To interact, communicate, and navigate the world successfully, people must retrieve relevant information from their semantic memory (memory for facts and general knowledge). Individuals with Alzheimer's disease have difficulty retrieving such knowledge from early in the course of the disease and progressively gets worse as the disease spreads, a process known as semantic decline. This project examines the mechanisms underlying semantic decline in individuals with Alzheimer's disease by developing and applying novel computational tools. The extent to which semantic memory is impaired in individuals with Alzheimer's disease can be probed using behavioral experiments. Individuals with Alzheimer's as well as those at-risk for the disease display a pattern of behavior on these tasks distinct from healthy individuals. Despite decades of research, explanations of these behavioral impairments focus almost exclusively on cognitive mechanisms that may explain a patient's current behavior at a given time point, but without an account of the transition from normal, pre-symptomatic behavior to fully impaired behavior. Existing models fail to explain the mechanisms by which semantic memory and memory retrieval processes degrade over time due to Alzheimer's, limiting our understanding of the development of the disease, as well as hindering our ability for prognosis, early detection measures, and possible interventions. This project will test computational models of how the disease spreads, making specific quantitative predictions about the decline of semantic memory. Additionally, we will develop a novel machine learning method that can be used to map the structure of an individual's semantic memory, creating opportunities for individualized behavioral interventions to improve semantic memory and improve the quality of life for individuals with Alzheimer's disease.

Public Health Relevance

This project examines the mechanisms underlying decline of semantic memory (memory for knowledge) in individuals with Alzheimer's disease by developing and applying novel computational models. This research will develop methods for testing neurocognitive theories of Alzheimer's and other neurodegenerative diseases, inform interventions for mitigating decline, and further our understanding of how the disease spreads.

Agency
National Institute of Health (NIH)
Institute
National Institute on Aging (NIA)
Type
Exploratory/Developmental Grants (R21)
Project #
5R21AG053467-02
Application #
9334048
Study Section
Cognition and Perception Study Section (CP)
Program Officer
Silverberg, Nina B
Project Start
2016-09-01
Project End
2018-05-31
Budget Start
2017-06-15
Budget End
2018-05-31
Support Year
2
Fiscal Year
2017
Total Cost
$193,393
Indirect Cost
$54,288
Name
University of Wisconsin Madison
Department
Psychology
Type
Other Domestic Higher Education
DUNS #
161202122
City
Madison
State
WI
Country
United States
Zip Code
53715
Zemla, Jeffrey C; Austerweil, Joseph L (2017) Modeling Semantic Fluency Data as Search on a Semantic Network. Cogsci 2017:3646-3651