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

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.

Keywords: Alzheimer's disease; cognition; cognitive tests; composite scores; data‐driven computational models; data‐sharing initiatives; mild cognitive impairment; predictive models.

FIGURE 1

Flowchart representing example (A) research questions and steps regarding (B) test selection, (C) quality control and standardization, and (D) computational/statistical methods. Example research questions (A) correspond closely to test selection (B), while subsequent processes outlined in steps C and D are broadly relevant across questions and tests. Example measures are reported in italics and section headings underlined. Dashed arrows indicate revisiting steps, for example, revisiting test selection owing to missing data