@article{396661715ec845b9aa825db9b2c21ec1,
title = "Analyzing Knowledge Retrieval Impairments Associated with Alzheimer's Disease Using Network Analyses",
abstract = "A defining characteristic of Alzheimer's disease is difficulty in retrieving semantic memories, or memories encoding facts and knowledge. While it has been suggested that this impairment is caused by a degradation of the semantic store, the precise ways in which the semantic store is degraded are not well understood. Using a longitudinal corpus of semantic fluency data (listing of items in a category), we derive semantic network representations of patients with Alzheimer's disease and of healthy controls. We contrast our network-based approach with analyzing fluency data with the standard method of counting the total number of items and perseverations in fluency data. We find that the networks of Alzheimer's patients are more connected and that those connections are more randomly distributed than the connections in networks of healthy individuals. These results suggest that the semantic memory impairment of Alzheimer's patients can be modeled through the inclusion of spurious associations between unrelated concepts in the semantic store. We also find that information from our network analysis of fluency data improves prediction of patient diagnosis compared to traditional measures of the semantic fluency task.",
author = "Zemla, {Jeffrey C.} and Austerweil, {Joseph L.}",
note = "Funding Information: We thank Bill Heindel, David Salmon, and the University of California-San Diego Shiley-Marcos Alzheimer{\textquoteright}s Disease Research Center for providing the longitudinal fluency data set and technical assistance. We would also like to thank Elizabeth Pettit, Jacqueline Erens, and Jacob Hurlburt for help with data transcription. This research was performed using the compute resources and assistance of the UW-Madison Center for High Throughput Computing (CHTC) in the Department of Computer Sciences. The CHTC is supported by UW-Madison, the Advanced Computing Initiative, the Wisconsin Alumni Research Foundation, the Wisconsin Institutes for Discovery, and the National Science Foundation and is an active member of the Open Science Grid, which is supported by the National Science Foundation and the U.S. Department of Energy{\textquoteright}s Office of Science. Support for this research was provided by NIH R21AG0534676 and the Office of the VCGRE at UW-Madison with funding from the WARF. The fluency dataset was collected by the University of California-San Diego Shiley-Marcos Alzheimer{\textquoteright}s Disease Research Center with support by NIH AG05131. Publisher Copyright: {\textcopyright} 2019 Jeffrey C. Zemla and Joseph L. Austerweil.",
year = "2019",
doi = "10.1155/2019/4203158",
language = "English (US)",
volume = "2019",
journal = "Complexity",
issn = "1076-2787",
publisher = "John Wiley and Sons Inc.",
}