A morphospace of functional configuration to assess configural breadth based on brain functional networks
AbstractThe quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transit...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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The MIT Press
2021-01-01
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| Series: | Network Neuroscience |
| Online Access: | https://direct.mit.edu/netn/article/5/3/666/98350/A-morphospace-of-functional-configuration-to |
| _version_ | 1818897721038209024 |
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| author | Duy Duong-Tran Kausar Abbas Enrico Amico Bernat Corominas-Murtra Mario Dzemidzic David Kareken Mario Ventresca Joaquín Goñi |
| author_facet | Duy Duong-Tran Kausar Abbas Enrico Amico Bernat Corominas-Murtra Mario Dzemidzic David Kareken Mario Ventresca Joaquín Goñi |
| author_sort | Duy Duong-Tran |
| collection | DOAJ |
| description |
AbstractThe quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states. |
| first_indexed | 2024-12-19T19:20:40Z |
| format | Article |
| id | doaj.art-21f12496059f46c6b3596150d25f59b2 |
| institution | Directory Open Access Journal |
| issn | 2472-1751 |
| language | English |
| last_indexed | 2024-12-19T19:20:40Z |
| publishDate | 2021-01-01 |
| publisher | The MIT Press |
| record_format | Article |
| series | Network Neuroscience |
| spelling | doaj.art-21f12496059f46c6b3596150d25f59b22022-12-21T20:09:00ZengThe MIT PressNetwork Neuroscience2472-17512021-01-015366668810.1162/netn_a_00193A morphospace of functional configuration to assess configural breadth based on brain functional networksDuy Duong-Tran0Kausar Abbas1Enrico Amico2Bernat Corominas-Murtra3Mario Dzemidzic4David Kareken5Mario Ventresca6Joaquín Goñi7School of Industrial Engineering, Purdue University, West Lafayette, IN, USASchool of Industrial Engineering, Purdue University, West Lafayette, IN, USASchool of Industrial Engineering, Purdue University, West Lafayette, IN, USADepartment of Zoology, Institute of Biology, Karl-Franzens University Graz, Graz, AustriaDepartment of Neurology, Indiana University School of Medicine, Indianapolis, IN, USADepartment of Neurology, Indiana University School of Medicine, Indianapolis, IN, USASchool of Industrial Engineering, Purdue University, West Lafayette, IN, USASchool of Industrial Engineering, Purdue University, West Lafayette, IN, USA AbstractThe quantification of human brain functional (re)configurations across varying cognitive demands remains an unresolved topic. We propose that such functional configurations may be categorized into three different types: (a) network configural breadth, (b) task-to task transitional reconfiguration, and (c) within-task reconfiguration. Such functional reconfigurations are rather subtle at the whole-brain level. Hence, we propose a mesoscopic framework focused on functional networks (FNs) or communities to quantify functional (re)configurations. To do so, we introduce a 2D network morphospace that relies on two novel mesoscopic metrics, trapping efficiency (TE) and exit entropy (EE), which capture topology and integration of information within and between a reference set of FNs. We use this framework to quantify the network configural breadth across different tasks. We show that the metrics defining this morphospace can differentiate FNs, cognitive tasks, and subjects. We also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence, and general intelligence. In essence, we put forth a framework to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity. This tool that can also quantify the FN reconfigurations that result from the brain switching between mental states.https://direct.mit.edu/netn/article/5/3/666/98350/A-morphospace-of-functional-configuration-to |
| spellingShingle | Duy Duong-Tran Kausar Abbas Enrico Amico Bernat Corominas-Murtra Mario Dzemidzic David Kareken Mario Ventresca Joaquín Goñi A morphospace of functional configuration to assess configural breadth based on brain functional networks Network Neuroscience |
| title | A morphospace of functional configuration to assess configural
breadth based on brain functional networks |
| title_full | A morphospace of functional configuration to assess configural
breadth based on brain functional networks |
| title_fullStr | A morphospace of functional configuration to assess configural
breadth based on brain functional networks |
| title_full_unstemmed | A morphospace of functional configuration to assess configural
breadth based on brain functional networks |
| title_short | A morphospace of functional configuration to assess configural
breadth based on brain functional networks |
| title_sort | morphospace of functional configuration to assess configural breadth based on brain functional networks |
| url | https://direct.mit.edu/netn/article/5/3/666/98350/A-morphospace-of-functional-configuration-to |
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