A fully synthetic three-dimensional human cerebrovascular model based on histological characteristics to investigate the hemodynamic fingerprint of the layer BOLD fMRI signal formation

bioRxiv (Cold Spring Harbor Laboratory)

Abstract

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.

Authors 7

1. Mario Gilberto Báez‐Yáñez corresponding

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands

   UMC Utrecht

2. Wouter Schellekens

   Radboud University Nijmegen · Utrecht University · Radboud University Medical Center · University Medical Center Utrecht · Donders Centre for Cognitive Neuroimaging

   Affiliation as printed

   Donders Centre for Cognitive Neuroimaging, Radboud UMC, Nijmegen, Netherlands

   Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands

   Radboud UMC

3. Alex A. Bhogal

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands

   UMC Utrecht

4. Emiel C. A. Roefs Aachen C.J. Gorter MRI Center Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

5. Matthias J.P. van Osch Aachen C.J. Gorter MRI Center Department of Radiology, Leiden University Medical Center

   Leiden University Medical Center

   Affiliation as printed

   C.J. Gorter MRI Center, Department of Radiology, Leiden University Medical Center, Leiden, the Netherlands

6. Jeroen C.W. Siero

   Utrecht University · University Medical Center Utrecht · Spinoza Centre for Neuroimaging

   Affiliation as printed

   Spinoza Centre for Neuroimaging Amsterdam, Amsterdam, the Netherlands

   Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands

   UMC Utrecht

7. Natalia Petridou

   Utrecht University · University Medical Center Utrecht

   Affiliation as printed

   Translational Neuroimaging Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, the Netherlands

   UMC Utrecht