Individual in vivo sub-parcellation of Broca’s region using functional connectivity glyphs

Broca's region is functionally involved in language production and is comprised of two adjacent cytoarchitectonic areas, 44 and 45. These regions have distinct connectivity to superior temporal and inferior parietal regions in both macaque monkeys and humans. We made use of prior knowledge of sulcal anatomy and resting-state functional connectivity, together with a novel visualization technique, to manually parcellate areas 44 and 45 in individual brains in vivo. 103 ICA-FIX denoised rfMRI datasets from healthy individuals, provided by the Human Connectome Project, were used. Left-hemisphere grayordinate correlation matrices were computed for every subject, and then visualized in brainGL. The full unthresholded connectivity profile is presented at each node of the surface­ based rendering in a small visual summary (glyph), representing the distribution of connections from the node to the rest of the cortical surface. By manipulating various visualization parameters of the glyphs, transitions between cortical areas can be observed. Using this technique, individual brains were manually parcellated, and then compared at the group-level. Additionally, the manual labeling approach was compared to a variety of pre-existing automatic clustering techniques. Group­-level probability maps of the two areas show significant spatial consistency across individuals, while still demonstrating individual variability in cortical topography. Automatic parcellation techniques produced clusters with varying degrees of spatial overlap with the manual labels, indicating the need for further investigation and validation of machine learning cortical segmentation approaches. We could clearly delineate areas 44 and 45 from each other and neighboring regions in all subjects. These results are consistent with previous studies showing distinct connectivity patterns of areas 44 and 45, and validate the usefulness of glyph visualization for individual-level manual cortical segmentation