Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique
Effective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification ha...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-12-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811921008879 |
| _version_ | 1819005331393478656 |
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| author | Melanie A. Matyi Sebastian M. Cioaba Marie T. Banich Jeffrey M. Spielberg |
| author_facet | Melanie A. Matyi Sebastian M. Cioaba Marie T. Banich Jeffrey M. Spielberg |
| author_sort | Melanie A. Matyi |
| collection | DOAJ |
| description | Effective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification has yet to occur, hampering our ability to understand both normative and pathological processes that rely on the amygdala. We developed and applied novel graph theory methods to diffusion-based anatomical networks in a large sample (n = 1,052, 54.28% female, mean age=28.75) to identify nodes that critically support amygdalar interactions with the larger brain network. We examined three graph properties, each indexing a different emergent aspect of amygdalar network communication: current-flow betweenness centrality (amygdalar influence on information flowing between other pairs of nodes), node communicability (clarity of communication between the amygdala and other nodes), and subgraph centrality (amygdalar influence over local network processing). Findings demonstrate that each of these aspects of amygdalar communication is associated with separable sets of regions and, in some cases, these sets map onto previously identified sub-circuits. For example, betweenness and communicability were each associated with different sub-circuits that have been identified in previous work as supporting distinct aspects of memory-guided behavior. Other regions identified span basic (e.g., visual cortex) to higher-order (e.g., insula) sensory processing and executive functions (e.g., dorsolateral prefrontal cortex). Present findings expand our current understanding of amygdalar function by showing that there is no single ‘amygdala network’, but rather multiple networks, each supporting different modes of amygdalar interaction with the larger brain network. Additionally, our novel method allowed for the identification of how such regions support the amygdala, which has not been previously explored. |
| first_indexed | 2024-12-20T23:51:05Z |
| format | Article |
| id | doaj.art-f4ed2d1f1ea845a385d271c1bbc678bb |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-20T23:51:05Z |
| publishDate | 2021-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-f4ed2d1f1ea845a385d271c1bbc678bb2022-12-21T19:22:50ZengElsevierNeuroImage1095-95722021-12-01244118614Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory techniqueMelanie A. Matyi0Sebastian M. Cioaba1Marie T. Banich2Jeffrey M. Spielberg3Department of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USA; Corresponding author.Department of Mathematical Sciences, University of Delaware, Newark, DE 19716, USADepartment of Psychology and Neuroscience, University of Colorado Boulder, Boulder, CO 80309, USADepartment of Psychological and Brain Sciences, University of Delaware, Newark, DE 19716, USAEffective amygdalar functionality depends on the concerted activity of a complex network of regions. Thus, the role of the amygdala cannot be fully understood without identifying the set of brain structures that allow the processes performed by the amygdala to emerge. However, this identification has yet to occur, hampering our ability to understand both normative and pathological processes that rely on the amygdala. We developed and applied novel graph theory methods to diffusion-based anatomical networks in a large sample (n = 1,052, 54.28% female, mean age=28.75) to identify nodes that critically support amygdalar interactions with the larger brain network. We examined three graph properties, each indexing a different emergent aspect of amygdalar network communication: current-flow betweenness centrality (amygdalar influence on information flowing between other pairs of nodes), node communicability (clarity of communication between the amygdala and other nodes), and subgraph centrality (amygdalar influence over local network processing). Findings demonstrate that each of these aspects of amygdalar communication is associated with separable sets of regions and, in some cases, these sets map onto previously identified sub-circuits. For example, betweenness and communicability were each associated with different sub-circuits that have been identified in previous work as supporting distinct aspects of memory-guided behavior. Other regions identified span basic (e.g., visual cortex) to higher-order (e.g., insula) sensory processing and executive functions (e.g., dorsolateral prefrontal cortex). Present findings expand our current understanding of amygdalar function by showing that there is no single ‘amygdala network’, but rather multiple networks, each supporting different modes of amygdalar interaction with the larger brain network. Additionally, our novel method allowed for the identification of how such regions support the amygdala, which has not been previously explored.http://www.sciencedirect.com/science/article/pii/S1053811921008879AmygdalaGraph theoryBrain network |
| spellingShingle | Melanie A. Matyi Sebastian M. Cioaba Marie T. Banich Jeffrey M. Spielberg Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique NeuroImage Amygdala Graph theory Brain network |
| title | Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique |
| title_full | Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique |
| title_fullStr | Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique |
| title_full_unstemmed | Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique |
| title_short | Identifying brain regions supporting amygdalar functionality: Application of a novel graph theory technique |
| title_sort | identifying brain regions supporting amygdalar functionality application of a novel graph theory technique |
| topic | Amygdala Graph theory Brain network |
| url | http://www.sciencedirect.com/science/article/pii/S1053811921008879 |
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