Item Details

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)

2019 (issued)

degree

Psychology (discipline)

embargo

2024-06-01 (terms)

2024-06-01 (liftdate)

identifier

http://hdl.handle.net/10339/93946 (uri)

language

en (iso)

publisher

Wake Forest University

type

Thesis