A statistical model of the penetrating arterioles and venules in the human cerebral cortex

El-Bouri, Wahbi K.; Payne, Stephen J.

doi:10.1111/micc.12318

Cited by 24 publications

(40 citation statements)

References 33 publications

(126 reference statements)

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“…As their diameters and constituent cell types are changed, the vessels make a transition to capillaries. The capillaries then increase their diameter again and transition into the post-capillary venules, which join to form collecting venules that collect into larger veins (Landau and Davis, 1957;Harnarine-Singh et al, 1972;Onodera, 2011;Itoh and Suzuki, 2012;El-Bouri and Payne, 2016). In the small vessels (from the arterioles to venules), there are two types of mural cells separately located outside of endothelial layer: (1) vascular smooth muscle cells (SMCs) and (2) pericytes (Figure 1).…”

Section: Mural Cells In the Brain Small Vessels: Vascular Smooth Muscmentioning

confidence: 99%

Brain Microvascular Pericytes in Vascular Cognitive Impairment and Dementia

Uemura

Maki

Ihara

et al. 2020

Front. Aging Neurosci.

157

112

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Pericytes are unique, multi-functional mural cells localized at the abluminal side of the perivascular space in microvessels. Originally discovered in 19th century, pericytes had drawn less attention until decades ago mainly due to lack of specific markers. Recently, however, a growing body of evidence has revealed that pericytes play various important roles: development and maintenance of blood-brain barrier (BBB), regulation of the neurovascular system (e.g., vascular stability, vessel formation, cerebral blood flow, etc.), trafficking of inflammatory cells, clearance of toxic waste products from the brain, and acquisition of stem cell-like properties. In the neurovascular unit, pericytes perform these functions through coordinated crosstalk with neighboring cells including endothelial, glial, and neuronal cells. Dysfunction of pericytes contribute to a wide variety of diseases that lead to cognitive impairments such as cerebral small vessel disease (SVD), acute stroke, Alzheimer's disease (AD), and other neurological disorders. For instance, in SVDs, pericyte degeneration leads to microvessel instability and demyelination while in stroke, pericyte constriction after ischemia causes a no-reflow phenomenon in brain capillaries. In AD, which shares some common risk factors with vascular dementia, reduction in pericyte coverage and subsequent microvascular impairments are observed in association with white matter attenuation and contribute to impaired cognition. Pericyte loss causes BBB-breakdown, which stagnates amyloid β clearance and the leakage of neurotoxic molecules into the brain parenchyma. In this review, we first summarize the characteristics of brain microvessel pericytes, and their roles in the central nervous system. Then, we focus on how dysfunctional pericytes contribute to the pathogenesis of vascular cognitive impairment including cerebral 'small vessel' and 'large vessel' diseases, as well as AD. Finally, we discuss therapeutic implications for these disorders by targeting pericytes.

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Section: Mural Cells In the Brain Small Vessels: Vascular Smooth Muscmentioning

confidence: 99%

Brain Microvascular Pericytes in Vascular Cognitive Impairment and Dementia

Uemura

Maki

Ihara

et al. 2020

Front. Aging Neurosci.

157

112

View full text Add to dashboard Cite

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“…The model parameters are summarized in Table 1. The number-density of the terminal vessels over the brain surface area [34] and the artery-vein ratio [9,27] are obtained from the observational data. However, there are no clear data for the other parameters, and we therefore introduce some assumptions.…”

Section: Model Parametersmentioning

confidence: 99%

“…There are two typical approaches. One employs a data-driven concept that reproduces the statistical features of the vascular morphology evaluated by real-world data [23][24][25][26][27]. Recently, a whole-scale cerebrovascular model of a mouse that reflects the statistical features of the microvascular morphology has been developed and used for blood circulation simulations [28].…”

Section: Introductionmentioning

confidence: 99%

Multiscale modeling of human cerebrovasculature: A hybrid approach using image-based geometry and a mathematical algorithm

et al. 2020

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The cerebral vasculature has a complex and hierarchical network, ranging from vessels of a few millimeters to superficial cortical vessels with diameters of a few hundred micrometers, and to the microvasculature (arteriole/venule) and capillary beds in the cortex. In standard imaging techniques, it is difficult to segment all vessels in the network, especially in the case of the human brain. This study proposes a hybrid modeling approach that determines these networks by explicitly segmenting the large vessels from medical images and employing a novel vascular generation algorithm. The framework enables vasculatures to be generated at coarse and fine scales for individual arteries and veins with vascular subregions, following the personalized anatomy of the brain and macroscale vasculatures. In this study, the vascular structures of superficial cortical (pial) vessels before they penetrate the cortex are modeled as a mesoscale vasculature. The validity of the present approach is demonstrated through comparisons with partially observed data from existing measurements of the vessel distributions on the brain surface, pathway fractal features, and vascular territories of the major cerebral arteries. Additionally, this validation provides some biological insights: (i) vascular pathways may form to ensure a reasonable supply of blood to the local surface area; (ii) fractal features of vascular pathways are not sensitive to overall and local brain geometries; and (iii) whole pathways connecting the upstream and downstream entire-scale cerebral circulation are highly dependent on the local curvature of the cerebral sulci.

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“…Additionally, we have neglected the 3‐dimensional nature of the vasculature, considering a purely hierarchical network, as shown in Figure . A more detailed model that considered the full 3‐dimensional nature of the cerebral vasculature, such as those described in the Introduction, embedded within a spatially varying tissue model would be a valuable next step and this is currently under development, following the work of El‐Bouri and Payne . The aim of the model presented here was to provide a simple means of gaining an insight into the dynamic model behavior without the complexity of a 3‐dimensional model.…”

Section: Discussionmentioning

confidence: 99%

“…The difficulties involved with this approach, which are the strong dependence of any results on the choice of boundary conditions and the high computational expense of simulating the flow in volumes of any significant size, have motivated a parallel approach to modeling these networks. Based on the generation of artificial networks that match the available experimental data, Su et al, homogenization techniques have been proposed that allow for the scaling up of networks to a voxel length scale, El‐Bouri and Payne,. Such approaches can then be linked to voxel‐based imaging data through the use of metrics such as perfusion and transit time distributions, Park and Payne …”

Section: Introductionmentioning

confidence: 99%