Mathematical synthesis of the cortical circulation for the whole mouse brain—part II: Microcirculatory closure

Hartung, Grant; Badr, Shoale; Mihelic, Samuel A.; Dunn, Andrew K.; Kura, Sreekanth; Boas, David A.; Kleinfeld, David; Alaraj, Ali; Linninger, Andreas A.

doi:10.1111/micc.12687

Cited by 20 publications

(28 citation statements)

References 46 publications

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“…In our tests, the average oxygen partial pressure in the tissue is about 60 mmHg, with a spatial variation of approximately 35 mmHg (see Figure 12). These data are in agreement with values reported in the literature for brain tissue 42 . We also remark in the discussion that the range of variation of hematocrit (

H_{italicin}

, see Table 2) ensures in any case good blood oxygenation.…”

Section: A Geometrical Model Of the Microvasculature And A Physical M...supporting

confidence: 92%

Sensitivity analysis of a multi‐physics model for the vascular microenvironment

Vitullo

Cicci

Possenti

et al. 2023

Numer Methods Biomed Eng

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The vascular microenvironment is the scale at which microvascular transport, interstitial tissue properties and cell metabolism interact. The vascular microenvironment has been widely studied by means of quantitative approaches, including multi‐physics mathematical models as it is a central system for the pathophysiology of many diseases, such as cancer. The microvascular architecture is a key factor for fluid balance and mass transfer in the vascular microenvironment, together with the physical parameters characterizing the vascular wall and the interstitial tissue. The scientific literature of this field has witnessed a long debate about which factor of this multifaceted system is the most relevant. The purpose of this work is to combine the interpretative power of an advanced multi‐physics model of the vascular microenvironment with state of the art and robust sensitivity analysis methods, in order to determine the factors that most significantly impact quantities of interest, related in particular to cancer treatment. We are particularly interested in comparing the factors related to the microvascular architecture with the ones affecting the physics of microvascular transport. Ultimately, this work will provide further insight into how the vascular microenvironment affects cancer therapies, such as chemotherapy, radiotherapy or immunotherapy.

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H_{italicin}

, see Table 2) ensures in any case good blood oxygenation.…”

Section: A Geometrical Model Of the Microvasculature And A Physical M...supporting

confidence: 92%

Sensitivity analysis of a multi‐physics model for the vascular microenvironment

Vitullo

Cicci

Possenti

et al. 2023

Numer Methods Biomed Eng

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“…While this simplification gave acceptable system-wide predictions, it is not able to account for biphasic blood flow phenomena 163 and network effects 164 that occur in the microcirculation. Significant progress has been made to develop realistic microvascular networks models 135,165,166 which could be integrated with the proposed system models in future work.…”

Section: Discussionmentioning

confidence: 99%

“…It has an impact on fundamental hemodynamic characteristics of the cardiovascular system and plays a determinant role in setting the functional level of the cardiovascular system under physiological and under pathological conditions. Previous works 39,134,135 have shown the benefits of considering viscoelastic properties of vessel walls in arterial circulation comparing model predictions and in vivo measurements of pressure and flow at different location. The effects of viscoelasticity become more significant in the periphery, especially on the flow wave.…”

Section: Arteries and Veinsmentioning

confidence: 99%

Cerebrospinal fluid dynamics coupled to the global circulation in holistic setting: Mathematical models, numerical methods and applications

Toro

Celant

Contarino

et al. 2021

Numer Methods Biomed Eng

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This paper presents a mathematical model of the global, arterio-venous circulation in the entire human body, coupled to a refined description of the cerebrospinal fluid (CSF) dynamics in the craniospinal cavity. The present model represents a substantially revised version of the original Müller-Toro mathematical model. It includes one-dimensional (1D), non-linear systems of partial differential equations for 323 major blood vessels and 85 zero-dimensional, differential-algebraic systems for the remaining components. Highlights include the myogenic mechanism of cerebral blood regulation; refined vasculature for the inner ear, the brainstem and the cerebellum; and viscoelastic, rather than purely elastic, models for all blood vessels, arterial and venous. The derived 1D parabolic systems of partial differential equations for all major vessels are approximated by hyperbolic systems with stiff source terms following a relaxation approach. A major novelty of this paper is the coupling of the circulation, as described, to a refined description of the CSF dynamics in the craniospinal cavity, following Linninger et al. The numerical solution methodology employed to approximate the hyperbolic non-linear systems of partial differential equations with stiff source terms is based on the Arbitrary DERivative Riemann problem finite volume framework, supplemented with a well-balanced formulation, and a local time stepping procedure. The full model is validated through comparison of computational results against published data and bespoke MRI measurements. Then we present two medical applications: (i) transverse sinus stenoses and their relation to Idiopathic Intracranial Hypertension; and (ii) extra-cranial venous strictures and their impact in the inner ear circulation, and its implications for Ménière's disease.

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“…Nonetheless, the multi-frame image also produced the greatest number of CDFs for strand metrics that were not significantly different from those of a standard-resolution image. We consider these results from the multi-frame model to be within acceptable tolerance for vectorization accuracy and similarity in strand statistics for our previously described purposes of characterizing the structural properties of the vessels of a particular network 22,23 .…”

Section: Discussionmentioning

confidence: 99%

A deep learning approach for improving two-photon vascular imaging speeds

Zhou

Mihelic

Engelmann

et al. 2022

Preprint

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A potential method for tracking neurovascular disease progression over time in preclinical models is multiphoton fluorescence microscopy (MPM), which can image cerebral vasculature with capillary-level resolution. However, obtaining high-quality, three-dimensional images with a traditional point scanning MPM is time-consuming and limits sample sizes for chronic studies. Here, we present a convolutional neural network-based algorithm for fast upscaling of low-resolution or sparsely sampled images and combine it with a segmentation-less vectorization process for 3D reconstruction and statistical analysis of vascular network structure. In doing so, we also demonstrate that the use of semi-synthetic training data can replace the expensive and arduous process of acquiring low- and high-resolution training pairs without compromising vectorization outcomes, and thus open the possibility of utilizing such approaches for other MPM tasks where collecting training data is challenging. We applied our approach to large field of view images and show that our method generalizes across imaging depths, disease states and other differences in neurovasculature. Our pre-trained models and lightweight architecture can be used to reduce MPM imaging time by up to fourfold without any changes in underlying hardware, thereby enabling deployability across a range of settings.

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Mathematical synthesis of the cortical circulation for the whole mouse brain—part II: Microcirculatory closure

Cited by 20 publications

References 46 publications

Sensitivity analysis of a multi‐physics model for the vascular microenvironment

Sensitivity analysis of a multi‐physics model for the vascular microenvironment

Cerebrospinal fluid dynamics coupled to the global circulation in holistic setting: Mathematical models, numerical methods and applications

A deep learning approach for improving two-photon vascular imaging speeds

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