Top-Down Control of Attention

描述（由申请人提供）：心理物理和生理实验已经证明，在预期的刺激实际发生之前，注意它可以增强其感觉处理。控制预期注意的神经机制的破坏是包括精神分裂症和癫痫在内的许多精神和神经疾病的症状。神经影像学研究已经确定了有助于预期注意的大脑区域，并提出了自上而下的控制机制。实现这种控制的神经生理信号仍未得到很好的理解。本项目有三个目标。首先，我们希望确定局部场电位和表面脑电图中的α范围（10hz）活动是视觉皮层中自上而下注意控制的不同方面的中介信号。其次，我们希望确定，颞下皮层可以作为一个中转站，将自上而下的控制信号从前额皮质引导到较低的视觉区域。第三，通过比较刺激发生前后的注意效应，我们希望检验自上而下控制和自下而上加工之间的相互作用。这些目标将通过利用一个独特的资源——120 GB高质量的多电极局部场电位、表面脑电图和多单元活动数据来解决，这些数据来自执行多模式选择性注意任务的猴子。实现这些目标的关键组成部分是一套用于分析多元神经记录的信号处理方法，这些方法是PI和他的同事在过去十年中开发和验证的。对数据的分析方法的初步应用已经产生了一些见解，以支持我们的研究目标，为成功执行拟议项目奠定了基础。

DESCRIPTION (provided by applicant): Psychophysical and physiological experiments have demonstrated that attending to an anticipated stimulus before its actual onset can enhance its sensory processing. Disruption of the neural mechanism that controls anticipatory attention is symptomatic of a number of psychiatric and neurological disorders including schizophrenia and epilepsy. Neuroimaging studies have identified the brain areas that contribute to anticipatory attention and suggested a top-down control mechanism. What remains not well understood is the neurophysiological signal that implements such control. The present project has three objectives. First, we wish to establish that alpha range (10 Hz) activities in local field potentials and in surface EEGs are the signals that mediate different aspects of top-down attentional control in the visual cortex. Second, we wish to establish that the inferotemporal cortex may serve as a way station that directs the top-down control signal from the prefrontal cortex to lower visual areas. Third, by comparing the attentional effect before and after stimulus onset, we wish to examine the interplay between top-down control and bottom-up processing. These objectives will be addressed by utilizing a unique resource - 120 GB of high quality multielectrode local field potential, surface EEG and multiunit activity data recorded from monkeys performing an intermodal selective attention task. A key enabling component for accomplishing these objectives is a set of signal processing methods for analyzing multivariate neural recordings developed and validated over the past decade by the PI and his colleagues. Initial applications of the analysis methods to the data have already yielded a number of insights in support of our research aims which lay the foundation for the successful execution of the proposed project.