Open AccessAnalyzing large Alzheimer's disease cognitive datasets: Considerations and challenges
Author(s) -
Bellio Maura,
Oxtoby Neil P.,
Walker Zuzana,
Henley Susie,
Ribbens Annemie,
Blandford Ann,
Alexander Daniel C.,
Yong Keir X. X.
Publication year - 2020
Publication title -
alzheimer's and dementia: diagnosis, assessment and disease monitoring
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.497
H-Index - 37
ISSN - 2352-8729
DOI - 10.1002/dad2.12135
Subject(s) - cognition , computer science , transparency (behavior) , data sharing , data science , disease , cognitive computing , perspective (graphical) , cognitive aging , computational model , psychology , artificial intelligence , medicine , neuroscience , alternative medicine , computer security , pathology
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.