Alzheimers is a devastating disease, the more so because the onset can be confusing and difficult to detect.
, professor of Human Development at Cornell University, and her colleagues want to discover and define changes in language function that occur in early and preclinical Alzheimer’s disease (AD) in order to contrast these changes with those that may occur normally with aging.
With a grant from the Institute for the Social Sciences, and seed grant funding from both CITRA (Cornell Institute for Translational Research) and BLCC (Bronfenbrenner Life Course Center), the interdisciplinary team will complete collection and analysis of pilot data and share their initial findings. In addition to Barbara Lust, the team includes Janet Sherman, Massachusetts General Hospital; Suzanne Flynn, Massachusetts Institute of Technology; and Alexander Immerman, Cornell Language Acquisition Lab. For the study, the researchers will administer a set of language and thought tasks to participants and look for differences among three groups: a healthy aging group, a group of patients with early Alzheimers’ Disease, and a group of young adults, 20-29 years old. In addition, a study of the role of bilingualism will be initiated.
Results from the cognitive and linguistic tasks will be correlated to data from a detailed background questionnaire, designed to gather information about potential mediating social and personal factors. This will allow them test a wealth of hypotheses regarding the development and impairment of language and thought in normal aging and clinical AD.
If they find changes in language function in early and preclinical AD, it may facilitate the development of sensitive preclinical diagnostic tools that could aid in early detection of AD and assessment of its progress. In doing so, this project aims to contribute to the development of appropriate clinical and social interventions.
Human Development Outreach & Extension
and have received a two-year challenge grant from the National Institutes of Health to improve the prediction and diagnosis of cognitive impairment. Memory declines, especially in recall, are hallmarks of healthy aging and conversion to cognitive impairment. The project will use highly sensitive mathematical modeling techniques to improve the ability of clinical recall tests to predict future cognitive impairment and to diagnose current impairment. Their research will focus on one of the most widely used clinical tests of such declines, the Rey Auditory Verbal Learning Test (RAVLT).
The specific aims of the project are to apply mathematical models to RAVLT data in order to: (a) substantially improve the ability of the RAVLT and similar clinical recall tests to predict future impairment and to diagnose current impairment; (b) separate different clinically important components of memory from one another in accordance with current theories of the memory processes that underlie performance on the RAVLT and similar tests; (c) identify the components of memory that differentiate cognitive changes that are associated with normal aging from changes that are associated with conversion to impairment; and (d) provide separate scores for different memory components of RAVLT data, which can be used to better predict behavioral and biological markers of future impairment and to identify current impairment.
The research will consist of 2 phases, spanning 2 years. Both phases will rely on mathematical modeling tools and software that the investigators have previously developed. Preliminary studies have shown that RAVLT-type tests are inherently noisy measures of impairment because 3 different memory processes are responsible for performance, but only 1 of them (gist-based reconstruction) is responsible for conversion to impairment. The investigators will look at the effectiveness of modeling tools to remove this noise.
The first phase will establish whether noise-free scores improve the ability to separate groups of subjects that differ on biological markers of impairment, behavioral markers of impairment, and clinical diagnoses of impairment. This question will be investigated using a very large sample of subjects who participated in the Aging, Demographics, and Memory Study (ADAMS) portion of NIA’s Healthy Retirement Study. The second phase will establish whether noise-free scores improve the ability to differentiate individual people who differ in biological markers of impairment, behavioral markers of impairment, and clinical diagnoses of impairment. This question will be investigated in a longitudinal study of 200 adults (aged 70 and above) who will be administered a neuropsychological test battery as well as 3 versions of the RAVLT, spaced at 6-month intervals.