Unraveling the mesoscopic functional organization of the human visual cortex using high-field MRI

使用高场 MRI 揭示人类视觉皮层的细观功能组织

• 批准号: RGPIN-2020-06930
• 负责人: Shmuel, Amir
• 金额: $ 3.42万
• 依托单位: McGill University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2022
• 资助国家: 加拿大
• 起止时间: 2022-01-01 至 2023-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=761358
• 关键词: Unraveling; mesoscopic; functional; organization; human

The uniformity of the mammalian cortex has led to the proposition that there exist elementary cortical units of operation, consisting of several hundred or thousand neurons that are repeated within and across cortical areas (Lorente de No', 1938). Cortical columns and layers of the neocortex are prominent examples of such structurally and functionally specialized units. These fine-scale anatomical constructs appear throughout cortical areas. Based on numerous animal experiments, the functional properties are similar for neurons within a column but are known to vary between columns. It can, therefore, be argued that the optimal spatial scale for studying the relationship between brain function and behavior is that of cortical columns (and layers, for similar reasons). The responses of these fine-scale structures to visual stimuli as well as their spontaneous activity can now be probed in the human brain by combining high-resolution functional MRI (fMRI) at ultra-high (7 Tesla or higher) magnetic field and analysis methods specifically tailored for this task. The long term goal of my research program is to unravel the functional organization, the function, and the interactions of cortical columns and layers in lower visual areas of the human cerebral cortex, in order to understand their mechanistic roles in support of perception. Our first objective is to develop a novel method for detecting and correcting small head movements in high-resolution fMRI time-series of a small brain volume. In our second objective, we will compare the spatial specificity of cerebral blood volume-based fMRI and Gradient and Spin-Echo (GRASE) blood oxygenation level-dependent (BOLD) fMRI in the human visual cortex. Our third objective is to compute the spatial point-spread function of cerebral blood volume-based fMRI and GRASE BOLD fMRI in the human visual cortex. Our fourth and fifth objectives are to simultaneously image, and quantitatively characterize and model the complete maps of ocular dominance columns and orientation columns, respectively, from the entire primary visual area of human subjects. To realize these objectives, we will image human subjects using the recently installed Siemens 7 Tesla Terra scanner of the Brain Imaging Centre of the Montreal Neurological Institute and McGill University. We will apply model-based analysis as well as data-driven machine learning techniques. We expect that the findings from addressing objectives 1-3 will significantly improve high-resolution fMRI at the scale of cortical columns and layers. They can provide the basis for using fMRI and decoding techniques for discovering fine-scale functional organizations in regions of the brain where fine cortical maps have not been revealed, such as higher-order association areas. The results of addressing aims 4-5 will set the stage for investigating the neuronal mechanisms of human perception of shape, stereopsis and binocular rivalry at the scale of cortical columns.

哺乳动物皮层的一致性导致了这样一个命题，即存在基本的皮层操作单元，由数百或数千个神经元组成，这些神经元在皮层区域内和皮层区域之间重复（Lorente de No', 1938）。新皮层的皮层柱和皮层层是这种结构和功能特化单位的突出例子。这些精细的解剖结构出现在整个皮质区域。根据大量的动物实验，同一列内的神经元的功能特性是相似的，但在不同的列之间是不同的。因此，可以认为，研究大脑功能和行为之间关系的最佳空间尺度是皮质柱（和层，出于类似的原因）。这些精细结构对视觉刺激的反应及其自发活动现在可以在人脑中通过结合超高（7特斯拉或更高）磁场的高分辨率功能性MRI （fMRI）和专门为这项任务量身定制的分析方法来探测。我的研究计划的长期目标是揭示人类大脑皮层下视觉区皮层柱和层的功能组织、功能和相互作用，以了解它们在支持感知方面的机制作用。我们的第一个目标是开发一种新方法，用于在小脑容量的高分辨率fMRI时间序列中检测和纠正小头部运动。在我们的第二个目标中，我们将比较基于脑血容量的功能磁共振成像和梯度和自旋回声（GRASE）血氧水平依赖（BOLD）功能磁共振成像在人类视觉皮层中的空间特异性。我们的第三个目标是计算基于脑血容量的fMRI和GRASE BOLD fMRI在人类视觉皮层中的空间点扩散函数。我们的第四个和第五个目标是同时成像，定量表征和建模，分别从人类受试者的整个主要视觉区域的眼优势柱和方向柱的完整地图。为了实现这些目标，我们将使用蒙特利尔神经学研究所和麦吉尔大学脑成像中心最近安装的西门子7特斯拉Terra扫描仪对人类受试者进行成像。我们将应用基于模型的分析以及数据驱动的机器学习技术。我们期望目标1-3的研究结果将显著提高皮层柱和层尺度上的高分辨率fMRI。它们可以为使用功能磁共振成像（fMRI）和解码技术来发现精细的功能组织提供基础，这些组织位于大脑皮层精细图谱尚未揭示的区域，如高阶关联区。解决目标4-5的结果将为在皮质柱尺度上研究人类形状感知、立体视觉和双目竞争的神经元机制奠定基础。