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高空间分辨率功能磁共振成像:对血氧水平依赖模型的影响

fMRI at High Spatial Resolution: Implications for BOLD-Models.

作者信息

Goense Jozien, Bohraus Yvette, Logothetis Nikos K

机构信息

Department of Psychology, Institute of Neuroscience and Psychology, University of Glasgow Glasgow, UK.

Department of Physiology of Cognitive Processes, Max-Planck Institute for Biological Cybernetics Tübingen, Germany.

出版信息

Front Comput Neurosci. 2016 Jun 28;10:66. doi: 10.3389/fncom.2016.00066. eCollection 2016.

Abstract

As high-resolution functional magnetic resonance imaging (fMRI) and fMRI of cortical layers become more widely used, the question how well high-resolution fMRI signals reflect the underlying neural processing, and how to interpret laminar fMRI data becomes more and more relevant. High-resolution fMRI has shown laminar differences in cerebral blood flow (CBF), volume (CBV), and neurovascular coupling. Features and processes that were previously lumped into a single voxel become spatially distinct at high resolution. These features can be vascular compartments such as veins, arteries, and capillaries, or cortical layers and columns, which can have differences in metabolism. Mesoscopic models of the blood oxygenation level dependent (BOLD) response therefore need to be expanded, for instance, to incorporate laminar differences in the coupling between neural activity, metabolism and the hemodynamic response. Here we discuss biological and methodological factors that affect the modeling and interpretation of high-resolution fMRI data. We also illustrate with examples from neuropharmacology and the negative BOLD response how combining BOLD with CBF- and CBV-based fMRI methods can provide additional information about neurovascular coupling, and can aid modeling and interpretation of high-resolution fMRI.

摘要

随着高分辨率功能磁共振成像(fMRI)以及皮质层fMRI的应用越来越广泛,高分辨率fMRI信号在多大程度上反映潜在神经处理过程以及如何解释层流fMRI数据的问题变得越来越重要。高分辨率fMRI已显示出脑血流量(CBF)、血容量(CBV)和神经血管耦合方面的层流差异。以前集中在单个体素中的特征和过程在高分辨率下在空间上变得清晰可辨。这些特征可以是血管腔室,如静脉、动脉和毛细血管,也可以是皮质层和皮质柱,它们在代谢方面可能存在差异。因此,血氧水平依赖(BOLD)反应的介观模型需要扩展,例如,纳入神经活动、代谢和血流动力学反应之间耦合的层流差异。在这里,我们讨论影响高分辨率fMRI数据建模和解释的生物学和方法学因素。我们还通过神经药理学和负BOLD反应的例子说明,将BOLD与基于CBF和CBV的fMRI方法相结合如何能够提供有关神经血管耦合的额外信息,并有助于高分辨率fMRI的建模和解释。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ab8d/4923185/d8cda07837ca/fncom-10-00066-g0001.jpg

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