The Hidden Control Architecture of Complex Brain Networks
Summary: The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to d...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2019-03-01
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| Series: | iScience |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2589004219300513 |
| _version_ | 1818298356031553536 |
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| author | Byeongwook Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho |
| author_facet | Byeongwook Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho |
| author_sort | Byeongwook Lee |
| collection | DOAJ |
| description | Summary: The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency. : Neuroscience; Systems Neuroscience; Systems Biology; Control Theory Subject Areas: Neuroscience, Systems Neuroscience, Systems Biology, Control Theory |
| first_indexed | 2024-12-13T04:34:01Z |
| format | Article |
| id | doaj.art-453a3a5b7ab04fa9b8b36c3a97d63754 |
| institution | Directory Open Access Journal |
| issn | 2589-0042 |
| language | English |
| last_indexed | 2024-12-13T04:34:01Z |
| publishDate | 2019-03-01 |
| publisher | Elsevier |
| record_format | Article |
| series | iScience |
| spelling | doaj.art-453a3a5b7ab04fa9b8b36c3a97d637542022-12-21T23:59:29ZengElsevieriScience2589-00422019-03-0113154162The Hidden Control Architecture of Complex Brain NetworksByeongwook Lee0Uiryong Kang1Hongjun Chang2Kwang-Hyun Cho3Laboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of KoreaLaboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of KoreaLaboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of KoreaLaboratory for Systems Biology and Bio-inspired Engineering, Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Republic of Korea; Corresponding authorSummary: The brain controls various cognitive functions in a robust and efficient way. What is the control architecture of brain networks that enables such robust and optimal control? Is this brain control architecture distinct from that of other complex networks? Here, we developed a framework to delineate a control architecture of a complex network that is compatible with the behavior of the network and applied the framework to structural brain networks and other complex networks. As a result, we revealed that the brain networks have a distributed and overlapping control architecture governed by a small number of control nodes, which may be responsible for the robust and efficient brain functions. Moreover, our artificial network evolution analysis showed that the distributed and overlapping control architecture of the brain network emerges when it evolves toward increasing both robustness and efficiency. : Neuroscience; Systems Neuroscience; Systems Biology; Control Theory Subject Areas: Neuroscience, Systems Neuroscience, Systems Biology, Control Theoryhttp://www.sciencedirect.com/science/article/pii/S2589004219300513 |
| spellingShingle | Byeongwook Lee Uiryong Kang Hongjun Chang Kwang-Hyun Cho The Hidden Control Architecture of Complex Brain Networks iScience |
| title | The Hidden Control Architecture of Complex Brain Networks |
| title_full | The Hidden Control Architecture of Complex Brain Networks |
| title_fullStr | The Hidden Control Architecture of Complex Brain Networks |
| title_full_unstemmed | The Hidden Control Architecture of Complex Brain Networks |
| title_short | The Hidden Control Architecture of Complex Brain Networks |
| title_sort | hidden control architecture of complex brain networks |
| url | http://www.sciencedirect.com/science/article/pii/S2589004219300513 |
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