Unfolding how the brain functionally shifts within the cognitive space remains an unresolved question . From a brain connectivity perspective, there exist two main concepts: cognitive shifts and cognitive flexibility. Although the former is the proxy of the latter, the biggest challenge, in terms of bridging these two concepts, lies in the fact that cognitive shifts are governed by topological rules whereas cognitive flexibility is purely numerical. In this paper, we bridge the aforementioned concepts while preserving the complexity of cognition by proposing a formalism based on a 2D network morphospace that quantifies trapping and exit characteristics of network subsystems, naturally interpreted as functional communities. We show that the constructed measurements reflect the emergent phenomenon of higher-order cognitive states in addition to being able to quantify cognitive flexibility, as a direct output. Leveraging this analytic framework , cognitive shifts among traversedly integrated/segregated states of cognition are shown to be projected from subject specific cognitive signatures. The evidence of individual fingerprint emerged from cognitive flexibility domain legitimizes the quest to explore the intrinsic relationship between flexibility of functional networks and behavioral measures, including fluid intelligence. The constructed multi-linear models using flexibility descriptors demonstrate an above chance level of specificity. Finally, through the associations between behavioral measures and flexibility theory, we found that frontoparietal (FP) activation level, expressed through FP preconfiguration, and default mode network (DMN) efficiency, expressed through DMN preconfiguration, are positively correlated with all behavioral measures .
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 .
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Unfolding how the brain functionally shifts within the cognitive space remains an unresolved question . From a brain connectivity perspective, there exist two main concepts: cognitive shifts and cognitive flexibility. Although the former is the proxy of the latter, the biggest challenge, in terms of bridging these two concepts, lies in the fact that cognitive shifts are governed by topological rules whereas cognitive flexibility is purely numerical. In this paper, we bridge the aforementioned concepts while preserving the complexity of cognition by proposing a formalism based on a
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
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that quantifies trapping and exit characteristics of network subsystems, naturally interpreted as functional communities. We show that the constructed measurements reflect the emergent phenomenon of higher-order cognitive states in addition to being able to quantify cognitive flexibility, as a direct output. Leveraging this analytic framework , cognitive shifts among traversedly integrated/segregated states of cognition are shown to be projected from subject specific cognitive signatures. The evidence of individual fingerprint emerged from cognitive flexibility domain legitimizes the quest to explore the intrinsic relationship between flexibility of functional networks and behavioral measures, including fluid intelligence. The constructed multi-linear models using flexibility descriptors demonstrate an above chance level of specificity. Finally, through the associations between behavioral measures and flexibility theory, we found that frontoparietal (FP) activation level, expressed through FP preconfiguration, and default mode network (DMN) efficiency, expressed through DMN preconfiguration, are positively correlated with all behavioral measures
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