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首页> 外文期刊>Experimental Brain Research >An additive-factors design to disambiguate neuronal and areal convergence: measuring multisensory interactions between audio, visual, and haptic sensory streams using fMRI.
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An additive-factors design to disambiguate neuronal and areal convergence: measuring multisensory interactions between audio, visual, and haptic sensory streams using fMRI.

机译:可消除神经元和区域融合歧义的累加因子设计:使用fMRI测量音频,视觉和触觉感觉流之间的多感觉交互。

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摘要

It can be shown empirically and theoretically that inferences based on established metrics used to assess multisensory integration with BOLD fMRI data, such as superadditivity, are dependent on the particular experimental situation. For example, the law of inverse effectiveness shows that the likelihood of finding superadditivity in a known multisensory region increases with decreasing stimulus discriminability. In this paper, we suggest that Sternberg's additive-factors design allows for an unbiased assessment of multisensory integration. Through the manipulation of signal-to-noise ratio as an additive factor, we have identified networks of cortical regions that show properties of audio-visual or visuo-haptic neuronal convergence. These networks contained previously identified multisensory regions and also many new regions, for example, the caudate nucleus for audio-visual integration, and the fusiform gyrus for visuo-haptic integration. A comparison of integrative networks across audio-visual and visuo-haptic conditions showed very little overlap, suggesting that neural mechanisms of integration are unique to particular sensory pairings. Our results provide evidence for the utility of the additive-factors approach by demonstrating its effectiveness across modality (vision, audition, and haptics), stimulus type (speech and non-speech), experimental design (blocked and event-related), method of analysis (SPM and ROI), and experimenter-chosen baseline. The additive-factors approach provides a method for investigating multisensory interactions that goes beyond what can be achieved with more established metric-based, subtraction-type methods.
机译:可以从经验和理论上证明,基于用于评估与BOLD fMRI数据的多传感器整合(例如超可加性)的既定指标的推论取决于特定的实验情况。例如,逆有效性定律表明,在已知的多感官区域中发现超可加性的可能性随刺激性可辨别性的降低而增加。在本文中,我们建议Sternberg的加性因子设计可以对多感官整合进行公正的评估。通过操纵信噪比作为加性因子,我们已经确定了表现出视听或触觉神经元融合特性的皮质区域网络。这些网络包含以前确定的多感官区域,还包含许多新的区域,例如,用于视听整合的尾状核,以及用于视觉-触觉整合的梭状回。跨视听和视觉触觉条件的整合网络的比较显示出很少的重叠,表明整合的神经机制对于特定的感觉配对是独特的。我们的结果通过展示跨模态(视觉,听觉和触觉),刺激类型(语音和非语音),实验设计(阻止和事件相关),方法的有效性,证明了加性因子方法的效用。分析(SPM和ROI)以及实验者选择的基准。加性因子方法提供了一种研究多感官相互作用的方法,该方法超出了使用更成熟的基于度量的减法类型方法可以实现的范围。

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