Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference
Alexandra L. Young,Razvan V. Marinescu,Neil P. Oxtoby,Martina Bocchetta,Keir Yong,Nicholas C. Firth,David M. Cash,David L. Thomas,Katrina M. Dick,Jorge Cardoso,Jorge Cardoso,John C. van Swieten,Barbara Borroni,Daniela Galimberti,Daniela Galimberti,Mario Masellis,Maria Carmela Tartaglia,James B. Rowe,Caroline Graff,Fabrizio Tagliavini,Giovanni B. Frisoni,Robert Laforce,Elizabeth Finger,Alexandre de Mendonça,Sandro Sorbi,Jason D. Warren,Sebastian J. Crutch,Nick C. Fox,Sebastien Ourselin,Jonathan M. Schott,Jonathan D. Rohrer,Daniel C. Alexander +31 more
Reads0
Chats0
TLDR
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 is introduced, using two neurodegenerative disease cohorts.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.read more
Citations
More filters
Journal ArticleDOI
Four distinct trajectories of tau deposition identified in Alzheimer’s disease
Jacob W. Vogel,Alexandra L. Young,Neil P. Oxtoby,Ruben Smith,Rik Ossenkoppele,Rik Ossenkoppele,Olof Strandberg,Renaud La Joie,Leon M Aksman,Michel J. Grothe,Michel J. Grothe,Yasser Iturria-Medina,Alzheimer's Disease Neuroimaging Initiative,Michael J. Pontecorvo,Michael D. Devous,Gil D. Rabinovici,Daniel C. Alexander,Chul Hyoung Lyoo,Alan C. Evans,Oskar Hansson +19 more
TL;DR: Using tau-positron emission tomography scans from 1,612 individuals, this paper identified four distinct spatiotemporal trajectories of tau pathology, ranging in prevalence from 18 to 33%.
Journal ArticleDOI
An update on genetic frontotemporal dementia.
TL;DR: Increased knowledge about genetic FTD has led to more clinical presymptomatic genetic testing but this has not yet been mirrored in the development of either an accepted FTD-specific testing protocol or provision of appropriate psychological support mechanisms for those living through the at-risk phase.
Journal Article
TMEM106B is a Genetic Modifier of Frontotemporal Lobar Degeneration with C9orf72 Hexanucleotide Repeat Expansions (P2.150)
Alice Chen-Plotkin,Michael D. Gallagher,EunRan Suh,Murray Grossman,Lauren Elman,Leo McCluskey,John Q. Trojanowski,Virginia M.-Y. Lee,Vivianna M. Van Deerlin +8 more
TL;DR: Chen et al. as mentioned in this paper evaluated TMEM106B as a genetic modifier in C9orf72-associated frontotemporal lobar degeneration (FTLD)-associated TAR DNA binding protein of 43kDa.
Journal ArticleDOI
NODDI in clinical research.
TL;DR: The applications of NODDI in clinical research are reviewed and future perspectives for investigations toward the implementation of dMRI microstructure imaging in clinical practice are discussed.
References
More filters
Journal ArticleDOI
Clinical and Biomarker Changes in Dominantly Inherited Alzheimer’s Disease
Randall J. Bateman,Chengjie Xiong,Tammie L.S. Benzinger,Anne M. Fagan,Alison Goate,Nick C. Fox,Daniel S. Marcus,Nigel J. Cairns,Xianyun Xie,Tyler Blazey,David M. Holtzman,Anna Santacruz,Virginia Buckles,Angela Oliver,Krista L. Moulder,Paul S. Aisen,Bernardino Ghetti,William E. Klunk,Eric McDade,Ralph N. Martins,Colin L. Masters,Richard Mayeux,John M. Ringman,Martin N. Rossor,Peter R. Schofield,Reisa A. Sperling,Stephen Salloway,John C. Morris +27 more
TL;DR: In this paper, a longitudinal study of 128 patients with Alzheimer's disease was conducted, where the authors used the participant's age at baseline assessment and the parent's age to calculate the estimated years from expected symptom onset (age of the participant minus parent's ages at symptom onset).
Journal ArticleDOI
Cerebrospinal fluid biomarker signature in Alzheimer's disease neuroimaging initiative subjects.
Leslie M. Shaw,Hugo Vanderstichele,Malgorzata Knapik-Czajka,Christopher M. Clark,Paul S. Aisen,Ronald C. Petersen,Kaj Blennow,Holly Soares,Adam J. Simon,Piotr Lewczuk,Robert A. Dean,Eric Siemers,William Z. Potter,Virginia M.-Y. Lee,John Q. Trojanowski +14 more
TL;DR: Develop a cerebrospinal fluid biomarker signature for mild Alzheimer's disease (AD) in Alzheimer's Disease Neuroimaging Initiative (ADNI) subjects.