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Updated: Jun 2, 2025

Automated, Long-term Behavioral Assay for Cognitive Functions in Multiple Genetic Models of Alzheimer's Disease, Using IntelliCage
Published on: August 4, 2018
Detecting early cognitive deficits in preclinical Alzheimer's disease using a remote digital multi-day learning
Roos J Jutten1, Daniel Soberanes2, Cassidy P Molinare2
1Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02129, USA. rjutten@mgh.harvard.edu.
Abstract:
Remote, digital cognitive testing on an individual's own device provides the opportunity to deploy previously understudied but promising cognitive paradigms in preclinical Alzheimer's disease (AD). The Boston Remote Assessment for NeuroCognitive Health (BRANCH) captures a personalized learning curve for the same information presented over seven consecutive days. Here, we examined BRANCH multi-day learning curves (MDLCs) in 167 cognitively unimpaired older adults (age = 74.3 ± 7.5, 63% female) with different amyloid-β (A) and tau (T) biomarker profiles on positron emission tomography. MDLC scores decreased across ascending biomarker groups, with the A + T- group performing numerically worse (β = -0.24, 95%CI[-0.55,0.07], p = 0.128) and the A + T+ group performing significantly worse (β = -0.58, 95%CI[-1.06,-0.10], p = 0.018) than the A-T- group. Further, lower MDLC scores were associated with greater cortical thinning (β = 0.18, 95%CI[0.04,0.34], p = 0.013). Our results suggest that diminished MDLCs track with advanced AD pathophysiology, and demonstrate how a digital multi-day learning paradigm can provide novel insights about cognitive decline during preclinical AD.
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