Work

Features of Learning From High-frequency Cognitive Assessments as Digital Biomarkers of Cognitive Change

Year: 2023

Type: conference-abstract

Abstract: In measurement burst designs, participants’ cognitive performance is measured multiple times per day, for several days, forming a measurement burst. Ideally, these are repeated once or twice a year as people age. Such rich longitudinal data are generated by multiple processes (e.g., aging and learning) that operate on multiple timescales. We propose a Bayesian process model that can extract person-specific, substantively meaningful features of learning and change from such data. We show how to model retest gains across measurement bursts, as well as warm-up effects within a burst, while quantifying change across bursts in peak performance and accounting for short-term within-person variability. Individual differences in these features are also linked with psychosocial variables and biomarkers of cognitive decline in a one-step analysis. We also highlight how this approach allows for drawing intuitive inferences on cognitive decline with Bayesian posterior probabilities.

Source: Innovation in Aging

DOI: 10.1093/geroni/igad104.2043

Authors: Zita Oravecz, Joachim Vandekerckhove, Cuiling Wang, Mindy Katz, Jonathan Hakun, Martin Sliwinski

Institutions: Pennsylvania State University, University of California, Irvine, Albert Einstein College of Medicine

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,615