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A morphospace framework to assess cognitive flexibility based on brain functional networks

D. Duong-Tran,E. Amico,B. Corominas-Murtra,M. Ventresca,J. Goñi

Published 2019 in arXiv: Neurons and Cognition

ABSTRACT

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.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    arXiv: Neurons and Cognition

  • Publication date

    2019-01-30

  • Fields of study

    Biology, Mathematics, Computer Science

  • Identifiers
    arXiv 1901.10962
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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