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Merging Clinical Neuropsychology and Functional Neuroimaging to Evaluate the Construct Validity and Neural Network Engagement of the n-Back Task

Published online by Cambridge University Press:  25 June 2014

Tonisha E. Kearney-Ramos
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Jennifer S. Fausett
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Jennifer L. Gess
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Ashley Reno
Affiliation:
University of Virginia School of Medicine, Charlottesville, Virginia
Jennifer Peraza
Affiliation:
New Mexico VA Health Care System, Albuquerque, New Mexico
Clint D. Kilts
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
G. Andrew James
Affiliation:
Psychiatric Research Institute, University of Arkansas for Medical Sciences, Little Rock, Arkansas
Corresponding
E-mail address:

Abstract

The n-back task is a widely used neuroimaging paradigm for studying the neural basis of working memory (WM); however, its neuropsychometric properties have received little empirical investigation. The present study merged clinical neuropsychology and functional magnetic resonance imaging (fMRI) to explore the construct validity of the letter variant of the n-back task (LNB) and to further identify the task-evoked networks involved in WM. Construct validity of the LNB task was investigated using a bootstrapping approach to correlate LNB task performance across clinically validated neuropsychological measures of WM to establish convergent validity, as well as measures of related but distinct cognitive constructs (i.e., attention and short-term memory) to establish discriminant validity. Independent component analysis (ICA) identified brain networks active during the LNB task in 34 healthy control participants, and general linear modeling determined task-relatedness of these networks. Bootstrap correlation analyses revealed moderate to high correlations among measures expected to converge with LNB (|ρ|≥0.37) and weak correlations among measures expected to discriminate (|ρ|≤0.29), controlling for age and education. ICA identified 35 independent networks, 17 of which demonstrated engagement significantly related to task condition, controlling for reaction time variability. Of these, the bilateral frontoparietal networks, bilateral dorsolateral prefrontal cortices, bilateral superior parietal lobules including precuneus, and frontoinsular network were preferentially recruited by the 2-back condition compared to 0-back control condition, indicating WM involvement. These results support the use of the LNB as a measure of WM and confirm its use in probing the network-level neural correlates of WM processing. (JINS, 2014, 20, 1–15)

Type
Research Articles
Copyright
Copyright © The International Neuropsychological Society 2014 

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