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PREDICTING WORKING MEMORY PERFORMANCE FROM RESTING AND TASK STATE FUNCTIONAL CONNECTIVITY

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title
PREDICTING WORKING MEMORY PERFORMANCE FROM RESTING AND TASK STATE FUNCTIONAL CONNECTIVITY
author
Bailey, Erika
abstract
Network science techniques provide powerful tools to investigate functional brain connections. Recent studies have successfully used resting-state functional connectivity to predict cognitive behavioral outcomes. However, some research indicates that task state functional connectivity may be a stronger predictor of behavior. The current study examined the predictive capabilities of resting-state and task state functional connectivity in regard to working memory to determine whether one or both are capable of predicting working memory scores. Additionally, this study compared two different methods of thresholding edges to determine which method produces the better predictive model. The results indicated that functional connectivity for n-back task state data was able to successfully predict performance on a 2-back task. However, resting-state functional connectivity was unable to predict task performance. Furthermore, differences in thresholding techniques did not alter results. In tests of convergent and discriminant validity, models created using edges identified during task-state as relevant for the n-back task trended towards predicting another behavioral measure of working memory better than they were able to predict a theoretically unrelated measure of life satisfaction, although these results failed to reach significance and remain somewhat inconclusive.
subject
cognition
fMRI
n-back
Network science
psychology
Working memory
contributor
Dagenbach, Dale (committee chair)
Jennings, Janine M. (committee member)
Sali, Anthony W. (committee member)
Laurienti, Paul (committee member)
date
2019-05-24T08:35:43Z (accessioned)
2024-06-01T08:30:08Z (available)
2019 (issued)
degree
Psychology (discipline)
embargo
2024-06-01 (terms)
identifier
http://hdl.handle.net/10339/93946 (uri)
language
en (iso)
publisher
Wake Forest University
type
Thesis

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