Utilizing network analysis and fMRI to infer key language modules and their circuits from healthy human controls

Traditional task-based functional Magnetic Resonance Imaging (tb-fMRI) statistical analysis has served as a powerful tool to identify brain areas associated with language. However, it does not provide an explanation of how different functional areas interact and integrate with each other to form comprehensive language tasks.
We abstracted task-correlated areas at different anatomical locations as network modules where each voxel within the module is a network node and utilized statistical inference methods to infer the links between each node pair from their correlation matrix. We applied this network analysis to language tb-fMRI scans acquired from 9 healthy right-handed individuals.
Our results show that a robust fully-connected functional language network exists across 8 out of 9 healthy individuals, which entangles the Brocas Area, Wernickes Area, Supplementary Motor Area, and Pre-Motor Area, all in the left hemisphere. Furthermore, we uncovered the functional connectivity of the anatomical sub-divisions (pars-opercularis and pars-triangularis) of the Broca's Area.