Experimental digital atlas of blood supply zones of the internal carotid artery
- Authors: Gubskiy I.L.1,2, Namestnikova D.D.1,2, Cherkashova E.A.1,2, Gumin I.S.1, Gubsky L.V.1,2, Baklaushev V.P.1,2, Chekhonin V.P.2,3, Yarygin K.N.4,5
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Affiliations:
- Federal Center of Brain Research and Neurotechnologies
- The Russian National Research Medical University named after N.I. Pirogov
- V. Serbsky National Medical Research Centre of Psychiatry and Narcology
- Institute of Biomedical Chemistry
- Russian Medical Academy of Continuous Professional Education
- Issue: Vol 16, No 1 (2025)
- Pages: 30-37
- Section: Original Study Articles
- URL: https://bakhtiniada.ru/clinpractice/article/view/295967
- DOI: https://doi.org/10.17816/clinpract642757
- ID: 295967
Cite item
Abstract
Background: The compilation of a neuroanatomic atlas based on a large sample is essentially a fundamental research work, but compiling a digital atlas during the epoch of wide usage of radiodiagnostics methods in the clinical and experimental practice along with using the artificial intelligence systems brings a significant applied relevance to the research. Rats are the main species of laboratory animals, in which the studies of modeling the ischemic stroke, of testing the cerebroprotective drugs and of developing new strategies of regenerative therapy of stroke consequences are carried out. At the present moment, there is no available and comprehensive digital atlas of the arterial blood supply of the rat brain, while single research works are based on small groups of animals and their histological description. Within this context, it is deemed very interesting and important to take the first step in addressing this issue. AIM: to compile an atlas of blood supply zones within the intracranial branches of the internal carotid artery in the settings of experimentally induced occlusion of the medial cerebral artery. METHODS: The archived data were used from the magnetic resonance imaging scans in rats with modeling the transient occlusion of the medial cerebral artery with a monofilament (n=243). The system of automatic brain segmentation based on artificial intelligence was used for objective mapping of the cerebral infarction area, the obtained data were added to a single coordinate space, unified and analyzed for highlighting the arterial blood supply zones. RESULTS: A digital atlas of the arterial circulation was compiled based on the intravitam data of high-resolution magnetic resonance imaging with an isotropic voxel. CONCLUSION: The compiled atlas may be used for increasing the quality of modeling the cerebral infarction by means of transient occlusion of the medial cerebral artery with a monofilament and it allows for using the additional objective parameters in the evaluation of the treatment effects in cases of experimentally induced ischemic stroke. The methodology developed by us is applicable for high-performance retrospective analysis of the neurovisualization data from the ischemic stroke patients, obtained within a framework of the implementation of the Vascular Medicine Program in the Russian Federation.
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##article.viewOnOriginalSite##About the authors
Ilya L. Gubskiy
Federal Center of Brain Research and Neurotechnologies; The Russian National Research Medical University named after N.I. Pirogov
Author for correspondence.
Email: gubskiy.ilya@gmail.com
ORCID iD: 0000-0003-1726-6801
SPIN-code: 9181-3091
MD, PhD
Russian Federation, 1 Ostrovityanova st, build 10, Moscow, 117513; MoscowDaria D. Namestnikova
Federal Center of Brain Research and Neurotechnologies; The Russian National Research Medical University named after N.I. Pirogov
Email: dadnam89@gmai.com
ORCID iD: 0000-0001-6635-511X
SPIN-code: 1576-1860
MD, PhD
Russian Federation, 1 Ostrovityanova st, build 10, Moscow, 117513; MoscowElvira A. Cherkashova
Federal Center of Brain Research and Neurotechnologies; The Russian National Research Medical University named after N.I. Pirogov
Email: tchere@yandex.ru
ORCID iD: 0000-0001-9549-9104
SPIN-code: 3735-3277
MD, PhD
Russian Federation, 1 Ostrovityanova st, build 10, Moscow, 117513; MoscowIvan S. Gumin
Federal Center of Brain Research and Neurotechnologies
Email: ivangumin@mail.ru
ORCID iD: 0000-0003-2360-3261
SPIN-code: 3454-2665
Scopus Author ID: 57223430019
MD
Russian Federation, 1 Ostrovityanova st, build 10, Moscow, 117513Leonid V. Gubsky
Federal Center of Brain Research and Neurotechnologies; The Russian National Research Medical University named after N.I. Pirogov
Email: gubskii@mail.ru
ORCID iD: 0000-0002-7423-1229
MD, PhD, Professor
Russian Federation, Moscow; MoscowVladimir P. Baklaushev
Federal Center of Brain Research and Neurotechnologies; The Russian National Research Medical University named after N.I. Pirogov
Email: baklaushev.vp@fnkc-fmba.ru
ORCID iD: 0000-0003-1039-4245
SPIN-code: 3968-2971
MD, PhD, Assistant Professor
Russian Federation, Moscow; MoscowVladimir P. Chekhonin
The Russian National Research Medical University named after N.I. Pirogov; V. Serbsky National Medical Research Centre of Psychiatry and Narcology
Email: chekhoninnew@yandex.ru
ORCID iD: 0000-0003-4386-7897
SPIN-code: 8292-2807
MD, PhD, Professor, member of the Russian Academy of Sciences
Russian Federation, Moscow; MoscowKonstantin N. Yarygin
Institute of Biomedical Chemistry; Russian Medical Academy of Continuous Professional Education
Email: kyarygin@yandex.ru
ORCID iD: 0000-0002-2261-851X
SPIN-code: 7567-1230
PhD, Professor
Russian Federation, Moscow; MoscowReferences
- Srivastava N, Verma S, Singh A, et al. Advances in artificial intelligence-based technologies for increasing the quality of medical products. Daru. 2024;33(1):1. doi: 10.1007/s40199-024-00548-5 EDN: ZRACJW
- Fajardo-Ortiz D, Thijs B, Glanzel W, Sipido KR. Evolution of funding for collaborative health research towards higher-level patient-oriented research. A comparison of the European Union Framework Programmes to the program funding by the United States National Institutes of Health. 2023. 37 р. doi: 10.48550/arXiv.2308.07162
- Smith JR, Bolton ER, Dwinell MR. The rat: A model used in biomedical research. Methods Mol Biol. 2019;2018:1–41. doi: 10.1007/978-1-4939-9581-3_1 EDN: ZYKRSG
- Li Y, Tan L, Yang C, et al. Distinctions between the Koizumi and Zea Longa methods for middle cerebral artery occlusion (MCAO) model: A systematic review and meta-analysis of rodent data. Sci Rep. 2023;13(1):10247. doi: 10.1038/s41598-023-37187-w EDN: LRWMGN
- Li Y, Zhang J. Animal models of stroke. Animal Model Exp Med. 2021;4(3):204–219. doi: 10.1002/AME2.12179
- Gerriets T, Stolz E, Walberer M, et al. Complications and pitfalls in rat stroke models for middle cerebral artery occlusion: A comparison between the suture and the macrosphere model using magnetic resonance angiography. Stroke. 2004;35(10):2372–2377. doi: 10.1161/01.STR.0000142134.37512.a7
- He Z, Yang SH, Naritomi H, et al. Definition of the anterior choroidal artery territory in rats using intraluminal occluding technique. J Neurol Sci. 2000;182(1):16–28. doi: 10.1016/S0022-510X(00)00434-2
- Guan Y, Wang Y, Yuan F, et al. Effect of suture properties on stability of middle cerebral artery occlusion evaluated by synchrotron radiation angiography. Stroke. 2012;43(3):888–891. doi: 10.1161/STROKEAHA.111.636456
- Liang D, Wiart M, Chauveau F, et al. Pipeline for automatic segmentation of multiparametric MRI data in a rat model of ischemic stroke. Clin Biomed Imaging. 2024. P. 26. doi: 10.1117/12.3006149
- Kuo DP, Kuo PC, Chen YC, et al. Machine learning-based segmentation of ischemic penumbra by using diffusion tensor metrics in a rat model. J Biomed Sci. 2020;27(1):80. doi: 10.1186/s12929-020-00672-9 EDN: ENCAEI
- Fan Y, Song Z, Zhang M. Emerging frontiers of artificial intelligence and machine learning in ischemic stroke: A comprehensive investigation of state-of-the-art methodologies, clinical applications, and unraveling challenges. EPMA J. 2023;14(4):645–661. doi: 10.1007/s13167-023-00343-3 EDN: YGLIYW
- Denic A, Macura SI, Mishra P, et al. MRI in rodent models of brain disorders. Neurotherapeutics. 2011;8(1):3–18. doi: 10.1007/s13311-010-0002-4 EDN: LEUNDA
- Gubskiy IL, Namestnikova DD, Cherkashova EA, et al. MRI guiding of the middle cerebral artery occlusion in rats aimed to improve stroke modeling. Transl Stroke Res. 2018;9(4):417–425. doi: 10.1007/s12975-017-0590-y EDN: BYVFPP
- Hatamizadeh A, Nath V, Tang Y, et al. Swin UNETR: Swin transformers for semantic segmentation of brain tumors in MRI images. In: Conference paper, 22 July 2022. P. 272–284. doi: 10.1007/978-3-031-08999-2_22
- Van Rossum G, Drake FL. Python 3 reference manual (Python Documentation Manual Part 2). CreateSpace Independent Publishing Platform, Brand: CreateSpace Independent Publishing Platform; 2009. 242 p.
- Yaniv Z, Lowekamp BC, Johnson HJ, Beare R. SimpleITK image-analysis notebooks: A collaborative environment for education and reproducible research. J Digit Imaging. 2018;31(3):290–303. doi: 10.1007/s10278-017-0037-8 EDN: YBZILM
- Kapur T, Pieper S, Fedorov A, et al. Increasing the impact of medical image computing using community-based open-access hackathons: The NA-MIC and 3D Slicer experience. Med Image Anal. 2016;33:176–180. doi: 10.1016/j.media.2016.06.035
- Li F, Omae T, Fisher M, et al. Spontaneous hyperthermia and its mechanism in the intraluminal suture middle cerebral artery occlusion model of rats editorial comment. Stroke. 1999;30(11):2464–2471. doi: 10.1161/01.STR.30.11.2464
- Dorr A, Sled JG, Kabani N. Three-dimensional cerebral vasculature of the CBA mouse brain: A magnetic resonance imaging and micro computed tomography study. NeuroImage. 2007;35(4):1409–1423. doi: 10.1016/j.neuroimage.2006.12.040
- El Amki M, Clavier T, Perzo N, et al. Hypothalamic, thalamic and hippocampal lesions in the mouse MCAO model: Potential involvement of deep cerebral arteries? J Neurosci Methods. 2015;254:80–85. doi: 10.1016/j.jneumeth.2015.07.008
- Sokolowski JD, Soldozy S, Sharifi KA, et al. Preclinical models of middle cerebral artery occlusion: New imaging approaches to a classic technique. Front Neurol. 2023;14:1170675. doi: 10.3389/fneur.2023.1170675 EDN: MNAKLW
- Sutherland BA, Neuhaus AA, Couch Y, et al. The transient intraluminal filament middle cerebral artery occlusion model as a model of endovascular thrombectomy in stroke. J Cereb Blood Flow Metab. 2016;36(2):363–369. doi: 10.1177/0271678X15606722
- Themistoklis KM, Papasilekas TI, Melanis KS, et al. transient intraluminal filament middle cerebral artery occlusion stroke model in rats: A step-by-step guide and technical considerations. World Neurosurg. 2022;168:43–50. doi: 10.1016/j.wneu.2022.09.043
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