Biology subjects

Veraart, J.

Publications and source records attributed to Veraart, J..

6 recordsLinked to original sources

bioRxiv2026

Integration of Neuroimaging and Histopathology in a Squirrel Monkey Model of Spontaneous Cerebral Amyloid Angiopathy and Amyloid-Related Imaging Abnormalities

Cerebral amyloid angiopathy (CAA) is a prevalent cerebrovascular pathology in AD and contributes to amyloid-related imaging abnormalities (ARIA), a major complication of current anti-amyloid immunotherapy. Despite its growing clinical importance, critical gaps remain in the development of reliable biomarkers for antemortem diagnosis of CAA. Squirrel monkeys (SQMs) are a valuable non-human primate model with translational relevance due to their universal propensity to develop abundant CAA and spontaneous ARIA in geriatric animals. The present study employed R2*, the apparent transverse relaxation rate, which is sensitive to susceptibility-related tissue properties, including iron content and blood oxygenation levels, to monitor age-related brain pathology in SQMs with varying CAA severity. Young, early geriatric, and late geriatric SQMs underwent 7T MRI using a dual-purpose T2*-w 3D multi-gradient-echo sequence for high-resolution anatomy and quantitative R2* mapping, alongside T2-w/FLAIR imaging. Whole-brain and gray matter R2* increased with age. In contrast, white matter R2* showed an initial increase followed by a late-life reversal, likely reflecting age-related changes in myelin integrity, tissue microstructure, and tissue water content. MRI-histopathology correlation linked T2*-w MRI hypointensities to CAA-positive iron-laden vessels associated with perivascular inflammation and, in some cases, hemosiderin deposits. Here we confirmed the occurrence of ARIA-H and ARIA-E events in geriatric animals. SQMs provided a valuable opportunity to examine the underlying pathology of spontaneous ARIA-E, revealing distinctive tissue signatures associated with its manifestation. ARIA-E hyperintensities were characterized by reactive gliosis and BBB disruption, whereas more advanced lesions exhibited perivascular lymphocytic infiltration and evidence of myelin and axonal injury. The current study presents compelling evidence linking R2* alterations with CAA severity and ARIA-related tissue injury, providing a foundation for further advancing early detection of CAA, ARIA risk assessment, and treatment strategies.

neuroscience↗

bioRxiv2025

Cellular basis for cortical network aging in primates

Large-scale brain networks are vulnerable to change with aging and become dysregulated. How these networks are altered at the cellular level remains unclear owing to challenges of bridging data across scales. Here, we integrate in vivo cortical similarity networks with whole brain spatial transcriptomics to characterize the aging brain in a lifespan cohort of macaques (N=64, ages 1-26 years). Deep-layer excitatory neurons and oligodendrocytes emerged as dominant correlates of cortical similarity, linking infragranular cell type composition to macroscopic network structure. Age-related declines in network strength were most pronounced in transmodal networks, including default mode and limbic, and aligned with regions enriched in inhibitory and glial cell types. Parvalbumin-enriched chandelier cells showed the strongest association with regional vulnerability, suggesting a role in network disconnection. Cell-type enrichment was conserved across species, with both human and macaque transcriptomic data aligning with the cortical functional hierarchy. These findings uncover a cellular basis for cortical network aging and highlight the value of imaging-transcriptomic integration across scales.

neuroscience↗

bioRxiv2024

Enhanced Structural Brain Connectivity Analyses Using High Diffusion-weighting Strengths

Tractography is a unique modality for the in vivo measurement of structural connectivity, crucial for understanding brain networks and neurological conditions. With increasing b-value, the diffusion-weighting signal becomes primarily sensitive to the intra-axonal signal. However, it remains unclear how tractography is affected by this observation. Here, using open-source datasets, we showed that at high b-values, DWI reduces the uncertainty in estimating fiber orientations. Specifically, we found the ratio of biologically-meaningful longer-range connections increases, accompanied with downstream impact of redistribution of connectome and network metrics. However, when going beyond b=6000 s/mm2, the loss of SNR imposed a penalty. Lastly, we showed that the data reaches satisfactory reproducibility with b-value above 1200 s/mm2. Overall, the results suggest that using b-values above 2500 s/mm2 is essential for more accurate connectome reconstruction by reducing uncertainty in fiber orientation estimation, supporting the use of higher b-value protocols in standard diffusion MRI scans and pipelines.

neuroscience↗

bioRxiv2021

PyDesigner: A Pythonic Implementation of the DESIGNER Pipeline for Diffusion Tensor and Diffusional Kurtosis Imaging

PyDesigner is an open-source and containerized Python software package, adapted from the DESIGNER pipeline, for diffusion weighted magnetic resonance imaging preprocessing and tensor estimation. PyDesigner combines tools from FSL and MRtrix3 to reduce the effects of signal noise and imaging artifacts on multiple diffusion measures that can be derived from the diffusion and kurtosis tensors. This publication describes the main features of PyDesigner and highlights its ease of use across platforms, while examining its accuracy and robustness in deriving commonly used diffusion and kurtosis metrics.

neuroscience↗

bioRxiv2019

Multi-parametric quantitative spinal cord MRI with unified signal readout and image denoising

Multi-parametric quantitative MRI (qMRI) of the spinal cord is a promising non-invasive tool to probe early microstructural damage in neurological disorders. It is usually performed by combining acquisitions with multiple signal readouts, which exhibit different thermal noise levels, geometrical distortions and susceptibility to physiological noise. This ultimately hinders joint multi-contrast modelling and makes the geometric correspondence of parametric maps challenging. We propose an approach to overcome these limitations, by implementing state-of-the-art microstructural MRI of the spinal cord with a unified signal readout. We base our acquisition on single-shot echo planar imaging with reduced field-of-view, and obtain data from two different vendors (vendor 1: Philips Achieva; vendor 2: Siemens Prisma). Importantly, the unified acquisition allows us to compare signal and noise across contrasts, thus enabling overall quality enhancement via Marchenko-Pastur (MP) Principal Component Analysis (PCA) denoising. MP-PCA is a recent method relying on redundant acquisitions, i.e. such that the number of measurements is much larger than the number of informative principal components. Here we used in vivo and synthetic data to test whether a unified readout enables more efficient denoising of less redundant acquisitions, since these can be denoised jointly with more redundant ones. We demonstrate that a unified readout provides robust multi-parametric maps, including diffusion and kurtosis tensors from diffusion MRI, myelin metrics from two-pool magnetisation transfer, and T1 and T2 from relaxometry. Moreover, we show that MP-PCA improves the quality of our multi-contrast acquisitions, since it reduces the coefficient of variation (i.e. variability) by up to 15% for mean kurtosis, 8% for bound pool fraction (BPF, myelin-sensitive), and 13% for T1, while enabling more efficient denoising of modalities limited in redundancy (e.g. relaxometry). In conclusion, multi-parametric spinal cord qMRI with unified readout is feasible and provides robust microstructural metrics with matched resolution and distortions, whose quality benefits from MP-PCA denoising, a useful pre-processing tool for spinal cord MRI.

neuroscience↗

bioRxiv2018

Electron microscopy 3-dimensional segmentation and quantification of axonal dispersion and diameter distribution in mouse brain corpus callosum

To model the diffusion MRI signal in brain white matter, general assumptions have been made about the microstructural properties of axonal fiber bundles, such as the axonal shape and the fiber orientation dispersion. In particular, axons are modeled by perfectly circular cylinders with no diameter variation within each axon, and their directions obey a specific orientation distribution. However, these assumptions have not been validated by histology in 3-dimensional high-resolution neural tissue. Here, we reconstructed sequential scanning electron microscopy images in mouse brain corpus callosum, and introduced a semi-automatic random-walker (RaW) based algorithm to rapidly segment individual intra-axonal spaces and myelin sheaths of myelinated axons. Confirmed with a conventional machine-learning-based interactive segmentation method, our semi-automatic algorithm is reliable and less time-consuming. Based on the segmentation, we calculated histological estimates of size-related (e.g., inner axonal diameter, g-ratio) and orientation-related (e.g., Fiber orientation distribution and its rotational invariants, dispersion angle) quantities, and simulated how these quantities would be observed in actual diffusion MRI experiments by considering diffusion time-dependence. The reported dispersion angle is consistent with previous 2-dimensional histology studies and diffusion MRI measurements, though the reported diameter is larger than those in other mouse brain studies. Our results show that the orientation-related metrics have negligible diffusion time-dependence; however, inner axonal diameters demonstrate a non-trivial time-dependence at diffusion times typical for clinical and preclinical use. In other words, the fiber dispersion estimated by diffusion MRI modeling is relatively independent, while the \"apparent\" axonal size estimated by axonal diameter mapping potentially depends on experimental MRI settings.

neuroscience↗