Department of Radiology, Centre for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands.
Spinoza Centre for Neuroimaging Amsterdam, Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands.
NMR Biomed. 2023 Dec;36(12):e5026. doi: 10.1002/nbm.5026. Epub 2023 Aug 29.
Blood oxygenation level-dependent (BOLD) functional magnetic resonance imaging (fMRI) is one of the most used imaging techniques to map brain activity or to obtain clinical information about human cortical vasculature, in both healthy and disease conditions. Nevertheless, BOLD fMRI is an indirect measurement of brain functioning triggered by neurovascular coupling. The origin of the BOLD signal is quite complex, and the signal formation thus depends, among other factors, on the topology of the cortical vasculature and the associated hemodynamic changes. To understand the hemodynamic evolution of the BOLD signal response in humans, it is beneficial to have a computational framework available that virtually resembles the human cortical vasculature, and simulates hemodynamic changes and corresponding MRI signal changes via interactions of intrinsic biophysical and magnetic properties of the tissues. To this end, we have developed a mechanistic computational framework that simulates the hemodynamic fingerprint of the BOLD signal based on a statistically defined, three-dimensional, vascular model that approaches the human cortical vascular architecture. The microvasculature is approximated through a Voronoi tessellation method and the macrovasculature is adapted from two-photon microscopy mice data. Using this computational framework, we simulated hemodynamic changes-cerebral blood flow, cerebral blood volume, and blood oxygen saturation-induced by virtual arterial dilation. Then we computed local magnetic field disturbances generated by the vascular topology and the corresponding blood oxygen saturation changes. This mechanistic computational framework also considers the intrinsic biophysical and magnetic properties of nearby tissue, such as water diffusion and relaxation properties, resulting in a dynamic BOLD signal response. The proposed mechanistic computational framework provides an integrated biophysical model that can offer better insights regarding the spatial and temporal properties of the BOLD signal changes.
血氧水平依赖（BOLD）功能磁共振成像（fMRI）是一种最常用于绘制大脑活动图谱或获取健康和疾病状态下人类皮质血管临床信息的成像技术。然而，BOLD fMRI 是一种由神经血管耦联引发的大脑功能的间接测量。BOLD 信号的起源非常复杂，因此信号的形成取决于皮质血管的拓扑结构和相关的血液动力学变化等因素。为了理解人类 BOLD 信号响应的血液动力学演变，最好有一个可模拟人类皮质血管的计算框架，通过组织的固有生物物理和磁特性的相互作用来模拟血液动力学变化和相应的 MRI 信号变化。为此，我们开发了一种基于统计定义的三维血管模型的机制计算框架，该模型模拟 BOLD 信号的血液动力学特征，该模型接近人类皮质血管结构。微血管通过 Voronoi 细分方法近似化，宏观血管从双光子显微镜小鼠数据中适应。使用这个计算框架，我们模拟了虚拟动脉扩张引起的血液动力学变化，如脑血流量、脑血容量和血氧饱和度。然后，我们计算了由血管拓扑和相应的血氧饱和度变化引起的局部磁场干扰。这个机制计算框架还考虑了附近组织的固有生物物理和磁特性，如水分子扩散和弛豫特性，从而产生动态的 BOLD 信号响应。所提出的机制计算框架提供了一个集成的生物物理模型，可以更好地了解 BOLD 信号变化的时空特性。
