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4 results on '"Young, Alexandra"'

1. Disease progression modelling reveals heterogeneity in trajectories of Lewy-type α-synuclein pathology

Author

Mastenbroek, Sophie E., Vogel, Jacob W., Collij, Lyduine E., Serrano, Geidy E., Tremblay, Cécilia, Young, Alexandra L., Arce, Richard A., Shill, Holly A., Driver-Dunckley, Erika D., Mehta, Shyamal H., Belden, Christine M., Atri, Alireza, Choudhury, Parichita, Barkhof, Frederik, Adler, Charles H., Ossenkoppele, Rik, Beach, Thomas G., and Hansson, Oskar

Published
2024
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2. Uncovering the heterogeneity and temporal complexity of neurodegenerative diseases with Subtype and Stage Inference.

Author

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, Genetic FTD Initiative (GENFI), and Alzheimer’s Disease Neuroimaging Initiative (ADNI)

Subjects
Genetic FTD Initiative, Alzheimer’s Disease Neuroimaging Initiative, Humans, Alzheimer Disease, Neurodegenerative Diseases, Reproducibility of Results, Genotype, Phenotype, Models, Neurological, Time Factors, Frontotemporal Dementia, Models, Neurological, Alzheimer's Disease, Brain Disorders, Genetics, Aging, Neurodegenerative, Acquired Cognitive Impairment, Dementia, Alzheimer's Disease including Alzheimer's Disease Related Dementias, Neurosciences, Rare Diseases, 2.1 Biological and endogenous factors, MD Multidisciplinary
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.

Published
2018

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