Higher general intelligence is associated with stable, efficient, and typical dynamic functional brain connectivity patterns

Justin Ng, Ju-Chi Yu, J. D. Feusner, Colin Hawco
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Abstract

Abstract General intelligence, referred to as g, is hypothesized to emerge from the capacity to dynamically and adaptively reorganize macroscale brain connectivity. Temporal reconfiguration can be assessed using dynamic functional connectivity (dFC), which captures the propensity of brain connectivity to transition between a recurring repertoire of distinct states. Conventional dFC metrics commonly focus on categorical state switching frequencies which do not fully assess individual variation in continuous connectivity reconfiguration. Here, we supplement frequency measures by quantifying within-state connectivity consistency, dissimilarity between connectivity across states, and conformity of individual connectivity to group-average state connectivity. We utilized resting-state functional magnetic resonance imaging (fMRI) data from the large-scale Human Connectome Project and applied data-driven multivariate Partial Least Squares Correlation to explore emergent associations between dynamic network properties and cognitive ability. Our findings reveal a positive association between g and the stable maintenance of states characterized by distinct connectivity between higher-order networks, efficient reconfiguration (i.e., minimal connectivity changes during transitions between similar states, large connectivity changes between dissimilar states), and ability to sustain connectivity close to group-average state connectivity. This hints at fundamental properties of brain–behavior organization, suggesting that general cognitive processing capacity may be supported by the ability to efficiently reconfigure between stable and population-typical connectivity patterns.
较高的一般智力与稳定、高效和典型的动态大脑功能连接模式有关
摘要 一般智能(General Intelligence),简称为 "g",被假定为产生于动态和适应性地重组宏观尺度大脑连接的能力。动态功能连通性(dFC)能捕捉大脑连通性在重复出现的不同状态之间转换的倾向,从而评估时间重组。传统的 dFC 指标通常侧重于分类状态切换频率,无法全面评估连续连接重组的个体差异。在这里,我们通过量化状态内连通性的一致性、不同状态间连通性的差异性以及个体连通性与组平均状态连通性的一致性来补充频率测量。我们利用大规模人类连接组计划的静息态功能磁共振成像(fMRI)数据,并应用数据驱动的多元偏最小二乘法相关性来探索动态网络属性与认知能力之间的关联。我们的研究结果表明,g 与稳定的状态维持之间存在正相关,其特点是高阶网络之间的连通性不同、重构效率高(即相似状态之间过渡时的连通性变化最小,而不同状态之间的连通性变化较大),以及维持接近群体平均状态连通性的能力。这暗示了大脑行为组织的基本特性,表明一般认知处理能力可能是由在稳定和群体典型连接模式之间高效重组的能力支持的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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