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A morphospace of functional configuration to assess configural breadth based on brain functional networks
Duy Duong-Tran1, Kausar Abbas1, Enrico Amico1
1School of Industrial Engineering, Purdue University, West Lafayette, IN, USA.
Network Neuroscience (Cambridge, Mass.)
|November 8, 2021
Summary
We introduce a new framework to quantify brain functional configurations, categorizing them into network configural breadth, task transition, and within-task reconfiguration. This approach enhances understanding of cognitive processes and individual differences.
Area of Science:
- Neuroscience
- Cognitive Science
- Network Science
Background:
- Quantifying human brain functional (re)configurations under varying cognitive demands is challenging.
- Functional brain configurations can be categorized into network configural breadth, task-to-task transitional reconfiguration, and within-task reconfiguration.
- These reconfigurations are subtle at the whole-brain level, necessitating a mesoscopic approach.
Purpose of the Study:
- To propose a mesoscopic framework for quantifying functional (re)configurations using functional networks (FNs).
- To introduce a 2D network morphospace with novel metrics (trapping efficiency and exit entropy) to capture network topology and information integration.
- To utilize this framework to quantify network configural breadth and its relationship with cognitive abilities.
Main Methods:
- Development of a mesoscopic framework focusing on functional networks (FNs).
- Introduction of a 2D network morphospace utilizing trapping efficiency (TE) and exit entropy (EE) metrics.
- Application of the framework to quantify network configural breadth across different cognitive tasks and subjects.
Main Results:
- The proposed metrics successfully differentiate functional networks, cognitive tasks, and individual subjects.
- Network configural breadth was found to significantly predict behavioral measures including episodic memory, verbal episodic memory, fluid intelligence, and general intelligence.
- The framework demonstrated the ability to quantify functional network reconfigurations during shifts between mental states.
Conclusions:
- A novel framework is presented for comprehensively exploring cognitive space at individual and granular levels.
- The framework provides a tool to quantify functional network reconfigurations associated with transitions between mental states.
- This approach offers new insights into the relationship between brain functional organization and cognitive performance.
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