Trade-offs among cost, integration, and segregation in the human connectome
AbstractThe human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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The MIT Press
2023-01-01
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| Series: | Network Neuroscience |
| Online Access: | https://direct.mit.edu/netn/article/7/2/604/113760/Trade-offs-among-cost-integration-and-segregation |
| _version_ | 1797796691313688576 |
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| author | Junji Ma Xitian Chen Yue Gu Liangfang Li Ying Lin Zhengjia Dai |
| author_facet | Junji Ma Xitian Chen Yue Gu Liangfang Li Ying Lin Zhengjia Dai |
| author_sort | Junji Ma |
| collection | DOAJ |
| description |
AbstractThe human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis. |
| first_indexed | 2024-03-13T03:36:51Z |
| format | Article |
| id | doaj.art-d494166dcdfd4dd2aed908d0991039c3 |
| institution | Directory Open Access Journal |
| issn | 2472-1751 |
| language | English |
| last_indexed | 2024-03-13T03:36:51Z |
| publishDate | 2023-01-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Network Neuroscience |
| spelling | doaj.art-d494166dcdfd4dd2aed908d0991039c32023-06-23T18:35:16ZengThe MIT PressNetwork Neuroscience2472-17512023-01-017260463110.1162/netn_a_00291Trade-offs among cost, integration, and segregation in the human connectomeJunji Ma0Xitian Chen1Yue Gu2Liangfang Li3Ying Lin4Zhengjia Dai5Department of Psychology, Sun Yat-sen University, Guangzhou, ChinaDepartment of Psychology, Sun Yat-sen University, Guangzhou, ChinaDepartment of Psychology, Sun Yat-sen University, Guangzhou, ChinaDepartment of Psychology, Sun Yat-sen University, Guangzhou, ChinaDepartment of Psychology, Sun Yat-sen University, Guangzhou, ChinaDepartment of Psychology, Sun Yat-sen University, Guangzhou, China AbstractThe human brain structural network is thought to be shaped by the optimal trade-off between cost and efficiency. However, most studies on this problem have focused on only the trade-off between cost and global efficiency (i.e., integration) and have overlooked the efficiency of segregated processing (i.e., segregation), which is essential for specialized information processing. Direct evidence on how trade-offs among cost, integration, and segregation shape the human brain network remains lacking. Here, adopting local efficiency and modularity as segregation factors, we used a multiobjective evolutionary algorithm to investigate this problem. We defined three trade-off models, which represented trade-offs between cost and integration (Dual-factor model), and trade-offs among cost, integration, and segregation (local efficiency or modularity; Tri-factor model), respectively. Among these, synthetic networks with optimal trade-off among cost, integration, and modularity (Tri-factor model [Q]) showed the best performance. They had a high recovery rate of structural connections and optimal performance in most network features, especially in segregated processing capacity and network robustness. Morphospace of this trade-off model could further capture the variation of individual behavioral/demographic characteristics in a domain-specific manner. Overall, our results highlight the importance of modularity in the formation of the human brain structural network and provide new insights into the original cost-efficiency trade-off hypothesis.https://direct.mit.edu/netn/article/7/2/604/113760/Trade-offs-among-cost-integration-and-segregation |
| spellingShingle | Junji Ma Xitian Chen Yue Gu Liangfang Li Ying Lin Zhengjia Dai Trade-offs among cost, integration, and segregation in the human connectome Network Neuroscience |
| title | Trade-offs among cost, integration, and segregation in the human
connectome |
| title_full | Trade-offs among cost, integration, and segregation in the human
connectome |
| title_fullStr | Trade-offs among cost, integration, and segregation in the human
connectome |
| title_full_unstemmed | Trade-offs among cost, integration, and segregation in the human
connectome |
| title_short | Trade-offs among cost, integration, and segregation in the human
connectome |
| title_sort | trade offs among cost integration and segregation in the human connectome |
| url | https://direct.mit.edu/netn/article/7/2/604/113760/Trade-offs-among-cost-integration-and-segregation |
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