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一种用于区分神经元和区域汇聚的加法因子设计:使用功能磁共振成像测量音频、视觉和触觉感觉流之间的多感官相互作用。

An additive-factors design to disambiguate neuronal and areal convergence: measuring multisensory interactions between audio, visual, and haptic sensory streams using fMRI.

作者信息

Stevenson Ryan A, Kim Sunah, James Thomas W

机构信息

Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.

出版信息

Exp Brain Res. 2009 Sep;198(2-3):183-94. doi: 10.1007/s00221-009-1783-8. Epub 2009 Apr 8.

Abstract

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.

摘要

从经验和理论上都可以表明,基于用于评估与BOLD功能磁共振成像数据的多感官整合的既定指标(如超相加性)所做的推断,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相加性的可能性会随着刺激辨别力的降低而增加。在本文中,我们认为斯特恩伯格的相加因素设计能够对多感官整合进行无偏评估。通过将信噪比作为相加因素进行操控,我们识别出了显示视听或视觉触觉神经元汇聚特性的皮质区域网络。这些网络包含先前已识别出的多感官区域以及许多新区域,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。对跨视听和视觉触觉条件的整合网络进行比较发现,重叠极少,这表明整合的神经机制对于特定的感官配对而言是独特的。我们的结果通过展示其在不同模态(视觉、听觉和触觉)、刺激类型(语音和非语音)、实验设计(组块设计和事件相关设计)、分析方法(统计参数映射和感兴趣区域)以及实验者选择的基线方面的有效性,为相加因素方法的实用性提供了证据。相加因素方法提供了一种研究多感官相互作用的方法,超越了更成熟的基于指标的相减型方法所能达到的范围。

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