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与具有较高流体智力的个体更接近的关键静息态神经动力学。

Closer to critical resting-state neural dynamics in individuals with higher fluid intelligence.

机构信息

PRESTO, Japan Science and Technology Agency, Kawaguchi, Saitama, Japan.

Research Center for Advanced Science and Technology, The University of Tokyo, Meguro-ku, Tokyo, Japan.

出版信息

Commun Biol. 2020 Feb 3;3(1):52. doi: 10.1038/s42003-020-0774-y.

Abstract

According to the critical brain hypothesis, the brain is considered to operate near criticality and realize efficient neural computations. Despite the prior theoretical and empirical evidence in favor of the hypothesis, no direct link has been provided between human cognitive performance and the neural criticality. Here we provide such a key link by analyzing resting-state dynamics of functional magnetic resonance imaging (fMRI) networks at a whole-brain level. We develop a data-driven analysis method, inspired from statistical physics theory of spin systems, to map out the whole-brain neural dynamics onto a phase diagram. Using this tool, we show evidence that neural dynamics of human participants with higher fluid intelligence quotient scores are closer to a critical state, i.e., the boundary between the paramagnetic phase and the spin-glass (SG) phase. The present results are consistent with the notion of "edge-of-chaos" neural computation.

摘要

根据关键大脑假说,大脑被认为处于接近临界状态,从而实现高效的神经计算。尽管此前有理论和经验证据支持这一假说,但人类认知表现与神经临界性之间尚未建立直接联系。在这里,我们通过分析全脑功能磁共振成像 (fMRI) 网络的静息态动力学提供了这样一个关键联系。我们开发了一种受统计物理自旋系统理论启发的数据驱动分析方法,将全脑神经动力学映射到相图上。使用这个工具,我们有证据表明,具有更高流体智力商数得分的人类参与者的神经动力学更接近临界状态,即顺磁相和自旋玻璃 (SG) 相之间的边界。目前的结果与“混沌边缘”神经计算的概念一致。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/f111/6997374/909d0c33b8cd/42003_2020_774_Fig1_HTML.jpg

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