Oscillatory dynamics and sensory processing

描述（由申请人提供）：人类感觉系统不断受到比单次处理的输入多得多的输入的轰击。因此，注意力机制已经发展，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，最重要的刺激支配着知觉意识，并有特权进入控制如何与外部物体进行最佳互动的决定的神经机制。最近，研究人员提出了一个违反直觉的假设，即选择性注意可以及时地通过θ和α波段（即~4- 12 Hz）的缓慢内在皮层振荡来门控相关的感觉信息。例如，基本视觉任务的行为表现随着阿尔法振荡的时间而增减，实验性地破坏这些节奏会损害感知。 在这里，我们将测试的假设，这些节奏波动的行为反映了振荡的保真度的感觉表征在早期皮质区（感觉调制假说）。我们将使用决策的正式模型与新颖的EEG分析技术相结合，这些技术可以无创地测量具有高度时间分辨率的特征选择性表示。因此，这项工作将测试一个集中的假设，内在的皮层振荡的性质和他们的联系，在人类信息处理的节奏变化。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，符合NIH的战略目标，即开发新的工具和方法，以了解神经细胞群体如何在大脑区域内和之间共同工作。

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.