Neural mechanisms: Learned audio-visuo-motor integration

这项研究回答了人类大脑中大规模神经网络的基本问题， 跨模态认知为了揭示听觉和视觉刺激以及运动行为是如何被任意地组合为一个 跨模态学习的结果，并整合到超模态符号表征，我们将研究 罗马字母的神经表征这些由四个单峰表示组成 （书写和口语的视觉，听觉和运动表征）和学习之间的联系 这些，也就是他们的视听识别和运动产生的基础过程。准确 字母表现出符号的所有必要属性，这一事实有助于实验控制。 跨模态表示，但在物理上简单和准确的格式携带没有语义关联 可能会混淆神经生理学对结果的解释。组合式3特斯拉功能磁 磁共振成像（fMRI）和306通道脑磁图/ 128通道 将采用同步行为记录的脑电图（MEG/EEG）技术， 精确定位潜在的神经机制这种方法结合了空间上 具有时间特异性MEG/EEG的精确fMRI，能够准确地对大脑进行时空表征 活动完全无创。为了直接观察大规模的神经认知网络是如何在 跨模态联想学习，我们还将进行fMRI/MEG/EEG测量之前和之后，我们的 受试者被教授（以前不熟悉）日语假名字母。具体目的是阐明 结构，功能和振荡机制的完全建立的跨模态神经网络的基础上， 以前广泛的联想学习（罗马字母）和目前不断发展的网络， 跨模态联想（日语字母）。我们将描述脑深部核团的相对作用， 小脑，内侧颞叶，以及感觉特异性和多感觉关联皮层， 网络.多维实验设计允许分离神经机制， 知觉、工作记忆、记忆编码和回忆。

This study answers fundamental questions of large-scale neural networks in the human brain supporting crossmodal cognition. To reveal how auditory and visual stimuli and motor acts are arbitrarily combined as a result of crossmodal learning and integrated to supramodal symbolic representations, we will study the neural representations of the letters of the Roman alphabet. These consist of four unimodal representations (visual, auditory, and motor representations for writing and speaking) and learned connections between these, that is, the processes that underlie their audiovisual recognition and motor production. Accurate experimental control is facilitated by the fact that letters exhibit all the necessary properties of symbolic crossmodal representations but in a physically simple and exact format carrying no semantic associations that could confound the neurophysiological interpretation of results. Combined 3-Tesla functional magnetic resonance imaging (fMRI) and 306-channel magnetoencephalographic / 128-channel electroencephalographic (MEG/EEG) techniques with simultaneous behavioral recordings will be applied to pinpoint the exact underlying neural mechanisms. This approach combines the advantages of spatially accurate fMRI with temporally specific MEG/EEG, enabling accurate spatiotemproal characterization of brain activity totally noninvasively. To directly observe how large-scale neurocognitive networks evolve during crossmodal associative learning, we will also conduct fMRI/MEG/EEG measurements before and after our subjects are taught (previously unfamiliar) Japanese kana-letters. The specific aims are to elucidate structure, function, and oscillatory mechanisms of fully established crossmodal neural networks based on previous extensive associative learning (Roman letters) and currently evolving networks representing novel crossmodal associations (Japanese letters). We will characterize the relative roles of deep brain nuclei, cerebellum, medial temporal lobe, and sensory-specific and multisensory association cortices in such networks. The multidimensional experimental design allows isolation of neural mechanisms utilized by perception, working memory, memory encoding, and recall.