Biology subjects

Roefs, E. C. A.

Publications and source records attributed to Roefs, E. C. A..

3 recordsLinked to original sources

bioRxiv2026

Cerebrovascular pulsatility differs across vascular compartments and is altered by hypercapnic stimuli: a BOLD fMRI study

Cerebral small vessel disease and neurodegenerative disorders have been associated with increased cerebrovascular pulsatility. Recently, BOLD fMRI-based methods have emerged for assessing pulsatility, however their interpretability is limited because the relation between estimated pulsatility indices (PI) and vascular anatomy and physiology remains poorly understood. To improve interpretability, we introduce a cardiac-specific BOLD fMRI- based PI, investigate its relationship to the cortical vasculature, and validate its sensitivity by introducing the known physiological vascular modulation of hypercapnia. Using high-resolution 7T BOLD fMRI with gradient-echo (GE) and spin-echo (SE) sequences, we disentangled macro- and microvascular contributions to the PI and quantified it across cortical depth. PI maps revealed anatomically plausible patterns, with elevated GE-PI near large veins and in white matter while SE-PI remained largely constant across cortical depth. GE-PI decreased during hypercapnia consistent with altered vascular tone, SE-PI on the other hand did not. PI correlated with cerebrovascular reactivity and venous blood volume suggesting sensitivity to vascular density and vessel mechanics. Our findings demonstrate that BOLD-derived PI provides a spatially and physiologically specific measure of vascular pulsatility. The BOLD fMRI-based PI method is readily applicable to existing datasets and has potential for assessing potential microvascular damage in cerebrovascular and neurodegenerative disease.

neuroscience↗

bioRxiv2024

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

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.

neuroscience↗

bioRxiv2021

The many layers of BOLD. On the contribution of different vascular compartments to laminar fMRI.

Ultra-high field functional Magnetic Resonance Imaging (fMRI) offers the spatial resolution to measure neural activity at the scale of cortical layers. Most fMRI studies make use of the Blood-Oxygen-Level Dependent (BOLD) signal, arising from a complex interaction of changes in cerebral blood flow (CBF) and volume (CBV), and venous oxygenation. However, along with cyto- and myeloarchitectural changes across cortical depth, laminar fMRI is confronted with additional confounds related to vascularization differences that exist across cortical depth. In the current study, we quantify how the non-uniform distribution of macro- and micro-vascular compartments, as measured with Gradient-Echo (GE) and Spin-Echo (SE) scan sequences, respectively, affect laminar BOLD fMRI responses following evoked hypercapnic and hyperoxic breathing conditions. We find that both macro- and micro-vascular compartments are capable of comparable theoretical maximum signal intensities, as represented by the M-scaling parameter. However, the capacity for vessel dilation, as reflected by the cerebrovascular reactivity (CVR), is approximately three times larger for the macro-compared to the micro-vasculature at superficial layers. Finally, there is roughly a 35% difference in CBV estimates between the macro- and micro-vascular compartments, although this relative difference is approximately uniform across cortical depth.

neuroscience↗