High resolution atlas of the venous brain vasculature from 7 T quantitative susceptibility maps

Abstract

The vascular organization of the human brain can determine neurological and neurophysiological functions, yet thus far it has not been comprehensively mapped. Aging and diseases such as dementia are known to be associated with changes to the vasculature and normative data could help detect these vascular changes in neuroimaging studies. Furthermore, given the well-known impact of venous vessels on the blood oxygen level dependent (BOLD) signal, information about the common location of veins could help detect biases in existing datasets. In this work, a quantitative atlas of the venous vasculature using quantitative susceptibility maps (QSM) acquired with a 0.6 mm isotropic resolution is presented. The Venous Neuroanatomy (VENAT) atlas was created from 5 repeated 7 Tesla MRI measurements in young and healthy volunteers (n = 20, 10 females, mean age = 25.1 ± 2.5 years) using a two-step registration method on 3D segmentations of the venous vasculature. This cerebral vein atlas includes the average vessel location, diameter (mean:0.84 ± 0.33 mm) and curvature (0.11 ± 0.05 mm -1 ) from all participants and provides an in vivo measure of the angio-architectonic organization of the human brain and its variability.This atlas can be used as a basis to understand changes in the vasculature during aging and neurodegeneration, as well as vascular and physiological effects in neuroimaging. Keywords: venous vasculature; QSM, UHF-MRI; vein atlas; vein segmentation; cerebral vasculature information about venous properties, which we seek to leverage here. In this paper we introduce the Venous Neuroanatomy (VENAT) atlas. This atlas provides several advantages over existing venous atlases, since it was acquired with a higher field strength (higher SNR), has a higher image resolution (0.6 mm isotropic) and uses multiple repetitions to increase SNR. This image resolution allows detection of vessels up to 0.3 mm in diameter. TheVENAT atlas is open source and available on figshare (https://figshare.com/articles/VENAT_Probability_map_nii_gz/7205960). This atlas was built using an optimized approach through registration of vascular segmentations and using five repeated QSM images per participant. The VENAT atlas includes a partial volume (PV) map, which shows vessel location, maps of the diameter, curvature of vessels, and venous density maps.

Physiological and motion signatures in static and time-varying functional connectivity and their subject identifiability

“…SLFOs related to changes in heart rate and breathing patterns were found to affect mostly sensory regions including the visual and somatosensory cortices (particularly of the face) ( Figure 1A ), which correspond to regions with a high density of veins ( Bernier et al, 2018 ; Huck et al, 2019 ). The spatial pattern of SLFOs is very similar to statistical maps reported in prior works, which have highlighted brain regions highly correlated with the global signal ( Billings and Keilholz, 2018 ; Glasser et al, 2016 ; Li et al, 2019a ; Power et al, 2017 ; Tong et al, 2013 ; Zhang et al, 2020 ).…”

Investigating the oxygenation of brain arteriovenous malformations using quantitative susceptibility mapping

“…Some studies have developed digital atlases of the brain vasculature including either the arteries (Dunas et al, 2017) or the veins (Ward et al, 2018). Others have developed such digital atlases including both the arteries and the veins with (Bernier et al, 2018;Huck et al, 2018) or without (Passat et al, 2005;Viviani, 2016) separate labelling for these two vessel types. Future work could explore the use of these probabilistic digital atlases of the cerebral vasculature in patients with a vascular malformation.…”

Macrovascular contributions to resting-state fMRI signals: A comparison between EPI and bSSFP at 9.4 Tesla

“…However, this effect is not seen in our plots, which means that merely removing voxels inside veins or close to veins will not result in an elimination of their effect on the signal (fluctuation), especially at the used spatial resolution. Previous work also showed that the effects of the large veins (≥ 0.3 mm in diameter) can induce perturbations, which are perceptible even 6.7 mm away from the vein (Huck et al, 2023). Much smaller veins are probably not detectable with our method (Bause et al, 2020;Bollmann et al, 2022).…”