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Understanding neural flexibility from a multifaceted definition

Lookup NU author(s): Professor Marcus Kaiser



This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


© 2021Flexibility is a hallmark of human intelligence. Emerging studies have proposed several flexibility measurements at the level of individual regions, to produce a brain map of neural flexibility. However, flexibility is usually inferred from separate components of brain activity (i.e., intrinsic/task-evoked), and different definitions are used. Moreover, recent studies have argued that neural processing may be more than a task-driven and intrinsic dichotomy. Therefore, the understanding to neural flexibility is still incomplete. To address this issue, we propose a multifaceted definition of neural flexibility according to three key features: broad cognitive engagement, distributed connectivity, and adaptive connectome dynamics. For these three features, we first review the advances in computational approaches, their functional relevance, and their potential pitfalls. We then suggest a set of metrics that can help us assign a flexibility rating to each region. Subsequently, we present an emergent probabilistic view for further understanding the functional operation of individual regions in the unified framework of intrinsic and task-driven states. Finally, we highlight several areas related to the multifaceted definition of neural flexibility for future research. This review not only strengthens our understanding of flexible human brain, but also suggests that the measure of neural flexibility could bridge the gap between understanding intrinsic and task-driven brain function dynamics.

Publication metadata

Author(s): Yin D, Kaiser M

Publication type: Review

Publication status: Published

Journal: NeuroImage

Year: 2021

Volume: 235

Print publication date: 15/07/2021

Online publication date: 06/04/2021

Acceptance date: 02/04/2018

ISSN (print): 1053-8119

ISSN (electronic): 1095-9572

Publisher: Academic Press Inc.


DOI: 10.1016/j.neuroimage.2021.118027

PubMed id: 33836274