Parcellation‐based tractographic modeling of the salience network through meta‐analysis
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 structu...
| Main Authors: | , , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2022-07-01
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| Series: | Brain and Behavior |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/brb3.2646 |
| _version_ | 1817971234248327168 |
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| author | Robert G. Briggs Isabella M. Young Nicholas B. Dadario R. Dineth Fonseka Jorge Hormovas Parker Allan Micah L. Larsen Yueh‐Hsin Lin Onur Tanglay B. David Maxwell Andrew K. Conner Jordan F. Stafford Chad A. Glenn Charles Teo Michael E. Sughrue |
| author_facet | Robert G. Briggs Isabella M. Young Nicholas B. Dadario R. Dineth Fonseka Jorge Hormovas Parker Allan Micah L. Larsen Yueh‐Hsin Lin Onur Tanglay B. David Maxwell Andrew K. Conner Jordan F. Stafford Chad A. Glenn Charles Teo Michael E. Sughrue |
| author_sort | Robert G. Briggs |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T20:44:06Z |
| format | Article |
| id | doaj.art-13d8bf0170bd49dda7f2698932322c9a |
| institution | Directory Open Access Journal |
| issn | 2162-3279 |
| language | English |
| last_indexed | 2024-04-13T20:44:06Z |
| publishDate | 2022-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Brain and Behavior |
| spelling | doaj.art-13d8bf0170bd49dda7f2698932322c9a2022-12-22T02:30:44ZengWileyBrain and Behavior2162-32792022-07-01127n/an/a10.1002/brb3.2646Parcellation‐based tractographic modeling of the salience network through meta‐analysisRobert G. Briggs0Isabella M. Young1Nicholas B. Dadario2R. Dineth Fonseka3Jorge Hormovas4Parker Allan5Micah L. Larsen6Yueh‐Hsin Lin7Onur Tanglay8B. David Maxwell9Andrew K. Conner10Jordan F. Stafford11Chad A. Glenn12Charles Teo13Michael E. Sughrue14Department of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USAOmniscient Neurotechnology Sydney New South Wales AustraliaRobert Wood Johnson Medical School, Rutgers University New Brunswick New Jersey USACentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaCentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaDepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USADepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USACentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaCentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaDepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USADepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USADepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USADepartment of Neurosurgery University of Oklahoma Health Sciences Center Oklahoma City Oklahoma USACentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaCentre for Minimally Invasive Neurosurgery Prince of Wales Private Hospital Sydney New South Wales AustraliaAbstract 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.https://doi.org/10.1002/brb3.2646anatomyparcellationsalience networktractography |
| spellingShingle | Robert G. Briggs Isabella M. Young Nicholas B. Dadario R. Dineth Fonseka Jorge Hormovas Parker Allan Micah L. Larsen Yueh‐Hsin Lin Onur Tanglay B. David Maxwell Andrew K. Conner Jordan F. Stafford Chad A. Glenn Charles Teo Michael E. Sughrue Parcellation‐based tractographic modeling of the salience network through meta‐analysis Brain and Behavior anatomy parcellation salience network tractography |
| title | Parcellation‐based tractographic modeling of the salience network through meta‐analysis |
| title_full | Parcellation‐based tractographic modeling of the salience network through meta‐analysis |
| title_fullStr | Parcellation‐based tractographic modeling of the salience network through meta‐analysis |
| title_full_unstemmed | Parcellation‐based tractographic modeling of the salience network through meta‐analysis |
| title_short | Parcellation‐based tractographic modeling of the salience network through meta‐analysis |
| title_sort | parcellation based tractographic modeling of the salience network through meta analysis |
| topic | anatomy parcellation salience network tractography |
| url | https://doi.org/10.1002/brb3.2646 |
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