Brain multiphysics最新文献

{"title":"Dynamic characteristics of impact-induced brain strain in the corpus callosum","authors":"Songbai Ji ,&nbsp;Shaoju Wu ,&nbsp;Wei Zhao","doi":"10.1016/j.brain.2022.100046","DOIUrl":"10.1016/j.brain.2022.100046","url":null,"abstract":"<div><p>Impact-induced brain strains are spatially rich and intrinsically dynamic. However, the dynamic information of brain strain is not typically used in any injury investigation. Here, we study the dynamic characteristics of maximum and minimum principal strain (maxPS and minPS) of the corpus callosum and highlight the significance of impact simulation time window. Three datasets are used: laboratory reconstructed National Football League (NFL; N=53), measured impacts from Stanford (SF; N=110) and Prevent Biometric (PB; N=314). Impact cases are discarded (by 20.8%, 11.8%, and 66.2%, respectively), when the simulation time window is considered inadequate to capture sufficient strain temporal responses. Fitted Gaussian peaks (with average relative root mean squared error of ∼5% and R² &gt;0.9) from all datasets have a similar median (15–18 ms) and inter-quantile range (5–9 ms) for the full width at half maximum (FWHM). FWHM significantly and negatively correlates with strain magnitude for NFL and SF, but not for PB. However, ratios between the largest minPS and maxPS magnitudes are similar across datasets (median of 0.5–0.6 with inter-quantile range of 0.2–0.7). Dynamic strain features improve injury prediction. This study motivates further development of advanced deep learning models to instantly estimate the complete details of spatiotemporal history of brain strains, beyond spatially detailed peak strains obtained at maximum values currently available. In addition, this study highlights the time lag between impact kinematics and corpus callosum strain deep in the brain, which has important implications for impact simulation and result interpretation as well as impact sensor designs in the future.</p></div><div><h3>Statement of significance</h3><p></p><ul><li><span>•</span><span><p>First study to systematically characterize the temporal history of corpus callosum strain in contact sports head impact.</p></span></li><li><span>•</span><span><p>Allows to rapidly launch multiscale modeling of concussion in the corpus callosum without a costly whole brain model simulation.</p></span></li><li><span>•</span><span><p>Motivates further development of advanced deep learning models that will instantly reproduce the complete spatiotemporal details of strain in the entire brain.</p></span></li><li><span>•</span><span><p>Highlights the importance of sufficient impact simulation time window in order to capture the complete strain responses deep in the brain.</p></span></li></ul></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100046"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266652202200003X/pdfft?md5=3bf2a50ac67bc1175a2b8da33406f64e&pid=1-s2.0-S266652202200003X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54405913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multiscale co-simulation of deep brain stimulation with brain networks in neurodegenerative disorders","authors":"Hina Shaheen,&nbsp;Swadesh Pal,&nbsp;Roderick Melnik","doi":"10.1016/j.brain.2022.100058","DOIUrl":"10.1016/j.brain.2022.100058","url":null,"abstract":"<div><p>Deep brain stimulation (DBS) has been used successfully as symptomatic treatment in several neurodegenerative disorders, including Parkinson’s disease (PD). However, the mechanisms of its activity inside the brain network are unclear. Many virtual DBS models investigate the dynamics of a subnetwork surrounding the basal ganglia (BG) as a spiking network has been attracting a growing body of research in neuroscience. Connectomic data, on the other hand, show that DBS has a wide range of impacts on many distinct cortical and subcortical sites. Notably, the nonlinear reaction–diffusion multiscale mathematical models demonstrate the effectiveness of capturing crucial disease characteristics and are used to simulate large-scale brain activity. The BG and associated nuclei comprise many subcortical cell groups in the brain, and their couplings commonly revealed MRI-based assessments of the strength of anatomical connections. We have developed a hybrid modeling formalism and a unique co-simulation technique that allows us to compute electrodiffusive ion dynamics for the cortex–BG–thalamus (BGTH) brain network model within a large-scale brain connectome. We collect data from the Human Connectome Project (HCP) and propose a closed-loop DBS approach based on the brain network model. Moreover, we select regions in the parameter space that reflect the healthy and Parkinsonian states as well as the impact of DBS on the subthalamic nucleus (STN) and globus pallidus internus (GPi) neurons. We predicted that if we apply the DBS to the system described by the temporal model, the brain maintains a healthy state until <span><math><mrow><mn>0</mn><mo>.</mo><mn>05</mn><mtext>ms</mtext></mrow></math></span> for STN neurons and <span><math><mrow><mn>0</mn><mo>.</mo><mn>035</mn><mtext>ms</mtext></mrow></math></span> for GPi neurons. A local regulatory mechanism known as feedback inhibition control (FIC) points to the existence of underlying network dynamics in the white matter of connected brain regions. The model showed unanticipated effects that in the presence of diffusion, the human brain maintained a healthy state for a long time after the DBS had been applied to STN and GPi neurons. This research helps us better understand the changes in brain activity caused by DBS and enhances this clinical therapy, thus shedding new light on the importance of DBS mechanisms in BGTH brain network models of neurodegenerative disorders.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100058"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000156/pdfft?md5=f3d3b3f593e0afe0ed66d9103a9635b7&pid=1-s2.0-S2666522022000156-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48566232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparing the effects of linear and one-term Ogden elasticity in a model of glioblastoma invasion.","authors":"Meghan E. Rhodes ,&nbsp;Thomas Hillen ,&nbsp;Vakhtang Putkaradze","doi":"10.1016/j.brain.2022.100050","DOIUrl":"https://doi.org/10.1016/j.brain.2022.100050","url":null,"abstract":"<div><p>Our modelling of brain mechanics is based on observations of Budday and colleagues [6], who analyzed the elastic properties of human brain tissue samples under multiple loading modes. Using these data, Budday <em>et al.</em> determined a realistic constitutive model for brain tissue mechanics. In these studies, they found that compression and shear responses were best modelled by a non-linear one-term Ogden elasticity model, although other elasticity models are possible as well. Here we analyze the role of the elasticity model of brain tissue on the invasion speed of glioma and the resulting tissue deformation (mass effect). We present a one dimensional continuum model that couples cell dynamics to tissue mechanics. Since the mechanics of glioma-compromised brain tissue is not clear, for comprehensive studies, we incorporate both elastic and viscoelastic versions of two brain tissue elasticity models - the commonly employed linear model and the experimentally determined one-term Ogden model. For each elasticity model we identify travelling wave solutions in one dimension and calculate the corresponding invasion speeds. We find that the invasion speed is, in fact, independent of the chosen elasticity model. However, the deformations of the brain tissue, and resulting stress, between the linear and one-term Ogden models are drastically different: the Ogden model shows two orders of magnitude less deformation and three orders of magnitude less stress as compared to the linear model. Such a discrepancy might be relevant when looking at glioma-induced health complications.</p></div><div><h3>Statement of significance</h3><p>Cancers arising from glial cells, known as gliomas, form in the spine and the brain. The spread of glioma is not fully understood, although recent studies have highlighted the role of tissue mechanics as a main factor in the invasion process. We present a one dimensional continuum model framework of glioma invasion that incorporates proliferation and invasion of glioma cells, as well as mass effects by coupling cell dynamics to tissue mechanics. We explore both elastic and viscoelastic versions of two brain tissue elasticity models - the commonly employed linear model and the experimentally determined one-term Ogden model. This is the first time the one-term Ogden model has been incorporated into a model of glioma invasion. We show that although the choice of elasticity model does not affect the invasion speed, the deformation and stress generated in the tissue are significantly different with the Ogden model producing three orders of magnitude less deformation and stress as compared to the linear model. Such a discrepancy might be relevant when looking at glioma-induced health complications.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100050"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000077/pdfft?md5=05c8f3cb032217c8f92618c86760ba5e&pid=1-s2.0-S2666522022000077-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136885200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Bok’s equi-volume principle: Translation, historical context, and a modern perspective","authors":"Jack Consolini ,&nbsp;Nagehan Demirci ,&nbsp;Andrew Fulwider ,&nbsp;Jeffrey J. Hutsler ,&nbsp;Maria A. Holland","doi":"10.1016/j.brain.2022.100057","DOIUrl":"10.1016/j.brain.2022.100057","url":null,"abstract":"<div><p>The human brain has a complex and unique structure, characterized by intricate three-dimensional folds. These folds, and the mechanisms for their formation, have been studied for over a hundred years. Here we offer a full translation of the pivotal (1929) work by Siegfried Bok, “Der Einflußder in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur” (“The Influence of the Curvature Occurring in the Folds and Turns of the Cerebral Cortex on Cortical Architecture”). This paper established the influential equi-volume principle, which stated that cortical and laminar thicknesses, along with neuronal shape and fiber orientation, change in order to preserve relative volume throughout the folds of the cortex. We also offer a commentary on the main points of the work, looking at Bok’s observations and predictions regarding the structure of neurons, cortical laminae, and the cortex itself, throughout the folds and curves of the brain. His equi-volume principle has held up to decades of experimentation and, even today, has important implications for the analysis of brain structure and function.</p><p><strong>Statement of Significance</strong>: This manuscript presents, for the first time, a full English translation of the foundational neuroanatomy article, “Der Einflußder in den Furchen und Windungen auftretenden Krümmungen der Großhirnrinde auf die Rindenarchitektur” (“The Influence of the Curvature Occurring in the Folds and Turns of the Cerebral Cortex on Cortical Architecture”), written over 90 years ago by Siegfried T. Bok and heavily cited since then. In addition, we provide an assessment of Bok’s main points, in light of his contemporaries in research at the time, as well as more recent work during the intervening decades.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100057"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000144/pdfft?md5=be617a5ddec75e8655ee28a2b4966db2&pid=1-s2.0-S2666522022000144-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42232293","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technical considerations on the use of Granger causality in neuromonitoring","authors":"Michał M. Placek ,&nbsp;Erta Beqiri ,&nbsp;Marek Czosnyka ,&nbsp;Peter Smielewski","doi":"10.1016/j.brain.2022.100044","DOIUrl":"10.1016/j.brain.2022.100044","url":null,"abstract":"<div><p>Neuromonitoring-derived indices play an important role in implementing personalised medicine for traumatic brain injury patients. A well-established example is the pressure reactivity index (PRx), calculated from spontaneous fluctuations of arterial blood pressure (ABP) and intracranial pressure (ICP). PRx assumes causal relationship between ABP and ICP but lacks the check for this assumption. Granger causality (GC) — a method of assessing causal interactions between time series data — is gaining popularity in neurosciences. In our work, we used ABP and ICP data recorded at the frequency of 100 Hz or higher from 235 traumatic brain injury patients. We focused on time domain GC. Analysis was first performed directly on high-resolution data, which included pulse waves. We showed that due to the measurement delay in high-resolution ABP data, GC analysis may erroneously indicate strong ICP→ABP causal relation. Subsequently, the data were downsampled to 0.1 Hz, effectively removing pulse and respiratory waves. We aimed to investigate how different ways of calculating GC influence results and which way should be recommended for ABP-ICP recordings. We considered aspects like selecting autoregressive model order and dealing with data non-stationarity. In addition, we generated simulated signals to investigate the influence of gaps and different procedures of missing data imputation on GC estimation. We showed that unlike methods which interpolate missing data, replacing missing data by white Gaussian noise did not increase the rate of false GC detection. Python source code used in this study is available at: <span>https://github.com/m-m-placek/python-icmplus-granger-causality</span><svg><path></path></svg>.</p></div><div><h3>Statement of significance</h3><p>Assessing causality between time series data is of particular interest when neuromonitoring indices are derived from those time series and causal interaction between them is assumed. Causality assessment can improve reliability of such indices and open pathways for their safe clinical implementation. Granger Causality (GC) has recently been investigated in data collected from traumatic brain injury patients. However, there are two main issues related to applications suggested in these studies. Firstly, they considered GC for entire multi-day data recordings or for 24-h long episodes. There is interest in considering causal relationships in finer granularity, also in terms of their potential real-time applications at the bedside. Secondly, GC calculation requires selecting some parameters and there is no unique nor standardised way of doing that. Many papers often provide very brief description of data pre-processing and GC calculation. For this reason, it can be harder to reproduce and compare results derived from GC application. Different ways of obtaining GC may potentially lead to inconsistent results. Here, we attempted to explore possibility of time-varying GC of finer granularity and to ","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100044"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000016/pdfft?md5=a4caecba7e9b8bdef859203d564add56&pid=1-s2.0-S2666522022000016-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45721509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mathematical modelling of axonal cortex contractility","authors":"D. Andrini ,&nbsp;V. Balbi ,&nbsp;G. Bevilacqua ,&nbsp;G. Lucci ,&nbsp;G. Pozzi ,&nbsp;D. Riccobelli","doi":"10.1016/j.brain.2022.100060","DOIUrl":"10.1016/j.brain.2022.100060","url":null,"abstract":"<div><p>The axonal cortex is composed of a regular structure of F-actin and spectrin able to contract thanks to myosin II motors. Such an active tension is of fundamental importance in controlling the physiological shape of axons. Recent experiments show that axons modulate the contraction of the cortex when subject to mechanical deformations, exhibiting a non-trivial coupling between the hoop and the axial active tension. However, the underlying mechanisms are still poorly understood. In this paper, we propose a continuum model of the axon based on the active strain theory. By using the Coleman–Noll procedure, we shed light on the coupling between the hoop and the axial active strain through the Mandel stress tensor. We propose a qualitative analysis of the system under the simplifying assumption of incompressibility, showing the existence of a stable equilibrium solution. In particular, our results show that the axon regulates the active contraction to maintain a homeostatic stress state. Finally, we propose numerical simulations of the model, using a more suitable compressible constitutive law. The results are compared with experimental data, showing an excellent quantitative agreement.</p><p><em>Statement of Significance</em> The mechanics of cortical contractility in axons is still poorly understood. Unravelling the mechanisms underlying axial and hoop stress generation in the cortex will give insight on the active regulation of axon diameter. The understanding of this phenomenon may shed new light on the physical causes of axonal morphological degeneration as a consequence of neurodegenerative diseases, viral infections, and traumatic brain injuries.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100060"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S266652202200017X/pdfft?md5=925a5c3798ed7399d63bd204406c8a88&pid=1-s2.0-S266652202200017X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46777511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to ‘Mechanical threshold for concussion based on computation of axonal strain using a finite element rat brain model’","authors":"Sumedha Premi,&nbsp;Caroline Deck,&nbsp;Brian D. Stemper,&nbsp;Rémy Willinger","doi":"10.1016/j.brain.2022.100048","DOIUrl":"10.1016/j.brain.2022.100048","url":null,"abstract":"","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100048"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000053/pdfft?md5=3d2534882ef8e4926c520ee23c43c430&pid=1-s2.0-S2666522022000053-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54406102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Anisotropic mechanical properties in the healthy human brain estimated with multi-excitation transversely isotropic MR elastography","authors":"Daniel R. Smith ,&nbsp;Diego A. Caban-Rivera ,&nbsp;Matthew D.J. McGarry ,&nbsp;L. Tyler Williams ,&nbsp;Grace McIlvain ,&nbsp;Ruth J. Okamoto ,&nbsp;Elijah E.W. Van Houten ,&nbsp;Philip V. Bayly ,&nbsp;Keith D. Paulsen ,&nbsp;Curtis L. Johnson","doi":"10.1016/j.brain.2022.100051","DOIUrl":"10.1016/j.brain.2022.100051","url":null,"abstract":"<div><p>Magnetic resonance elastography (MRE) is an MRI technique for imaging the mechanical properties of brain in vivo, and has shown differences in properties between neuroanatomical regions and sensitivity to aging, neurological disorders, and normal brain function. Past MRE studies investigating these properties have typically assumed the brain is mechanically isotropic, though the aligned fibers of white matter suggest an anisotropic material model should be considered for more accurate parameter estimation. Here we used a transversely isotropic, nonlinear inversion algorithm (TI-NLI) and multi-excitation MRE to estimate the anisotropic material parameters of individual white matter tracts in healthy young adults. We found significant differences between individual tracts for three recovered anisotropic parameters: substrate shear stiffness, <span><math><mi>μ</mi></math></span> (range: 2.57 – 3.02 kPa), shear anisotropy, <span><math><mi>φ</mi></math></span> (range: -0.026 – 0.164), and tensile anisotropy, <span><math><mi>ζ</mi></math></span> (range: 0.559 – 1.049). Additionally, we demonstrated the repeatability of these parameter estimates in terms of lower variability of repeated measures in a single subject relative to variability in our sample population. Further, we observed significant differences in anisotropic mechanical properties between segments of the corpus callosum (genu, body, and splenium), which is expected based on differences in axonal microstructure. This study shows the ability of MRE with TI-NLI to estimate anisotropic mechanical properties of white matter and presents reference properties for tracts throughout the healthy brain.</p></div><div><h3>Statement of significance</h3><p>In this study we use magnetic resonance elastography to determine the mechanical properties of white matter, which can be useful in characterizing neurological conditions such as multiple sclerosis and traumatic brain injury. However, due to its fibrous nature, accurate estimation of mechanical properties of white matter requires an anisotropic material model. In this work, we use a transversely isotropic inversion algorithm with data from multi-excitation MRE to determine the anisotropic mechanical properties of white matter in a healthy young population based upon an anisotropic material model. We display the ability of MRE to capture structural differences between different white matter tracts and sub-regions of these tracts, which are expected to reflect differences such as average axon thickness and myelin density. This robust estimation of white matter anisotropic properties in a young, healthy population provides an avenue for future studies to implement these methods to examine brain development, aging, and pathology.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100051"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9635552/pdf/nihms-1821069.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"40670047","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessment of brain injury biomechanics in soccer heading using finite element analysis","authors":"Richard A. Perkins ,&nbsp;Amirhamed Bakhtiarydavijani ,&nbsp;Athena E. Ivanoff ,&nbsp;Michael Jones ,&nbsp;Youssef Hammi ,&nbsp;Raj K. Prabhu","doi":"10.1016/j.brain.2022.100052","DOIUrl":"10.1016/j.brain.2022.100052","url":null,"abstract":"<div><p>This study presents an <em>in silico</em> finite element (FE) model-based biomechanical analysis of brain injury metrics and associated risks of a soccer ball impact to the head for aware and unaware athletes, considering ball impact velocity and direction. The analysis presented herein implements a validated soccer ball and 50^th percentile human head computational FE model for quantifying traumatic brain injury (TBI) metrics. The brain's mechanical properties are designated using a viscoelastic-viscoplastic constitutive material model for the white and gray matter within the human head FE model. FE results show a dynamic human head-soccer ball peak contact area of approximately seven times greater than those documented for helmet-to-helmet hits in American Football. Due to the deformable nature of the soccer ball, the impact dynamics are unique depending on the location and velocity of impact. TBI injury risks also depend on the location of impact and the impact velocity. Impacts to the rear (BrIC:0.48, HIC₁₅:180.7), side (BrIC:0.52, HIC₁₅:176.5), and front (BrIC:0.37, HIC₁₅:129.0) are associated with the highest injury risks. Furthermore, the FE results indicate when an athlete is aware of an incoming ball, HIC₁₅-based Abbreviated Injury Scale 1 (AIS 1) injury risks for the front, side, and rear impacts decrease from 10.5%, 18.5%, and 19.3%, respectively, to approximately 1% in front and side impacts and under 6% in a rear impact. Lastly, the unique contact area between the head and soccer ball produces pressure gradients in the ball that translate into distinguishable stress waves in the skull and the cerebral cortex.</p></div><div><h3>Statement of significance</h3><p>Mild traumatic brain injuries (mTBI) are a worrisome aspect of participation in most sports due to difficulties in their diagnosis in competitions and the potential of long-term neurological defects. These types of injuries are not well understood for athletes playing soccer, specifically pertaining to the risks of heading a soccer ball. Studies are warranted which investigate impacts in this game to improve current knowledge. Our computational study uses finite element modeling to investigate contact between a player's head and the soccer ball. The results of this study present potential injury mechanisms and risks caused by this contact interaction to contribute to the current understanding of brain injuries in soccer and the promotion of athlete safety.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100052"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000090/pdfft?md5=5f1e74cd718987c1eef54c6b910f2446&pid=1-s2.0-S2666522022000090-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44242380","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Computational pipeline for the generation and validation of patient-specific mechanical models of brain development","authors":"Mireia Alenyà ,&nbsp;Xiaoyu Wang ,&nbsp;Julien Lefèvre ,&nbsp;Guillaume Auzias ,&nbsp;Benjamin Fouquet ,&nbsp;Elisenda Eixarch ,&nbsp;François Rousseau ,&nbsp;Oscar Camara","doi":"10.1016/j.brain.2022.100045","DOIUrl":"10.1016/j.brain.2022.100045","url":null,"abstract":"<div><p>The human brain develops from a smooth cortical surface in early stages of fetal life to a convoluted one postnatally, creating an organized ensemble of folds. Abnormal folding patterns are linked to neurodevelopmental disorders. However, the complex multi-scale interactions involved in cortical folding are not fully known yet. Computational models of brain development have contributed to better understand the process of cortical folding, but still leave several questions unanswered. A major limitation of the existing models is that they have basically been applied to synthetic examples or simplified brain anatomies. However, the integration of patient-specific longitudinal imaging data is key for improving the realism of simulations. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development. Starting from the processing of fetal brain magnetic resonance images (MRI), personalised finite-element 3D meshes were generated, in which biomechanical models were run to simulate brain development. Several metrics were then employed to compare simulation results with neonatal images from the same subjects, on a common reference space. We applied the computational pipeline to a cohort of 29 subjects where fetal and neonatal MRI were available, including controls and ventriculomegaly cases. The neonatal brain simulations had several sulcal patterns similar to the ones observed in neonatal MRI data. However, the pipeline also revealed some limitations of the evaluated mechanical model and the importance of including patient-specific cortical thickness as well as regional and anisotropic growth to obtain more realistic and personalised brain development models.</p><p><strong>Statement of Significance:</strong> Computational modelling has emerged as a powerful tool to study the complex process of brain development during gestation. However, most of the studies performed so far have been carried out in synthetic or two-dimensional geometries due to the difficulties involved in processing real fetal data. Moreover, as there is no correspondence between meshes, comparing them or assessing whether they are realistic or not is not a trivial task. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development, mainly based on open-source tools.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"3 ","pages":"Article 100045"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522022000028/pdfft?md5=1de0fa8ca4d696974474b8b56de564dc&pid=1-s2.0-S2666522022000028-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43489821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}