A data-driven model of biomarker changes in sporadic Alzheimer's disease
- Submitting institution
-
University College London
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13931
- Type
- D - Journal article
- DOI
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10.1093/brain/awu176
- Title of journal
- BRAIN
- Article number
- -
- First page
- 2564
- Volume
- 137
- Issue
- 9
- ISSN
- 0006-8950
- Open access status
- Out of scope for open access requirements
- Month of publication
- July
- Year of publication
- 2014
- URL
-
-
- Supplementary information
-
https://dl.acm.org/action/downloadSupplement?doi=10.1145%2F2676726.2677011&file=p343-sidebyside.mpg&download=true
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
7
- Research group(s)
-
-
- Citation count
- 94
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The first realistic application of data-driven disease progression modeling to real-world sporadic-disease data. Real-world application required substantial further development of the original event-based model (Fonteijn Neuroimage 2012) to accommodate the lack of a well-defined control population and multi-modal biomarker distributions. Results verify and add substantial detail to the (Jack Lancet Neurology 2010) famous hypothetical model of Alzheimer’s disease progression. The reformulation has enabled widespread application of the event-based model in neurology, e.g. (Eshaghi Brain 2018; Wijeratne ACTN 2018; Byrne Sci. Trans. Med. 2018), and underpinned intense subsequent development (e.g. Venkatraghavan Neuroimage 2019; Young Nat. Comms. 2018; Lorenzi Neuroimage 2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -