Publication: Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults
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Date
2015
Published Version
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Mary Ann Liebert, Inc.
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Citation
Shaw, Emily E., Aaron P. Schultz, Reisa A. Sperling, and Trey Hedden. 2015. “Functional Connectivity in Multiple Cortical Networks Is Associated with Performance Across Cognitive Domains in Older Adults.” Brain Connectivity 5 (8): 505-516. doi:10.1089/brain.2014.0327. http://dx.doi.org/10.1089/brain.2014.0327.
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Abstract
Abstract Intrinsic functional connectivity MRI has become a widely used tool for measuring integrity in large-scale cortical networks. This study examined multiple cortical networks using Template-Based Rotation (TBR), a method that applies a priori network and nuisance component templates defined from an independent dataset to test datasets of interest. A priori templates were applied to a test dataset of 276 older adults (ages 65–90) from the Harvard Aging Brain Study to examine the relationship between multiple large-scale cortical networks and cognition. Factor scores derived from neuropsychological tests represented processing speed, executive function, and episodic memory. Resting-state BOLD data were acquired in two 6-min acquisitions on a 3-Tesla scanner and processed with TBR to extract individual-level metrics of network connectivity in multiple cortical networks. All results controlled for data quality metrics, including motion. Connectivity in multiple large-scale cortical networks was positively related to all cognitive domains, with a composite measure of general connectivity positively associated with general cognitive performance. Controlling for the correlations between networks, the frontoparietal control network (FPCN) and executive function demonstrated the only significant association, suggesting specificity in this relationship. Further analyses found that the FPCN mediated the relationships of the other networks with cognition, suggesting that this network may play a central role in understanding individual variation in cognition during aging.
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Keywords
cognition, cognitive control, default mode network, memory, resting-state functional connectivity MRI
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