Neuroimaging Studies of Human Binocular Vision and Stimulus Selection

正常的双眼视觉需要两只位于前方的眼睛的配合。每只眼睛受到的刺激从来不是相同的。值得注意的是，我们通常（但并非总是）在感知上体验到单一的观点。实现双眼组合的计算依赖于显示在每只眼睛上的刺激，并且存在不同的体制。我们的长期目标是提供这些双目组合机制的感知和生理解释，将我们对整个视觉系统的理解整合在一起。本应用程序的目的是关注双目竞争（BR）作为双目整合机制的窗口。我们提出了一种多学科的方法，将详细的视觉测试与大脑活动的高分辨率神经成像相结合，这样我们就可以评估双目视觉的竞争理论，并将我们的发现与当前对感知意识的理解联系起来。除了功能性磁共振成像（fMRI），我们也开始将脑磁图（MEG）纳入非侵入性人类神经成像。新的分析技术，如功能连接的网络建模和新兴的机器学习技术，将进一步加强这项工作。具体来说，我们建议进一步研究双眼竞争感知背后的大脑网络特征，包括与模式竞争、眼间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因素，以确定单眼和双眼神经竞争的位置。目的2 -确定双眼竞争中眼间分组的机制，进一步分离大区域空间内双眼竞争的神经基质。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。目标4 -将网络建模分析应用于fMRI数据和MEG数据，以描述皮质区域网络之间使用的通信机制。此外，我们还建议利用已知存在于正常视觉大脑中的强大个体差异，特别是在双目视觉中的行为。这种大脑行为差异的直接关联可以比过去更精细地应用于有效地解决假定的神经机制。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在未来几年影响我们对行为的理解。最后，将我们的竞争模型提升到单个受试者的水平，使得最终翻译到具有异常双目视觉的受试者的可能性大大增加，并进一步增加我们的影响。

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.