    Michael J. Pontecorvo & Michael D. Devous

  13. Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA

    Gil D. Rabinovici

  14. Departments of Neurology, Gangnam Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

    Chul Hyoung Lyoo

  15. Memory Clinic, Skåne University Hospital, Malmö, Sweden

    Oskar Hansson

  16. University of California San Francisco, San Francisco, CA, USA

    Michael Weiner, Howard J. Rosen & Bruce L. Miller

  17. University of California San Diego, San Diego, CA, USA

    Paul Aisen, Ronald G. Thomas, Michael Donohue, Sarah Walter, Devon Gessert, Tamie Sather, Gus Jiminez, James Brewer & Helen Vanderswag

  18. Mayo Clinic, Rochester, NY, USA

    Ronald Petersen, Clifford R. Jack Jr., Matthew Bernstein, Bret Borowski, Jeff Gunter, Matt Senjem, Prashanthi Vemuri, David Jones, Kejal Kantarci, Chad Ward, Sara S. Mason, Colleen S. Albers, David Knopman & Kris Johnson

  19. University of California Berkeley, Berkeley, CA, USA

    William Jagust & Susan Landau

  20. University of Pennsylvania, Philadelphia, PA, USA

    John Q. Trojanowki, Leslie M. Shaw, Steven E. Arnold, Jason H. Karlawish & David Wolk

  21. University of Southern California, Los Angeles, CA, USA

    Arthur W. Toga, Karen Crawford, Scott Neu, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro & Bryan M. Spann

  22. University of California Davis, Davis, CA, USA

    Laurel Beckett, Danielle Harvey & Charles DeCArli

  23. Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA

    Robert C. Green, Reisa A. Sperling, Keith A. Johnson, Gad Marshall & Meghan Frey

  24. Indiana University, Bloomington, IN, USA

    Andrew J. Saykin, Tatiana M. Foroud, Li Shen, Faber Kelley, Sungeun Kim, Kwangsik Nho, Martin R. Farlow, Ann Marie Hake, Brandy R. Matthews, Scott Herring & Cynthia Hunt

  25. Washington University St. Louis, St. Louis, MO, USA

    John Morris, Nigel J. Cairns, Erin Householder & Lisa Taylor Reinwald

  26. Janssen Alzheimer Immunotherapy, South San Francisco, CA, USA

    Enchi Liu

  27. University of Washington, Seattle, WA, USA

    Tom Montine

  28. University of London, London, UK

    Nick Fox

  29. University of Southern California School of Medicine, Los Angeles, CA, USA

    Paul Thompson

  30. University of California San Francisco MRI, San Francisco, CA, USA

    Norbert Schuff

  31. University of Michigan, Ann Arbor, MI, USA

    Robert A. Koeppe, Judith L. Heidebrink & Joanne L. Lord

  32. University of Utah, Salt Lake City, UT, USA

    Norm Foster

  33. Banner Alzheimer’s Institute, Phoenix, AZ, USA

    Eric M. Reiman, Kewei Chen, Adam Fleisher, Pierre Tariot & Stephanie Reeder

  34. University of Pittsburgh, Pittsburgh, PA, USA

    Chet Mathis, Oscar L. Lopez, MaryAnn Oakley & Donna M. Simpson

  35. University of Pennsylvania School of Medicine, Philadelphia, PA, USA

    Virginia Lee, Magdalena Korecka & Michal Figurski

  36. University of California Irvine, Irvine, CA, USA

    Steven Potkin, Steven G. Potkin, Adrian Preda & Dana Nguyen

  37. Khachaturian, Radebaugh & Associates, Inc. and Alzheimer’s Association’s Ronald and Nancy Reagan’s Research Institute, Chicago, IL, USA

    Zaven Kachaturian

  38. General Electric, Boston, MA, USA

    Richard Frank

  39. Brown University, Providence, RI, USA

    Peter J. Snyder

  40. National Institute on Aging/National Institutes of Health, Bethesda, MD, USA

    Susan Molchan

  41. Oregon Health and Science University, Portland, OR, USA

    Jeffrey Kaye, Joseph Quinn, Betty Lind, Raina Carter & Sara Dolen

  42. Baylor College of Medicine, Houston, TX, USA

    Rachelle S. Doody, Javier Villanueva Meyer, Munir Chowdhury, Susan Rountree & Mimi Dang

  43. Columbia University Medical Center, New York, NY, USA

    Yaakov Stern, Lawrence S. Honig & Karen L. Bell

  44. Washington University, St. Louis, MO, USA

    Beau Ances, John C. Morris, Maria Carroll, Sue Leon, Mark A. Mintun & Stacy Schneider

  45. University of Alabama at Birmingham, Birmingham, MO, USA

    Angela Oliver, Randall Griffith, David Clark, David Geldmacher, John Brockington & Erik Roberson

  46. Mount Sinai School of Medicine, New York, NY, USA

    Hillel Grossman & Effie Mitsis

  47. Rush University Medical Center, Chicago, IL, USA

    Leyla deToledo-Morrell & Raj C. Shah

  48. Wien Center, Vienna, Austria

    Ranjan Duara, Daniel Varon, Maria T. Greig & Peggy Roberts

  49. Johns Hopkins University, Baltimore, MD, USA

    Marilyn Albert, Chiadi Onyike, Daniel D’Agostino II & Stephanie Kielb

  50. New York University, New York, NY, USA

    James E. Galvin, Dana M. Pogorelec, Brittany Cerbone, Christina A. Michel, Henry Rusinek, Mony J. de Leon, Lidia Glodzik & Susan De Santi

  51. Duke University Medical Center, Durham, NC, USA

    P. Murali Doraiswamy, Jeffrey R. Petrella & Terence Z. Wong

  52. University of Kentucky, Lexington, KY, USA

    Charles D. Smith, Greg Jicha, Peter Hardy, Partha Sinha, Elizabeth Oates & Gary Conrad

  53. University of Rochester Medical Center, Rochester, NY, USA

    Anton P. Porsteinsson, Bonnie S. Goldstein, Kim Martin, Kelly M. Makino, M. Saleem Ismail & Connie Brand

  54. University of California, Irvine, CA, USA

    Ruth A. Mulnard, Gaby Thai & Catherine McAdams Ortiz

  55. University of Texas Southwestern Medical School, Dallas, TX, USA

    Kyle Womack, Dana Mathews, Mary Quiceno, Ramon Diaz Arrastia, Richard King, Myron Weiner, Kristen Martin Cook & Michael DeVous

  56. Emory University, Atlanta, GA, USA

    Allan I. Levey, James J. Lah & Janet S. Cellar

  57. University of Kansas Medical Center, Lawrence, KS, USA

    Jeffrey M. Burns, Heather S. Anderson & Russell H. Swerdlow

  58. University of California, Los Angeles, CA, USA

    Liana Apostolova, Kathleen Tingus, Ellen Woo, Daniel H. S. Silverman, Po H. Lu & George Bartzokis

  59. Mayo Clinic, Jacksonville, FL, USA

    Neill R. Graff Radford, Francine Parfitt, Tracy Kendall & Heather Johnson

  60. Yale University School of Medicine, New Haven, CT, USA

    Christopher H. van Dyck, Richard E. Carson & Martha G. MacAvoy

  61. McGill University, Montreal Jewish General Hospital, Montreal, Quebec, Canada

    Howard Chertkow, Howard Bergman & Chris Hosein

  62. Sunnybrook Health Sciences, Toronto, Ontario, Canada

    Sandra Black, Bojana Stefanovic & Curtis Caldwell

  63. University of British Columbia Clinic for AD & Related Disorders, Vancouver, British Columbia, Canada

    Ging Yuek Robin Hsiung, Howard Feldman, Benita Mudge & Michele Assaly Past

  64. Cognitive Neurology St. Joseph’s, Toronto, Ontario, Canada

    Andrew Kertesz, John Rogers & Dick Trost

  65. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, USA

    Charles Bernick & Donna Munic

  66. Northwestern University, Evanston, IL, USA

    Diana Kerwin, Marek Marsel Mesulam, Kristine Lipowski, Chuang Kuo Wu & Nancy Johnson

  67. Premiere Research Institute Palm Beach Neurology, West Palm Beach, FL, USA

    Carl Sadowsky, Walter Martinez & Teresa Villena

  68. Georgetown University Medical Center, Washington, DC, USA

    Raymond Scott Turner, Kathleen Johnson & Brigid Reynolds

  69. Stanford University, Stanford, CA, USA

    Jerome Yesavage, Joy L. Taylor, Barton Lane, Allyson Rosen & Jared Tinklenberg

  70. Banner Sun Health Research Institute, Sun City, AZ, USA

    Marwan N. Sabbagh, Christine M. Belden, Sandra A. Jacobson & Sherye A. Sirrel

  71. Boston University, Boston, MA, USA

    Neil Kowall, Ronald Killiany, Andrew E. Budson, Alexander Norbash & Patricia Lynn Johnson

  72. Howard University, Washington, DC, USA

    Thomas O. Obisesan, Saba Wolday & Joanne Allard

  73. Case Western Reserve University, Cleveland, OH, USA

    Alan Lerner, Paula Ogrocki & Leon Hudson

  74. University of California Davis Medical Center, Sacramento, CA, USA

    Evan Fletcher, Owen Carmichael, John Olichney & Charles DeCarli

  75. Neurological Care of CNY, New York, NY, USA

    Smita Kittur

  76. Parkwood Hospital, Parkwood, CA, USA

    Michael Borrie, T. Y. Lee & Rob Bartha

  77. University of Wisconsin, Madison, WI, USA

    Sterling Johnson, Sanjay Asthana & Cynthia M. Carlsson

  78. Dent Neurologic Institute, Amherst, NY, USA

    Vernice Bates, Horacio Capote & Michelle Rainka

  79. Ohio State University, Columbus, OH, USA

    Douglas W. Scharre, Maria Kataki & Anahita Adeli

  80. Albany Medical College, Albany, NY, USA

    Earl A. Zimmerman, Dzintra Celmins & Alice D. Brown

  81. Hartford Hospital, Olin Neuropsychiatry Research Center, Hartford, CT, USA

    Godfrey D. Pearlson, Karen Blank & Karen Anderson

  82. Dartmouth Hitchcock Medical Center, Albany, NY, USA

    Robert B. Santulli, Tamar J. Kitzmiller & Eben S. Schwartz

  83. Wake Forest University Health Sciences, Winston-Salem, NC, USA

    Kaycee M. Sink, Jeff D. Williamson, Pradeep Garg & Franklin Watkins

  84. Rhode Island Hospital, Providence, RI, USA

    Brian R. Ott, Henry Querfurth & Geoffrey Tremont

  85. Butler Hospital, Providence, RI, USA

    Stephen Salloway, Paul Malloy & Stephen Correia

  86. Medical University of South Carolina, Charleston, SC, USA

    Jacobo Mintzer, Kenneth Spicer & David Bachman

  87. St. Joseph’s Health Care, Toronto, Ontario, Canada

    Elizabether Finger, Stephen Pasternak, Irina Rachinsky & Dick Drost

  88. Nathan Kline Institute, Orangeburg, SC, USA

    Nunzio Pomara, Raymundo Hernando & Antero Sarrael

  89. University of Iowa College of Medicine, Iowa City, IA, USA

    Susan K. Schultz, Laura L. Boles Ponto, Hyungsub Shim & Karen Elizabeth Smith

  90. Cornell University, Ithaca, NY, USA

    Norman Relkin, Gloria Chaing & Lisa Raudin

  91. University of South Florida Health/Byrd Alzheimer’s Institute, Tampa, FL, USA

    Amanda Smith, Kristin Fargher & Balebail Ashok Raj

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  1. Jacob W. Vogel
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  • , Amanda Smith
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Contributions

J.W.V., O.H. and A.C.E. conceptualized the work. J.W.V., A.L.Y., N.P.O., L.M.A., M.J.P., M.J.G and O.H. contributed to the analytical design. M.J.P., M.D.D., G.D.R., C.H.L. and O.H. acquired and provided the data. R.S., R.O., O.T.S. and R.L.J. contributed to data curation and processing. J.W.V. analyzed the data. A.L.Y. and D.C.A. originally created the SuStaIn algorithm, and N.P.O. and L.M.A. contributed to its execution. Y.I.M. created the ESM algorithm. J.W.V. and O.H. drafted the manuscript. All authors interpreted the data and contributed substantively to revising the manuscript.

Corresponding authors

Correspondence to Jacob W. Vogel or Oskar Hansson.

Ethics declarations

Competing interests

M.J.P. and M.D.D. are employees of AVID Radiopharmaceuticals, a wholly owned subsidiary of Eli Lilly and Company, and are minor stockholders in Eli Lilly and Company. O.H. has acquired research support (for the institution) from AVID Radiopharmaceuticals, Biogen, Eli Lilly and Company, Eisai, GE Healthcare, Pfizer and Roche. In the past two years, he has received consultancy/speaker fees from AC Immune, ALZpath, Biogen, Cerveau and Roche. The remaining authors declare no competing interests.

Additional information

Peer reviewer information Nature Medicine thanks Giovanni Frisoni and the other, anonymous, reviewer(s), for their contribution to the peer review of this work. Jerome Staal was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Methodological details.

Methodological details. a, SuStaIn requires both spatial (for example brain regions) and pseudotemporal (for example Z-score waypoints representing advancing biomarker severity) features as input. SuStaIn models linear change between waypoints across multiple biomarkers and uses clustering to fit subtype trajectories representing distinct biomarker sequences. b, Each spatial feature was z-scored in order to derive interpretable waypoints. Example: (top left), SUVR distribution in the left temporal lobe. (bottom-left) Distribution of standardized residuals after regression of choroid plexus. Gaussian mixture-modeling identifies ‘normal’ (grey) and ‘abnormal’ (red) tau-PET values within this distribution. (bottom right) Mean and SD of ‘normal’ distribution used to normalize the whole distribution, creating ‘Tau Z-scores’. Tau Z-scores of 2, 5 and 10 are used as waypoints. (top-right) Tau-z scores superimposed onto the original SUVR distribution. For each subtype model (k = 1-7), c) distribution of average negative log-likelihood, d) CVIC, and e) distribution of the probability of the maximum-likelihood subtype across cross-validation folds of left-out individuals. Higher log-likelihood, lower CVIC represents better model fit. f, Visualization of subtype solutions k = 2-7. For each subtype, the rendered brains show significant regional tau difference between the subtype and all other subtypes in its solution. Connecting-line thickness indicates how many subjects are shared between a subtype and each subtype from its parent and child solutions. Circle color represents which k = 4 subtypes (outlined in the dashed box) each subtype is most similar to, in terms of the number of overlapping subjects. Red arrowheads indicate subtypes that were formed by pooling individuals from two different parent subtypes. For boxplots in c,e, center line=median, box=inner quartiles, whiskers=extent of data distribution except *=outliers.

Extended Data Fig. 2 Details of subtype assignment.

Details of subtype assignment. a, Several individuals classified as S2 (MTL-Sparing) were found to be tau-negative (that is no significant tau in the entorhinal cortex or precuneus; S2: False). Cortical rendering shows the overall mean tau Z-scores (see Extended Data Fig. 1b) of S2: False individuals. Slightly elevated signal was observed throughout the cortex (but not MTL areas), including in regions where pathological tau is not typically observed until late AD. b, Proportion of Ab + (top) and cognitively impaired (bottom) individuals in S2: False (n = 54) compared to other S2 individuals (S2: True, n = 79) and tau-negative individuals (S0, n = 646). Using, χ2-tests with Tukey’s posthoc correction, a higher proportion of S2: False and S0 individuals were Aβ- (S0 p[adj]=8.5e-13; S2:F p[adj]=9.7e-08) and cognitively unimpaired (S0 p[adj]=6.2e-68; S2:F p[adj]=2.0e-10) than S2: True individuals, but did not differ significantly from one another (Ab p[adj]=0.94; cog p[adj]=0.07). c, Using two-sided ANOVAs with Tukey’s posthoc correction, S0 and S2: False individuals were older (S0 p[adj]=0.001; S2:F p([adj]=0.024) and had higher MMSE scores (S0 adj]=0.001; S2:F p([adj]=0.001) than S2: True individuals, but did not differ from one another (Age p[adj]=0.48; MMSE p[adj]=0.21). d, SuStaIn stage of all individuals stratified by subtype, with the poorly fitting subjects (those that had <0.5 probability of falling into any subtype) shown separately. All but one poorly fit subject exhibited very low SuStaIn stages. e, Probability of maximum likelihood subtype is low at early SuStaIn stage and quickly increases with increasing SuStaIn stage. f, Distribution of clinical diagnoses across SuStaIn stages. g, Distribution of clinical diagnoses across subtypes (same colors as in f). h, Distribution of maximum-likelihood subtype probabilities for each clinical diagnosis. i, Distribution of PCA and lvPPA subjects from the UCSF sample into each subtype. For boxplots in c,e,h, center line=median, box=inner quartiles, whiskers=extent of data distribution except *=outliers.

Extended Data Fig. 3 Regional comparisons.

Regional comparisons. Comparison of the mean tau-PET signal (tau-Z) across all ROIs, after adjustment for total cortical tau. A value of 0 represents regional tau Z-score proportionate to the average cortical tau Z-score in that subtype. The left panel represents left hemisphere, the right panel represents right hemisphere. Values represent means +/− SEM.

Extended Data Fig. 4 Subtypes observable across all contributing cohorts.

Subtypes observable across all contributing cohorts. The top panel shows the proportions of each subtype (plus S0) within each of the cohorts. All cohorts included individuals from each subtype. The bottom shows the mean tau Z image of each subtypes in a given cohort. Variation be observed across cohorts, particularly regarding phenotypic severity, but subtype patterns are fairly consistent.

Extended Data Fig. 5 Individual fit to stereotypical subtype progression.

Individual fit to stereotypical subtype progression. a) Progression plots are created for each subtyped individual based on their progression through events specific to their subtype. The outer images show regional tau z scores (see Extended Data Fig. 1) for an S2 (left) and S3 (right) individual. This data is summarized in lobar ROI z-scores (inner images). In progression plots under the images, each box represents a biomarker event, SuStaIn stage. A SuStaIn stage represents tau reaching a given severity (Z) score at a given region (see Extended Data Fig. 1). Filled (boxes indicate an individual fulfills the criteria for that SuStaIn stage. An empty (black) box indicates an individual does not. Note that each subtype has a different event order. b) A stepwise progression plot is shown for each subtype. Each row represents an individual, and each column represents SuStaIn stage. A perfect fit would be represented by an individual (row) having every box filled before a given stage, and no boxes filled after. y-axes (subjects) are sorted from the least (top) to most (bottom) stages fulfilled. Across the population, this should be represented as a stepwise progression. Each subtype demonstrates a stepwise progression indicating good general fit. The average subject fit imperfection was 2.1 boxes.

Extended Data Fig. 6 Replication using different initial parameters.

Replication using different initial parameters. SuStaIn creates nearly identical subtypes when initialized with different parameters (Supplementary Table 5) Methods: Replication Analysis). SuStaIn was rerun allowing a data-driven methodology to determine the number and value of z-score waypoints for each ROI. a) Qualitative contrasts of each subtype as defined using the original (Orig) parameters and the new data-driven (DD) parameters, where maps show regions significantly different between one subtype and all others (excluding S0) within the cohort (after FDR correction). b) Confusion matrix comparing subtypes identified in the original (orig) sample (y-axis), and subtypes separately identified in the data-driven parameter replication sample (x-axis). Values represent spatial correlation between average regional tau for each subtype. Values along the diagonal indicates similarity between the same subtype across both parameter sets.

Extended Data Fig. 7 Stability of subtypes across train-test split and replication datasets.

Stability of subtypes across train-test split and replication datasets. (Top) Cortical renders showing, for each subtype across each dataset, regions with significantly different tau-PET signal compared to other within-dataset subtypes after FDR correction. Hot regions show greater tau-PET signal, whereas cooler regions show lower signal. Remarkable similarity can be observed across subtypes, except S4, where lateralization switches from left to right. (Bottom) A heatmap showing similarity (spatial correlation) between subtypes across all four datasets. The diagonal represents identity, whereas outlined boxes represent comparisons of the same subtype across datasets.

Extended Data Fig. 8 Subtypes present with differing clinical profiles.

Subtypes present with differing clinical profiles. For all plots, a * below a box indicates the subtype is significantly different (corrected p < 0.05) from other subtypes combined (one vs. all), while a χ represents a trend (p < 0.1). Thick horizontal lines above boxes indicate significant (p < 0.05) differences between two subtypes (one vs one). Dashed horizontal lines represent the mean of the S0 group (controlling for covariates), where relevant. All statistics are adjusted for demographics, disease status, cohort and SuStaIn stage. For boxplots, the center line=median, box=inner quartiles, whiskers=extent of data distribution except *=outliers.

Extended Data Fig. 9 Lateralization across disease progression as measured with SuStaIn stage.

Lateralization across disease progression as measured with SuStaIn stage. a, Tau lateralization was measured as the mean left to right ratio of scores for all ten tau features. Higher positive numbers represent greater left hemisphere tau lateralization, whereas lower negative numbers represent greater right hemisphere lateralization. The progression of laterality over SuStaIn stage was visualized for each subtype. Lateralization generally increased with increasing SuStaIn stage. In some subtypes (particularly S2 and S3), strong lateralization was seen in both hemispheres later stages. b, The absolute (that is agnostic to hemisphere) lateralization was visualized against SuStaIn stage, indicating general increase in lateralization with more severe tau expression. c, A three-way relationship between age, SuStaIn stage and absolute lateralization is visualized, indicating these relationships covary but are independent of one another. For b, confidence interval represents 95% around line-of-best-fit across 1000 bootstrap samples.

Extended Data Fig. 10 Replication of subtype-specific epidemic spreading model.

Replication of subtype-specific epidemic spreading model. We repeated analyses from Fig. 4, this time using functional connectivity from a sample of elderly healthy and MCI individuals, over a higher-resolution cortical atlas, as the connectome input to the model. The ESM was fit separately for each subtype; once using an entorhinal cortex epicenter (a), gray], and once with a subtype-specific best-fitting epicenter (b), blue]. For each plot, each dot represents a region. The x-axis represents the mean simulated tau-positive probabilities across the population, while the y-axis represents mean observed tau-positive probability. Each column represents a subtype. Confidence interval represents 95% CI around line-of-best-fit across 1000 bootstrap samples c) Visualization of the best-fitting epicenter selected by the model. d) For each subtype, the probability that each region’s best fitting epicenter for that subtype, based on bootstrap resampling (1000 samples).

Supplementary information

Supplementary Information

Supplementary Notes 1–3 and Tables 1–6.

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Vogel, J.W., Young, A.L., Oxtoby, N.P. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat Med 27, 871–881 (2021). https://doi.org/10.1038/s41591-021-01309-6

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