Brain multiphysics最新文献

{"title":"Traumatic brain injury or traumatic brain disease: A scientific commentary","authors":"Adedunsola Adewunmi Obasa ,&nbsp;Funmilayo Eniola Olopade ,&nbsp;Sharon Leah Juliano ,&nbsp;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":"2024-03-18","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":"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":"2024-02-06","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":"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":"2024-01-20","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":"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":"2023-12-23","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":"2023-12-19","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":"BMPHI_ Editorial 2024","authors":"","doi":"10.1016/j.brain.2023.100087","DOIUrl":"10.1016/j.brain.2023.100087","url":null,"abstract":"","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"7 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138619819","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":"Neuroimaging, wearable sensors, and blood-based biomarkers reveal hyperacute changes in the brain after sub-concussive impacts","authors":"Carissa Grijalva ,&nbsp;Veronica A. Mullins ,&nbsp;Bryce R. Michael ,&nbsp;Dallin Hale ,&nbsp;Lyndia Wu ,&nbsp;Nima Toosizadeh ,&nbsp;Floyd H. Chilton ,&nbsp;Kaveh Laksari","doi":"10.1016/j.brain.2023.100086","DOIUrl":"https://doi.org/10.1016/j.brain.2023.100086","url":null,"abstract":"<div><p>Impacts in mixed martial arts (MMA) have been studied mainly in regard to the long-term effects of concussions. However, repetitive sub-concussive head impacts at the hyperacute phase (minutes after impact), are not understood. The head experiences rapid acceleration similar to a concussion, but without clinical symptoms. We utilize portable neuroimaging technology – transcranial Doppler (TCD) ultrasound and functional near infrared spectroscopy (fNIRS) – to estimate the extent of pre- and post-differences following contact and non-contact sparring sessions in nine MMA athletes. In addition, the extent of changes in neurofilament light (NfL) protein biomarker concentrations, and neurocognitive/balance parameters were determined following impacts. Athletes were instrumented with sensor-based mouth guards to record head kinematics. TCD and fNIRS results demonstrated significantly increased blood flow velocity (<em>p</em> = 0.01) as well as prefrontal (<em>p</em> = 0.01) and motor cortex (<em>p</em> = 0.04) oxygenation, only following the contact sparring sessions. This increase after contact was correlated with the cumulative angular acceleration experienced during impacts (<em>p</em> = 0.01). In addition, the NfL biomarker demonstrated positive correlations with angular acceleration (<em>p</em> = 0.03), and maximum principal and fiber strain (<em>p</em> = 0.01). On average athletes experienced 23.9 ± 2.9 g peak linear acceleration, 10.29 ± 1.1 rad/s peak angular velocity, and 1,502.3 ± 532.3 rad/s² angular acceleration. Balance parameters were significantly increased following contact sparring for medial-lateral (ML) center of mass (COM) sway, and ML ankle angle (<em>p</em> = 0.01), illustrating worsened balance. These combined results reveal significant changes in brain hemodynamics and neurophysiological parameters that occur immediately after sub-concussive impacts and suggest that the physical impact to the head plays an important role in these changes.</p></div><div><h3>Statement of significance</h3><p>: Brain injuries sustained during sport participation have received much attention since it is a common occurrence among participants. Although protective technologies have been developed over the years, the mechanism of injury is still unclear. There is less focus on the repetitive exposure to sub-concussive impacts on the functional integrity of the brain. Sub-concussive impacts are defined as a lesser impact force resulting in acceleration of the head without symptoms of concussion. Diminished neurocognitive performance has been associated with increased sparring exposure in amateur MMA/boxers suggesting that repeated sub-concussive blows may be just as harmful. However, no one has studied the potential effect of repeated sub-concussive head impacts at the hyperacute level defined as within minutes after impact. We apply novel mobile sensing tools such as head impact sensors and portable neuroimaging devices that allow us ","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"5 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2023-11-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522023000242/pdfft?md5=a8a634b88fb553fe97ba690d24f3e7c9&pid=1-s2.0-S2666522023000242-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138448920","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":"2023-11-14","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":"Predicting the spatio-temporal response of recurrent glioblastoma treated with rhenium-186 labelled nanoliposomes","authors":"Chase Christenson ,&nbsp;Chengyue Wu ,&nbsp;David A. Hormuth II ,&nbsp;Shiliang Huang ,&nbsp;Ande Bao ,&nbsp;Andrew Brenner ,&nbsp;Thomas E. Yankeelov","doi":"10.1016/j.brain.2023.100084","DOIUrl":"https://doi.org/10.1016/j.brain.2023.100084","url":null,"abstract":"<div><p>Rhenium-186 (¹⁸⁶Re) labeled nanoliposome (RNL) therapy for recurrent glioblastoma patients has shown promise to improve outcomes by locally delivering radiation to affected areas. To optimize the delivery of RNL, we have developed a framework to predict patient-specific response to RNL using image-guided mathematical models.</p></div><div><h3>Methods</h3><p>We calibrated a family of reaction-diffusion type models with multi-modality imaging data from ten patients (NCR01906385) to predict the spatio-temporal dynamics of each patient's tumor. The data consisted of longitudinal magnetic resonance imaging (MRI) and single photon emission computed tomography (SPECT) to estimate tumor burden and local RNL activity, respectively. The optimal model from the family was selected and used to predict future growth. A simplified version of the model was used in a leave-one-out analysis to predict the development of an individual patient's tumor, based on cohort parameters.</p></div><div><h3>Results</h3><p>Across the cohort, predictions using patient-specific parameters with the selected model were able to achieve Spearman correlation coefficients (SCC) of 0.98 and 0.93 for tumor volume and total cell number, respectively, when compared to the measured data. Predictions utilizing the leave-one-out method achieved SCCs of 0.89 and 0.88 for volume and total cell number across the population, respectively.</p></div><div><h3>Conclusion</h3><p>We have shown that patient-specific calibrations of a biology-based mathematical model can be used to make early predictions of response to RNL therapy. Furthermore, the leave-one-out framework indicates that radiation doses determined by SPECT can be used to assign model parameters to make predictions directly following the conclusion of RNL treatment.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"5 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2023-10-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666522023000229/pdfft?md5=9f035232901ef898e9f65d2a3f7361a5&pid=1-s2.0-S2666522023000229-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91958665","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":"Human whole-brain models of cerebral blood flow and oxygen transport","authors":"Stephen Payne,&nbsp;Van-Phung Mai","doi":"10.1016/j.brain.2023.100083","DOIUrl":"https://doi.org/10.1016/j.brain.2023.100083","url":null,"abstract":"<div><p>The cerebral vasculature plays a critical role in the transport of oxygen and other nutrients to brain tissue. However, the size, complexity, and paucity of detailed anatomical information of this system makes understanding cerebral behaviour in normal and pathological conditions, as well as its response to stimuli, highly challenging. Whole-brain mathematical models have a valuable role to play in the understanding and measurement of cerebral parameters. However, for the same reasons, whole-brain models are highly complex to construct. In this study, we propose a novel multi-compartment approach to blood flow and oxygen transport. Building on prior models, we propose a new formulation based on a multiple compartment porous medium approach. Using non-dimensional analysis, we derive the most compact form of the equations and constrain the parameter space using clinically measurable quantities, such as baseline perfusion and blood volume. We illustrate the spatially and temporally varying response of the brain by simulating the response to changes in both arterial blood pressure and arterial oxygen saturation, showing that the oxygen response is strongly dependent upon depth, with large but slow responses being found deep in the brain and small but fast responses nearer the surface, whereas the flow response is very rapid in comparison. Blood flow and oxygenation are thus shown to exhibit very different characteristic time scales. This has significant implications for how we consider the response of the brain to external stimuli, such the autoregulation and reactivity responses, and how we model the brain at different time scales.</p></div><div><h3>Statement of Significance</h3><p>In this study we present a new mathematical model for simulations of blood flow and oxygen transport in the human brain. A compact representation is obtained from analysis of the governing equations and different time scales are identified. We show that the behaviour is strongly depth dependent and that 3D models exhibit very different behaviour from simplified 1D models. This will be important in developing further models of the brain, particularly in simulating its active response.</p></div>","PeriodicalId":72449,"journal":{"name":"Brain multiphysics","volume":"5 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2023-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49856610","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}