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Title: Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Authors: Young, Alexandra L.
Marinescu, Razvan V.
Oxtoby, Neil P.
Bocchetta, Martina
Yong, Keir
Firth, Nicholas C.
Cash, David M.
Thomas, David L.
Dick, Katrina M.
Cardoso, Jorge
van Swieten, John
Borroni, Barbara
Galimberti, Daniela
Masellis, Mario
Tartaglia, Maria Carmela
Rowe, James B.
Graff, Caroline
Tagliavini, Fabrizio 
Frisoni, Giovanni B.
Laforce, Robert
Finger, Elizabeth
de Mendonça, Alexandre
Sorbi, Sandro
Warren, Jason D.
Crutch, Sebastian
Fox, Nick C. 
Ourselin, Sebastien
Schott, Jonathan M. 
Rohrer, Jonathan D. 
Alexander, Daniel C.
Ferreira, Carlos 
et al.
Issue Date: 15-Oct-2018
Publisher: Springer Nature
Serial title, monograph or event: Nature Communications
Volume: 9
Issue: 1
Abstract: 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.
ISSN: 2041-1723
DOI: 10.1038/s41467-018-05892-0
Rights: openAccess
Appears in Collections:I&D ICNAS - Artigos em Revistas Internacionais

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