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Connectivity in math-gifted adolescents: Comparing structural equation modeling, granger causality, and dynamic causal modeling

机译:数学天赋青少年的连通性:比较结构方程模型,格兰杰因果关系和动态因果关系模型

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摘要

Major challenges in brain research include understanding how the brain retrieves, processes, and transmits information along with understanding how information is stored. Therefore, connectivity analyses are vital in exploring information flow and temporal interactions between particular brain regions. This paper presents the results of two different types of connectivity analysis on previously acquired fMRI data of mathematically gifted adolescents and control subjects performing a mental rotation task. It has been hypothesized that mathematically gifted children rely on the parietal region and right hemisphere, along with utilizing inter-hemispheric interactions that may be a more efficient network during mental rotation tasks. Granger causality and dynamic causal modeling (DCM) were used to model the connectivity in the two groups. The model outputs are compared with connectivity paths determined from structural equation modeling (SEM) in a previous study [1]. Although these methods can be used as confirmatory and/or exploratory tools, they may provide complementary, rather than redundant, information about connectivity networks within the brain.
机译:大脑研究的主要挑战包括了解大脑如何检索,处理和传输信息,以及了解如何存储信息。因此,连通性分析对于探索特定大脑区域之间的信息流和时间相互作用至关重要。本文介绍了两种不同类型的连接分析结果,这些分析是对先前获得的数学天才青少年和执行心理旋转任务的控制对象的fMRI数据进行的。据推测,数学上有天赋的孩子依靠顶叶区域和右半球,以及利用半球间相互作用,这可能是在心理旋转任务中更有效的网络。使用格兰杰因果关系和动态因果建模(DCM)来对两组的连通性进行建模。在先前的研究中,将模型输出与通过结构方程模型(SEM)确定的连通性路径进行比较[1]。尽管这些方法可以用作确认和/或探索性工具,但它们可以提供有关大脑内部连接网络的补充而非冗余信息。

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