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.

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, 2019).…”

Denoising High-Field Multi-Dimensional MRI With Local Complex PCA

“…Finally, we tested the capabilities of the denoising to maintain fine details by segmenting the vasculature with the method of Huck et al (2019) on R2 ∗ quantitative maps. The algorithm uses a spatial vessel filter followed by global diffusion, and is particularly sensitive to small vessels (Bazin et al, 2016).…”

Evolution of Human Brain Atlases in Terms of Content, Applications, Functionality, and Availability

“…From cerebral parts (e.g., the basal ganglia (Yelnik et al 2007), thalamus and basal ganglia (Morel 2007), thalamus (Krauth et al 2010), and deep brain structures (Lemaire et al 2019)) to the whole brain (Kikinis et al 1996;Hoehne 2001;Tzourio-Mazoyer et al, 2002;Nowinski et al 2011b); b. From structural neuroanatomy (Rohlfing et al 2010;Mandal et al 2012;Nowinski and Chua 2014) to vascular neuroanatomy (Nowinski et al 2009b;2011a;Huck et al 2019) to connectional neuroanatomy (Mori et al 2005;Nowinski et al 2012b;Van Essen 2013;Van Essen et al 2013;Baker et al 2018;Briggs et al 2018) to gene expression (Sunkin et al 2013) including gene expression in brain development (Kanton et al 2019); c. From brain to head (Tiede et al 1996;Chen et al 2018), and to head and neck (Nowinski 2017b); d. From structure to function, including functional atlases (Minoshima et al 1994;Zhao et al 2017;Haegelen et al 2018;Varoquaux et al 2018;Lehman et al 2020), integrated anatomic-functional atlases (Nowinski 2004;Nowinski et al 2010), and functional connectivity atlases (Craddock et al 2012;James et al 2016).…”