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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Smartphone-based detection of subtle memory decline in prodromal Alzheimer's disease
Sarah E Polk1, Lindsay R Clark2,3,4, Kristin Basche2
1German Center for Neurodegenerative Diseases (DZNE), Magdeburg, Germany. sarah.polk@dzne.de.
None:
The slow progression of Alzheimer's disease (AD) poses a challenge for the quantification of early disease-driven cognitive decline. Here, we show that frequently administered remote and unsupervised digital cognitive assessments can detect differences in cognitive decline within 30 weeks in early AD. The sample comprised 202 individuals (52-85 years old) recruited from longitudinal observational studies, who were cognitively unimpaired (CU, n = 152) or had a diagnosis of mild cognitive impairment (MCI, n = 50). Participants self-administered remote tasks testing memory precision for objects and scenes, associative memory, and familiarity-dependent memory. The MCI group showed greater decline than the CU group in the familiarity-dependent task, while stratifying the MCI group by beta-amyloid (Aβ) status (n = 21 Aβ-; n = 24 Aβ+) revealed greater change in memory precision for objects and familiarity-dependent memory in the MCI Aβ+ group. A 30-week change in the remote familiarity-dependent task was correlated with a multi-year change in annual in-person neuropsychological assessments. In conclusion, frequent remote cognitive testing is a promising tool to feasibly capture and monitor subtle and short-term cognitive decline.
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