明尼苏达大学Kendrick Kay团队经过不懈努力，提出使用时间分解分析方法可识别静脉在基于任务的功能磁共振成像（fMRI）的影响，从而提高fMRI空间分辨率。 相关论文于2020年9月7日发表在《自然—方法学》杂志上。

该研究组描述一个分析方法，它提供了数据驱动的对于基于任务的功能磁共振成像中静脉效应影响的估计。该方法包括拟合用于描述在特定数据集观察到的响应timecourses的变异的一维流形，然后使用识别的早期和晚期timecourses作为基函数将响应分解为与微脉管系统（毛细血管和小静脉）和大脉管系统（大血管）分别相关的组分。课题组展示将后者（大血管）的组分去除可以减少fMRI响应的表面皮质深度偏差，并有助于消除皮质活动映射中的人为假象。

该方法提供了fMRI信号起源的信息，并且可被用于提高fMRI的空间准确性。流形拟合介导的时间分解（temporal decomposition through manifold fitting, TDM）作为一种分析技术可以在基于任务的fMRI数据中将血氧水平依赖(BOLD)的响应分解成不同组分，这些组分可能对应于微脉管系统和大脉管系统相关的信号。

据了解，功能性磁共振成像的空间分辨率从根本上受限于大引流静脉产生的效应。

附：英文原文

Title: A temporal decomposition method for identifying venous effects in task-based fMRI

Author: Kendrick Kay, Keith W. Jamison, Ru-Yuan Zhang, Kamil Uurbil

Issue&Volume: 2020-09-07

Abstract: The spatial resolution of functional magnetic resonance imaging (fMRI) is fundamentally limited by effects from large draining veins. Here we describe an analysis method that provides data-driven estimates of these effects in task-based fMRI. The method involves fitting a one-dimensional manifold that characterizes variation in response timecourses observed in a given dataset, and then using identified early and late timecourses as basis functions for decomposing responses into components related to the microvasculature (capillaries and small venules) and the macrovasculature (large veins), respectively. We show the removal of late components substantially reduces the superficial cortical depth bias of fMRI responses and helps eliminate artifacts in cortical activity maps. This method provides insight into the origins of the fMRI signal and can be used to improve the spatial accuracy of fMRI. Temporal decomposition through manifold fitting (TDM) is an analysis technique that decomposes blood oxygenation level dependent (BOLD) responses in task-based fMRI into different components that likely correspond to microvasculature- and macrovasculature-driven signals.

DOI: 10.1038/s41592-020-0941-6

Source: https://www.nature.com/articles/s41592-020-0941-6