Parcellation-based anatomic model of the semantic network

Abstract

Introduction: The semantic network is an important mediator of language, enabling both speech production and the comprehension of multimodal stimuli. A major challenge in the field of neurosurgery is preventing semantic deficits. Multiple cortical areas have been linked to semantic processing, though knowledge of network connectivity has lacked anatomic specificity. Using attentional task-based fMRI studies, we built a neuroanatomical model of this network.

Methods: One hundred and fifty-five task-based fMRI studies related to categorization of visual words and objects, and auditory words and stories were used to generate an activation likelihood estimation (ALE). Cortical parcellations overlapping the ALE were used to construct a preliminary model of the semantic network based on the cortical parcellation scheme previously published under the Human Connectome Project. Deterministic fiber tractography was performed on 25 randomly chosen subjects from the Human Connectome Project, to determine the connectivity of the cortical parcellations comprising the network.

Results: The ALE analysis demonstrated fourteen left hemisphere cortical regions to be a part of the semantic network: 44, 45, 55b, IFJa, 8C, p32pr, SFL, SCEF, 8BM, STSdp, STSvp, TE1p, PHT, and PBelt. These regions showed consistent interconnections between parcellations. Notably, the anterior temporal pole, a region often implicated in semantic function, was absent from our model.

Conclusions: We describe a preliminary cortical model for the underlying structural connectivity of the semantic network. Future studies will further characterize the neurotractographic details of the semantic network in the context of medical application.

Keywords: dual stream; language network; parcellation; tractography.

FIGURE 1

Flow diagram demonstrating the methods used in this study

FIGURE 2

Activation Likelihood Estimation (ALE) of 155 task‐based fMRI experiments related to goal‐oriented attentional processing, wherein red data represents the ALE of the visual word stimuli studies, blue represents the auditory words and stories studies, and green is the ALE data of visual image stimuli studies. The three‐dimensional ALE data are displayed in Mango on a brain normalized to the MNI coordinate space. ALE data highlighting the left lateral occipital lobe. (a–d) ALE data highlighting the left superior parietal lobule and intraparietal sulcus. (c and d) ALE data highlighting the left frontal eye field of the frontal lobe

FIGURE 3

Comparison overlays between the cortical parcellation data (red) and activation likelihood estimation (ALE) cluster data (blue) of the semantic network. Regions were visually assessed for inclusion in the network if they overlapped with the ALE data. To confirm these findings, we underwent an analysis of how much each parcellation overlapped with the ALE clusters, which were provided as an output of the ALE data. Any parcellation that fell more than 15% within the ALE cluster was included in the network

FIGURE 4

Fiber tracking analysis for the semantic network. Shown on T1‐weighted MR images in the left cerebral hemisphere. TOP ROW: sagittal sections from most medial to most lateral demonstrating the superior longitudinal fasciculus and its projections between the frontal, parietal, and temporal clusters of the dorsal attention network. ROW TWO AND THREE: Partially oblique (left column) and pure (middle and right column) coronal sections. BOTTOM ROW: axial sections through the frontal and parietal clusters of the network. The fronto‐parietal projections of the SLF are particularly apparent

FIGURE 5

Simplified schematic of the white matter connections identified between individual parcellations of the semantic network during the fiber tracking analysis. Connections are labeled with the average strength measured across all 25 subjects