Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.

Published online

Journal Article

The heterogeneity of neurodegenerative diseases is a key confound to disease understanding and treatment development, as study cohorts typically include multiple phenotypes on distinct disease trajectories. Here we introduce a machine-learning technique-Subtype and Stage Inference (SuStaIn)-able to uncover data-driven disease phenotypes with distinct temporal progression patterns, from widely available cross-sectional patient studies. Results from imaging studies in two neurodegenerative diseases reveal subgroups and their distinct trajectories of regional neurodegeneration. In genetic frontotemporal dementia, SuStaIn identifies genotypes from imaging alone, validating its ability to identify subtypes; further the technique reveals within-genotype heterogeneity. In Alzheimer's disease, SuStaIn uncovers three subtypes, uniquely characterising their temporal complexity. SuStaIn provides fine-grained patient stratification, which substantially enhances the ability to predict conversion between diagnostic categories over standard models that ignore subtype (p = 7.18 × 10-4) or temporal stage (p = 3.96 × 10-5). SuStaIn offers new promise for enabling disease subtype discovery and precision medicine.

Full Text

Duke Authors

Cited Authors

  • Young, AL; Marinescu, RV; Oxtoby, NP; Bocchetta, M; Yong, K; Firth, NC; Cash, DM; Thomas, DL; Dick, KM; Cardoso, J; van Swieten, J; Borroni, B; Galimberti, D; Masellis, M; Tartaglia, MC; Rowe, JB; Graff, C; Tagliavini, F; Frisoni, GB; Laforce, R; Finger, E; de Mendonça, A; Sorbi, S; Warren, JD; Crutch, S; Fox, NC; Ourselin, S; Schott, JM; Rohrer, JD; Alexander, DC; Genetic FTD Initiative (GENFI), ; Alzheimer’s Disease Neuroimaging Initiative (ADNI),

Published Date

  • October 15, 2018

Published In

Volume / Issue

  • 9 / 1

Start / End Page

  • 4273 -

PubMed ID

  • 30323170

Pubmed Central ID

  • 30323170

Electronic International Standard Serial Number (EISSN)

  • 2041-1723

Digital Object Identifier (DOI)

  • 10.1038/s41467-018-05892-0

Language

  • eng

Conference Location

  • England