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乘法网络的有效连接:功能磁共振成像和多变量 Granger 因果关系映射研究。

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

机构信息

Department of Molecular Neuroscience, George Mason University, Fairfax, VA 22030, USA.

出版信息

Hum Brain Mapp. 2011 Sep;32(9):1419-31. doi: 10.1002/hbm.21119. Epub 2010 Aug 16.

Abstract

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.

摘要

发展神经心理学和功能神经影像学的证据表明,简单和复杂的心理计算由额顶网络提供。然而,额顶网络内各区域之间的有效连接(连接方向和强度)仍未得到探索。结合事件相关 fMRI 和多元 Granger 因果关系映射(GCM),我们让健康参与者进行乘法验证任务,要求他们解决一位数和两位数的乘法。我们研究的目的首先是确定乘法网络的有效连接,其次是比较低和高算术能力(AC)组之间的有效连接模式。乘法难度的操作揭示了一个额顶网络,包括双侧顶内沟(IPS)、左辅助运动区(PreSMA)、左中央前回(PreCG)和右背外侧前额叶皮层(DLPFC)。该网络由一个顶内 IPS-IPS 回路驱动,该回路承载着数值数量的表示,与一个额顶 DLPFC-IPS 回路交织在一起,该回路负责临时存储和更新算术运算。这两个回路都从 PreCG 和 PreSMA 获得额外的输入,在心理计算中扮演着更具支持性的角色。与低 AC 组相比,高 AC 组在右 IPS 中显示出更大的激活,并且其计算更多地基于反馈驱动的顶内 IPS-IPS 回路,而低能力组则更多地基于反馈驱动的额顶 DLPFC-IPS 回路。这项研究首次提供了证据,表明多元 GCM 是一种敏感的方法,可以研究心理计算中涉及的心理过程的有效连接,并比较不同人群的组间表现。

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