Within-person dynamics of cognition and personality in healthy aging and Alzheimer's disease.

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.

Grant Number: 7K01AG071847-06
NIH Institute/Center: NIH
Principal Investigator: Andrew ASCHENBRENNER