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.
Highlights
The operation of the human brain depends on a complex, hierarchical system of interactions that we are beginning to probe through the use of functional imaging tools and connectivity analyses
Graph theory allows for the ability to quantify complex network structure and to locate nodes with special network properties
Our study constitutes a novel extension that builds on both univariate task-related methods as well as previously implemented resting state functional connectivity methods to yield important insight into how reading, a high-level cognitive skill, relates to patterns of neural interaction
Summary
The operation of the human brain depends on a complex, hierarchical system of interactions that we are beginning to probe through the use of functional imaging tools and connectivity analyses. Graph theory allows for the ability to quantify complex network structure and to locate nodes with special network properties (see [6,7] for comprehensive reviews of graph theoretical applications to brain connectivity). We distinguish these large-scale, graph theoretical analyses from seed- or ROI-based analyses that restrict the areas investigated to a priori defined anatomical regions of interest and do not necessarily investigate general network patterns in connectivity
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