Báez-Yáñez Mario Gilberto, Schellekens Wouter, Bhogal Alex A, Roefs Emiel C A, van Osch Matthias J P, Siero Jeroen C W, Petridou Natalia
Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands.
Donders Centre for Cognitive Neuroimaging, Radboud UMC, Nijmegen, Netherlands.
bioRxiv. 2024 May 26:2024.05.24.595716. doi: 10.1101/2024.05.24.595716.
Recent advances in functional magnetic resonance imaging (fMRI) at ultra-high field (≥7 tesla), novel hardware, and data analysis methods have enabled detailed research on neurovascular function, such as cortical layer-specific activity, in both human and nonhuman species. A widely used fMRI technique relies on the blood oxygen level-dependent (BOLD) signal. BOLD fMRI offers insights into brain function by measuring local changes in cerebral blood volume, cerebral blood flow, and oxygen metabolism induced by increased neuronal activity. Despite its potential, interpreting BOLD fMRI data is challenging as it is only an indirect measurement of neuronal activity. Computational modeling can help interpret BOLD data by simulating the BOLD signal formation. Current developments have focused on realistic 3D vascular models based on rodent data to understand the spatial and temporal BOLD characteristics. While such rodent-based vascular models highlight the impact of the angioarchitecture on the BOLD signal amplitude, anatomical differences between the rodent and human vasculature necessitate the development of human-specific models. Therefore, a computational framework integrating human cortical vasculature, hemodynamic changes, and biophysical properties is essential. Here, we present a novel computational approach: a three-dimensional VAscular MOdel based on Statistics (3D VAMOS), enabling the investigation of the hemodynamic fingerprint of the BOLD signal within a model encompassing a fully synthetic human 3D cortical vasculature and hemodynamics. Our algorithm generates microvascular and macrovascular architectures based on morphological and topological features from the literature on human cortical vasculature. By simulating specific oxygen saturation states and biophysical interactions, our framework characterizes the intravascular and extravascular signal contributions across cortical depth and voxel-wise levels for gradient-echo and spin-echo readouts. Thereby, the 3D VAMOS computational framework demonstrates that using human characteristics significantly affects the BOLD fingerprint, making it an essential step in understanding the fundamental underpinnings of layer-specific fMRI experiments.
超高场（≥7特斯拉）功能磁共振成像（fMRI）、新型硬件及数据分析方法的最新进展，使得在人类和非人类物种中对神经血管功能进行详细研究成为可能，比如皮层特定层的活动。一种广泛使用的fMRI技术依赖于血氧水平依赖（BOLD）信号。BOLD fMRI通过测量因神经元活动增加而引起的脑血容量、脑血流量和氧代谢的局部变化，来深入了解脑功能。尽管其具有潜力，但解读BOLD fMRI数据具有挑战性，因为它只是对神经元活动的间接测量。计算建模可以通过模拟BOLD信号形成来帮助解读BOLD数据。目前的进展集中在基于啮齿动物数据的真实三维血管模型，以了解BOLD信号的时空特征。虽然这种基于啮齿动物的血管模型突出了血管结构对BOLD信号幅度的影响，但啮齿动物和人类血管系统之间的解剖差异使得有必要开发针对人类的模型。因此，一个整合人类皮层血管系统、血液动力学变化和生物物理特性的计算框架至关重要。在此，我们提出一种新颖的计算方法：基于统计学的三维血管模型（3D VAMOS），能够在一个包含完全合成的人类三维皮层血管系统和血液动力学的模型中，研究BOLD信号的血液动力学特征。我们的算法基于人类皮层血管系统文献中的形态和拓扑特征生成微血管和大血管结构。通过模拟特定的氧饱和度状态和生物物理相互作用，我们的框架在皮层深度和体素水平上，针对梯度回波和自旋回波读出，表征血管内和血管外信号贡献。由此，3D VAMOS计算框架表明，使用人类特征会显著影响BOLD特征，使其成为理解特定层fMRI实验基本基础的关键一步。
