An unresolved question in network neuroscience is the quantification of reconfiguration in functional networks in response to varying cognitive demands  . We propose that  a mesoscopic generalizable framework would be most apt to investigate the breadth of functional (re-) configurations. We propose a 2D network morphospace using novel mesoscopic metrics, Trapping Efficiency (TE) and Exit Entropy (EE), that characterize the topology of mesoscopic structures and the flow of information within and between them. This framework captures the behavior of a reference set of functional networks (FNs) with changing mental states. We show that this morphospace is sensitive to different FNs, cognitive tasks and subjects. We propose that functional connectivity changes in FNs may be categorized into three different types of reconfigurations: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration; and quantify the Network Configural Breadth across different tasks . In essence, we put forth a framework that can be used to explore the cognitive space in a comprehensive manner, for each individual separately, and at different levels of granularity ; a tool that can also quantify the changes that result from such an exploration, as the brain switches between mental states.
The best approach to quantify human brain functional reconfigurations in response to varying cognitive demands remains an unresolved topic in network neuroscience . We propose that such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. 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. In this study, 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.
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An unresolved question in network neuroscience is the quantification of reconfiguration in functional networks
The best approach to quantify human brain functional reconfigurations
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remains an unresolved topic in network neuroscience
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such functional reconfigurations may be categorized into three different types: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration. In order to quantify these reconfigurations, we propose a mesoscopic framework focused on functional networks (FNs) or communities. To do so, we introduce
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that characterize the topology of mesoscopic structures and the flow
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this morphospace is sensitive to different
the metrics defining this morphospace can differentiate
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propose that functional connectivity changes in FNs may be categorized into three different types of reconfigurations: i) Network Configural Breadth, ii) Task-to-Task transitional reconfiguration, and iii) Within-Task reconfiguration; and quantify the Network Configural Breadth across different tasks
also show that network configural breadth significantly predicts behavioral measures, such as episodic memory, verbal episodic memory, fluid intelligence and general intelligence
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