Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Data-driven modelling of neurodegenerative disease progression: thinking outside the black box

Nature Reviews Neuroscience volume 25, pages 111–130 (2024)Cite this article

Subjects

Abstract

Data-driven disease progression models are an emerging set of computational tools that reconstruct disease timelines for long-term chronic diseases, providing unique insights into disease processes and their underlying mechanisms. Such methods combine a priori human knowledge and assumptions with large-scale data processing and parameter estimation to infer long-term disease trajectories from short-term data. In contrast to ‘black box’ machine learning tools, data-driven disease progression models typically require fewer data and are inherently interpretable, thereby aiding disease understanding in addition to enabling classification, prediction and stratification. In this Review, we place the current landscape of data-driven disease progression models in a general framework and discuss their enhanced utility for constructing a disease timeline compared with wider machine learning tools that construct static disease profiles. We review the insights they have enabled across multiple neurodegenerative diseases, notably Alzheimer disease, for applications such as determining temporal trajectories of disease biomarkers, testing hypotheses about disease mechanisms and uncovering disease subtypes. We outline key areas for technological development and translation to a broader range of neuroscience and non-neuroscience applications. Finally, we discuss potential pathways and barriers to integrating disease progression models into clinical practice and trial settings.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Framework for data-driven disease progression modelling.
Fig. 2: Selected phenomenological data-driven disease progression models.
Fig. 3: Network illustration of hypotheses that underpin pathophysiological models.
Fig. 4: Examples of pathophysiological models trained on observed data from studies of neurodegenerative diseases.
Fig. 5: Example applications of data-driven disease progression modelling.

Similar content being viewed by others

References

  1. Bateman, R. J. et al. Clinical and biomarker changes in dominantly inherited Alzheimer’s disease. N. Engl. J. Med. 367, 795–804 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Savica, R., Rocca, W. A. & Ahlskog, J. E. When does Parkinson disease start? Arch. Neurol. 67, 798–801 (2010).

    Article  PubMed  Google Scholar 

  3. Rohrer, J. D. et al. Presymptomatic cognitive and neuroanatomical changes in genetic frontotemporal dementia in the Genetic Frontotemporal Dementia Initiative (GENFI) study: a cross-sectional analysis. Lancet Neurol. 14, 253–262 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Paulsen, J. S. et al. Detection of Huntington’s disease decades before diagnosis: the Predict-HD study. J. Neurol. Neurosurg. Psychiatry 79, 874–880 (2008).

    Article  CAS  PubMed  Google Scholar 

  5. Hansson, O. Biomarkers for neurodegenerative diseases. Nat. Med. 27, 954–963 (2021).

    Article  CAS  PubMed  Google Scholar 

  6. Donohue, M. C. et al. Estimating long-term multivariate progression from short-term data. Alzheimer’s Dement. 10, S400–S410 (2014). This work presents an early latent-time regression model of Alzheimer disease that has informed and inspired many subsequent data-driven disease progression models.

    Article  Google Scholar 

  7. Oxtoby, N. P. & Alexander, D. C. Imaging plus X: multimodal models of neurodegenerative disease. Curr. Opin. Neurol. 30, 371–379 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Hampel, H. et al. Blood-based biomarkers for Alzheimer disease: mapping the road to the clinic. Nat. Rev. Neurol. 14, 639–652 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Poulakis, K. & Westman, E. Clustering and disease subtyping in neuroscience, toward better methodological adaptations. Front. Comput. Neurosci. 17, 1243092 (2021).

    Article  Google Scholar 

  10. Ferreira, D., Nordberg, A. & Westman, E. Biological subtypes of Alzheimer disease: a systematic review and meta-analysis. Neurology 94, 436–448 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  11. Makhani, N. & Tremlett, H. The multiple sclerosis prodrome. Nat. Rev. Neurol. 17, 515–521 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  12. Lange, P. et al. Lung-function trajectories leading to chronic obstructive pulmonary disease. N. Engl. J. Med. 373, 111–122 (2015).

    Article  CAS  PubMed  Google Scholar 

  13. Eshaghi, A. et al. Progression of regional grey matter atrophy in multiple sclerosis. Brain 141, 1665–1677 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Eshaghi, A. et al. Identifying multiple sclerosis subtypes using unsupervised machine learning and MRI data. Nat. Commun. 12, 2078 (2021). This work applies the SuStaIn algorithm to brain MRI scans in multiple sclerosis, identifying three subtypes with distinct disability progression and treatment response in clinical trials.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Lopez, S. M. et al. Event-based modeling in temporal lobe epilepsy demonstrates progressive atrophy from cross-sectional data. Epilepsia 63, 2081–2095 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Xiao, F. et al. Identification of different MRI atrophy progression trajectories in epilepsy by Subtype and Stage Inference. Brain 146, 4702–4716 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  17. Wen, J. et al. Characterizing heterogeneity in neuroimaging, cognition, clinical symptoms, and genetics among patients with late-life depression. JAMA Psychiatry 79, 464–474 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  18. Chen, D. et al. Neurophysiological stratification of major depressive disorder by distinct trajectories. Nat. Mental Health 1, 863–875 (2023).

    Article  Google Scholar 

  19. Jiang, Y. et al. Neuroimaging biomarkers define neurophysiological subtypes with distinct trajectories in schizophrenia. Nat. Mental Health 1, 186–199 (2023).

    Article  Google Scholar 

  20. Wojcik, C. et al. Staging and stratifying cognitive dysfunction in multiple sclerosis. Mult. Scler. J. 28, 463–471 (2022).

    Article  Google Scholar 

  21. Pontillo, G. et al. Stratification of multiple sclerosis patients using unsupervised machine learning: a single-visit MRI-driven approach. Eur. Radiol. 32, 5382–5391 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Li, M. et al. Identifying the phenotypic and temporal heterogeneity of knee osteoarthritis: data from the Osteoarthritis Initiative. Front. Public Health 9, 1–10 (2021).

    CAS  Google Scholar 

  23. Young, A. L. et al. Disease progression modeling in chronic obstructive pulmonary disease. Am. J. Respir. Crit. Care Med. 201, 294–302 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Rangelov, B. et al. Delineating COVID-19 subgroups using routine clinical data identifies distinct in-hospital outcomes. Sci. Rep. 13, 1–14 (2023).

    Google Scholar 

  25. Braak, H. & Braak, E. Neuropathological stageing of Alzheimer-related changes. Acta Neuropathol. 82, 239–259 (1991).

    Article  CAS  PubMed  Google Scholar 

  26. Thal, D. R., Rüb, U., Orantes, M. & Braak, H. Phases of Aβ-deposition in the human brain and its relevance for the development of AD. Neurology 58, 1791–1800 (2002).

    Article  PubMed  Google Scholar 

  27. Mirra, S. S. et al. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Part II. Standardization of the neuropathologic assessment of Alzheimer’s disease. Neurology 41, 479 (1991).

    Article  CAS  PubMed  Google Scholar 

  28. Nelson, P. T. et al. Limbic-predominant age-related TDP-43 encephalopathy (LATE): consensus working group report. Brain 142, 1503–1527 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  29. Josephs, K. A. et al. Staging TDP-43 pathology in Alzheimer’s disease. Acta Neuropathol. 127, 441–450 (2014).

    Article  CAS  PubMed  Google Scholar 

  30. Brettschneider, J., Del Tredici, K., Lee, V. M. Y. & Trojanowski, J. Q. Spreading of pathology in neurodegenerative diseases: a focus on human studies. Nat. Rev. Neurosci. 16, 109–120 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Braak, H. et al. Staging of brain pathology related to sporadic Parkinson’s disease. Neurobiol. Aging 24, 197–211 (2003).

    Article  PubMed  Google Scholar 

  32. Jack, C. R. et al. Hypothetical model of dynamic biomarkers of the Alzheimer’s pathological cascade. Lancet Neurol. 9, 119–128 (2010). This work presents an influential hypothetical model of Alzheimer disease progression that inspired the development of data-driven disease progression models.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Jack, C. R. et al. Tracking pathophysiological processes in Alzheimer’s disease: an updated hypothetical model of dynamic biomarkers. Lancet Neurol. 12, 207–216 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Frisoni, G. B., Fox, N. C., Jack, C. R., Scheltens, P. & Thompson, P. M. The clinical use of structural MRI in Alzheimer disease. Nat. Rev. Neurol. 6, 67–77 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  35. Frisoni, G. B. et al. The probabilistic model of Alzheimer disease: the amyloid hypothesis revised. Nat. Rev. Neurosci. 23, 53–66 (2022).

    Article  CAS  PubMed  Google Scholar 

  36. Fleisher, A. S. et al. Associations between biomarkers and age in the presenilin 1 E280A autosomal dominant Alzheimer disease kindred: a cross-sectional study. JAMA Neurol. 72, 316–324 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Scahill, R. I., Schott, J. M., Stevens, J. M., Rossor, M. N. & Fox, N. C. Mapping the evolution of regional atrophy in Alzheimer’s disease: unbiased analysis of fluid-registered serial MRI. Proc. Natl Acad. Sci. USA 99, 1–5 (2002).

    Article  Google Scholar 

  38. Ridha, B. H. et al. Tracking atrophy progression in familial Alzheimer’s disease: a serial MRI study. Lancet Neurol. 5, 828–834 (2006).

    Article  PubMed  Google Scholar 

  39. Fox, N. C. & Schott, J. M. Imaging cerebral atrophy: normal ageing to Alzheimer’s disease. Lancet 363, 392–394 (2004).

    Article  PubMed  Google Scholar 

  40. Jack, C. R. et al. Serial PIB and MRI in normal, mild cognitive impairment and Alzheimer’s disease: implications for sequence of pathological events in Alzheimer’s disease. Brain 132, 1355–1365 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  41. Lo, R. Y. et al. Longitudinal change of biomarkers in cognitive decline. Arch. Neurol. 68, 1257–1266 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Landau, S. M. et al. Amyloid deposition, hypometabolism, and longitudinal cognitive decline. Ann. Neurol. 72, 578–586 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Jack, C. R. et al. Shapes of the trajectories of 5 major biomarkers of Alzheimer disease. Arch. Neurol. 69, 856–867 (2012).

    PubMed  PubMed Central  Google Scholar 

  44. Caroli, A. & Frisoni, G. B. The dynamics of Alzheimer’s disease biomarkers in the Alzheimer’s Disease Neuroimaging Initiative cohort. Neurobiol. Aging 31, 1263–1274 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Sabuncu, M. R. et al. The dynamics of cortical and hippocampal atrophy in Alzheimer disease. Arch. Neurol. 68, 1040–1048 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Jack, C. R. et al. Evidence for ordering of Alzheimer disease biomarkers. Arch. Neurol. 68, 1526–1535 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  47. Ryman, D. C. et al. Symptom onset in autosomal dominant Alzheimer disease a systematic review and meta-analysis. Neurology 83, 253–260 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Schmidt-Richberg, A. et al. Learning biomarker models for progression estimation of Alzheimer’s disease. PLoS ONE 11, e0153040 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  49. Guerrero, R. et al. Instantiated mixed effects modeling of Alzheimer’s disease markers. Neuroimage 142, 113–125 (2016).

    Article  CAS  PubMed  Google Scholar 

  50. Poulakis, K. et al. Multi-cohort and longitudinal Bayesian clustering study of stage and subtype in Alzheimer’s disease. Nat. Commun. 13, 4566 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Buchhave, P. et al. Cerebrospinal fluid levels of β-amyloid 1–42, but not of tau, are fully changed already 5 to 10 years before the onset of Alzheimer dementia. Arch. Gen. Psychiatry 69, 98–106 (2012).

    Article  CAS  PubMed  Google Scholar 

  52. Habes, M. et al. Disentangling heterogeneity in Alzheimer’s disease and related dementias using data-driven methods. Biol. Psychiatry 88, 70–82 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  53. Fonteijn, H. M. et al. An event-based model for disease progression and its application in familial Alzheimer’s disease and Huntington’s disease. Neuroimage 60, 1880–1889 (2012). This work introduces the event-based model, a discrete phenomenological disease progression model that has since been widely used.

    Article  PubMed  Google Scholar 

  54. Young, A. L. et al. A data-driven model of biomarker changes in sporadic Alzheimer’s disease. Brain 137, 2564–2577 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  55. Firth, N. C. et al. Sequences of cognitive decline in typical Alzheimer’s disease and posterior cortical atrophy estimated using a novel event-based model of disease progression. Alzheimer’s Dement. 16, 965–973 (2020).

    Article  Google Scholar 

  56. Young, A. L. et al. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference. Nat. Commun. 9, 4273 (2018). This work introduces the SuStaIn algorithm, which combines clustering with disease progression modelling to uncover disease subtypes with distinct progression patterns.

    Article  PubMed  PubMed Central  Google Scholar 

  57. Young, A. L. et al. Ordinal sustain: Subtype and Stage Inference for clinical scores, visual ratings, and other ordinal data. Front. Artif. Intell. 4, 1–13 (2021).

    Article  Google Scholar 

  58. Huang, J. & Alexander, D. Probabilistic event cascades for Alzheimer’s disease. In Advances in Neural Information Processing Systems (eds. Pereira, F. et al.) 3095–3103 (Curran, 2012).

  59. Venkatraghavan, V., Bron, E. E., Niessen, W. J. & Klein, S. Disease progression timeline estimation for Alzheimer’s disease using discriminative event based modeling. Neuroimage 186, 518–532 (2019).

    Article  PubMed  Google Scholar 

  60. Tandon, R., Kirkpatrick, A. & Mitchell, C. S. sEBM: scaling event based models to predict disease progression via implicit biomarker selection and clustering. In Information Processing in Medical Imaging 2023; Lecture Notes in Computer Science 13939 (eds. Frangi, A. et al.) 208–221 (Springer, 2023).

  61. Parker, C., Oxtoby, N., Alexander, D., Zhang, H. & Alzheimer’s Disease Neuroimaging Initiative. S-EBM: generalising event-based modelling of disease progression for simultaneous events. Preprint at bioRxiv https://doi.org/10.1101/2022.07.10.499471 (2022).

  62. Du, J. & Zhou, Y. Filtered trajectory recovery: a continuous extension to event-based model for Alzheimer’s disease progression modeling. In Information Processing in Medical Imaging 2023; Lecture Notes in Computer Science 13939 (eds. Frangi, A. et al.) 95–106 (Springer, 2023).

  63. Wijeratne, P. A. & Alexander, D. C. Learning transition times in event sequences: the temporal event-based model of disease progression. In Proc. Information Processing in Medical Imaging; Lect. Notes Computer Sci. 12729 (eds. Feragen, A. et al.) 583–595 (Springer, 2021).

  64. Wijeratne, P. A. et al. The temporal event-based model: learning event timelines in progressive diseases. Imaging Neurosci. 1, 1–19 (2023). This work combines an event-based model with a hidden Markov model to enable the estimation of an event-based model with an absolute timescale from short-term longitudinal data.

    Article  Google Scholar 

  65. Severson, K. A. et al. Personalized input-output hidden Markov models for disease progression modeling. In Proc. 5th Machine Learning for Healthcare Conference (eds. Doshi-Velez, F. et al.) 309–330 (PMLR, 2020).

  66. Severson, K. A. et al. Discovery of Parkinson’s disease states and disease progression modelling: a longitudinal data study using machine learning. Lancet Digit. Health 3, e555–e564 (2021).

    Article  CAS  PubMed  Google Scholar 

  67. Samtani, M. N. et al. Disease progression model in subjects with mild cognitive impairment from the Alzheimer’s Disease Neuroimaging Initiative: CSF biomarkers predict population subtypes. Br. J. Clin. Pharmacol. 75, 146–161 (2013).

    Article  PubMed  Google Scholar 

  68. Villemagne, V. L. et al. Amyloid β deposition, neurodegeneration, and cognitive decline in sporadic Alzheimer’s disease: a prospective cohort study. Lancet Neurol. 12, 357–367 (2013). This work applies differential equation modelling to amyloid-PET data to derive timescales of amyloid-β deposition, demonstrating that amyloid-β deposition is likely to extend for more than two decades.

    Article  CAS  PubMed  Google Scholar 

  69. Jack, C. R. et al. Brain β-amyloid load approaches a plateau. Neurology 80, 890–896 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Oxtoby, N. P. et al. in Bayesian and Graphical Models for Biomedical Imaging (eds Simpson, I., Arbel, T., Ribbens, A., Cardoso, M. J. & Precup, D.) Vol. 8677, 85–94 (Springer International, 2014).

  71. Betthauser, T. J. et al. Multi-method investigation of factors influencing amyloid onset and impairment in three cohorts. Brain 145, 4065–4079 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  72. Saelens, W., Cannoodt, R., Todorov, H. & Saeys, Y. A comparison of single-cell trajectory inference methods. Nat. Biotechnol. 37, 547–554 (2019).

    Article  CAS  PubMed  Google Scholar 

  73. Saint-Jalmes, M. et al. Disease progression modelling of Alzheimer’s disease using probabilistic principal components analysis. Neuroimage 278, 120279 (2023).

    Article  CAS  PubMed  Google Scholar 

  74. Iturria-Medina, Y., Khan, A. F., Adewale, Q. & Shirazi, A. H. Blood and brain gene expression trajectories mirror neuropathology and clinical deterioration in neurodegeneration. Brain 143, 661–673 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Iturria-Medina, Y. et al. Unified epigenomic, transcriptomic, proteomic, and metabolomic taxonomy of Alzheimer’s disease progression and heterogeneity. Sci. Adv. 8, 1–19 (2022).

    Article  Google Scholar 

  76. McCarthy, J. et al. Data-driven staging of genetic frontotemporal dementia using multi-modal MRI. Hum. Brain Mapp. 43, 1821–1835 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  77. Jedynak, B. M. et al. A computational neurodegenerative disease progression score: method and results with the Alzheimer’s Disease Neuroimaging Initiative cohort. Neuroimage 63, 1478–1486 (2012). This work presents the first latent-time regression model of Alzheimer disease, which learns a disease progression score from multiple biomarkers and the trajectories of biomarkers.

    Article  PubMed  Google Scholar 

  78. Li, D., Iddi, S., Thompson, W. K. & Donohue, M. C. Bayesian latent time joint mixed effect models for multicohort longitudinal data. Stat. Methods Med. Res. 28, 835–845 (2019).

    Article  PubMed  Google Scholar 

  79. Lorenzi, M., Filippone, M., Frisoni, G. B., Alexander, D. C. & Ourselin, S. Probabilistic disease progression modeling to characterize diagnostic uncertainty: application to staging and prediction in Alzheimer’s disease. NeuroImage 190, 56–68 (2019).

    Article  PubMed  Google Scholar 

  80. Raket, L. L. Statistical disease progression modeling in Alzheimer disease. Front. Big Data 3, 1–18 (2020).

    Article  Google Scholar 

  81. Therneau, T. M. et al. Relationships between β-amyloid and tau in an elderly population: an accelerated failure time model. Neuroimage 242, 118440 (2021).

    Article  CAS  PubMed  Google Scholar 

  82. Wei, L. J. The accelerated failure time model: a useful alternative to the Cox regression model in survival analysis. Stat. Med. 11, 1871–1879 (1992).

    Article  CAS  PubMed  Google Scholar 

  83. Durrleman, S., Pennec, X., Trouve, A., Gerig, G. & Ayache, N. Spatiotemporal atlas estimation for developmental delay detection in longitudinal datasets. Int. Conf. Med. Image Comput. Comput. Interv. 12, 297–304 (2009).

    Google Scholar 

  84. Schiratti, J. B., Allassonnière, S., Colliot, O. & Durrleman, S. Learning spatiotemporal trajectories from manifold-valued longitudinal data. In Adv. Neural Inf. Process. Syst. 2015 (eds. Cortes, C. et al) 2404–2412 (Curran, 2015). This work presents the first spatiotemporal disease progression model, which generalizes latent-time regression models to work with longitudinal manifold-valued data.

  85. Schiratti, J. B., Allassonnière, S., Routier, A., Colliot, O. & Durrleman, S. A mixed-effects model with time reparametrization for longitudinal univariate manifold-valued data. In Proc. Information Processing in Medical Imaging; Lect. Notes Computer Sci. 9123 (eds. Ourselin, S. et al.) 564–575 (Springer, 2015).

  86. Schiratti, J., Allassonniere, S., Colliot, O. & Durrleman, S. Mixed-effects model for the spatiotemporal analysis of longitudinal manifold-valued data. In Proc. 5th MICCAI Work. Math. Found. Comput. Anat. (2015).

  87. Durrleman, S. et al. Toward a comprehensive framework for the spatiotemporal statistical analysis of longitudinal shape data. Int. J. Comput. Vis. 103, 22–59 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Lorenzi, M., Pennec, X., Frisoni, G. B. & Ayache, N. Disentangling normal aging from Alzheimer’s disease in structural magnetic resonance images. Neurobiol. Aging 36, S42–S52 (2015).

    Article  PubMed  Google Scholar 

  89. Schiratti, J. B., Allassonnière, S., Colliot, O. & Durrleman, S. A Bayesian mixed-effects model to learn trajectories of changes from repeated manifold-valued observations. J. Mach. Learn. Res. 18, 1–33 (2017).

    Google Scholar 

  90. Koval, I. et al. AD course map charts Alzheimer’s disease progression. Sci. Rep. 11, 1–16 (2021).

    Article  Google Scholar 

  91. Poulet, P. E. & Durrleman, S. Multivariate disease progression modeling with longitudinal ordinal data. Stat. Med. 42, 3164–3183 (2023).

    Article  PubMed  Google Scholar 

  92. Sivera, R., Delingette, H., Lorenzi, M., Pennec, X. & Ayache, N. A model of brain morphological changes related to aging and Alzheimer’s disease from cross-sectional assessments. Neuroimage 198, 255–270 (2019).

    Article  PubMed  Google Scholar 

  93. Louis, M., Couronné, R., Koval, I., Charlier, B. & Durrleman, S. Riemannian geometry learning for disease progression modelling. In Proc. Information Processing in Medical Imaging; Lect. Notes Comput. Sci. 11492 (eds. Chung, A. et al.) 542–553 (Springer, 2019).

  94. Abi Nader, C., Ayache, N., Robert, P. & Lorenzi, M. Monotonic Gaussian process for spatio-temporal disease progression modeling in brain imaging data. Neuroimage 205, 116266 (2020).

    Article  PubMed  Google Scholar 

  95. Bilgel, M., Prince, J. L., Wong, D. F., Resnick, S. M. & Jedynak, B. M. A multivariate nonlinear mixed effects model for longitudinal image analysis: application to amyloid imaging. Neuroimage 134, 658–670 (2016). This work proposes a voxel-wise disease progression model of amyloid-β PET images.

    Article  PubMed  Google Scholar 

  96. Whittington, A., Sharp, D. J. & Gunn, R. N. Spatiotemporal distribution of β-Amyloid in Alzheimer disease is the result of heterogeneous regional carrying capacities. J. Nucl. Med. 59, 822–827 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Marinescu, R. V. et al. DIVE: a spatiotemporal progression model of brain pathology in neurodegenerative disorders. Neuroimage 192, 166–177 (2019).

    Article  PubMed  Google Scholar 

  98. Aksman, L. M. et al. pySuStaIn: a Python implementation of the Subtype and Stage Inference algorithm. SoftwareX 16, 100811 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Young, A. L., Aksman, L. M., Alexander, D. C. & Wijeratne, P. A. Subtype and Stage Inference with Timescales Vol. 2 (Springer Nature, 2023).

  100. Chen, I. Y., Krishnan, R. G. & Sontag, D. Clustering interval-censored time-series for disease phenotyping. AAAI Conf. 1, https://doi.org/10.1609/aaai.v36i6.20570 (2022).

  101. Poulet, P. E. & Durrleman, S. Mixture modeling for identifying subtypes in disease course mapping. In Information Processing for Medical Imaging 2021; Lecture Notes in Computer Science 12729 (eds. Feragen, A. et al.) 571–582 (Springer, 2021).

  102. Zhou, J., Gennatas, E. D., Kramer, J. H., Miller, B. L. & Seeley, W. W. Predicting regional neurodegeneration from the healthy brain functional connectome. Neuron 73, 1216–27 (2012). This is the first study to evaluate evidence for different competing mechanistic hypotheses, demonstrating that transneuronal spread from an epicentre best describes observed atrophy patterns in neurodegenerative diseases.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Altmann, A. et al. Analysis of brain atrophy and local gene expression in genetic frontotemporal dementia. Brain Commun. 2, 1–13 (2020).

    Article  CAS  Google Scholar 

  104. Seeley, W. W., Crawford, R. K., Zhou, J., Miller, B. L. & Greicius, M. D. Neurodegenerative diseases target large-scale human brain networks. Neuron 62, 42–52 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  105. Saxena, S. & Caroni, P. Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron 71, 35–48 (2011).

    Article  CAS  PubMed  Google Scholar 

  106. de Haan, W., Mott, K., van Straaten, E. C. W., Scheltens, P. & Stam, C. J. Activity dependent degeneration explains hub vulnerability in Alzheimer’s disease. PLoS Comput. Biol. 8, e1002582 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Fornito, A., Zalesky, A. & Breakspear, M. The connectomics of brain disorders. Nat. Rev. Neurosci. 16, 159–172 (2015).

    Article  CAS  PubMed  Google Scholar 

  108. Fu, H., Hardy, J. & Duff, K. E. Selective vulnerability in neurodegenerative diseases. Nat. Neurosci. 21, 1350–1358 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  109. Bateman, R. J. et al. Human amyloid-β synthesis and clearance rates as measured in cerebrospinal fluid in vivo. Nat. Med. 12, 856–861 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Clavaguera, F. et al. ‘Prion-Like’ templated misfolding in tauopathies. Brain Pathol. 23, 342–349 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Bourdenx, M. et al. Progress in neurobiology protein aggregation and neurodegeneration in prototypical neurodegenerative diseases: examples of amyloidopathies, tauopathies and synucleinopathies. Prog. Neurobiol. 155, 171–193 (2017).

    Article  CAS  PubMed  Google Scholar 

  112. Weickenmeier, J., Jucker, M., Goriely, A. & Kuhl, E. A physics-based model explains the prion-like features of neurodegeneration in Alzheimer’s disease, Parkinson’s disease, and amyotrophic lateral sclerosis. J. Mech. Phys. Solids 124, 264–281 (2019).

    Article  Google Scholar 

  113. Soto, C. & Pritzkow, S. Protein misfolding, aggregation, and conformational strains in neurodegenerative diseases. Nat. Neurosci. 21, 1332–1340 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Garbarino, S. et al. Differences in topological progression profile among neurodegenerative diseases from imaging data. eLife 8, 1–27 (2019). This work introduces the concept of a topological profile, which evaluates the relative contributions of multiple pathophysiological mechanisms, rather than considering a single mechanism in isolation.

    Article  Google Scholar 

  115. Oxtoby, N. P. et al. Data-driven sequence of changes to anatomical brain connectivity in sporadic Alzheimer’s disease. Front. Neurol. 8, 1–11 (2017).

    Article  Google Scholar 

  116. Cope, T. E. et al. Tau burden and the functional connectome in Alzheimer’s disease and progressive supranuclear palsy. Brain 141, 550–567 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  117. Brown, J. A. et al. Patient-tailored, connectivity-based forecasts of spreading brain atrophy. Neuron 104, 856–868.e5 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  118. Sintini, I. et al. Tau and amyloid relationships with resting-state functional connectivity in atypical Alzheimer’s disease. Cereb. Cortex 31, 1693–1706 (2021).

    Article  PubMed  Google Scholar 

  119. Franzmeier, N. et al. Functional brain architecture is associated with the rate of tau accumulation in Alzheimer’s disease. Nat. Commun. 11, 1–17 (2020).

    Article  Google Scholar 

  120. Franzmeier, N. et al. Tau deposition patterns are associated with functional connectivity in primary tauopathies. Nat. Commun. 13, 1–18 (2022).

    Article  Google Scholar 

  121. Lee, W. J. et al. Regional Aβ–tau interactions promote onset and acceleration of Alzheimer’s disease tau spreading. Neuron 110, 1932–1943.e5 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Jucker, M. & Walker, L. C. Self-propagation of pathogenic protein aggregates in neurodegenerative diseases. Nature 501, 45–51 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  123. Tijms, B. M. et al. Single-subject grey matter graphs in Alzheimer’s disease. PLoS ONE 8, 1–9 (2013).

    Article  Google Scholar 

  124. Pelkmans, W. et al. Grey matter network markers identify individuals with prodromal Alzheimer’s disease who will show rapid clinical decline. Brain Commun. 4, 1–9 (2022).

    Article  CAS  Google Scholar 

  125. Frost, B. & Diamond, M. I. Prion-like mechanisms in neurodegenerative diseases. Nat. Rev. Neurosci. 11, 155–159 (2010).

    Article  CAS  PubMed  Google Scholar 

  126. Prusiner, S. B. Some speculations about prions, amyloid and Alzheimer’s disease. Nejm 310, 661–663 (1984).

    Article  CAS  PubMed  Google Scholar 

  127. Zott, B. et al. A vicious cycle of β amyloid-dependent neuronal hyperactivation. Science 365, 559–565 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  128. Appel, S. H. A unifying hypothesis for the cause of amyotrophic lateral sclerosis, parkinsonism, and Alzheimer disease. Ann. Neurol. 10, 499–505 (1981).

    Article  CAS  PubMed  Google Scholar 

  129. Salehi, A. et al. Increased App expression in a mouse model of down’s syndrome disrupts NGF transport and causes cholinergic neuron degeneration. Neuron 51, 29–42 (2006).

    Article  CAS  PubMed  Google Scholar 

  130. Cioli, C., Abdi, H., Beaton, D., Burnod, Y. & Mesmoudi, S. Differences in human cortical gene expression match the temporal properties of large-scale functional networks. PLoS ONE 9, 1–28 (2014).

    Article  Google Scholar 

  131. Rittman, T. et al. Regional expression of the MAPT gene is associated with loss of hubs in brain networks and cognitive impairment in Parkinson disease and progressive supranuclear palsy. Neurobiol. Aging 48, 153–160 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  132. Leng, K. et al. Molecular characterization of selectively vulnerable neurons in Alzheimer’s disease. Nat. Neurosci. 24, 276–287 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  133. Damoiseaux, J. S. & Greicius, M. D. Greater than the sum of its parts: a review of studies combining structural connectivity and resting-state functional connectivity. Brain Struct. Funct. 213, 525–533 (2009).

    Article  PubMed  Google Scholar 

  134. Chennu, S. et al. Brain networks predict metabolism, diagnosis and prognosis at the bedside in disorders of consciousness. Brain 140, 2120–2132 (2017).

    Article  PubMed  Google Scholar 

  135. Raj, A., Kuceyeski, A. & Weiner, M. A network diffusion model of disease progression in dementia. Neuron 73, 1204–15 (2012). This work presents the first dynamical systems model of disease progression, which models disease progression as diffusion along the brain’s connectivity network.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  136. Schäfer, A., Peirlinck, M., Linka, K. & Kuhl, E. Bayesian physics-based modeling of tau propagation in Alzheimer’s disease. Front. Physiol. 12, 1–12 (2021).

    Article  Google Scholar 

  137. Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J. & Evans, A. C. Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders. PLoS Comput. Biol. 10, e1003956 (2014). This work proposes an ESM that accounts for production and clearance of pathogenic proteins.

    Article  PubMed  PubMed Central  Google Scholar 

  138. Raj, A. et al. Network diffusion model of progression predicts longitudinal patterns of atrophy and metabolism in Alzheimer’s disease. Cell Rep. 10, 359–369 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Mišić, B. et al. Cooperative and competitive spreading dynamics on the human connectome. Neuron 86, 1518–1529 (2015).

    Article  PubMed  Google Scholar 

  140. Weickenmeier, J., Kuhl, E. & Goriely, A. Multiphysics of prionlike diseases: progression and atrophy. Phys. Rev. Lett. 121, 158101 (2018). This work simulates the anisotropic propagation and accumulation of toxic proteins, demonstrating that a single model with different initial seeding regions reproduces the expected progression of protein aggregation in different neurodegenerative diseases.

    Article  CAS  PubMed  Google Scholar 

  141. Garbarino, S. & Lorenzi, M. Modeling and inference of spatio-temporal protein dynamics across brain networks. In Information Processing in Medical Imaging 2019; Lecture Notes in Computer Science 11492 (eds. Chung, A. et al.) 57–69 (Springer, 2019).

  142. Bertsch, M., Franchi, B., Meacci, L., Primicerio, M. & Tesi, M. C. The amyloid cascade hypothesis and Alzheimer’s disease: a mathematical model. Eur. J. Appl. Math. 32, 749–768 (2021).

    Article  Google Scholar 

  143. Powell, F., Tosun, D. & Raj, A. Network-constrained technique to characterize pathology progression rate in Alzheimer’s disease. Brain Commun. 3, 1–16 (2021).

    Article  Google Scholar 

  144. Garbarino, S. & Lorenzi, M. Investigating hypotheses of neurodegeneration by learning dynamical systems of protein propagation in the brain. Neuroimage 235, 117980 (2021).

    Article  CAS  PubMed  Google Scholar 

  145. Vogel, J. W. et al. Spread of pathological tau proteins through communicating neurons in human Alzheimer’s disease. Nat. Commun. 11, 2612 (2020). This work uses an ESM to simulate tau spreading in Alzheimer disease, with evaluation of the model against tau-PET and amyloid-PET data demonstrating that the spatial pattern of tau spread is unaffected by amyloid-β, but that amyloid-β may play a role in accelerating tau spread.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  146. Iaccarino, L. et al. NeuroImage: clinical local and distant relationships between amyloid, tau and neurodegeneration in Alzheimer’s disease. NeuroImage Clin. 17, 452–464 (2018).

    Article  PubMed  Google Scholar 

  147. Torok, J., Maia, P. D., Powell, F., Pandya, S. & Raj, A. A method for inferring regional origins of neurodegeneration. Brain 141, 863–876 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  148. Iturria-Medina, Y., Carbonell, F. M., Sotero, R. C., Chouinard-Decorte, F. & Evans, A. C. Multifactorial causal model of brain (dis)organization and therapeutic intervention: application to Alzheimer’s disease. Neuroimage 152, 60–77 (2017).

    Article  PubMed  Google Scholar 

  149. Iturria-Medina, Y., Sotero, R. C., Toussaint, P. J., Mateos-Perez, J. M. & Evans, A. C. Early role of vascular dysregulation on late-onset Alzheimer’s disease based on multifactorial data-driven analysis. Nat. Commun. 7, 11934 (2016). This work uses a multifactorial data-driven model to provide evidence for the early role of vascular dysregulation in Alzheimer disease.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  150. Iturria-Medina, Y., Carbonell, F. M. & Evans, A. C. Multimodal imaging-based therapeutic fingerprints for optimizing personalized interventions: application to neurodegeneration. Neuroimage 179, 40–50 (2018).

    Article  PubMed  Google Scholar 

  151. He, T. et al. A coupled-mechanisms modelling framework for neurodegeneration. In Medical Image Computing and Computer Assisted Intervention 2023 (eds. Greenspan, H. et al.) 459–469 (Springer, 2023).

  152. Ashford, J. W. & Schmitt, F. A. Modeling the time-course of Alzheimer dementia. Curr. Psychiatry Rep. 3, 20–28 (2001).

    Article  CAS  PubMed  Google Scholar 

  153. Gomeni, R. et al. Modeling Alzheimer’s disease progression using the disease system analysis approach. Alzheimer’s Dement. 8, 39–50 (2012).

    Article  Google Scholar 

  154. Budgeon, C. A. et al. Constructing longitudinal disease progression curves using sparse, short-term individual data with an application to Alzheimer’s disease. Stat. Med. 36, 2720–2734 (2017).

    Article  CAS  PubMed  Google Scholar 

  155. Oxtoby, N. P. et al. Data-driven models of dominantly-inherited Alzheimer’s disease progression. Brain 141, 1529–1544 (2018). This work applies an event-based model and a differential equation model to characterize timelines of dominantly inherited Alzheimer disease progression and predict symptom onset.

    Article  PubMed  PubMed Central  Google Scholar 

  156. Ahmadi, K. et al. Gray matter hypoperfusion is a late pathological event in the course of Alzheimer’s disease. J. Cereb. Blood Flow. Metab. 43, 565–580 (2023).

    Article  CAS  PubMed  Google Scholar 

  157. Aisen, P. S. et al. Clinical core of the Alzheimer’s Disease Neuroimaging Initiative: progress and plans. Alzheimer’s Dement. 6, 239–246 (2010).

    Article  Google Scholar 

  158. Byrne, L. M. et al. Evaluation of mutant huntingtin and neurofilament proteins as potential markers in Huntington’s disease. Sci. Transl. Med. 10, eaat7108 (2018).

    Article  PubMed  Google Scholar 

  159. Bilgel, M. & Jedynak, B. M. Predicting time to dementia using a quantitative template of disease progression. Alzheimer’s Dement. 11, 205–215 (2019).

    Google Scholar 

  160. Maheux, E. et al. Forecasting individual progression trajectories in Alzheimer’s disease. Nat. Commun. 14, 761 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  161. Insel, P. S., Donohue, M. C., Berron, D., Hansson, O. & Mattsson-Carlgren, N. Time between milestone events in the Alzheimer’s disease amyloid cascade. Neuroimage 227, 117676 (2021).

    Article  CAS  PubMed  Google Scholar 

  162. Iddi, S. et al. Estimating the evolution of disease in the Parkinson’s Progression Markers Initiative. Neurodegener. Dis. 18, 173–190 (2018).

    Article  PubMed  Google Scholar 

  163. Koval, I. et al. Forecasting individual progression trajectories in Huntington disease enables more powered clinical trials. Sci. Rep. 12, 1–14 (2022).

    Article  Google Scholar 

  164. Leuzy, A. et al. Biomarker-based prediction of longitudinal tau positron emission tomography in Alzheimer disease. JAMA Neurol. 79, 149–158 (2022).

    Article  PubMed  Google Scholar 

  165. Berron, D. et al. Early stages of tau pathology and its associations with functional connectivity, atrophy and memory. Brain 144, 2771–2783 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  166. Wijeratne, P. A. et al. An image-based model of brain volume biomarker changes in Huntington’s disease. Ann. Clin. Transl. Neurol. 5, 570–582 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  167. Oxtoby, N. P. et al. Sequence of clinical and neurodegeneration events in Parkinson’s disease progression. Brain 144, 975–988 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  168. Dekker, I. et al. The sequence of structural, functional and cognitive changes in multiple sclerosis. NeuroImage Clin. 29, 102550 (2021).

    Article  PubMed  Google Scholar 

  169. Young, A. L. et al. Characterizing the clinical features and atrophy patterns of MAPT-related frontotemporal dementia with disease progression modeling. Neurology 97, e941–e952 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  170. Gabel, M. C. et al. Evolution of white matter damage in amyotrophic lateral sclerosis. Ann. Clin. Transl. Neurol. 7, 722–732 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  171. Broad, R. J. et al. Neurite orientation and dispersion density imaging (NODDI) detects cortical and corticospinal tract degeneration in ALS. J. Neurol. Neurosurg. Psychiatry 90, 404–411 (2019).

    Article  PubMed  Google Scholar 

  172. Wen, J. et al. Neurite density is reduced in the presymptomatic phase of C9orf72 disease. J. Neurol. Neurosurg. Psychiatry 90, 387–394 (2019).

    Article  PubMed  Google Scholar 

  173. Scotton, W. J. et al. A data-driven model of brain volume changes in progressive supranuclear palsy. Brain Commun. 4, fcac098 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  174. Pascuzzo, R. et al. Prion propagation estimated from brain diffusion MRI is subtype dependent in sporadic Creutzfeldt–Jakob disease. Acta Neuropathol. 140, 169–181 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  175. Firth, N. C. et al. Longitudinal neuroanatomical and cognitive progression of posterior cortical atrophy. Brain 142, 2082–2095 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  176. ten Kate, M. et al. Atrophy subtypes in prodromal Alzheimer’s disease are associated with cognitive decline. Brain 141, 3443–3456 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  177. Saito, Y. et al. Temporal progression patterns of brain atrophy in corticobasal syndrome and progressive supranuclear palsy revealed by Subtype and Stage Inference (SuStaIn). Front. Neurol. 13, 814768 (2022).

    Article  PubMed  PubMed Central  Google Scholar 

  178. Vogel, J. W. et al. Four distinct trajectories of tau deposition identified in Alzheimer’s disease. Nat. Med. 27, 871–881 (2021). This work applies the SuStaIn algorithm to multi-cohort and multi-tracer tau-PET data, identifying four subtypes of tau deposition in Alzheimer disease with distinct progression patterns and cognitive profiles.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  179. Collij, L. E. et al. Spatial-temporal patterns of amyloid-β accumulation: a Subtype and Stage Inference model analysis. Neurology 98, e1692–e1703 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  180. Tijms, B. M. et al. Pathophysiological subtypes of Alzheimer’s disease based on cerebrospinal fluid proteomics. Brain 143, 3776–3792 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  181. Chen, H. et al. Transferability of Alzheimer’s disease progression subtypes to an independent population cohort. Neuroimage 271, 120005 (2023).

    Article  PubMed  Google Scholar 

  182. Young, A. L. et al. Data-driven neuropathological staging and subtyping of TDP-43 proteinopathies. Brain 146, 2975–2988 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  183. Young, A. L. et al. Genomewide association study of data-driven Alzheimer’s disease subtypes. In Alzheimer’s Association International Conference 15 https://doi.org/10.3389/fnagi.2023.1290657 (2018).

  184. Ahmed, Z. et al. A novel in vivo model of tau propagation with rapid and progressive neurofibrillary tangle pathology: the pattern of spread is determined by connectivity, not proximity. Acta Neuropathol. 127, 667–683 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  185. Adewale, Q., Khan, A. F., Carbonell, F. & Iturria-Medina, Y. Integrated transcriptomic and neuroimaging brain model decodes biological mechanisms in aging and Alzheimer’s disease. eLife 10, 1–22 (2021).

    Article  Google Scholar 

  186. Sanz Perl, Y. et al. Model-based whole-brain perturbational landscape of neurodegenerative diseases. eLife 12, e83970 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  187. Petersen, R. C. et al. Vitamin E and donepezil for the treatment of mild cognitive impairment. N. Engl. J. Med. 352, 2379–2388 (2005).

    Article  CAS  PubMed  Google Scholar 

  188. Oxtoby, N. P., Shand, C., Cash, D. M., Alexander, D. C. & Barkhof, F. Targeted screening for Alzheimer’s disease clinical trials using data-driven disease progression models. Front. Artif. Intell. 5, 1–9 (2022).

    Article  Google Scholar 

  189. Abi Nader, C., Ayache, N., Frisoni, G. B., Robert, P. & Lorenzi, M. Simulating the outcome of amyloid treatments in Alzheimer’s disease from imaging and clinical data. Brain Commun. 3, 1–17 (2021).

    Article  Google Scholar 

  190. Leuzy, A. et al. Comparison of group-level and individualized brain regions for measuring change in longitudinal tau positron emission tomography in Alzheimer disease. JAMA Neurol. 80, 614–623 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  191. Staffaroni, A. M. et al. Temporal order of clinical and biomarker changes in familial frontotemporal dementia. Nat. Med. 28, 2194–2206 (2022). This work uses disease progression modelling to build multimodal models of familial frontotemporal dementia, finding that the temporal ordering differs by genotype and identifying subsets of biomarkers with optimal sensitivity for clinical trials at different disease stages.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  192. Shand, C. et al. Heterogeneity in preclinical Alzheimer’s disease trial cohort identified by image-based data-driven disease progression modelling. Preprint at medRxiv 1–19 https://doi.org/10.1101/2023.02.07.23285572 (2023).

  193. Sperling, R. A. et al. Association of factors with elevated amyloid burden in clinically normal older individuals. JAMA Neurol. 77, 735–745 (2020).

    Article  PubMed  Google Scholar 

  194. Sperling, R. A. et al. Trial of solanezumab in preclinical Alzheimer’s disease. N. Engl. J. Med. 389, 1096–1107 (2023).

    Article  CAS  PubMed  Google Scholar 

  195. Sauty, B. & Durrleman, S. Progression models for imaging data with longitudinal variational auto encoders. In Proc. Medical Image Computing and Computer Assisted Intervention 2022; Lect. Notes Comput. Sci. 13431 (eds. Wang, L. et al.) 3–13 (Springer, 2022).

  196. Yang, Z. et al. A deep learning framework identifies dimensional representations of Alzheimer’s disease from brain structure. Nat. Commun. 12, 7065 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  197. Martí-Juan, G., Lorenzi, M. & Piella, G. MC-RVAE: multi-channel recurrent variational autoencoder for multimodal Alzheimer’s disease progression modelling. Neuroimage 268, 119892 (2023).

    Article  PubMed  Google Scholar 

  198. Zhou, Y. et al. A foundation model for generalizable disease detection from retinal images. Nature 622, 156–163 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  199. Budd Haeberlein, S. et al. Two randomized phase 3 studies of aducanumab in early Alzheimer’s disease. J. Prev. Alzheimer’s Dis. 9, 197–210 (2022).

    CAS  Google Scholar 

  200. van Dyck, C. H. et al. Lecanemab in early Alzheimer’s disease. N. Engl. J. Med. 388, 9–21 (2022).

    Article  PubMed  Google Scholar 

  201. Castro, D. C., Walker, I. & Glocker, B. Causality matters in medical imaging. Nat. Commun. 11, 1–10 (2020).

    Article  Google Scholar 

  202. Scelsi, M. A. et al. Genetic study of multimodal imaging Alzheimer’s disease progression score implicates novel loci. Brain 141, 2167–2180 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  203. Jones, D. et al. A computational model of neurodegeneration in Alzheimer’s disease. Nat. Commun. 13, 1643 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  204. Verdi, S., Marquand, A. F., Schott, J. M. & Cole, J. H. Beyond the average patient: how neuroimaging models can address heterogeneity in dementia. Brain 144, 2946–2953 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  205. Cole, J. H. et al. Predicting brain age with deep learning from raw imaging data results in a reliable and heritable biomarker. Neuroimage 163, 115–124 (2017).

    Article  PubMed  Google Scholar 

  206. Cole, J. H., Marioni, R. E., Harris, S. E. & Deary, I. J. Brain age and other bodily ‘ages’: implications for neuropsychiatry. Mol. Psychiatry 24, 266–281 (2019).

    Article  PubMed  Google Scholar 

  207. Bethlehem, R. A. I. et al. Brain charts for the human lifespan. Nature 604, 525–533 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  208. Salive, M. E. Multimorbidity in older adults. Epidemiol. Rev. 35, 75–83 (2013).

    Article  PubMed  Google Scholar 

  209. Rahimi, J. & Kovacs, G. G. Prevalence of mixed pathologies in the aging brain. Alzheimer’s Res. Ther. 6, 1–11 (2014).

    Google Scholar 

  210. Zhang, X. et al. Bayesian model reveals latent atrophy factors with dissociable cognitive trajectories in Alzheimer’s disease. Proc. Natl Acad. Sci. USA 113, E6535–E6544 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  211. Edison, P. et al. Amyloid, hypometabolism, and cognition in Alzheimer disease: an [11C]PIB and [18F]FDG PET study. Neurology 68, 501–508 (2007).

    Article  CAS  PubMed  Google Scholar 

  212. Butterfield, D. A. & Halliwell, B. Oxidative stress, dysfunctional glucose metabolism and Alzheimer disease. Nat. Rev. Neurosci. 20, 148–160 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  213. Wiseman, F. K. et al. A genetic cause of Alzheimer disease: mechanistic insights from Down syndrome. Nat. Rev. Neurosci. 16, 564–574 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  214. Iadecola, C. The overlap between neurodegenerative and vascular factors in the pathogenesis of dementia. Acta Neuropathol. 120, 287–296 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  215. Iadecola, C. Neurovascular regulation in the normal brain and in Alzheimer’s disease. Nat. Rev. Neurosci. 5, 347–360 (2004).

    Article  CAS  PubMed  Google Scholar 

  216. Bell, R. D. & Zlokovic, B. V. Neurovascular mechanisms and blood–brain barrier disorder in Alzheimer’s disease. Acta Neuropathol. 118, 103–113 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  217. Vogels, T., Murgoci, A. N. & Hromádka, T. Intersection of pathological tau and microglia at the synapse. Acta Neuropathol. Commun. 7, 109 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  218. Terada, T. et al. In vivo direct relation of tau pathology with neuroinflammation in early Alzheimer’s disease. J. Neurol. 266, 2186–2196 (2019).

    Article  PubMed  Google Scholar 

  219. Heneka, M. T. et al. Neuroinflammation in Alzheimer’s disease. Lancet Neurol. 14, 388–405 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  220. Aglinskas, A., Hartshorne, J. K. & Anzellotti, S. Contrastive machine learning reveals the structure of neuroanatomical variation within autism. Science 376, 1070–1074 (2022).

    Article  CAS  PubMed  Google Scholar 

  221. Archetti, D. et al. Inter-cohort validation of sustain model for Alzheimer’s disease. Front. Big Data 4, 1–13 (2021).

    Article  Google Scholar 

  222. Archetti, D. et al. Multi-study validation of data-driven disease progression models to characterize evolution of biomarkers in Alzheimer’s disease. NeuroImage Clin. 24, 101954 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  223. Golriz Khatami, S., Salimi, Y., Hofmann-Apitius, M., Oxtoby, N. P. & Birkenbihl, C. Comparison and aggregation of event sequences across ten cohorts to describe the consensus biomarker evolution in Alzheimer’s disease. Alzheimer’s Res. Ther. 14, 1–14 (2022).

    Google Scholar 

  224. Clément, A. N. et al. SimulAD: a dynamical model for personalized simulation and disease staging in Alzheimer’s disease. Neurobiol. Aging 113, 73–83 (2022).

    Article  PubMed  Google Scholar 

  225. Young, A. L., Oxtoby, N. P., Ourselin, S., Schott, J. M. & Alexander, D. C. A simulation system for biomarker evolution in neurodegenerative disease. Med. Image Anal. 26, 47–56 (2015).

    Article  PubMed  Google Scholar 

  226. Dadgar-kiani, E. et al. Mesoscale connections and gene expression empower whole-brain modeling of α-synuclein spread, aggregation, and decay dynamics. CellReports 41, 111631 (2022).

    CAS  Google Scholar 

  227. Marinescu, R. V. et al. TADPOLE challenge: prediction of longitudinal evolution in Alzheimer’s disease. Preprint at arXiv https://doi.org/10.1007/978-3-030-32281-6_1 (2018).

  228. Marinescu, R. V. et al. The Alzheimer’s Disease Prediction Of Longitudinal Evolution (TADPOLE) challenge: results after 1 year follow-up. J. Mach. Learn. Biomed. Imaging 19, 1–60 (2021).

    Google Scholar 

  229. Bron, E. E. et al. Standardized evaluation of algorithms for computer-aided diagnosis of dementia based on structural MRI: the CADDementia challenge. Neuroimage 111, 562–579 (2015).

    Article  PubMed  Google Scholar 

  230. Bron, E. E. et al. Ten years of image analysis and machine learning competitions in dementia. Neuroimage 253, 119083 (2022).

    Article  PubMed  Google Scholar 

  231. Jack, C. R. et al. NIA-AA research framework: toward a biological definition of Alzheimer’s disease. Alzheimer’s Dement. 14, 535–562 (2018).

    Article  Google Scholar 

Download references

Acknowledgements

A.L.Y. was supported by the Wellcome Trust (227341/Z/23/Z) and the Medical Research Council (MR/T027800/1). N.P.O. acknowledges funding from his UKRI Future Leaders Fellowship (MR/S03546X/1) and the Early Detection of Alzheimer’s Disease Subtypes project (E-DADS; EU JPND), (MR/T046422/1). S.G. acknowledges the financial contribution from the National Group of Scientific Computing with the INdAM–GNCS Project (CUP_E53C22001930001) — Computational methods for modelling neurodegenerative diseases progression, the support of NEXTGENERATIONEU (NGEU), funding by the Italian Ministry of University and Research (MUR), the National Recovery and Resilience Plan (NRRP) and project MNESYS (PE0000006) — A Multiscale integrated approach to the study of the nervous system in health and disease (DN. 1553 11.10.2022), and the financial contribution from the Italian Ministry of Health with the project NeuroArtP3 (NET-2018-12366666) — Artificial intelligence of imaging and clinical neurological data for predictive, preventive, and personalized medicine. F.B. is supported by the National Institute for Health and Care Research (NIHR) Biomedical Research Centre at University College London Hospitals NHS Foundation Trust (UCLH). J.M.S. acknowledges the support of the NIHR UCLH Biomedical Research Centre, Wolfson Foundation, Alzheimer’s Research UK, Brain Research UK, Weston Brain Institute, Medical Research Council, British Heart Foundation and Alzheimer’s Association. D.C.A.’s work on this topic is supported by The Wellcome Trust (221915/Z/20/Z), JPND/MRC grant (MR/T046422/1) and the NIHR UCLH Biomedical Research Centre support. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 666992.

Author information

Author notes

  1. These authors contributed equally: Alexandra L. Young, Neil P. Oxtoby.

Authors and Affiliations

  1. UCL Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK

    Alexandra L. Young, Neil P. Oxtoby, Frederik Barkhof & Daniel C. Alexander

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

    Alexandra L. Young

  3. Life Science Computational Laboratory, IRCCS Ospedale Policlinico San Martino, Genova, Italy

    Sara Garbarino

  4. Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, UK

    Nick C. Fox & Jonathan M. Schott

  5. Department of Radiology & Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, The Netherlands

    Frederik Barkhof

Authors
  1. Alexandra L. Young
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Neil P. Oxtoby
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Sara Garbarino
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nick C. Fox
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Frederik Barkhof
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Jonathan M. Schott
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Daniel C. Alexander
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The authors contributed to all aspects of the article.

Corresponding authors

Correspondence to Alexandra L. Young or Neil P. Oxtoby.

Ethics declarations

Competing interests

N.P.O. consults for TheraPanacea (FR) and Queen Square Analytics (UK). F.B. and D.C.A. hold an equity stake in Queen Square Analytics (UK). F.B. is a steering committee or Data Safety Monitoring Board member for Biogen, Merck, ATRI/ACTC and Prothena; a consultant for Roche, Celltrion, Rewind Therapeutics, Merck, IXICO, Jansen and Combinostics; and has research agreements with Merck, Biogen, GE Healthcare and Roche. The other authors declare no competing interests.

Peer review

Peer review information

Nature Reviews Neuroscience thanks A. Ibáñez, who co-reviewed with V. Medel, B. Jedynak and E. Kuhl for their contribution to the peer review of this work.

Additional information

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

Related links

TADPOLE: https://tadpole.grand-challenge.org

Supplementary information

Glossary

Amyloid-β

The main component of amyloid plaques, a key pathological hallmark of Alzheimer disease.

Atrophy

A reduced cross-sectional brain volume measured relative to a population, or longitudinal brain volume loss compared with previous visits.

Network distance

The length of the shortest path between two locations along a network.

Prion-like spreading

Templated growth and spreading of misfolded proteins.

Protein propagation

The spreading of proteins from one region to another.

Tau

A protein for which abnormal, hyperphosphorylated forms are associated with neurofibrillary tangles, a key pathological hallmark of Alzheimer disease.

TDP43

A protein for which abnormal forms are associated with frontotemporal dementia, amyotrophic lateral sclerosis and the recently defined condition limbic-predominant age-related TDP43 encephalopathy.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Young, A.L., Oxtoby, N.P., Garbarino, S. et al. Data-driven modelling of neurodegenerative disease progression: thinking outside the black box. Nat. Rev. Neurosci. 25, 111–130 (2024). https://doi.org/10.1038/s41583-023-00779-6

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41583-023-00779-6

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing