Comparison of the mathematical modelling results of the relationship between cerebral ventricular size and capillary pressure based on experimental and clinical data

Aim of the study was to compare the results of mathematical modelling of the dependence between brain ventricle size and capillary pressure for humans and animals based on the equations of multicomponent poroelastic filtration for brain parenchyma.
Material and methods. The study included two groups of animals - 4 male mice of each inbred line C57Bl/6 and BALB/C at the age of 12 weeks – and 4 healthy volunteers. The brain and cerebrospinal fluid system images of mice were obtained using an 11.7 T horizontal MR scanner, group of humans were examined using the Ingenia 3.0 T MRI scanner. An axial section at the level of –0.5 mm from bregma in the mouse groups and a frontal slice at the level of the middle of the bodies of the lateral and third ventricles, posterior to the foramen of Monroe in the human group were chosen as the geometry for mathematical modelling. Mathematical modelling is based on the stationary mathematical model of multicomponent poroelastic filtration. Multiple linear regression of mean ventricular wall displacement on fluid media interaction parameters was constructed to compare results obtained. Regression coefficients were compared using nonparametric analysis of variance based on the Kraskell–Wallis criterion and post-hoc Dunn’s criterion with Hill’s correction
Results. A qualitative coincidence in the behavior of capillary pressure and mean ventricular wall displacement was demonstrated for the human and mouse groups. No significant differences were found between the two animal lines. For the animals characterized by small ventricular size (BALB/c), greater similarity to humans is observed than for the genetic line with hypertrophied ventricles (C57Bl/6). A significant difference between humans and mice is observed only for capillary-venous interaction.
Conclusions. The low variance within groups and insignificant discrepancy between groups indicate the possibility of further accumulation of empirical data to establish correction coefficients of the animal model, which will bring it more in line with the model for humans. Thus, the analyzed models are sufficiently comparable with each other.

1. World Health Organization. World health statistics 2022: monitoring health for the SDGs, sustainable development goals. Available at: https://www.who.int/publications/i/item/9789240051157

2. Rekate H.L., Raybaud C. Radiology of Hydrocephalus: From Morphology to Hydrodynamics and Pathogenesis. In: Pediatric Hydrocephalus. SpringerLink, 2018. 390–449, 480–482.

3. Miskin N., Patel H., Franceschi A.M., AdesAron B., Le A., Damadian B.E., Stanton C., Serulle Y., Golomb J., Gonen O., Rusinek H., George A.E. Diagnosis of normal-pressure hydrocephalus: Use of traditional measures in the era of volumetric MR imaging. Radiology. 2017;285(1):197–205. doi: 10.1148/radiol.2017161216

4. Zhou X., Xia J. Application of Evans index in normal pressure hydrocephalus patients: a mini review. Front. Aging Neurosci. 2022;13:783092. doi: 10.3389/fnagi.2021.783092

5. He W., Fang X., Wang X., Gao P., Gao X., Zhou X., Mao R., Hu J., Hua Y., Xia J. A new index for assessing cerebral ventricular volume in idiopathic normal-pressure hydrocephalus: a comparison with Evans’ index. Neuroradiology. 2020;62(6):661–667. doi: 10.1007/s00234-020-02361-8

6. Ye W., Chen Q. Potential applications and perspectives of humanized mouse models. Annu. Rev. Anim. Biosci. 2022;10:395–417. doi: 10.1146/annurev-animal-020420-033029

7. Valova G., Bogomyakova O., Tulupov A., Cherevko A. Influence of interaction of cerebral fluids on ventricular deformation: A mathematical approach. PLoS One. 2022;17(2):e0264395. doi: 10.1371/journal.pone.0264395

8. Ma Y., Hof P.R., Grant S.C., Blackband S.J., Bennett R., Slatest L., McGuigan M.D., Benveniste H. A three-dimensional digital atlas database of the adult C57BL/6J mouse brain by magnetic resonance microscopy. Neuroscience. 2005;135(4):1203–1215. doi: 10.1016/j.neuroscience.2005.07.014

9. The R Project for Statistical Computing. Available at: http://www.R-project.org