Advance-CTR

Louisa Thompson, PhD

Assistant Professor (Research), Department of Psychiatry & Human Behavior, Warren Alpert Medical School of Brown University

Awards

Advance RI-CTR Pilot Projects Program (Cycle 7)

"Preparing for primary care-based cognitive screening interventions for the early detection of Alzheimer's disease"

Dr. Thompson's pilot project addresses a serious gap in health care for older adults in the US. Improving the detection of MCI is a critical first step toward diagnosing ADRD earlier. When left undetected, individuals with cognitive impairment are unlikely to receive a diagnostic evaluation, and without a diagnosis, they are unlikely to receive post-diagnostic medical care, and caregivers do not receive critical support and resources. This research project will lay the foundation for developing primary care-based cognitive screening interventions to promote earlier detection and treatment of ADRD. 

The early detection of cognitive impairment is one of the most important challenges in Alzheimer’s disease (AD) research. Digital assessment tools have the potential to improve the efficiency and accuracy of cognitive screening and make it possible to conduct brief assessments across different timepoints and settings. This project will test the feasibility and validity of a multi-pronged cognitive screening approach using novel digital tools to detect subtle cognitive changes in early-stage AD within at-home and primary care settings.

First, the Boston Online Cognitive Assessment (BOCA) web-based cognitive test will be used to assess cognition remotely prior to the participant’s annual primary care follow-up visit. Preliminary findings from Dr. Thompson's research and others' have shown that brief remote cognitive testing is  feasible and well tolerated by older adults; however, these findings have yet to be replicated in an ethnically diverse sample or applied in clinical settings.

Second, DCTclock, a 5-minute, tablet-based clock drawing task to assess cognitive function at each participant’s annual follow-up visit, will be administered. DCTclock uses machine learning to detect nuanced information about cognitive performance (e.g., response latencies). DCTclock performance has been associated with cerebral amyloid and tau burden (pathologic signs of AD) in older adults.

The objective of this research is to establish optimized methods for cognitive screening that are accessible, accurate and suitable for primary care settings. This work is closely aligned with Brown’s commitment to Alzheimer’s disease research.

Mentors