A Minireview on Brain Models Simulating Geometrical, Physical, and Biochemical Properties of the Human Brain
Yassine Bouattour
1* 
Valérie Sautou1 Rodayna Hmede2 Youssef El Ouadhi2,3 Dimitri Gouot2 Philip Chennell1 Yuri Lapusta2
Frédéric Chapelle
2
Jean-Jacques Lemaire
2,3*- 1 Université Clermont Auvergne, CHU Clermont Ferrand, Clermont Auvergne INP, CNRS, ICCF, F-63000, Clermont-Ferrand, France
- 2 Universite Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, F-63000, Clermont-Ferrand, France
- 3 Service de Neurochirurgie, CHU Clermont Ferrand, F-63000, Clermont-Ferrand, France
There is a growing body of evidences that brain surrogates will be of great interest for researchers and physicians in the medical field. They are currently mainly used for education and training purposes or to verify the appropriate functionality of medical devices. Depending on the purpose, a variety of materials have been used with specific and accurate mechanical and biophysical properties, More recently they have been used to assess the biocompatibility of implantable devices, but they are still not validated to study the migration of leaching components from devices. This minireview shows the large diversity of approaches and uses of brain phantoms, which converge punctually. All these phantoms are complementary to numeric models, which benefit, reciprocally, of their respective advances. It also suggests avenues of research for the analysis of leaching components from implantable devices.
Introduction
The human brain is a complex organ at both functional and structural levels, which is placed in a particular biomechanical environment, the intracranial space. In the world of materials aiming to simulate biophysical properties of the brain, the words model, phantom, and surrogate are often used indifferently (Reinertsen and Collins, 2006; Forte et al., 2018; Zhang et al., 2019) even if models and phantoms should rather be representations, whereas the true surrogate should substitute the brain. Realistically there is no true surrogate of the brain, and models and phantoms are in their infancy. Nevertheless, few and partial structures and functions of the brain can already be surrogated. Indeed restoration of brain, by repair and regeneration, can be feasible using biomaterials such as bioscaffolds (Modo and Badylak, 2019) and bioengineering of the environment of stem cells (Zimmermann and Schaffer, 2019). Brain computer interfaces working via neuronal signal analysis and/or activation of neuronal population or body segment or exoskeleton, can surrogate inefficient auto repairing or treatments and must deal with biomaterials (Jeong et al., 2020). The bright future of replacements and surrogates will have to face the complexity of interactions between multiple domains from materials to regulatory processes (Handa et al., 2020).
The physical simulation of the different dimensions of the brain is extremely challenging and there is no model, phantom or surrogate that simulates the function, the structure, the aspect and the biomechanics all at the same time. The types of materials and their assembling, in a more or less realistic way, are essentially determined by the uses, such as imaging and biomechanical studies, education, surgery, developments of medical devices (MDs), and assessment of numeric models. Thus the choice of materials is not dissociable from the purpose of the physical representation accounting the context of the applications and uses.
Our goal was to carry out a mini-review of the materials proposed to simulate mechanical, chemical and biological properties of the human brain, as well as some of its structural elements such as architecture and aspect (Figure 1). The neural simulation of the brain function is particular because the functioning is so complex that it is still just not possible to simulate all the neuronal activity of the brain simultaneously, even with supercomputers. Recent programs illustrate the high level of the challenge (Amunts et al., 2016; Yamaura et al., 2020). Consequently, it was beyond our objective to integrate directly this dimension, in term of materials. The digital aspects of models are not addressed in this minireview. Hence we focused on the structural, physical and chemical properties of the brain, with the perspective of future medical applications, notably in neurosurgery, such as innovative treatments including surgery planning, as well as educational and training programs, which can be linked.
FIGURE 1. Overview of materials, models, phantoms and surrogates simulating the human brain.
Human Brain Models for Education, Training and Planning of Surgery
The realistic aspect of models, like their precise shape, size and colors, was largely skipped until recently. The most common human brain models for education are semi-realistic in the sense that they mostly aim to show the gross anatomy of the brain, in a more or less simplistic way (see e.g., search engine: “brain”+“model”+“education”), with some capabilities to see the “interior of the brain”, such as the ventricles. The targeted population of users is mainly undergraduate or graduate non-medical students. The models are generally made in rigid plastic (Azer and Azer, 2016) such as thermoplastic polyurethane (Goh et al., 2021), usually colored with different shades of pink, and specific colors highlighting particular regions, such as the hemispheres, functional territories or vessels. With the introduction of 3D printing, it has become easier to produce realistic “home-made” models, used for example, to explain diseases and therapeutic options to patients or relatives (van de Belt et al., 2018). Nevertheless, beyond the technological issues, the quality of data used for the 3D printing is variable. This quality is linked to the quality of medical images (geometrical and contrast resolutions, adequation between the type of image and the goal), the patience and diligence of the person in charge of the data extraction (as the best data is still extracted by skilled users), and the chain of data transfer from the raw data to the 3D printer. An advantage of additive manufacturing is that it enables the development of much more complex models, which could be able to integrate several physical dimensions of the brain (Zhao et al., 2020). The models used by neurosurgeons for training, preoperative planning and intraoperative guidance are promising (Rehder et al., 2016; Garcia et al., 2018; Qiu et al., 2018). However these models are still limited because the information embedded, such as topography, colors and texture, is not precise and they compete with virtual numeric models and historical anatomic dissections. For surgical training, physical models should add intracranial structures such as the vessels and the braincase (Ryan et al., 2016; Nagassa et al., 2019). One could expect that 3D bioprinting of physiologic or pathologic material, could be also used for training in surgery in line with the concept of mini-brain (Heinrich et al., 2019). However molding of synthetic materials can offer advantages such as low cost and easy making of brain surrogates, such as polymers and gelatins (Forte et al., 2018). More simple phantoms, made of radiopaque printed sheets intercalated with polyethylene foam layers, enable the design of anthropomorphic surrogates for training of interventional radiologists, with a fair CT-scan anatomic aspect, although they offer still limited haptic sensations (Jahnke et al., 2018).
Human Brain Models for the Study of Medical Devices
The different materials used for the simulation of biophysical properties of the human brain aim to model at best one or more biophysical dimensions. The phantoms and models built from these materials depend on the usages, which are chiefly brain imaging analysis and study of mechanical stress. Specific models have been developed for special studies, such as agarose gel for intraparenchymal diffusion (Chen et al., 2004) or composite gel for dosimetry (Pavoni et al., 2015).
The phantoms used for experimental brain magnetic resonance imaging (MRI) or ultrasonic imaging, or those devoted to assessment of imaging, are essentially made of gels (Hellerbach et al., 2013). They enable the measurement of mechanical and thermic stress (Hellerbach et al., 2013; Sammartino et al., 2016), as well as MRI parameters such as diffusion and relaxation time (Fieremans and Lee, 2018), irrespectively of the architecture, at least the meso-architecture of gray nuclei, such as those of the thalamus and prethalamus, and of white matter (WM) tracts and fascicles, such as the cingulum and the brachium conjunctivum. Some agarose based phantoms allow the mimicking of metabolites during 7-T spectroscopic imaging, such as glutamic acid, creatine and phospho-creatine, myo-inositol, gamma-aminobutyric acid (GABA), choline chloride, sodium lactate and N-acetyl aspartate (Jona et al., 2021). Phantoms were also developed specifically for the neonatal brain (Kozana et al., 2018). The main limitation of these phantoms remains their non-realistic characteristics, notably structural, hence MRI brain models based on anatomic specimen are still relevant (Droby et al., 2015). Physical phantoms for ionizing imaging, CT-scan and Pet-Scan, are anterior and were designed for imaging and radiotherapy, notably in oncology. These phantoms can embed true bony or resin braincases. They are also able to simulate blood infusion (Boese et al., 2013) and they continue to be updated [e.g., (Mansor et al., 2017; Pourmorteza et al., 2017].
Additive manufacturing or 3D-printing, already enables to shape phantoms and to fill them with specific materials (liquid or solid) depending on the usages (Filippou and Tsoumpas, 2018). In the same line, the microarchitecture of WM fiber bundles could be embedded in the near future (Altermatt et al., 2019). In medicine, the measurement of mechanical stress distributed within the brain tissue enables the evaluation of the risks of lesioning and consequently of dysfunctions, although it is still challenging to infer functions from lesions. Future robotic and robotized surgeries will beneficiate from such data (Martin et al., 2009; Ruby et al., 2020). Besides the measurement of stress values, the determination of thresholds is pertinent as it enables the conception of protective solutions such as helmets, airbags and smart retractors. Phantoms were made of silicone (Margulies et al., 1990; Chanda et al., 2018; Zhang et al., 2019), gel (Reinertsen and Collins, 2006; Pomfret et al., 2013a; Awad et al., 2015) and dual material such as gel-polymer (Alley et al., 2011; Zhu et al., 2012). Agarose gels of 0.4–0.6% seem close to strain and rheology of bovine brain tissue (Pervin and Chen, 2011). Recent complex head models with a silicone rubber brain are used to study the dynamics of impact tests (Petrone et al., 2019). In parallel, the development of numeric models (Gabrieli et al., 2020) and atlases (Hiscox et al., 2020) continues to explore the complex biomechanics of the brain. It seems feasible in the near future to embed micro models of brain components, such as vascular tissue using silicone elastomer or hydrogel models (Sato and Sato, 2018), blood-brain barrier using hybrid silicone elastomer - plastic polycarbonate (Nguyen et al., 2019), up to mini-brains, organoids and brain-cell models using true human brain cells (Camp and Treutlein, 2017; Quadrato and Arlotta, 2017; Korhonen et al., 2018; Lovett et al., 2020). It is noticeable that most phantoms and models could be used to develop brain surrogates for education, training and surgery planning.
The simulation of electrical conductivity of the brain tissue is of upmost importance since the growing interest in invasive, such as the deep brain stimulation (Fariba and Gupta, 2020), and non-invasive, such as the transcranial magnetic stimulation (Lefaucheur, 2019), acute or chronic stimulations at frequencies usually below 200 Hz, of neurons and axons. Physical head phantoms have been developed to measure in situ computational models of electric fields, either caused by neurons or by external sources such as transcranial electric stimulation (Hunold et al., 2018; Magsood and Hadimani, 2021). Gel phantoms seem particularly interesting to study the electric conductivity (Kandadai et al., 2012; Pomfret et al., 2013b; Chew et al., 2014).
More recently, medical device biocompatibility, which relies on the ability of materials to perform with an appropriate host response in a specific application, gains increasingly in significance. At the tissue-material interface, two coupled aspects are present, the biotic factor that represents the cell and tissue reactions against the device, and the abiotic factor that represents the physico-chemical reactions at the surface of the material (Gulino et al., 2019). The study of biotic reaction relies on immortalized cells (Chapman et al., 2016; Mantione et al., 2016; Rejmontová et al., 2016; Koss et al., 2017; O’Rourke et al., 2017; Bradley et al., 2018; Johnson et al., 2018), organoids (Nasr et al., 2018; Nzou et al., 2018) and cultures (Persheyev et al., 2011; Mantione et al., 2016). Yet the study of the abiotic factor is still to be done, the related brain models being in the infancy, focusing on molecules and nanoparticles with animal protocols (Gulino et al., 2021; Ojeda-Hernández et al., 2021). The International Organization for Standardization (ISO) norm 10,993 evaluating the biocompatibility of medical devices, precises in part 18 (Chemical characterization of medical device materials within a risk management process, revised in May 2020) that an exhaustive investigation of extractible compounds must be performed and that the simulated extraction should be only performed when the total extractable components exceeds a tolerable limit. Anyway this approach could be insufficient to investigate the security of use of a medical device for two reasons: 1) exhaustive extractables need to be completed with a simulation performed in a physiological environment (Paskiet et al., 2013), and 2) because some leaching component are by nature endocrine disruptors (bisphenol A for instance) and could be more toxic in lower quantity than in high doses (Li et al., 2015).
Discussion
Our minireview on the materials used to simulate mechanical, chemical and biological properties of the human brain, and structural features, shows that no model fulfills all these aspects. In parallel, the bio- mechanics and chemistry of the brain tissue should be present ideally in each brain models whatever the purpose. The biomechanical properties of the viscoelastic brain medium, is characterized by moduli, such as elastic and shear, and mechanical resonance. Recent MRI approaches, non-invasive, in-vivo and ex-vivo, yield more and more information, notably about the WM component such as the myelin density (Sepehrband et al., 2015), and about the WM anisotropy such as direction-dependent moduli (Smith et al., 2020). More specifically magnetic resonance elastography (MRE) enables the access to a large variety of physical parameters of the brain (Yin et al., 2018), notably the comparison of ex vivo and in vivo measurements of brain tissue (Chen et al., 2021) that enables to access to frequency-dependent behavior (Lv et al., 2020; Qiu et al., 2021). Interestingly, MRE fast analysis of regional variations of biomechanics could measure variations of neuronal activity as shown in rodent model (Patz et al., 2019). Whatever the efforts done, there are still limited, robust, consensual values of physical parameters of human brain specimen, although the non-linearity of mechanical responses and the region dependency of behavior seems demonstrated (Budday et al., 2017). Data from animal have been harvested, such as the stiffness modulus of WM 1.895 ± 0.592 kPa, and of GM 1.389 ± 0.289 kPa of bovine (Budday et al., 2015). Nevertheless, although of interest, ex vivo data must be extrapolated carefully to in vivo human conditions (Karimi et al., 2019). On the other hand, the mass density is known, 1,046 ± 6, WM = 1,041 ± 2 and GM = 1,045 ± 8 [1,039–1,050] (Duck, 1990; McIntosh and Anderson, 2011). The main chemical components of the brain, water, lipids (O’Brien and Sampson, 1965; Dawson, 2015), other molecules such amino-acids and amides (Daković et al., 2013), and elements such as iron, copper and zinc (Grochowski et al., 2019), are well-known. The water content (g water/g tissue or %) ranges from 67 to 72 in WM and 80 to 87 in GM (Alexander and Looney, 1938; Whittall et al., 1997; Tofts, 2004; Oros-Peusquens et al., 2019). The proton density (percentage; water = 100) ranges from 69 to 77 in WM and 78 to 86 in GM (Tofts, 2004). Lipids’ concentration, pH, temperature, viscoelastic behavior and Young modulus are precised in the Table 1.
TABLE 1. Macroscopic chemical and physical properties of the human brain.
Lipid’s concentration depends on age and most are glycerophosphatides (i.e., ethanolamine glycerophosphatides, serine glycerophosphatides and choline glycerophosphatides) and cholesterol (O’Brien and Sampson, 1965; Dawson, 2015).
Concerning the medical device biocompatibility, the migration of compounds into a medium is described by the laws of Fick (Fick, 1855; Kaufmann, 1998) that estimate the transfer of material from an initial medium to a final medium accounting the contact area, gradient of concentration and diffusion coefficient. The temperature (Einstein, 1905; Demir and Ulutan, 2013; Wei et al., 2019), lipophilicity (Stein, 1981; Lodish et al., 2000; Brodeur and Tardif, 2005) and pH (Pinheiro et al., 1998) of the final medium influence the migration. In polymer, which are frequent in medical devices, the diffusion can deviate from predicted values, as a result of interactions between polymer and solvent slowing down the diffusion kinetics and the polymer gelation. Hydrogels, for example, characterized by the presence of water (or water-based solutions) in the polymer that enters or leaves the system can give rise to volumetric deformations. The transport of water in the glass phase is mainly driven by diffusion, which most of the time does not follow a pure “fickian” behavior (Caccavo et al., 2016). Another diffusion may occur, called abnormal diffusion (Goychuk, 2009; Yasuda et al., 2017), which is representative of viscoelastic diffusion (diffusion in relaxing media) that is affected by the mechanics of the system (Caccavo et al., 2018). The diffusion coefficient in a semi-solid and the viscoelastic properties of medium are correlated (Tanaka et al., 1973; Fujiyabu et al., 2019). For the brain viscoelastic medium with a linear elastic behavior, it is Young’s modulus E which is the most described. Its determination is made on the basis of connection with the shear modulus, by estimating that the Poisson’s ratio ʋ is equal to 0.45 (Paulsen et al., 1999; Clatz et al., 2003; Soza et al., 2004; Miga et al., 2016). To summarize, an adequate simulant for the study of leachables from medical devices must take into consideration the bicompartmental property due to the physicochemical difference between gray matter and WM, and must be prepared from components of high purity and meet the physicochemical characteristics.
In conclusion, future brain models should cover a wide field of applications in medicine, from those used for education, training and planning of surgery to those enabling the advanced study of medical device uses, notably their biocompatibility. Brain models, or phantoms, and digital brain models should learn from each other (Seo et al., 2022). It is anticipated that artificial surrogates will integrate most biomechanical and biochemical properties of the living tissue. Functional brain surrogates could be hybrid, made of nonbiological and biological components, and should communicate with the central nervous system for invasive prosthetic applications.
Author Contributions
YB and J-JL : Writing original draft All authors: Review and editing.
Conflict of Interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Publisher’s Note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.
References
Alexander, L., and Looney, J. M. (1938). Physicochemical Properties of Brain, Especially in Senile Dementia and Cerebral Edema. Arch. Neurpsych 40, 877–902. doi:10.1001/archneurpsyc.1938.02270110031002
Alley, M. D., Schimizze, B. R., and Son, S. F. (2011). Experimental Modeling of Explosive Blast-Related Traumatic Brain Injuries. NeuroImage 54, S45–S54. doi:10.1016/j.neuroimage.2010.05.030
Altermatt, A., Santini, F., Deligianni, X., Magon, S., Sprenger, T., Kappos, L., et al. (2019). Design and Construction of an Innovative Brain Phantom Prototype for MRI. Magn. Reson. Med. 81, 1165–1171. doi:10.1002/mrm.27464
Amunts, K., Ebell, C., Muller, J., Telefont, M., Knoll, A., and Lippert, T. (2016). The Human Brain Project: Creating a European Research Infrastructure to Decode the Human Brain. Neuron 92, 574–581. doi:10.1016/j.neuron.2016.10.046
Awad, N., El-Dakhakhni, W. W., and Gilani, A. A. (2015). A Physical Head and Neck Surrogate Model to Investigate Blast-Induced Mild Traumatic Brain Injury. Arab J. Sci. Eng. 40, 945–958. doi:10.1007/s13369-015-1583-3
Azer, S. A., and Azer, S. (2016). 3D Anatomy Models and Impact on Learning: A Review of the Quality of the Literature. Health Professions Education 2, 80–98. doi:10.1016/j.hpe.2016.05.002
Boese, A., Gugel, S., Serowy, S., Purmann, J., Rose, G., Beuing, O., et al. (2013). Performance Evaluation of a C-Arm CT Perfusion Phantom. Int. J. CARS 8, 799–807. doi:10.1007/s11548-012-0804-4
Bradley, J. A., Luithardt, H. H., Metea, M. R., and Strock, C. J. (2018). In Vitro Screening for Seizure Liability Using Microelectrode Array Technology. Toxicol. Sci. 163, 240–253. doi:10.1093/toxsci/kfy029
Brodeur, J., and Tardif, R. (2005). “Absorption,” in Encyclopedia of Toxicology. Editor P. Wexler. Second Edition (New York: Elsevier), 1–6. doi:10.1016/B0-12-369400-0/00002-8
Budday, S., Nay, R., de Rooij, R., Steinmann, P., Wyrobek, T., Ovaert, T. C., et al. (2015). Mechanical Properties of gray and white Matter Brain Tissue by Indentation. J. Mech. Behav. Biomed. Mater. 46, 318–330. doi:10.1016/j.jmbbm.2015.02.024
Budday, S., Raybaud, C., and Kuhl, E. (2014). A Mechanical Model Predicts Morphological Abnormalities in the Developing Human Brain. Sci. Rep. 4, 5644. doi:10.1038/srep05644
Budday, S., Sommer, G., Birkl, C., Langkammer, C., Haybaeck, J., Kohnert, J., et al. (2017). Mechanical Characterization of Human Brain Tissue. Acta Biomater. 48, 319–340. doi:10.1016/j.actbio.2016.10.036
Caccavo, D., Cascone, S., Lamberti, G., and Barba, A. A. (2018). Hydrogels: Experimental Characterization and Mathematical Modelling of Their Mechanical and Diffusive Behaviour. Chem. Soc. Rev. 47, 2357–2373. doi:10.1039/C7CS00638A
Caccavo, D., Cascone, S., Lamberti, G., Barba, A. A., and Larsson, A. (2016). Swellable Hydrogel-Based Systems for Controlled Drug Delivery. Smart Drug Deliv. Syst. 1, 1. doi:10.5772/61792
Camp, J. G., and Treutlein, B. (2017). Advances in Mini-Brain Technology. Nature 545, 39–40. doi:10.1038/545039a
Chanda, A., Callaway, C., Clifton, C., and Unnikrishnan, V. (2018). Biofidelic Human Brain Tissue Surrogates. Mech. Adv. Mater. Structures 25, 1335–1341. doi:10.1080/15376494.2016.1143749
Chapman, C. A. R., Chen, H., Stamou, M., Lein, P. J., and Seker, E. (2016). Mechanisms of Reduced Astrocyte Surface Coverage in Cortical Neuron-Glia Co-cultures on Nanoporous Gold Surfaces. Cel. Mol. Bioeng. 9, 433–442. doi:10.1007/s12195-016-0449-4
Chen, Y., Qiu, S., He, Z., Yan, F., Li, R., and Feng, Y. (2021). Comparative Analysis of Indentation and Magnetic Resonance Elastography for Measuring Viscoelastic Properties. Acta Mech. Sin. 37, 527–536. doi:10.1007/s10409-020-01042-2
Chen, Z.-J., Gillies, G. T., Broaddus, W. C., Prabhu, S. S., Fillmore, H., Mitchell, R. M., et al. (2004). A Realistic Brain Tissue Phantom for Intraparenchymal Infusion Studies. J. Neurosurg. 101, 314–322. doi:10.3171/jns.2004.101.2.0314
Chew, K. M., Seman, N., Sudirman, R., and Yong, C. Y. (2014). A Comparison of Brain Phantom Relative Permittivity with CST Simulation Library and Existing Research. Bio-Medical Mater. Eng. 24, 2161–2167. doi:10.3233/BME-141027
Clatz, O., Delingette, H., Bardinet, E., Dormont, D., and Ayache, N. (2003). “Patient-Specific Biomechanical Model of the Brain: Application to Parkinson's Disease Procedure,” in Surgery Simulation And Soft Tissue Modeling Lecture Notes in Computer Science. Editors N. Ayache, and H. Delingette (Berlin, Heidelberg: Springer), 321–331. doi:10.1007/3-540-45015-7_31
Daković, M., Stojiljković, A. S., Bajuk-Bogdanović, D., Starčević, A., Puškaš, L., Filipović, B., et al. (2013). Profiling Differences in Chemical Composition of Brain Structures Using Raman Spectroscopy. Talanta 117, 133–138. doi:10.1016/j.talanta.2013.08.058
Dawson, G. (2015). Measuring Brain Lipids. Biochim. Biophys. Acta (Bba) - Mol. Cel Biol. Lipids 1851, 1026–1039. doi:10.1016/j.bbalip.2015.02.007
Droby, A., Lukas, C., Schänzer, A., Spiwoks-Becker, I., Giorgio, A., Gold, R., et al. (2015). A Human post-mortem Brain Model for the Standardization of Multi-centre MRI Studies. NeuroImage 110, 11–21. doi:10.1016/j.neuroimage.2015.01.028
Duck, F. A. (1990). “Mechanical Properties of Tissue,” in Physical Properties of Tissues (Elsevier), 137–165. doi:10.1016/B978-0-12-222800-1.50009-7
Einstein, A. (1905). Über die von der molekularkinetischen Theorie der Wärme geforderte Bewegung von in ruhenden Flüssigkeiten suspendierten Teilchen. Ann. Phys. 322, 549–560. doi:10.1002/andp.19053220806
Fariba, K., and Gupta, V. (2020). “Deep Brain Stimulation,” in StatPearls (Treasure Island (FL): StatPearls Publishing). 1. Available at: http://www.ncbi.nlm.nih.gov/books/NBK557847/(Accessed June 12, 2020).
Fick, A. (1855). V. On Liquid Diffusion. Lond. Edinb. Dublin Philosophical Mag. J. Sci. 10, 30–39. doi:10.1080/14786445508641925
Fieremans, E., and Lee, H.-H. (2018). Physical and Numerical Phantoms for the Validation of Brain Microstructural MRI: A Cookbook. Neuroimage 182, 39–61. doi:10.1016/j.neuroimage.2018.06.046
Filippou, V., and Tsoumpas, C. (2018). Recent Advances on the Development of Phantoms Using 3D Printing for Imaging with CT, MRI, PET, SPECT, and Ultrasound. Med. Phys. 45, e740–e760. doi:10.1002/mp.13058
Forte, A. E., Galvan, S., and Dini, D. (2018). Models and Tissue Mimics for Brain Shift Simulations. Biomech. Model. Mechanobiol 17, 249–261. doi:10.1007/s10237-017-0958-7
Friese, M. A., Craner, M. J., Etzensperger, R., Vergo, S., Wemmie, J. A., Welsh, M. J., et al. (2007). Acid-sensing Ion Channel-1 Contributes to Axonal Degeneration in Autoimmune Inflammation of the central Nervous System. Nat. Med. 13, 1483–1489. doi:10.1038/nm1668
Fujiyabu, T., Yoshikawa, Y., Kim, J., Sakumichi, N., Chung, U.-i., and Sakai, T. (2019). Shear Modulus Dependence of the Diffusion Coefficient of a Polymer Network. Macromolecules 52, 9613–9619. doi:10.1021/acs.macromol.9b01654
Gabrieli, D., Vigilante, N. F., Scheinfeld, R., Rifkin, J. A., Schumm, S. N., Wu, T., et al. (2020). A Multibody Model for Predicting Spatial Distribution of Human Brain Deformation Following Impact Loading. J. Biomechanical Eng. 142, 091015. doi:10.1115/1.4046866
Garcia, J., Yang, Z., Mongrain, R., Leask, R. L., and Lachapelle, K. (2018). 3D Printing Materials and Their Use in Medical Education: a Review of Current Technology and Trends for the Future. BMJ STEL 4, 27–40. doi:10.1136/bmjstel-2017-000234
Goh, G. D., Sing, S. L., Lim, Y. F., Thong, J. L. J., Peh, Z. K., Mogali, S. R., et al. (2021). Machine Learning for 3D Printed Multi-Materials Tissue-Mimicking Anatomical Models. Mater. Des. 211, 110125. doi:10.1016/j.matdes.2021.110125
Goychuk, I. (2009). Viscoelastic Subdiffusion: From Anomalous to normal. Phys. Rev. E 80, 046125. doi:10.1103/PhysRevE.80.046125
Grochowski, C., Blicharska, E., Krukow, P., Jonak, K., Maciejewski, M., Szczepanek, D., et al. (2019). Analysis of Trace Elements in Human Brain: Its Aim, Methods, and Concentration Levels. Front. Chem. 7, 115. doi:10.3389/fchem.2019.00115
Gulino, M., Kim, D., Pané, S., Santos, S. D., and Pêgo, A. P. (2019). Tissue Response to Neural Implants: The Use of Model Systems toward New Design Solutions of Implantable Microelectrodes. Front. Neurosci. 13, 689. doi:10.3389/fnins.2019.00689
Gulino, M., Santos, S. D., and Pêgo, A. P. (2021). Biocompatibility of Platinum Nanoparticles in Brain Ex Vivo Models in Physiological and Pathological Conditions. Front. Neurosci. 15, 1740. doi:10.3389/fnins.2021.787518
Handa, N., Mochizuki, S., Fujiwara, Y., Shimokawa, M., Wakao, R., and Arai, H. (2020). Future Development of Artificial Organs Related with Cutting Edge Emerging Technology and Their Regulatory Assessment: PMDA's Perspective. J. Artif. Organs 23, 203–206. doi:10.1007/s10047-020-01161-4
Heinrich, M. A., Bansal, R., Lammers, T., Zhang, Y. S., Michel Schiffelers, R., and Prakash, J. (2019). 3D‐Bioprinted Mini‐Brain: A Glioblastoma Model to Study Cellular Interactions and Therapeutics. Adv. Mater. 31, 1806590. doi:10.1002/adma.201806590
Hellerbach, A., Schuster, V., Jansen, A., and Sommer, J. (2013). MRI Phantoms - Are There Alternatives to Agar? PLoS ONE 8, e70343. doi:10.1371/journal.pone.0070343
Hiscox, L. V., McGarry, M. D. J., Schwarb, H., Van Houten, E. E. W., Pohlig, R. T., Roberts, N., et al. (2020). Standard‐space Atlas of the Viscoelastic Properties of the Human Brain. Hum. Brain Mapp. 41, 5282–5300. doi:10.1002/hbm.25192
Hunold, A., Strohmeier, D., Fiedler, P., and Haueisen, J. (2018). Head Phantoms for Electroencephalography and Transcranial Electric Stimulation: a Skull Material Study. Biomed. Eng./Biomedizinische Technik 63, 683–689. doi:10.1515/bmt-2017-0069
Jahnke, P., Schwarz, F. B., Ziegert, M., Almasi, T., Abdelhadi, O., Nunninger, M., et al. (2018). A Radiopaque 3D Printed, Anthropomorphic Phantom for Simulation of CT-guided Procedures. Eur. Radiol. 28, 4818–4823. doi:10.1007/s00330-018-5481-4
Jeong, Y. C., Lee, H. E., Shin, A., Kim, D. G., Lee, K. J., and Kim, D. (2020). Progress in Brain‐Compatible Interfaces with Soft Nanomaterials. Adv. Mater. 32, 1907522. doi:10.1002/adma.201907522
Johnson, C. D., D’Amato, A. R., Puhl, D. L., Wich, D. M., Vesperman, A., and Gilbert, R. J. (2018). Electrospun Fiber Surface Nanotopography Influences Astrocyte-Mediated Neurite Outgrowth. Biomed. Mater. 13, 054101. doi:10.1088/1748-605X/aac4de
Jona, G., Furman‐Haran, E., and Schmidt, R. (2021). Realistic Head‐shaped Phantom with Brain‐mimicking Metabolites for 7 T Spectroscopy and Spectroscopic Imaging. NMR Biomed. 34, e4421. doi:10.1002/nbm.4421
Kandadai, M. A., Raymond, J. L., and Shaw, G. J. (2012). Comparison of Electrical Conductivities of Various Brain Phantom Gels: Developing a 'brain Gel Model'. Mater. Sci. Eng. C 32, 2664–2667. doi:10.1016/j.msec.2012.07.024
Karimi, A., Rahmati, S. M., Razaghi, R., and Hasani, M. (2019). Mechanical Measurement of the Human Cerebellum under Compressive Loading. J. Med. Eng. Technology 43, 55–58. doi:10.1080/03091902.2019.1609609
Kaster, T., Sack, I., and Samani, A. (2011). Measurement of the Hyperelastic Properties of Ex Vivo Brain Tissue Slices. J. Biomech. 44, 1158–1163. doi:10.1016/j.jbiomech.2011.01.019
Kaufmann, R. S. (1998). “Fick's Law,” in Geochemistry (Dordrecht: Springer Netherlands), 245–246. doi:10.1007/1-4020-4496-8_123
Korhonen, P., Malm, T., and White, A. R. (2018). 3D Human Brain Cell Models: New Frontiers in Disease Understanding and Drug Discovery for Neurodegenerative Diseases. Neurochem. Int. 120, 191–199. doi:10.1016/j.neuint.2018.08.012
Koss, K. M., Churchward, M. A., Jeffery, A. F., Mushahwar, V. K., Elias, A. L., and Todd, K. G. (2017). Improved 3D Hydrogel Cultures of Primary Glial Cells for In Vitro Modelling of Neuroinflammation. JoVE 1, 56615. doi:10.3791/56615
Kozana, A., Boursianis, T., Kalaitzakis, G., Raissaki, M., and Maris, T. G. (2018). Neonatal Brain: Fabrication of a Tissue-Mimicking Phantom and Optimization of Clinical Τ1w and T2w MRI Sequences at 1.5 T. Physica Med. 55, 88–97. doi:10.1016/j.ejmp.2018.10.022
Lefaucheur, J.-P. (2019). “Transcranial Magnetic Stimulation,” in Handbook of Clinical Neurology (Elsevier), 559–580. doi:10.1016/B978-0-444-64032-1.00037-0
Li, L., Wang, Q., Zhang, Y., Niu, Y., Yao, X., and Liu, H. (2015). The Molecular Mechanism of Bisphenol A (BPA) as an Endocrine Disruptor by Interacting with Nuclear Receptors: Insights from Molecular Dynamics (MD) Simulations. PLOS ONE 10, e0120330. doi:10.1371/journal.pone.0120330
Lodish, H., Berk, A., Zipursky, S. L., Matsudaira, P., Baltimore, D., and Darnell, J. (2000). “Diffusion of Small Molecules across Phospholipid Bilayers,” in Molecular Cell Biology. 4th edition. 1. Available at: https://www.ncbi.nlm.nih.gov/books/NBK21626/(Accessed September 6, 2021).
Lovett, M. L., Nieland, T. J. F., Dingle, Y. T. L., and Kaplan, D. L. (2020). Innovations in 3D Tissue Models of Human Brain Physiology and Diseases. Adv. Funct. Mater. 30, 1909146. doi:10.1002/adfm.201909146
Lv, H., Kurt, M., Zeng, N., Ozkaya, E., Marcuz, F., Wu, L., et al. (2020). MR Elastography Frequency-dependent and Independent Parameters Demonstrate Accelerated Decrease of Brain Stiffness in Elder Subjects. Eur. Radiol. 30, 6614–6623. doi:10.1007/s00330-020-07054-7
Maddock, R. J., Buonocore, M. H., Copeland, L. E., and Richards, A. L. (2009). Elevated Brain Lactate Responses to Neural Activation in Panic Disorder: a Dynamic 1H-MRS Study. Mol. Psychiatry 14, 537–545. doi:10.1038/sj.mp.4002137
Magnotta, V. A., Heo, H.-Y., Dlouhy, B. J., Dahdaleh, N. S., Follmer, R. L., Thedens, D. R., et al. (2012). Detecting Activity-Evoked pH Changes in Human Brain. Proc. Natl. Acad. Sci. U.S.A. 109, 8270–8273. doi:10.1073/pnas.1205902109
Magsood, H., and Hadimani, R. L. (2021). Development of Anatomically Accurate Brain Phantom for Experimental Validation of Stimulation Strengths during TMS. Mater. Sci. Eng. C 120, 111705. doi:10.1016/j.msec.2020.111705
Mansor, S., Pfaehler, E., Heijtel, D., Lodge, M. A., Boellaard, R., and Yaqub, M. (2017). Impact of PET/CT System, Reconstruction Protocol, Data Analysis Method, and Repositioning on PET/CT Precision: An Experimental Evaluation Using an Oncology and Brain Phantom. Med. Phys. 44, 6413–6424. doi:10.1002/mp.12623
Mantione, D., Del Agua, I., Schaafsma, W., Diez-Garcia, J., Castro, B., Sardon, H., et al. (2016). Poly(3,4-ethylenedioxythiophene):GlycosAminoGlycan Aqueous Dispersions: Toward Electrically Conductive Bioactive Materials for Neural Interfaces. Macromol. Biosci. 16, 1227–1238. doi:10.1002/mabi.201600059
Margulies, S. S., Thibault, L. E., and Gennarelli, T. A. (1990). Physical Model Simulations of Brain Injury in the Primate. J. Biomech. 23, 823–836. doi:10.1016/0021-9290(90)90029-3
Martin, C., Chapelle, F., Lemaire, J. J., and Gogu, G. (2009). “Neurosurgical Robot Design and Interactive Motion Planning for Resection Task,” in 2009 IEEE/RSJ International Conference on Intelligent Robots and Systems, St. Louis, MO, USA (IEEE), 4505–4510. doi:10.1109/IROS.2009.5354647
McIntosh, R. L., and Anderson, V. (2011). A Comprehensive Tissue Properties Database provided for the Thermal Assessment of a Human at Rest. Biophys. Rev. Lett. 05, 129–151. doi:10.1142/S1793048010001184
Miga, M. I., Sun, K., Chen, I., Clements, L. W., Pheiffer, T. S., Simpson, A. L., et al. (2016). Clinical Evaluation of a Model-Updated Image-Guidance Approach to Brain Shift Compensation: Experience in 16 Cases. Int. J. CARS 11, 1467–1474. doi:10.1007/s11548-015-1295-x
Modo, M., and Badylak, S. F. (2019). A Roadmap for Promoting Endogenous In Situ Tissue Restoration Using Inductive Bioscaffolds after Acute Brain Injury. Brain Res. Bull. 150, 136–149. doi:10.1016/j.brainresbull.2019.05.013
Nagassa, R. G., McMenamin, P. G., Adams, J. W., Quayle, M. R., and Rosenfeld, J. V. (2019). Advanced 3D Printed Model of Middle Cerebral Artery Aneurysms for Neurosurgery Simulation. 3d Print Med. 5, 11. doi:10.1186/s41205-019-0048-9
Nasr, B., Chatterton, R., Yong, J., Jamshidi, P., D’Abaco, G., Bjorksten, A., et al. (2018). Self-Organized Nanostructure Modified Microelectrode for Sensitive Electrochemical Glutamate Detection in Stem Cells-Derived Brain Organoids. Biosensors 8, 14. doi:10.3390/bios8010014
Nguyen, P. Q. H., Duong, D. D., Kwun, J. D., and Lee, N. Y. (2019). Hybrid Elastomer-Plastic Microfluidic Device as a Convenient Model for Mimicking the Blood-Brain Barrier In Vitro. Biomed. Microdevices 21, 90. doi:10.1007/s10544-019-0446-1
Nzou, G., Wicks, R. T., Wicks, E. E., Seale, S. A., Sane, C. H., Chen, A., et al. (2018). Human Cortex Spheroid with a Functional Blood Brain Barrier for High-Throughput Neurotoxicity Screening and Disease Modeling. Sci. Rep. 8, 7413. doi:10.1038/s41598-018-25603-5
O’Brien, J. S., and Sampson, E. L. (1965). Lipid Composition of the normal Human Brain: gray Matter, white Matter, and Myelin. J. Lipid Res. 6, 537–544.
Ojeda-Hernández, D. D., Gomez-Pinedo, U., Hernández-Sapiéns, M. A., Canales-Aguirre, A. A., Espinosa-Andrews, H., Matias-Guiu, J., et al. (2021). Biocompatibility of Ferulic/succinic Acid-Grafted Chitosan Hydrogels for Implantation after Brain Injury: A Preliminary Study. Mater. Sci. Eng. C 121, 111806. doi:10.1016/j.msec.2020.111806
Orlowski, P., Chappell, M., Park, C. S., Grau, V., and Payne, S. (2011). Modelling of pH Dynamics in Brain Cells after Stroke. Interf. Focus. 1, 408–416. doi:10.1098/rsfs.2010.0025
Oros-Peusquens, A.-M., Loução, R., Abbas, Z., Gras, V., Zimmermann, M., and Shah, N. J. (2019). A Single-Scan, Rapid Whole-Brain Protocol for Quantitative Water Content Mapping with Neurobiological Implications. Front. Neurol. 10, 1333. doi:10.3389/fneur.2019.01333
O’Rourke, C., Lee-Reeves, C., Drake, R. A., Cameron, G. W., Loughlin, A. J., and Phillips, J. B. (2017). Adapting Tissue-Engineered In Vitro CNS Models for High-Throughput Study of Neurodegeneration. J. Tissue Eng. 8, 204173141769792. doi:10.1177/2041731417697920
Paskiet, D., Jenke, D., Ball, D., Houston, C., Norwood, D. L., and Markovic, I. (2013). The Product Quality Research Institute (PQRI) Leachables and Extractables Working Group Initiatives for Parenteral and Ophthalmic Drug Product (PODP). PDA J. Pharm. Sci. Technology 67, 430–447. doi:10.5731/pdajpst.2013.00936
Patz, S., Fovargue, D., Schregel, K., Nazari, N., Palotai, M., Barbone, P. E., et al. (2019). Imaging Localized Neuronal Activity at Fast Time Scales through Biomechanics. Sci. Adv. 5, eaav3816. doi:10.1126/sciadv.aav3816
Paulsen, K. D., Miga, M. I., Kennedy, F. E., Hoopens, P. J., Hartov, A., and Roberts, D. W. (1999). A Computational Model for Tracking Subsurface Tissue Deformation during Stereotactic Neurosurgery. IEEE Trans. Biomed. Eng. 46, 213–225. doi:10.1109/10.740884
Pavoni, J. F., Neves-Junior, W. F. P., Silveira, M. A., Ramos, P. A. M. M., Haddad, C. M. K., and Baffa, O. (2015). Feasibility on Using Composite Gel-Alanine Dosimetry on the Validation of a Multiple Brain Metastasis Radiosurgery VMAT Technique. J. Phys. Conf. Ser. 573, 012050. doi:10.1088/1742-6596/573/1/012050
Persheyev, S., Fan, Y., Irving, A., and Rose, M. J. (2011). BV-2 Microglial Cells Sense Micro-nanotextured Silicon Surface Topology. J. Biomed. Mater. Res. 99A, 135–140. doi:10.1002/jbm.a.33159
Pervin, F., and Chen, W. W. (2011). “Mechanically Similar Gel Simulants for Brain Tissues,” in Dynamic Behavior of Materials, Volume 1 Conference Proceedings of the Society for Experimental Mechanics Series. Editor T. Proulx (New York, NY: Springer New York), 9–13. doi:10.1007/978-1-4419-8228-5_3
Petrone, N., Candiotto, G., Marzella, E., Uriati, F., Carraro, G., Bäckström, M., et al. (2019). Feasibility of Using a Novel Instrumented Human Head Surrogate to Measure Helmet, Head and Brain Kinematics and Intracranial Pressure during Multidirectional Impact Tests. J. Sci. Med. Sport 22, S78–S84. doi:10.1016/j.jsams.2019.05.015
Pinheiro, J. P., Mota, A. M., Simões Gonçalves, M. L. S., and van Leeuwen, H. P. (1998). The pH Effect in the Diffusion Coefficient of Humic Matter: Influence in Speciation Studies Using Voltammetric Techniques. Colloids Surf. A: Physicochemical Eng. Aspects 137, 165–170. doi:10.1016/S0927-7757(97)00306-3
Pomfret, R., Miranpuri, G., and Sillay, K. (2013a). The Substitute Brain and the Potential of the Gel Model. ANS 20, 118–122. doi:10.5214/ans.0972.7531.200309
Pomfret, R., Sillay, K., and Miranpuri, G. (2013b). An Exploration of the Electrical Properties of Agarose Gel: Characterization of Concentration Using Nyquist Plot Phase Angle and the Implications of a More Comprehensive In Vitro Model of the Brain. ANS 20, 99–107. doi:10.5214/ans.0972.7531.200305
Pourmorteza, A., Symons, R., Reich, D. S., Bagheri, M., Cork, T. E., Kappler, S., et al. (2017). Photon-Counting CT of the Brain: In Vivo Human Results and Image-Quality Assessment. AJNR Am. J. Neuroradiol 38, 2257–2263. doi:10.3174/ajnr.A5402
P. Tofts (Editor) (2004). Quantitative MRI of the Brain: Measuring Changes Caused by Disease. Repr (Chichester, West Sussex: Wiley).
Qiu, K., Haghiashtiani, G., and McAlpine, M. C. (2018). 3D Printed Organ Models for Surgical Applications. Annu. Rev. Anal. Chem. 11, 287–306. doi:10.1146/annurev-anchem-061417-125935
Qiu, S., He, Z., Wang, R., Li, R., Zhang, A., Yan, F., et al. (2021). An Electromagnetic Actuator for Brain Magnetic Resonance Elastography with High Frequency Accuracy. NMR Biomed. 34, e4592. doi:10.1002/nbm.4592
Quadrato, G., and Arlotta, P. (2017). Present and Future of Modeling Human Brain Development in 3D Organoids. Curr. Opin. Cel Biol. 49, 47–52. doi:10.1016/j.ceb.2017.11.010
Rehder, R., Abd-El-Barr, M., Hooten, K., Weinstock, P., Madsen, J. R., and Cohen, A. R. (2016). The Role of Simulation in Neurosurgery. Childs Nerv Syst. 32, 43–54. doi:10.1007/s00381-015-2923-z
Reinertsen, I., and Collins, D. L. (2006). A Realistic Phantom for Brain-Shift Simulations. Med. Phys. 33, 3234–3240. doi:10.1118/1.2219091
Rejmontová, P., Capáková, Z., Mikušová, N., Maráková, N., Kašpárková, V., Lehocký, M., et al. (2016). Adhesion, Proliferation and Migration of NIH/3T3 Cells on Modified Polyaniline Surfaces. Ijms 17, 1439. doi:10.3390/ijms17091439
Ruby, J., Diana, S., Li, X., Tisa, J., Harry, W., Nedumaan, J., et al. (2020). Integrating Medical Robots for Brain Surgical Applications. J. Med. Surg. Res. 5 (1), 1–14.
Ryan, J. R., Almefty, K. K., Nakaji, P., and Frakes, D. H. (2016). Cerebral Aneurysm Clipping Surgery Simulation Using Patient-specific 3D Printing and Silicone Casting. World Neurosurg. 88, 175–181. doi:10.1016/j.wneu.2015.12.102
Sammartino, F., Krishna, V., Sankar, T., Fisico, J., Kalia, S. K., Hodaie, M., et al. (2017). 3-Tesla MRI in Patients with Fully Implanted Deep Brain Stimulation Devices: a Preliminary Study in 10 Patients. J. Neurosurg. 127, 892–898. doi:10.3171/2016.9.JNS16908
Sato, K., and Sato, K. (2018). Recent Progress in the Development of Microfluidic Vascular Models. Anal. Sci. 34, 755–764. doi:10.2116/analsci.17R006
Seo, Y., Bang, S., Son, J., Kim, D., Jeong, Y., Kim, P., et al. (2022). Brain Physiome: A Concept Bridging In Vitro 3D Brain Models and In Silico Models for Predicting Drug Toxicity in the Brain. Bioactive Mater. 13, 135–148. doi:10.1016/j.bioactmat.2021.11.009
Sepehrband, F., Clark, K. A., Ullmann, J. F. P., Kurniawan, N. D., Leanage, G., Reutens, D. C., et al. (2015). Brain Tissue Compartment Density Estimated Using Diffusion-Weighted MRI Yields Tissue Parameters Consistent with Histology. Hum. Brain Mapp. 36, 3687–3702. doi:10.1002/hbm.22872
Smith, D. R., Guertler, C. A., Okamoto, R. J., Romano, A. J., Bayly, P. V., and Johnson, C. L. (2020). Multi-Excitation Magnetic Resonance Elastography of the Brain: Wave Propagation in Anisotropic White Matter. J. Biomech. Eng. 142, 071005. doi:10.1115/1.4046199
Soza, G., Grosso, R., Nimsky, C., Greiner, G., and Hastreiter, P. (2004). “Estimating Mechanical Brain Tissue Properties with Simulation and Registration,” in Medical Image Computing And Computer-Assisted Intervention – MICCAI 2004 Lecture Notes in Computer Science. Editors C. Barillot, D. R. Haynor, and P. Hellier (Berlin, Heidelberg: Springer), 276–283. doi:10.1007/978-3-540-30136-3_35
Stein, W. D. (1981). “Chapter 1 Permeability for Lipophilic Molecules,” in New Comprehensive Biochemistry Membrane Transport. Editors S. L. Bonting, and J. J. H. H. M. de Pont (Elsevier), 1–28. doi:10.1016/S0167-7306(08)60029-0
Tanaka, T., Hocker, L. O., and Benedek, G. B. (1973). Spectrum of Light Scattered from a Viscoelastic Gel. J. Chem. Phys. 59, 5151–5159. doi:10.1063/1.1680734
Tüzüm Demir, A. P., and Ulutan, S. (2012). Migration of Phthalate and Non-phthalate Plasticizers Out of Plasticized PVC Films into Air. J. Appl. Polym. Sci. 128, a–n. doi:10.1002/app.38291
van de Belt, T. H., Nijmeijer, H., Grim, D., Engelen, L. J. L. P. G., Vreeken, R., van Gelder, M. M. H. J., et al. (2018). Patient-Specific Actual-Size Three-Dimensional Printed Models for Patient Education in Glioma Treatment: First Experiences. World Neurosurg. 117, e99–e105. doi:10.1016/j.wneu.2018.05.190
Wang, H., Wang, B., Normoyle, K. P., Jackson, K., Spitler, K., Sharrock, M. F., et al. (2014). Brain Temperature and its Fundamental Properties: a Review for Clinical Neuroscientists. Front. Neurosci. 8. doi:10.3389/fnins.2014.00307
Wei, X.-F., Linde, E., and Hedenqvist, M. S. (2019). Plasticiser Loss from Plastic or Rubber Products through Diffusion and Evaporation. Npj Mater. Degrad. 3, 1–8. doi:10.1038/s41529-019-0080-7
Whittall, K. P., Mackay, A. L., Graeb, D. A., Nugent, R. A., Li, D. K. B., and Paty, D. W. (1997). In Vivo measurement ofT2 Distributions and Water Contents in normal Human Brain. Magn. Reson. Med. 37, 34–43. doi:10.1002/mrm.1910370107
Xiong, Z.-G., Zhu, X.-M., Chu, X.-P., Minami, M., Hey, J., Wei, W.-L., et al. (2004). Neuroprotection in Ischemia. Cell 118, 687–698. doi:10.1016/j.cell.2004.08.026
Yamaura, H., Igarashi, J., and Yamazaki, T. (2020). Simulation of a Human-Scale Cerebellar Network Model on the K Computer. Front. Neuroinform. 14, 16. doi:10.3389/fninf.2020.00016
Yasuda, K., Okamoto, R., and Komura, S. (2017). Anomalous Diffusion in Viscoelastic media with Active Force Dipoles. Phys. Rev. E 95, 032417. doi:10.1103/PhysRevE.95.032417
Yin, Z., Romano, A. J., Manduca, A., Ehman, R. L., and Huston, J. (2018). Stiffness and beyond. Top. Magn. Reson. Imaging 27, 305–318. doi:10.1097/RMR.0000000000000178
Zhang, L., Jackson, W. J., and Bentil, S. A. (2019). The Mechanical Behavior of Brain Surrogates Manufactured from Silicone Elastomers. J. Mech. Behav. Biomed. Mater. 95, 180–190. doi:10.1016/j.jmbbm.2019.04.005
Zhao, J., Hussain, M., Wang, M., Li, Z., and He, N. (2020). Embedded 3D Printing of Multi-Internal Surfaces of Hydrogels. Additive Manufacturing 32, 101097. doi:10.1016/j.addma.2020.101097
Zhu, F., Wagner, C., Dal Cengio Leonardi, A., Jin, X., VandeVord, P., Chou, C., et al. (2012). Using a Gel/plastic Surrogate to Study the Biomechanical Response of the Head under Air Shock Loading: a Combined Experimental and Numerical Investigation. Biomech. Model. Mechanobiol 11, 341–353. doi:10.1007/s10237-011-0314-2
Keywords: brain surrogate, medical devices, mechanical properties, biocompatibility, leachables
Citation: Bouattour Y, Sautou V, Hmede R, El Ouadhi Y, Gouot D, Chennell P, Lapusta Y, Chapelle F and Lemaire J-J (2022) A Minireview on Brain Models Simulating Geometrical, Physical, and Biochemical Properties of the Human Brain. Front. Bioeng. Biotechnol. 10:818201. doi: 10.3389/fbioe.2022.818201
Received: 19 November 2021; Accepted: 08 March 2022;
Published: 28 March 2022.
Edited by:
Alan Raybould, University of Edinburgh, United KingdomReviewed by:
Yuan Feng, Shanghai Jiao Tong University, ChinaMaria Lurdes Pinto, University of Trás-os-Montes and Alto Douro, Portugal
Copyright © 2022 Bouattour, Sautou, Hmede, El Ouadhi, Gouot, Chennell, Lapusta, Chapelle and Lemaire. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Yassine Bouattour, ybouattour@chu-clermontferrand.fr; Jean-Jacques Lemaire, jjlemaire@chu-clermontferrand.fr