Oscillatory dynamics and sensory processing

描述(由申请人提供)：人类感官系统不断地受到远远超过一次处理能力的输入的轰炸。因此，注意力机制已经进化，以至于可用的处理能力专门用于编码环境中最突出和行为相关的刺激。反过来，最重要的刺激支配着知觉，并拥有特权进入神经机制，这些神经机制控制着如何最好地与外部物体互动的决定。最近，研究人员提出了一种违反直觉的假说，即选择性注意的作用是通过在theta和α频段(即~4-12赫兹)内缓慢的固有皮质振荡来及时地选通相关的感觉信息。例如，在基本视觉任务中的行为表现随着阿尔法振荡的时间而起伏不定，而实验性地扰乱这些节奏会损害知觉。在这里，我们将检验这样的假设，即这些行为的节律性波动反映了早期皮质区域感觉表征的保真度的振荡(感觉调制假说)。我们将使用决策的正式模型和新的脑电分析技术的组合，这些技术可以非侵入性地测量具有高时间分辨率的特征选择表征。因此，这项工作将测试一个关于固有皮质振荡的性质及其与人类信息处理中节奏变化的联系的重点假说。此外，我们分析高维EEG数据集的新方法将提供一种非侵入性和容忍度良好的手段，以高时间分辨率测量人类皮质的特征选择反应，这与NIH开发新的工具和方法以了解神经细胞群体如何在脑区内和脑区之间协同工作的战略目标是一致的。

DESCRIPTION (provided by applicant): Human sensory systems are continuously bombarded with far more input than can be processed at a single time. As a result, attentional mechanisms have evolved so that available processing capacity is dedicated to encoding only the most salient and behaviorally relevant stimuli in the environment. In turn, the most important stimuli dominate perceptual awareness and have privileged access to the neural mechanisms that control decisions about how to best interact with external objects. Recently, investigators have proposed the counterintuitive hypothesis that selective attention operates to gate relevant sensory information in time with slow intrinsic cortical oscillations in the theta and alpha bands (i.e. ~4-12Hz). For example, behavioral performance on basic visual tasks waxes and wanes in time with alpha oscillations, and experimentally disrupting these rhythms impairs perception. Here we will test the hypothesis that these rhythmic fluctuations in behavior reflect oscillations n the fidelity of sensory representations in early cortical areas (sensory-modulation hypothesis). We will use a combination of formal models of decision making coupled with novel EEG analysis techniques that can non- invasively measure feature-selective representations with a high degree of temporal resolution. Thus, this work will test a focused hypothesis about the nature of intrinsic cortical oscillations and their link to rhythmic changes in human information processing. In addition, our novel approach to analyzing high-dimensional EEG data sets will provide a non-invasive and well-tolerated means of measuring feature selective responses in human cortex with high temporal resolution, in line with the strategic aim of the NIH to develop novel tools and methodologies for understanding how populations of neural cells work together within and between brain regions.