Abstracts

Rationale: Expressive and receptive language processes are known to be linked to Broca’s area, in the posterior convolutions of the left inferior frontal gyrus, IFG, and Wernicke’s area in the posterior left superior temporal gyrus, respectively. However, the complexity of the widespread language network makes mapping functional interactions among critical regions and cortical structures challenging. We proposed whole-brain connectivity and graph-theoretical analysis of task-related magnetoencephalographic (MEG) responses to provide robust maps of both receptive and expressive language networks. Methods: MEG and fMRI data of healthy participants (n = 10, ages 22-36 years) were examined. Participants completed a covert word recognition task (WRT) and a covert auditory verb generation task (aVGT). MEG data were analyzed at two periods, baseline (-400 to -100 ms) and task (500 to 800 ms). Head model was defined by a regular 3D grid of individual’s MR with 8 mm resolution. Broadband sources, 0.1-40 Hz, were estimated using an LCVM beamformer with 5% regularization. Connectivity was quantified using phase locking value (PLV). Brain hubs were quantified using an eigenvector centrality, an extension of network degree centrality. The eigenvector centrality per subject was computed based on the difference of adjacency matrices of task and baseline periods. For group analyses, an automated 116-parcels anatomical labeling atlas was employed to summarize the network measures. To characterize hemispheric involvement, a conventional laterality index, (L-R)/(L+R), was derived for the eigenvector centrality scores within (n = 58) parcels in the right and left hemispheres. Values greater than 0.25 were considered as left hemispheric dominance, less than -0.25 as right hemispheric dominance, and intermediate values as a bilateral. Findings were qualitatively compared with t-contrasts of fMRI-GLM analysis. Results: Group MEG receptive language network, corresponding to the WRT, suggested bilateral and symmetrically distributed hubs in perisylvian sites (Wernicke’s area), consistent with fMRI (Fig.1A, an individual). Network analysis of expressive language network, corresponding to the aVGT, revealed hubs in the left prefrontal cortex (pars operculum, Broca’s area), right primary auditory cortex (Heschel’s gyrus), and bilateral supplementary motor area (Fig. 1B, an individual). A high level of consistency was observed between MEG networks and fMRI-GLM findings. Network maps showed a bilateral effect during receptive language (LI = -0.09) and a left-dominance (LI > 0.25) of eigenvector centrality values in 7/10 participants during expressive language, consistent with fMRI. Conclusions: This study demonstrated potential of a large-scale connectivity and graph theoretical analyses to identify language-specific network hubs. Our results were generally consistent with fMRI in terms of lateralization and localization, thus provide additional support for the underlying topological organization of the receptive and expressive language cortices. Whole brain connectivity and network analysis can potentially be used for mapping brain language in clinical evaluations. Funding: This study was funded by the Children’s Foundation Research Institute, Memphis, TN.