Project Abstract / Summary Intervention studies of Alzheimer disease (AD) require accurate measurement of cognitive function across many years. Adequate description of function is hampered by the fact that cognition can significantly fluctuate from moment-to-moment and from day-to-day. This additional variance adds measurement noise which impairs sensitivity to detect intervention effects. However, rather than ascribing cognitive variability to measurement error, there is accumulating evidence to suggest that fluctuations in performance are reflective of meaningful biological and psychological processes, including variations in daily mood, motivation and attention. Many of these psychological mechanisms can be captured via standard assessments of personality, which have been shown to be important behavioral predictors of AD risk. The overall goal of this project is to apply intensive longitudinal research techniques to the analysis of cognitive function in order to describe and explain performance variability in healthy aging and in individuals with mild or questionable cognitive impairment. If daily variability in cognition is predictive of later cognitive decline or other clinically meaningful outcomes, it may be useful as an additional or alternative cognitive endpoint in clinical trials. This proposal aims to apply dynamic structural equation models (DSEM) to a three-week intensive longitudinal research design. DSEM models allow for direct and robust statistical evaluation of variability as a sensitive marker of critical late life outcomes including cognitive decline and progression to AD. I will collect daily measures of cognitive and psychological (e.g., personality) function for a three-week period on healthy older adults and those with questionable impairment. DSEM will test the hypothesis that within-person variability in cognition is related to clinical status. Reanalysis of an existing dataset will provide further validation of this approach by relating cognitive variability to disease progression and in vivo AD biomarkers. I will supplement my extensive experience measuring cognition in healthy aging and early stage AD by gaining additional, didactic training in advanced analytical techniques including Bayesian modeling and dynamic structural equation modeling with emphasis on intensive longitudinal research designs. In addition, I will gain experience providing assessments of clinical function and judging the presence / severity of dementia. The mentors selected for this application, Drs. Joshua Jackson and John Morris are internationally recognized experts in the fields of personality assessment and longitudinal modeling in healthy aging, and clinical assessment of AD respectively and are well suited to serve as mentors on this project. Through the training and research plan described in this application, I will produce new cognitive endpoints for AD research. Techniques developed in this proposal can be readily extended to other neurodegenerative or clinical disorders where cognition plays a key role.

Project Narrative The research and training in this application will provide additional training and experience applying advanced intensive longitudinal statistical techniques to the study of aging and dementia. Outcomes in aging and Alzheimer disease (AD) research rely critically on the measurement of cognitive change and the innovative approaches described herein will provide novel and more sensitive analyses of longitudinal change. Ultimately, we will provide new cognitive endpoints for use in AD studies which will help establish the efficacy of candidate treatments.