Brain multiphysics最新文献

{"title":"Traumatic brain injury or traumatic brain disease: A scientific commentary","authors":"Adedunsola Adewunmi Obasa , Funmilayo Eniola Olopade , Sharon Leah Juliano , James Olukayode Olopade","doi":"10.1016/j.brain.2024.100092","DOIUrl":"10.1016/j.brain.2024.100092","url":null,"abstract":"<div><p>Traumatic Brain Injury (TBI) represents a major public health burden and a major contributor to disability and death, especially in the young population. It remains one of the most challenging human conditions to classify and the non-standardized classification is one of the numerous barriers in proper diagnosis and effective translation of experimental treatments in animal models. TBI is associated with numerous disorders, including amnesia, Parkinson's Disease, sleep disorders, Alzheimer's Disease, as well as disruption of physical, cognitive, and mental functioning. Several health care providers and the insurance industry see TBI as a singular 'event', meaning that the brain ''repairs'' over time, and does not require additional therapies. However, a single mild TBI can induce problems that self-propagate for months or years after the injury. There currently exist no diagnostic methods to quantify the extent of emotional and behavioral changes, cognitive impairment, fatigue, and sleep issues resulting from TBI in affected individual. The various animal and injury models available for TBI research are limited in clinical trials because a single TBI event is not fully understood. This review highlights the classifications of TBI, its heterogeneity, neuropathological lesions, long term sequelae, association with neurodegenerative disorders in human and animal studies, and attempts to modify the notion of TBI being viewed as a singular event.</p></div><div><h3>Statement of significance</h3><p>The significance and strength of this review article lies in its comprehensive exploration of Traumatic Brain Injury (TBI) by addressing various factors that contribute to its complexity. We carried out a careful and detailed review of TBI classifications with an aim to provide a clearer and more detailed understanding of the heterogeneity inherent in these injuries. The examination of neuropathological lesions associated with TBI offers critical insights into the intricate nature of brain damage, fostering a deeper comprehension of the diverse outcomes resulting from TBI.</p><p>Furthermore, this review critically evaluates the long-term sequelae of TBI, shedding light on the often-overlooked extended consequences that impact individuals well beyond the initial injury period. The findings from human and animal studies not only enriches our understanding of TBI but also highlights the translational implications for both clinical and preclinical research.</p><p>A pivotal aspect of our review involves investigating the association between TBI and neurodegenerative disorders. By combining information from human studies and animal models, we aim to contribute to the growing body of knowledge that elucidates the intricate links between TBI and the development of neurodegenerative conditions.</p><p>Most notably, this review challenges the conventional notion of TBI as a singular event by incorporating perspectives that emphasize its multifaceted nature. We c","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100092"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522024000030/pdfft?md5=6dac16cf2e985384f2746ca3c0637309&pid=1-s2.0-S2666522024000030-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140280106","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":"Revealing the heterogeneity of plasma protein and cognitive decline trajectory among Mild Cognitive Impairment patients by clustering of brain atrophy features","authors":"My Nguyen ,&nbsp;Bao Pham ,&nbsp;Toi Vo ,&nbsp;Huong Ha","doi":"10.1016/j.brain.2024.100093","DOIUrl":"https://doi.org/10.1016/j.brain.2024.100093","url":null,"abstract":"<div><p>Alzheimer's disease (AD) is suggested to be a heterogeneous disorder, but limited studies explore the heterogeneity of the Mild Cognitive Impairment (MCI) stage. This study aimed to tackle such problems using the CIMLR (Cancer Integration via Multikernel Learning) algorithm to cluster brain structural features extracted from T1-weighted Magnetic Resonance Images of MCI patients from Alzheimer's Disease Neuroimaging Initiative. The demographic and cognitive results, characteristics of brain structural features, plasma biomarkers, and longitudinal cognitive trajectory were analyzed for each cluster. The CIMLR clustering analysis revealed four distinct clusters. Cluster 1 is the oldest group but has had mild atrophy and moderate progression with elevated Tumor Necrosis Factor Receptor 2 level; and low Brain-Derived Neurotrophic Factor and CD40 Ligand levels. Cluster 2 showed the highest risk for aggressive MCI progression, with abnormal Leptin, Adiponectin, and Creatine kinase-MB values. Cluster 3 exhibited a low level of Monokine Induced by Gamma Interferon and mild atrophy that shared similar patterns with Cluster 1. Cluster 4 represented the healthiest group during longitudinal tracking, with the mildest Parahippocampal atrophy, which was found to be positively correlated with cognitive impairment and amino acid levels. The longitudinal analyses showed the potential of Hepatocyte Growth Factor as a marker for slow cognitive impairment; Cortisol and Neurofilament Light Polypeptide as prognosis markers for aggressive MCI progression. These findings may lay out new suggestions for further research contributing to the accurate diagnosis and precision medicine for dementia and AD.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100093"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522024000042/pdfft?md5=46b752042d20179714382c09e7dd6e1c&pid=1-s2.0-S2666522024000042-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140341135","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":"A review of brain injury at multiple time scales and its clinicopathological correlation through in silico modeling","authors":"Abhilash Awasthi ,&nbsp;Suryanarayanan Bhaskar ,&nbsp;Samhita Panda ,&nbsp;Sitikantha Roy","doi":"10.1016/j.brain.2024.100090","DOIUrl":"10.1016/j.brain.2024.100090","url":null,"abstract":"<div><p>Understanding the correlation between pathological changes and the type of brain injury is pivotal in mitigating the damage and planning reliable and improved treatment strategies. Swift identification of the underlying mechanisms behind brain injury is essential for early diagnosis, surgical planning, and post-operative therapies. Brain injury may stem from various sources, including trauma (resulting in traumatic brain injury), treatment (leading to surgical brain injury), and neurodegenerative mechanisms. These injuries can manifest spatially, affecting individual neurons to the entire organ and temporally, ranging from immediate to long-term degeneration. However, direct evidence linking injury mechanisms to short and long-term tissue damage in the human population is limited, posing challenges in establishing a clear clinicopathological connection. Recently, <em>in silico</em> modeling has emerged as a cost-effective approach that can assist clinicians in gaining deeper insights and uncover new injury pathways. Physics and machine learning-based <em>in silico</em> modeling offers valuable contributions to injury prevention, diagnosis, prognosis, treatment planning, and patient monitoring, especially given the complexities of acquiring patient-specific clinical data related to brain injuries. Considering the spatiotemporal complexity of brain tissue damage, developing a comprehensive, multiscale, and multiphysics model is imperative for a better understanding. This study aims to categorize and explore strategies for modeling brain injuries across three distinct time scales, review damage mechanisms at various length scales, and recommend the development of a comprehensive biomechanical model that integrates multimodal data and multiphysics. Such an integrated approach will provide personalized diagnosis and treatment strategies tailored to individual patients.</p><p><strong>Statement of Significance:</strong> The connection between clinical observations and brain pathology is crucial for managing brain injuries. Brain injuries result in brain damage via diverse factors across scales, from neurons to organs, from initial trauma to neurodegeneration. However, limited direct evidence linking injury mechanisms to long-term human tissue damage hinders clinicopathological connections. <em>In silico</em> modeling, a cost-effective approach utilizing physics and machine learning-based principles, can aid clinicians in uncovering injury pathways. A comprehensive, multimodal, and multiphysics model is vital for understanding complex brain tissue damage. This study categorizes modeling strategies, reviews damage mechanisms across scales, and recommends comprehensive biomechanical models for personalized treatment.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100090"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522024000017/pdfft?md5=916fa58bd537ef1a55a0f8582c7ef044&pid=1-s2.0-S2666522024000017-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139538219","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":"Post-mortem changes of anisotropic mechanical properties in the porcine brain assessed by MR elastography","authors":"Shuaihu Wang ,&nbsp;Kevin N. Eckstein ,&nbsp;Charlotte A. Guertler ,&nbsp;Curtis L. Johnson ,&nbsp;Ruth J. Okamoto ,&nbsp;Matthew D.J. McGarry ,&nbsp;Philip V. Bayly","doi":"10.1016/j.brain.2024.100091","DOIUrl":"https://doi.org/10.1016/j.brain.2024.100091","url":null,"abstract":"<div><p>Knowledge of the mechanical properties of brain tissue <em>in vivo</em> is essential to understanding the mechanisms underlying traumatic brain injury (TBI) and to creating accurate computational models of TBI and neurosurgical simulation. Brain white matter, which is composed of aligned, myelinated, axonal fibers, is structurally anisotropic. White matter <em>in vivo</em> also exhibits mechanical anisotropy, as measured by magnetic resonance elastography (MRE), but measurements of anisotropy obtained by mechanical testing of white matter <em>ex vivo</em> have been inconsistent. The minipig has a gyrencephalic brain with similar white matter and gray matter proportions to humans and therefore provides a relevant model for human brain mechanics. In this study, we compare estimates of anisotropic mechanical properties of the minipig brain obtained by identical, non-invasive methods in the live (<em>in vivo)</em> and dead animals <em>(in situ)</em>. To do so, we combine wave displacement fields from MRE and fiber directions derived from diffusion tensor imaging (DTI) with a finite element-based, transversely-isotropic nonlinear inversion (TI-NLI) algorithm. Maps of anisotropic mechanical properties in the minipig brain were generated for each animal alive and at specific times post-mortem. These maps show that white matter is stiffer, more dissipative, and more anisotropic than gray matter when the minipig is alive, but that these differences largely disappear post-mortem, with the exception of tensile anisotropy. Overall, brain tissue becomes stiffer, less dissipative, and less mechanically anisotropic post-mortem. These findings emphasize the importance of testing brain tissue properties <em>in vivo.</em></p></div><div><h3>Statement of Significance</h3><p>In this study, MRE and DTI in the minipig were combined to estimate, for the first time, anisotropic mechanical properties in the living brain and in the same brain after death. Significant differences were observed in the anisotropic behavior of brain tissue post-mortem. These results demonstrate the importance of measuring brain tissue properties <em>in vivo</em> as well as <em>ex vivo,</em> and provide new quantitative data for the development of computational models of brain biomechanics.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100091"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522024000029/pdfft?md5=7e0e05f1c8bd35197567d52bddf75b26&pid=1-s2.0-S2666522024000029-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139714852","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":"Alzheimer’s disease and the mathematical mind","authors":"Travis B. Thompson,&nbsp;Bradley Z. Vigil,&nbsp;Robert S. Young","doi":"10.1016/j.brain.2024.100094","DOIUrl":"10.1016/j.brain.2024.100094","url":null,"abstract":"<div><p>Throughout the 19th and 20th centuries, aided by advances in medical imaging, discoveries in physiology and medicine have added nearly 25 years to the average life expectancy. This resounding success brings with it a need to understand a broad range of age-related health conditions, such as dementia. Today, mathematics, neuroimaging and scientific computing are being combined with fresh insights, from animal models, to study the brain and to better understand the etiology and progression of Alzheimer’s disease, the most common cause of age-related dementia in humans. In this manuscript, we offer a brief primer to the reader interested in engaging with the exciting field of mathematical modeling and scientific computing to advance the study of the brain and, in particular, human AD research.</p><p><strong>Statement of Significance</strong> Modeling Alzheimer’s disease is a highly interdisciplinary field and finding an effective starting point can be a considerable challenge. To address this challenge, this manuscript briefly highlights some central components of AD related protein pathology, useful classes of mathematical models for brain and AD research and effective computational resources for the practical prospective practitioner.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100094"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522024000054/pdfft?md5=68c335b8652a866dd25fe222cee1915e&pid=1-s2.0-S2666522024000054-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140770887","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":"A novel framework for video-informed reconstructions of sports accidents: A case study correlating brain injury pattern from multimodal neuroimaging with finite element analysis","authors":"Qiantailang Yuan,&nbsp;Xiaogai Li,&nbsp;Zhou Zhou,&nbsp;Svein Kleiven","doi":"10.1016/j.brain.2023.100085","DOIUrl":"10.1016/j.brain.2023.100085","url":null,"abstract":"<div><p>Ski racing is a high-risk sport for traumatic brain injury. A better understanding of the injury mechanism and the development of effective protective equipment remains central to resolving this urgency. Finite element (FE) models are useful tools for studying biomechanical responses of the brain, especially in real-world ski accidents. However, real-world accidents are often captured by handheld monocular cameras; the videos are shaky and lack depth information, making it difficult to estimate reliable impact velocities and posture which are critical for injury prediction. Introducing novel computer vision and deep learning algorithms offers an opportunity to tackle this challenge. This study proposes a novel framework for estimating impact kinematics from handheld, shaky monocular videos of accidents to inform personalized impact simulations. The utility of this framework is demonstrated by reconstructing a ski accident, in which the extracted kinematics are input to a neuroimaging-informed, personalized FE model. The FE-derived responses are compared with imaging-identified brain injury sites of the victim. The results suggest that maximum principal strain may be a useful metric for brain injury. This study demonstrates the potential of video-informed accident reconstructions combined with personalized FE modeling to evaluate individual brain injury.</p></div><div><h3>Statement of significance</h3><p>Reconstructing real-world sports accidents combined with finite element (FE) models presents a unique opportunity to study brain injuries, as it enables simulating complex loading conditions experienced in reality. However, a significant challenge lies in accurately obtaining kinematics from the often shaky, handheld video footage of such accidents. We propose a novel framework that bridges the gap between real-world accidents and video-informed injury predictions. By integrating video analysis, 3D kinematics estimation, and personalized FE simulation, we extract accurate impact kinematics of a ski accident captured from handheld shaky monocular videos to inform personalized impact simulations, predicting the injury pathology identified by multimodal neuroimaging. This study provides important guidance on how best to estimate impact conditions from video-recorded accidents, opening new opportunities to better inform the biomechanical study of head trauma with improved boundary conditions.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100085"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522023000230/pdfft?md5=da49a10082b02cfc88996ee55d1c0486&pid=1-s2.0-S2666522023000230-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135764268","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":"Segmentation and quantification of venous structures and perivascular spaces in the thalamus in epilepsy using 7 Tesla MRI","authors":"Mackenzie T. Langan ,&nbsp;Gaurav Verma ,&nbsp;Bradley N. Delman ,&nbsp;Lara V. Marcuse ,&nbsp;Madeline C. Fields ,&nbsp;Rebecca Feldman ,&nbsp;Priti Balchandani","doi":"10.1016/j.brain.2023.100089","DOIUrl":"https://doi.org/10.1016/j.brain.2023.100089","url":null,"abstract":"<div><h3>Background and purpose</h3><p>Epilepsy is a complex neurological disorder affecting 50 million people worldwide. Persistent seizures may correlate with neural network, microstructural, and vascular changes within the thalamus. These thalamic changes may result from seizure activity or broader alterations involving neuronal vasculature and neuroinflammatory processes linked to glymphatic drainage. Improved resolution with Ultra-high field (UHF) magnetic resonance imaging (MRI) may be useful in identifying possible thalamic vascular abnormalities not otherwise detectable at lower field strengths.</p></div><div><h3>Materials and methods</h3><p>We outline a novel method which leverages UHF neuroimaging for detection and quantification of vessels and perivascular spaces (PVS) within the thalamus in 25 epilepsy patients and 16 controls, to uncover possible underlying imaging biomarkers of epilepsy. In our analysis, we optimize a MATLAB-based Frangi-based detection tool called Perivascular Space Semi-Automated Segmentation (PVSSAS) to detect thalamic PVSs, and additionally use a second Frangi-based segmentation tool method to automate detection of vascular structures in the thalamus. The resulting PVS and vessel masks were used to quantify differences in the number of vessels, PVS, overlaps, and number of PVS overlaps per vessel detected between groups, using a Hessian detection filter linked on an 18-connected network.</p></div><div><h3>Results</h3><p>We found significantly more thalamic PVS (<em>p</em> = 0.0307) and a significant increase in the number of thalamic vessels (<em>p</em> = 0.038) in patients compared to controls.</p></div><div><h3>Conclusion</h3><p>Here we have developed a novel process which leverages UHF MRI to quantify and detect thalamic vessels and PVS that may provide a potential neuroimaging biomarker of epilepsy.</p></div><div><h3>Statement of Significance</h3><p>We use 7T, ultra-high field MRI and employed an innovative combination of semi-automated perivascular space segmentation and automated vessel segmentation to visualize and quantify vessels and perivascular spaces (PVS) within the thalamus, a highly cited region of interest in epilepsy. To our knowledge, this is the first study to semi-automatically visualize and segment PVS in the thalamus and automatically detect thalamic vessels. We uncovered detectable differences in thalamic vasculature and PVS. These findings suggests that increases in the number of thalamic PVS and vessels may be a potential neuroimaging biomarker in epilepsy. This tool may be useful in the detection of subtle vascular changes in other regions of the brain related to epilepsy or can be employed in other neurological conditions.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100089"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522023000278/pdfft?md5=eab3f02d946851547d097cb1b85c9ede&pid=1-s2.0-S2666522023000278-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139099779","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":"Analysis of the pattern of microstructural changes in the brain after mTBI with diffusion tensor imaging and subject-specific FE models","authors":"Maryam Tayebi ,&nbsp;Eryn Kwon ,&nbsp;Alan Wang ,&nbsp;Justin Fernandez ,&nbsp;Samantha Holdsworth ,&nbsp;Vickie Shim","doi":"10.1016/j.brain.2023.100088","DOIUrl":"10.1016/j.brain.2023.100088","url":null,"abstract":"<div><p>Traumatic brain injury (TBI) is a major public health challenge. Up to 90 % of TBIs are on the mild spectrum of TBI (mTBI), where diagnosis is a major challenge. Majority of studies in this field have been conducted on human subjects, which inherently suffer from the lack of appropriate control group, selection bias, and individual differences in patients. To overcome these limitations, animal studies have been used as an alternative approach to provide deeper insights into the underlying mechanism related to the injury. Therefore our aim is to investigate various quantitative imaging biomarkers acquired from T1-W and diffusion tensor imaging (DTI) sequences to provide more information about the microstructural changes in the brain after mTBI. We then use this to generate subject-specific finite element models of the brain and examine how the changes in the brain material properties reflected in MR images affects strain distribution patterns on a subsequent head hit. Our study revealed a decrease in FA and an increase in diffusivity indices (MD, AD, RD) in the white matter tracts of the brain. This finding may represent the axonal damage, demyelination and gliosis after mild TBI, which have been shown in other animal and human studies. Moreover, our FE analysis showed that microstructural changes in the brain after mTBI might have weakened the structural integrity of the brain as the subsequent head hit led to wider and more severe brain deformations.</p></div><div><h3>Significance</h3><p>Animal models have been used to investigate biomechanical and pathophysiological aspects of mild traumatic brain injuries in the past. Still, most of them used small animals such as rats and mice. These models provided valuable insight into the pathophysiology of mTBI, but their findings have limitations due to their inherent differences to human brains. We have developed a large animal model of mTBI with sheep brains by combining advanced MRI and finite element analysis as they mimic the human brain better. To the best of our knowledge, this study is the first mTBI neuroimaging study conducted on large animal brains to investigate the diffusional changes in the white matter tracts after mTBI. Our FE analysis revealed that such microstructural changes resulted in tissue softening as the extent of brain deformation increased on a subsequent head hit, indicating increased brain vulnerability after head impacts.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"6 ","pages":"Article 100088"},"PeriodicalIF":0.0,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522023000266/pdfft?md5=86759ee9bf43f5af0f8c933aeb46cb77&pid=1-s2.0-S2666522023000266-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138988215","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":"Modal analysis of computational human brain dynamics during helmeted impacts","authors":"Fargol Rezayaraghi ,&nbsp;Javid Abderezaei ,&nbsp;Efe Ozkaya ,&nbsp;Devlin Stein ,&nbsp;Aymeric Pionteck ,&nbsp;Mehmet Kurt","doi":"10.1016/j.brain.2023.100082","DOIUrl":"10.1016/j.brain.2023.100082","url":null,"abstract":"<div><p>Sports-related mild traumatic brain injury (mTBI) is a growing public health concern, affecting millions in the U.S., annually. Current helmets are primarily designed to mitigate head kinematics, despite the importance of the brain substructures mechanics in mTBI mechanism. Therefore, it is crucial to consider the dynamical behavior of brain substructures, which has been shown in prior studies to be associated with strain concentration. Here, we studied the modal behavior and strain patterns of the substructures of the brain finite element (FE) model through Dynamic Mode Decomposition. We conducted side and front impact pendulum tests on a dummy headform equipped with hockey, football, ski, and bicycle helmets. After simulating the impact tests using a brain FE model, we calculated the dynamic modes of this computational model for the whole brain, corpus callosum, brainstem, and cerebellum. The main mode of oscillation in all regions for all helmet types occurred around the frequency regime of 7–15 Hz. Also, in cerebellum, a second harmonic was observed at 40–50 Hz in front impact, and 38 and 62 Hz in side impact in bicycle and ski helmets, respectively. Furthermore, we analyzed the correlation between the modal response and peak maximum principal strain (MPS). These analyses mostly showed a direct association between the computational modal behavior and MPS, where helmet tests with closely spaced modes and high-frequency modal amplitudes led to higher MPS values. This association between the computational modal behavior and strain patterns demonstrated a potential for improving helmet designs through a novel design objective.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"5 ","pages":"Article 100082"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46187431","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Noninvasive, patient-specific computational fluid dynamics simulations of dural venous sinus pressures in idiopathic intracranial hypertension","authors":"Patrick Fillingham ,&nbsp;Swati Rane Levendovszky ,&nbsp;Jalal Andre ,&nbsp;Michael Bindschadler ,&nbsp;Seth Friedman ,&nbsp;Mehmet Kurt ,&nbsp;Alberto Aliseda ,&nbsp;Michael R. Levitt","doi":"10.1016/j.brain.2023.100081","DOIUrl":"10.1016/j.brain.2023.100081","url":null,"abstract":"<div><h3>Background</h3><p>The pathophysiology of Idiopathic Intracranial Hypertension (IIH) is poorly understood, making the disease difficult to properly diagnose and treat. Endovascular venous stenting has emerged as an effective non-invasive treatment option for a select cohort of IIH patients with venous sinus stenosis and elevated venous sinus pressure gradient. Unfortunately, current methods of determining patient eligibility for stenting treatment depend on highly invasive and insufficient measurement methods such as venous manometry, which can only measure pressure gradients and not other components of the complex 3D hemodynamic environment. Thus, there is a need for a non-invasive methodology for determining the 3D flow environment of the dural venous sinuses.</p></div><div><h3>Objective</h3><p>To develop a novel method of non-invasive, patient-specific computational fluid dynamic (CFD) simulation of venous sinus hemodynamics for evaluating stenting eligibility.</p></div><div><h3>Method</h3><p>A patient with IIH and elevated sinus pressure gradient underwent MR venography, phase-contrast MR venography, and venous manometry. Patient-specific dural venous anatomy was segmented from the MR venography to construct 3D models of the venous sinuses. 3D transient patient-specific computational fluid dynamic simulations were conducted using flow velocities measured with phase-contrast MR venography as boundary conditions.</p></div><div><h3>Results</h3><p>Successful computational simulations were completed, allowing for the calculation of the spatio-temporal evolution of blood flow through the dural venous sinuses, and the quantitative examination of pressure gradients. Calculated pressure gradients from CFD were validated against venous manometry with an error of only ∼5%.</p></div><div><h3>Conclusions</h3><p>We have successfully developed time-resolved, patient-specific 3D computational simulations of the dural venous sinuses without assumptions at the boundary conditions for the first time. The methodology can accurately and non-invasively measure venous pressure gradients. This preliminary study serves as a proof of concept for our method to be used as a diagnostic tool for determining venous stenting eligibility, as well as a tool for advancing the general understanding of IIH pathophysiology.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"5 ","pages":"Article 100081"},"PeriodicalIF":0.0,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44886163","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}