Oscillatory dynamics and sensory processing

描述（由申请人提供）：人类感官系统不断受到远远超过一次处理能力的输入的轰击。因此，注意机制已经进化，使得可用的处理能力只用于编码环境中最显著和行为相关的刺激。反过来，最重要的刺激主导着感知意识，并有特权进入控制如何最好地与外部物体互动的决定的神经机制。最近，研究者提出了一种反直觉的假设，即选择性注意通过在θ和α波段（即~4-12Hz）缓慢的内在皮层振荡来及时门入相关的感觉信息。例如，基本视觉任务的行为表现随α振荡的变化而变化，实验表明，破坏这些节律会损害感知能力。在这里，我们将验证这样一个假设，即这些行为的节律波动反映了早期皮层区域感觉表征保真度的振荡（感觉调节假设）。我们将使用正式的决策模型与新颖的脑电图分析技术相结合，该技术可以非侵入性地测量具有高度时间分辨率的特征选择表示。因此，这项工作将测试一个关于内在皮层振荡的本质及其与人类信息处理节奏变化的联系的集中假设。此外，我们分析高维脑电图数据集的新方法将提供一种非侵入性和耐受性良好的方法，以高时间分辨率测量人类皮层的特征选择性反应，这符合美国国立卫生研究院开发新的工具和方法的战略目标，以了解神经细胞群体如何在大脑区域内和大脑区域之间协同工作。

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.