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Effective connectivity of the multiplication network: a functional MRI and multivariate Granger Causality Mapping study.

机译:乘法网络的有效连通性:功能性MRI和多元Granger因果关系映射研究。

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Developmental neuropsychology and functional neuroimaging evidence indicates that simple and complex mental calculation is subserved by a fronto-parietal network. However, the effective connectivity (connection direction and strength) among regions within the fronto-parietal network is still unexplored. Combining event-related fMRI and multivariate Granger Causality Mapping (GCM), we administered a multiplication verification task to healthy participants asking them to solve single and double-digit multiplications. The goals of our study were first, to identify the effective connectivity of the multiplication network, and second, to compare the effective connectivity patterns between a low and a high arithmetical competence (AC) group. The manipulation of multiplication difficulty revealed a fronto-parietal network encompassing bilateral intraparietal sulcus (IPS), left pre-supplementary motor area (PreSMA), left precentral gyrus (PreCG), and right dorsolateral prefrontal cortex (DLPFC). The network was driven by an intraparietal IPS-IPS circuit hosting a representation of numerical quantity intertwined with a fronto-parietal DLPFC-IPS circuit engaged in temporary storage and updating of arithmetic operations. Both circuits received additional inputs from the PreCG and PreSMA playing more of a supportive role in mental calculation. The high AC group compared to the low AC group displayed a greater activation in the right IPS and based its calculation more on a feedback driven intraparietal IPS-IPS circuit, whereas the low competence group more on a feedback driven fronto-parietal DLPFC-IPS circuit. This study provides first evidence that multivariate GCM is a sensitive approach to investigate effective connectivity of mental processes involved in mental calculation and to compare group level performances for different populations.
机译:发育神经心理学和功能性神经影像学证据表明,额顶网络可满足简单和复杂的心理计算。然而,额顶网络内各区域之间的有效连通性(连接方向和强度)仍待探索。结合事件相关的功能磁共振成像和多元格兰杰因果关系映射(GCM),我们对健康参与者进行了乘法验证任务,要求他们解决一位和两位数乘法。我们研究的目标首先是确定乘法网络的有效连通性,其次是比较低和高算术能力(AC)组之间的有效连通性模式。乘法难度的操作揭示了包括双侧顶壁沟(IPS),左前辅助运动区(PreSMA),左中前回(PreCG)和右后外侧前额皮质(DLPFC)的额顶网络。该网络由顶内IPS-IPS电路驱动,该电路托管着与额顶DLPFC-IPS电路交织在一起的数值表示,该电路参与临时存储和算术运算的更新。这两个电路都从PreCG和PreSMA接收了额外的输入,在心理计算中发挥了更多的支持作用。与低AC组相比,高AC组在正确的IPS中显示出更大的激活,其计算更多地基于反馈驱动的顶内IPS-IPS电路,而低能力组更多的基于反馈驱动的顶-顶DLPFC-IPS电路。这项研究提供了第一个证据,即多元GCM是研究参与心理计算的心理过程的有效连通性并比较不同人群的小组水平表现的灵敏方法。

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