Changes in Task-Related Functional Connectivity across Multiple Spatial Scales Are Related to Reading Performance
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Publication Details
Output type: Journal article
Author list: Wang JX, Bartolotti J, Amaral LAN, Booth JR
Publisher: Public Library of Science
Publication year: 2013
Journal: PLoS ONE (1932-6203)
Journal acronym: PLOS ONE
Volume number: 8
Issue number: 3
ISSN: 1932-6203
eISSN: 1932-6203
Languages: English-Great Britain (EN-GB)
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Open access status: gold
Full text URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0059204&type=printable
Abstract
Reading requires the interaction of a distributed set of cortical areas whose distinct patterns give rise to a wide range of individual skill. However, the nature of these neural interactions and their relation to reading performance are still poorly understood Functional connectivity analyses of fMRI data can be used to characterize the nature of interactivity of distributed brain networks, yet most previous studies have focused on connectivity during task-free (i.e., "resting state") conditions. Here, we report new methods for assessing task related functional connectivity using data-driven graph theoretical methods and describe how large-scale patterns of connectivity relate to individual variability in reading Performance among children. We found that connectivity Patterns of subjects Performing a reading task could be decomposed hierarchically into multiple sub-networks, and we observed stronger long-range interaction between sub-networks in subjects with higher task accuracy. Additionally, we found a network of hub regions known to be critical to reading that displays increased short-range synchronization in higher accuracy subjects. These individual differences in task-related functional connectivity reveal that increased interaction between distant regions, coupled with selective local integration within key regions, is associated with better reading performance. Importantly, we show that task-related neuroimaging data contains far more information than usually extracted via standard univariate analyses - information that can meaningfully relate neural connectivity patterns to cognition and task.
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