Non-Parametric Mixture Modelling and its Application to Disease Progression Modelling Article Swipe
Nicholas C. Firth
,
Neil P. Oxtoby
,
Silvia Primativo
,
Emilie Brotherhood
,
Alexandra L. Young
,
Keir Yong
,
Sebastian J. Crutch
,
Daniel C. Alexander
·
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1101/297978
YOU?
·
· 2018
· Open Access
·
· DOI: https://doi.org/10.1101/297978
Dementia is characterised by its progressive degeneration of cognitive abilities. In research cohorts, detailed neuropsychological test batteries are often administered to better understand how cognition changes over time. Understanding cognitive changes in dementia is of great importance, particularly in determining how structural changes in the brain may affect cognition and in facilitating earlier detection of symptomatic changes. Disease progression models are often applied to these data to understand how a disease changes over time from cross-sectional data or to disease trajectories from large numbers of individuals. Previous disease progression models used to build longitudinal models from cross-sectional data have focused on brain imaging data; however, these models are not directly applicable to cognitive data. Here we use the novel, non-parametric, Kernel Density Estimation Mixture Modelling (KDEMM) approach and demonstrate accurate modelling of the progression of cognitive test data. We found that using KDEMM resulted in more accurate models of disease progression in simulated data compared to Gaussian Mixture Models (GMMs) for the majority of parameters used to simulate the data. When comparing KDEMM and GMM to cognitive data collected in different Alzheimers Disease subtypes, we found the KDEMM resulted in a model much more in line with clinical phenotype. We anticipate that the KDEMM will be used to integrate cognitive test data, and other non-normally distributed datasets into complex disease progression models.
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/297978
- https://www.biorxiv.org/content/biorxiv/early/2018/04/10/297978.full.pdf
- OA Status
- green
- Cited By
- 2
- References
- 35
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2796586717
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2796586717Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/297978Digital Object Identifier
- Title
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Non-Parametric Mixture Modelling and its Application to Disease Progression ModellingWork title
- Type
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preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2018Year of publication
- Publication date
-
2018-04-10Full publication date if available
- Authors
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Nicholas C. Firth, Neil P. Oxtoby, Silvia Primativo, Emilie Brotherhood, Alexandra L. Young, Keir Yong, Sebastian J. Crutch, Daniel C. AlexanderList of authors in order
- Landing page
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https://doi.org/10.1101/297978Publisher landing page
- PDF URL
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https://www.biorxiv.org/content/biorxiv/early/2018/04/10/297978.full.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://www.biorxiv.org/content/biorxiv/early/2018/04/10/297978.full.pdfDirect OA link when available
- Concepts
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Cognition, Mixture model, Neuropsychology, Dementia, Disease, Parametric statistics, Computer science, Cognitive decline, Psychology, Cognitive psychology, Machine learning, Artificial intelligence, Medicine, Statistics, Neuroscience, Pathology, MathematicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2020: 1, 2018: 1Per-year citation counts (last 5 years)
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35Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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