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2017 Grants - Stricker
Can Subtle Cognitive Decline Predict Biomarker Positivity and Incident MCI?
Nikki Stricker, Ph.D.
2017 Alzheimer’s Association Research Grant (AARG)
Do people in middle age develop an early form of memory loss that can predict their risk for dementia?
Because Alzheimer’s disease is a progressive disorder, scientists are examining biological changes (or biomarkers) that can identify the disorder at its earliest stages, when potential therapies will be most effective. Much of their research has focused on tau and beta-amyloid, two brain molecules known to accumulate into clumps long before clinical symptoms of dementia arise. These clumps have been shown to promote brain cell dysfunction and death. They may also lead to mild cognitive impairment (MCI), a condition of mild impairment in memory and other cognitive functions that may precede Alzheimer’s. However, scientists have not yet devised an effective method for determining which individuals are at risk of MCI.
Nikki Stricker, Ph.D., and colleagues hypothesize that certain people have an identifiable form of cognitive decline that can predict (1) whether they may have harmful levels of beta-amyloid and tau and (2) whether they will go on to develop MCI and Alzheimer’s disease. For their grant, the researchers will characterize this subtle form of decline using a novel, computer-based analytical tool called CogState. CogState will analyze memory tests and brain scans taken over time from a group of cognitively normal participants aged 50 and over, most of whom are already enrolled in another study of aging. The investigators will also assess their participants’ brain health using a more traditional tool, the Auditory Verbal Learning Test (AVLT). They will then determine whether CogState is more effective than AVLT at predicting harmful protein accumulation and MCI among the study participants.
If successful, Dr. Stricker’s effort could shed new light on how early forms of cognitive decline in middle age may lead to the onset of Alzheimer’s disease and other dementias. Such knowledge could lead to more effective, cost-efficient tools for identifying Alzheimer’s risk and preventing disease onset.