Mechanisms of high-resolution functional imaging and of decoding information conveyed by cortical columns

高分辨率功能成像和解码皮质柱传达的信息的机制

• 批准号: RGPIN-2015-05103
• 负责人: Shmuel, Amir
• 金额: $ 4.15万
• 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: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=665087
• 关键词: Mechanisms; resolution; functional; imaging; decoding

The long term goal of my research program is to elucidate the neurophysiological and hemodynamic mechanisms underlying fMRI. The goal for the upcoming 5 years is to elucidate the mechanisms underlying high-resolution fMRI and fMRI-based decoding of information conveyed in fine cortical structures.******Multivariate machine learning algorithms applied to human fMRI data can decode information conveyed by cortical columns, despite the voxel size being large relative to the width of the columns. Initial speculations suggested that aliasing of high spatial-frequency components could underlie this phenomenon. However, we have proven that these speculations were wrong. Several other mechanisms have been proposed. These include contributions from: (I) local irregularities in the columnar organization, (II) large-scale non-columnar organizations, (III) functionally selective veins with biased draining regions, and (IV) complex spatiotemporal filtering of neuronal activity by fMRI voxels that may carry information on the fine-scale neurophysiological response. Although a large body of human imaging studies uses decoding based on information conveyed in fine structures, the link to the underlying neural activity and hemodynamic mechanisms remains tenuous.*******We hypothesize that the successful decoding of information conveyed by cortical columns relies on a combination of the mechanisms (I)-(IV) mentioned above. *** ***Our first aim is to determine the extent to which different spatial components of the hemodynamic response, including the responses of gray matter and macroscopic veins of different diameters, carry information on neuronal responses organized in cortical columns.*******Even if we find that coarse resamples of the spatial patterns of the responses do carry information on the fine-scale structure organized neuronal responses, their contributions may account for only part of the overall information available to decoding algorithms. Therefore, in our second aim, we will test whether utilizing information conveyed by the spatiotemporal response rather than the response along the spatial dimension alone contributes additional information towards successful decoding.****In aim 3, we will compare the extent to which large-scale organizations and irregularities in the structure of ocular dominance columns contribute to successful decoding.*In aim 4, we will determine the extent to which successful decoding through contributions from large-scale organizations or irregularities depends on responses from macroscopic veins and venules.*In our fifth objective, we will quantify the spatiotemporal point-spread function of the blood-oxygenation and blood volume responses relative to the neurophysiological response. *** Our studies will advances high-resolution functional MRI, and the method of decoding brain responses conveyed by fine structure.**

我的研究计划的长期目标是阐明功能磁共振成像背后的神经生理学和血液动力学机制。未来5年的目标是阐明高分辨率功能磁共振成像和基于功能磁共振成像的精细皮质结构中传递的信息的解码机制。*应用于人类功能磁共振数据的多变量机器学习算法可以解码皮质列传递的信息，尽管体素大小相对于列的宽度较大。最初的推测表明，高空间频率成分的混叠可能是这种现象的基础。然而，我们已经证明，这些猜测是错误的。还提出了其他几种机制。这些贡献包括：(I)柱状组织中的局部不规则，(Ii)大规模的非柱状组织，(Iii)具有偏向引流区域的功能性选择性静脉，以及(Iv)通过可能携带关于精细神经生理反应的信息的fMRI体素对神经元活动的复杂时空过滤。尽管大量的人体成像研究使用基于精细结构所传达的信息的解码，但与潜在的神经活动和血流动力学机制的联系仍然很薄弱。*我们假设，成功地解码由皮质柱传递的信息依赖于上述机制(I)-(IV)的组合。我们的第一个目标是确定血液动力学反应的不同空间成分，包括不同直径的灰质和宏观静脉的反应，在多大程度上携带以皮质柱组织的神经元反应的信息。*即使我们发现反应的空间模式的粗略重采样确实携带了关于精细结构组织的神经元反应的信息，它们的贡献可能只占解码算法可用的整体信息的一部分。因此，在我们的第二个目标中，我们将测试利用时空反应所传达的信息，而不是仅利用空间维度上的反应是否有助于成功解码。*在目标3中，我们将比较大规模组织和眼优势列结构中的不规则对成功解码的贡献。*在目标4中，我们将确定通过大规模组织或不规则的贡献而成功解码在多大程度上依赖于宏观静脉和小静脉的响应。*在我们的第五个目标中，我们将量化血液氧合和血容量响应相对于神经生理响应的时空点扩散函数。*我们的研究将推进高分辨率功能磁共振，以及精细结构传达的解码大脑反应的新方法。**