Automated individual-level parcellation of Broca's region based on functional connectivity.

Broca's region can be subdivided into its constituent areas 44 and 45 based on established differences in connectivity to superior temporal and inferior parietal regions. The current study builds on our previous work manually parcellating Broca's area on the individual-level by applying these anatomical criteria to functional connectivity data. Here we present an automated observer-independent and anatomy-informed parcellation pipeline with comparable precision to the manual labels at the individual-level. The method first extracts individualized connectivity templates of areas 44 and 45 by assigning to each surface vertex within the ventrolateral frontal cortex the partial correlation value of its functional connectivity to group-level templates of areas 44 and 45, accounting for other template connectivity patterns. To account for cross-subject variability in connectivity, the partial correlation procedure is then repeated using individual-level network templates, including individual-level connectivity from areas 44 and 45. Each node is finally labeled as area 44, 45, or neither, using a winner-take-all approach. The method also incorporates prior knowledge of anatomical location by weighting the results using spatial probability maps. The resulting area labels show a high degree of spatial overlap with the gold-standard manual labels, and group-average area maps are consistent with cytoarchitectonic probability maps of areas 44 and 45. To facilitate reproducibility and to demonstrate that the method can be applied to resting-state fMRI datasets with varying acquisition and preprocessing parameters, the labeling procedure is applied to two open-source datasets from the Human Connectome Project and the Nathan Kline Institute Rockland Sample. While the current study focuses on Broca's region, the method is adaptable to parcellate other cortical regions with distinct connectivity profiles.

Body Topography Parcellates Human Sensory and Motor Cortex.

The cytoarchitectonic map as proposed by Brodmann currently dominates models of human sensorimotor cortical structure, function, and plasticity. According to this model, primary motor cortex, area 4, and primary somatosensory cortex, area 3b, are homogenous areas, with the major division lying between the two. Accumulating empirical and theoretical evidence, however, has begun to question the validity of the Brodmann map for various cortical areas. Here, we combined in vivo cortical myelin mapping with functional connectivity analyses and topographic mapping techniques to reassess the validity of the Brodmann map in human primary sensorimotor cortex. We provide empirical evidence that area 4 and area 3b are not homogenous, but are subdivided into distinct cortical fields, each representing a major body part (the hand and the face). Myelin reductions at the hand-face borders are cortical layer-specific, and coincide with intrinsic functional connectivity borders as defined using large-scale resting state analyses. Our data extend the Brodmann model in human sensorimotor cortex and suggest that body parts are an important organizing principle, similar to the distinction between sensory and motor processing.

Subdivision of Broca's region based on individual-level functional connectivity.

Broca's region is composed of two adjacent cytoarchitectonic areas, 44 and 45, which have distinct connectivity to superior temporal and inferior parietal regions in both macaque monkeys and humans. The current study aimed to make 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. One hundred and one resting-state functional magnetic resonance imaging datasets from the Human Connectome Project were used. Left-hemisphere surface-based correlation matrices were computed and visualized in brainGL. By observation of differences in the connectivity patterns of neighbouring nodes, areas 44 and 45 were manually parcellated in individual brains, and then compared at the group-level. Additionally, the manual labelling approach was compared with parcellation results based on several data-driven clustering techniques. Areas 44 and 45 could be clearly distinguished from each other in all individuals, and the manual segmentation method showed high test-retest reliability. Group-level probability maps of areas 44 and 45 showed spatial consistency across individuals, and corresponded well to cytoarchitectonic probability maps. Group-level connectivity maps were consistent with previous studies showing distinct connectivity patterns of areas 44 and 45. Data-driven 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. The current study provides a reliable method for individual-level cortical parcellation that could be applied to regions distinguishable by even the most subtle differences in patterns of functional connectivity.

Connexel visualization: a software implementation of glyphs and edge-bundling for dense connectivity data using brainGL.

The visualization of brain connectivity becomes progressively more challenging as analytic and computational advances begin to facilitate connexel-wise analyses, which include all connections between pairs of voxels. Drawing full connectivity graphs can result in depictions that, rather than illustrating connectivity patterns in more detail, obfuscate patterns owing to the data density. In an effort to expand the possibilities for visualization, we describe two approaches for presenting connexels: edge-bundling, which clarifies structure by grouping geometrically similar connections; and, connectivity glyphs, which depict a condensed connectivity map at each point on the cortical surface. These approaches can be applied in the native brain space, facilitating interpretation of the relation of connexels to brain anatomy. The tools have been implemented as part of brainGL, an extensive open-source software for the interactive exploration of structural and functional brain data.

A novel frontal pathway underlies verbal fluency in primary progressive aphasia.

The frontal aslant tract is a direct pathway connecting Broca's region with the anterior cingulate and pre-supplementary motor area. This tract is left lateralized in right-handed subjects, suggesting a possible role in language. However, there are no previous studies that have reported an involvement of this tract in language disorders. In this study we used diffusion tractography to define the anatomy of the frontal aslant tract in relation to verbal fluency and grammar impairment in primary progressive aphasia. Thirty-five patients with primary progressive aphasia and 29 control subjects were recruited. Tractography was used to obtain indirect indices of microstructural organization of the frontal aslant tract. In addition, tractography analysis of the uncinate fasciculus, a tract associated with semantic processing deficits, was performed. Damage to the frontal aslant tract correlated with performance in verbal fluency as assessed by the Cinderella story test. Conversely, damage to the uncinate fasciculus correlated with deficits in semantic processing as assessed by the Peabody Picture Vocabulary Test. Neither tract correlated with grammatical or repetition deficits. Significant group differences were found in the frontal aslant tract of patients with the non-fluent/agrammatic variant and in the uncinate fasciculus of patients with the semantic variant. These findings indicate that degeneration of the frontal aslant tract underlies verbal fluency deficits in primary progressive aphasia and further confirm the role of the uncinate fasciculus in semantic processing. The lack of correlation between damage to the frontal aslant tract and grammar deficits suggests that verbal fluency and grammar processing rely on distinct anatomical networks.