Das Aniruddha, Murphy Kevin, Drew Patrick J
Department of Neuroscience, Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA.
Cardiff University Brain Research Imaging Centre (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff CF24 4HQ, UK.
Philos Trans R Soc Lond B Biol Sci. 2021 Jan 4;376(1815):20190635. doi: 10.1098/rstb.2019.0635. Epub 2020 Nov 16.
Fluctuations in blood oxygenation and flow are widely used to infer brain activity during resting-state functional magnetic resonance imaging (fMRI). However, there are strong systemic and vascular contributions to resting-state signals that are unrelated to ongoing neural activity. Importantly, these non-neural contributions to haemodynamic signals (or 'rude mechanicals') can be as large as or larger than the neurally evoked components. Here, we review the two broad classes of drivers of these signals. One is systemic and is tied to fluctuations in external drivers such as heart rate and breathing, and the robust autoregulatory mechanisms that try to maintain a constant milieu in the brain. The other class comprises local, active fluctuations that appear to be intrinsic to vascular tissue and are likely similar to active local fluctuations seen in vasculature all over the body. In this review, we describe these non-neural fluctuations and some of the tools developed to correct for them when interpreting fMRI recordings. However, we also emphasize the links between these vascular fluctuations and brain physiology and point to ways in which fMRI measurements can be used to exploit such links to gain valuable information about neurovascular health and about internal brain states. This article is part of the theme issue 'Key relationships between non-invasive functional neuroimaging and the underlying neuronal activity'.
在静息态功能磁共振成像(fMRI)中,血氧合和血流的波动被广泛用于推断大脑活动。然而,静息态信号存在强大的全身和血管因素,这些因素与正在进行的神经活动无关。重要的是,这些对血液动力学信号的非神经因素(或“粗暴机械因素”)可能与神经诱发成分一样大或更大。在这里,我们回顾了这些信号的两大类驱动因素。一类是全身性的,与诸如心率和呼吸等外部驱动因素的波动以及试图在大脑中维持恒定环境的强大自动调节机制有关。另一类包括局部的、活跃的波动,这些波动似乎是血管组织固有的,可能类似于在全身血管系统中看到的活跃局部波动。在这篇综述中,我们描述了这些非神经波动以及在解释fMRI记录时为校正这些波动而开发的一些工具。然而,我们也强调了这些血管波动与大脑生理学之间的联系,并指出了利用fMRI测量来利用这种联系以获取有关神经血管健康和大脑内部状态的有价值信息的方法。本文是主题为“非侵入性功能神经成像与潜在神经元活动之间的关键关系”的一部分。