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Meta-Analysis
. 2022 Jul;12(7):e2646.
doi: 10.1002/brb3.2646. Epub 2022 Jun 22.

Parcellation-based tractographic modeling of the salience network through meta-analysis

Robert G Briggs  1 , Isabella M Young  2 , Nicholas B Dadario  3 , R Dineth Fonseka  4 , Jorge Hormovas  4 , Parker Allan  1 , Micah L Larsen  1 , Yueh-Hsin Lin  4 , Onur Tanglay  4 , B David Maxwell  1 , Andrew K Conner  1 , Jordan F Stafford  1 , Chad A Glenn  1 , Charles Teo  4 , Michael E Sughrue  4   2
Affiliations
Meta-Analysis

Parcellation-based tractographic modeling of the salience network through meta-analysis

Robert G Briggs et al. Brain Behav. 2022 Jul.

Abstract

Background: The salience network (SN) is a transitory mediator between active and passive states of mind. Multiple cortical areas, including the opercular, insular, and cingulate cortices have been linked in this processing, though knowledge of network connectivity has been devoid of structural specificity.

Objective: The current study sought to create an anatomically specific connectivity model of the neural substrates involved in the salience network.

Methods: A literature search of PubMed and BrainMap Sleuth was conducted for resting-state and task-based fMRI studies relevant to the salience network according to PRISMA guidelines. Publicly available meta-analytic software was utilized to extract relevant fMRI data for the creation of an activation likelihood estimation (ALE) map and relevant parcellations from the human connectome project overlapping with the ALE data were identified for inclusion in our SN model. DSI-based fiber tractography was then performed on publicaly available data from healthy subjects to determine the structural connections between cortical parcellations comprising the network.

Results: Nine cortical regions were found to comprise the salience network: areas AVI (anterior ventral insula), MI (middle insula), FOP4 (frontal operculum 4), FOP5 (frontal operculum 5), a24pr (anterior 24 prime), a32pr (anterior 32 prime), p32pr (posterior 32 prime), and SCEF (supplementary and cingulate eye field), and 46. The frontal aslant tract was found to connect the opercular-insular cluster to the middle cingulate clusters of the network, while mostly short U-fibers connected adjacent nodes of the network.

Conclusion: Here we provide an anatomically specific connectivity model of the neural substrates involved in the salience network. These results may serve as an empiric basis for clinical translation in this region and for future study which seeks to expand our understanding of how specific neural substrates are involved in salience processing and guide subsequent human behavior.

Keywords: anatomy; parcellation; salience network; tractography.

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Conflict of interest statement

Michael Sughrue is the chief medical officer, co‐founder, and shareholder of Omniscient Neurotechnology. Charles Teo is also a consultant for Aesculap and Omniscient Neurotechnology. Isabella Young is an employee of Omniscient Neurotechnology. No products related to this were discussed in this paper. No other authors report any conflict of interest.

Figures

FIGURE 1
FIGURE 1
Activation likelihood estimation (ALE) of 12 task‐based fMRI experiments related to goal‐oriented attentional processing. The three‐dimensional ALE data are displayed in Mango on a brain normalized to the MNI coordinate space. (a) ALE data highlighting the insula. (b) ALE data highlighting the middle cingulate gyrus. (c) ALE data highlighting the cingulate gyrus
FIGURE 2
FIGURE 2
Comparison overlays between the cortical parcellation data (green) and ALE data (red) from Figure 1 in the left cerebral hemisphere. Regions were visually assessed for inclusion in the network if they overlapped with the ALE data. Parcellations included in the model of salience were identified in the insula, including AVI, FOP4, FOP5, and MI (top row); the middle cingulate gyrus, including a24pr, a32pr, p32pr, and SCEF (bottom row); and the dorsolateral prefrontal cortex, including 46 (middle). The labels indicate the parcellation shown in each panel. Abbreviations: a24pr, anterior 24 prime; a32pr, anterior 32 prime; AVI, areas anterior ventral insula; FOP4, frontal operculum 4; FOP5, frontal operculum 5; MI, middle insula; p32pr, posterior 32 prime; SCEF, supplementary and cingulate eye field
FIGURE 3
FIGURE 3
Fiber tracking analysis for the salience network (SN). T1‐weighted MR images in the left cerebral hemisphere are shown. Top row: sagittal sections from most posterior to most anterior demonstrating the frontal aslant tract (FAT) and its projections between the opercular, insular, and middle cingulate clusters of the SN. Middle row: coronal sections from medial to lateral through the parietal and occipital clusters demonstrate the FAT and the short fiber connections within the network. Bottom row: axial sections from inferior to superior provide another view of the FAT and short fiber connections within the network. Abbreviations: a24pr, anterior 24 prime; a32pr, anterior 32 prime; AVI, areas anterior ventral insula; FOP4, frontal operculum 4; FOP5, frontal operculum 5; MI, middle insula; p32pr, posterior 32 prime; SCEF, supplementary and cingulate eye field
FIGURE 4
FIGURE 4
Simplified schematic of the white matter connections identified between individual parcellations of the SN during the fiber tracking analysis. Connections are labeled with the average strength measured across all 25 subjects. Abbreviations: a24pr, anterior 24 prime; a32pr, anterior 32 prime; AVI, areas anterior ventral insula; FOP4, frontal operculum 4; FOP5, frontal operculum 5; MI, middle insula; p32pr, posterior 32 prime; SCEF, supplementary and cingulate eye field
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