Neuroimaging Studies of Human Binocular Vision and Stimulus Selection

正常的双眼视觉需要两个位于前方的眼睛的合作。每只眼睛接收到的刺激永远不会相同。值得注意的是，我们通常，但并不总是，在感知上经历一个单一的视图。实现双眼组合的计算取决于向每只眼睛显示的刺激，并且存在不同的机制。我们的长期目标是提供一个感性和生理帐户，这些双目组合制度，整合与我们的理解作为一个整体的视觉系统。本申请的目的是集中在双眼竞争（BR）作为一个窗口，双眼整合的机制。我们提出了一个多学科的方法，结合详细的视觉测试与高分辨率的大脑活动神经成像，使我们可以评估竞争的理论双目视觉，并将我们的研究结果与当前的理解知觉意识。除了功能性磁共振成像（fMRI），我们还开始将脑磁图（MEG）用于非侵入性人类神经成像。这项工作将通过新的分析技术得到进一步加强，如功能连接的网络建模和新兴的机器学习技术。 具体来说，我们建议在这里推进表征的大脑网络的基础上的感知双眼竞争，包括模式竞争，interocular分组，和选择性注意的关系。目的1 -比较和对比双眼和模式竞争的神经相关，以确定单眼和双眼神经竞争的网站。目的2 -确定双眼竞争中双眼间分组的机制，以进一步分离大空间区域内双眼竞争的神经基础。目的3 -将自愿视觉注意的影响与刺激对比对竞争的影响联系起来，以更好地理解选择性注意的作用。目的4 -将网络建模分析应用于功能磁共振成像数据和脑磁图数据，以描述皮层区域网络之间的通信机制。此外，我们还建议利用已知存在于正常视觉大脑中的强大的个体差异，特别是在双眼视觉中的行为。这种大脑行为差异的直接相关性可以应用于有效地解决假定的神经机制，甚至比过去更精细。此外，一些感知差异将与遗传特征有关，这种认知神经科学与分子生物学的整合可能会影响我们对未来几年行为的理解。最后，将我们的竞争模型提升到单个受试者的水平，使得最终翻译到双眼视觉异常的受试者的可能性更大，并进一步增加了我们的影响。

Normal binocular vision requires the cooperation of two frontally placed eyes. The stimulation received by each eye is never identical. Remarkably, we usually, but not always, experience a single view perceptually. The computations that achieve binocular combination depend on the stimuli shown to each eye, and distinct regimes exits. Our long-term goal is to provide a perceptual and physiological account of these binocular combination regimes that integrates with our understanding of the visual system as a whole. The objective of this application is to focus on binocular rivalry (BR) as a window onto mechanisms of binocular integration. We propose a multidisciplinary approach that combines detailed vision testing with high-resolution neuroimaging of brain activity so that we can evaluate competing theories of binocular vision, and relate our findings to current understanding of perceptual awareness. In addition to functional magnetic resonance imaging (fMRI), we have also begun to incorporate magnetoencephalography (MEG) for non-invasive human neuroimaging. This work will be further enhanced by new analysis techniques such as network modelling of functional connectivity, and emerging machine learning techniques. Specifically, we propose here to advance the characterization of the brain networks that underlie the perception of binocular rivalry, including the relationship to pattern rivalry, interocular grouping, and selective attention. Aim 1 - Compare and contrast the neural correlates of binocular and pattern rivalry, to define the sites of both monocular and binocular neural competition. Aim 2 - Determine the mechanisms of interocular grouping in binocular rivalry, to further isolate the neural substrates of binocular competition across large regions of space. Aim 3 - Relate the effects of voluntary visual attention to the effects of stimulus contrast on rivalry, to better understand the role of selective attention. Aim 4 - Apply network modelling analysis to both fMRI data and MEG data, to describe the mechanisms of communication used between networks of cortical regions. In addition, we also propose to take advantage of the robust individual differences known to exist in the normal visual brain, and behaviourally in binocular vision in particular. Direct correlation of such brain-behaviour differences can be applied to effectively address the putative neural mechanisms even more finely than in the past. Moreover, some perceptual differences will relate to genetic traits, and such cognitive neuroscience-to-molecular biology integration will likely impact our understanding of behaviour in the years to come. Finally, advancing our models of rivalry to the level of single subjects makes the eventual translation to subjects with abnormal binocular vision far more likely, and increases our impact even further.