Characterizing Information Flux within the Distributed Pediatric Expressive Language Network - a Core Region Mapped through fMRI-Constrained MEG Effective Connectivity Analyses.

Brain Connect. 2015 Oct 11;

Authors: Kadis DS, Dimitrijevic A, Toro Serey CA, Smith ML, Holland SK

Abstract
Using noninvasive neuroimaging, researchers have shown that young children have bilateral and diffuse language networks, which become increasingly left-lateralized and focal with development. Connectivity within the distributed pediatric language network has been minimally studied, and conventional neuroimaging approaches do not distinguish task-related signal changes from those that are task-essential. Here, we propose a novel multimodal method to map core language sites from patterns of information flux. We retrospectively analyze neuroimaging data collected in two groups of children, ages 5 to 18 years, performing verb generation in fMRI (n = 343) and MEG (n = 21). The fMRI data were conventionally analyzed and the group activation map parcellated in order to define node locations. Neuronal activity at each node was estimated from MEG data using a linearly constrained minimum variance beamformer, and effective connectivity within canonical frequency bands computed using the phase slope index (PSI) metric. We observed significant (p ≤ 0.05) effective connections in all subjects. The number of suprathreshold connections was significantly and linearly correlated with participant age (r = 0.50, n = 21, p ≤ 0.05), suggesting that core language sites emerge as part of the normal developmental trajectory. Across frequencies, we observed significant effective connectivity among proximal left frontal nodes. Within the low frequency bands, information flux was rostrally-directed within a focal, left frontal region, approximating Broca's area. At higher frequencies, we observed increased connectivity involving bilateral perisylvian nodes. Frequency-specific differences in patterns of information flux were resolved through fast (i.e., MEG) neuroimaging.

PMID: 26456242 [PubMed - as supplied by publisher]