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Analyzing large Alzheimer’s disease cognitive datasets: Considerations and challenges

Bellio, M; Oxtoby, NP; Walker, Z; Henley, S; Ribbens, A; Blandford, A; Alexander, DC; (2020) Analyzing large Alzheimer’s disease cognitive datasets: Considerations and challenges. Alzheimer’s & Dementia: Diagnosis, Assessment & Disease Monitoring , 12 (1) , Article e12135. 10.1002/dad2.12135. Green open access

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Abstract

Recent data-sharing initiatives of clinical and preclinical Alzheimer’s disease (AD) have led to a growing number of non-clinical researchers analyzing these datasets using modern data-driven computational methods. Cognitive tests are key components of such datasets, representing the principal clinical tool to establish phenotypes and monitor symptomatic progression. Despite the potential of computational analyses in complementing the clinical understanding of AD, the characteristics and multifactorial nature of cognitive tests are often unfamiliar to computational researchers and other non-specialist audiences. This perspective paper outlines core features, idiosyncrasies, and applications of cognitive test data. We report tests commonly featured in data-sharing initiatives, highlight key considerations in their selection and analysis, and provide suggestions to avoid risks of misinterpretation. Ultimately, the greater transparency of cognitive measures will maximize insights offered in AD, particularly regarding understanding the extent and basis of AD phenotypic heterogeneity.

Type: Article
Title: Analyzing large Alzheimer’s disease cognitive datasets: Considerations and challenges
Open access status: An open access version is available from UCL Discovery
DOI: 10.1002/dad2.12135
Publisher version: https://doi.org/10.1002/dad2.12135
Language: English
Additional information: This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/4.0/
Keywords: Alzheimer’s disease, cognition, cognitive tests, composite scores, data-sharing initiatives, datadriven computational models, mild cognitive impairment, predictive models
UCL classification: UCL
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Division of Psychiatry
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > UCL Queen Square Institute of Neurology > Neurodegenerative Diseases
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10117376
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