Crossmodal processing in the human brain: Insights from functional neuroimaging studies
Crossmodal processing in the human brain: Insights from functional neuroimaging studies
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DOI：
10.1093/cercor/11.12.1110
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发表时间：
2001-12-01
期刊：
影响因子：
3.7
通讯作者：
Calvert, GA
中科院分区：
文献类型：
--
作者：
Calvert, GA
现代脑成像技术现在已经使研究人脑中跨通道处理的神经部位和机制成为可能。本文综述了正电子发射断层扫描、功能磁共振成像（fMRI）、事件相关电位和脑磁图在跨通道匹配、内容和空间信息的跨通道整合以及跨通道学习等方面的研究。这些调查开始产生一些一致的结果，涉及这些不同的跨模态操作的神经网络。越来越多的人对上级颞沟、顶叶下沟、额叶皮层、额叶皮层和屏状核的特定作用进行了定义。然而，任何一项研究所涉及的大脑区域的精确网络，似乎都严重依赖于所使用的实验范式、所结合的信息的性质以及所研究的模式的特定组合。不同群体采用的不同分析策略也可能是造成调查结果差异的一个重要因素。在本文中，我们证明了计算交叉点，连接和互动效应的视听整合网站的识别，使用现有的功能磁共振成像数据从我们自己的实验室的影响。这项工作突出了潜在的价值，使用统计的相互作用的影响，以确定可能的网站在人脑中的多感官整合的crossmodal刺激的电生理反应模型。
Modern brain imaging techniques have now made it possible to study the neural sites and mechanisms underlying crossmodal processing in the human brain. This paper reviews positron emission tomography, functional magnetic resonance imaging (fMRI), event-related potential and magnetoencephalographic studies of crossmodal matching, the crossmodal integration of content and spatial information, and crossmodal learning. These investigations are beginning to produce some consistent findings regarding the neuronal networks involved in these distinct crossmodal operations. Increasingly, specific roles are being defined for the superior temporal sulcus, the inferior parietal sulcus, regions of frontal cortex, the insula cortex and claustrum. The precise network of brain areas implicated in any one study, however, seems to be heavily dependent on the experimental paradigms used, the nature of the information being combined and the particular combination of modalities under investigation. The different analytic strategies adopted by different groups may also be a significant factor contributing to the variability in findings. In this paper, we demonstrate the impact of computing intersections, conjunctions and interaction effects on the identification of audiovisual integration sites using existing fMRI data from our own laboratory. This exercise highlights the potential value of using statistical interaction effects to model electrophysiological responses to crossmodal stimuli in order to identify possible sites of multisensory integration in the human brain.