Brain multiphysics最新文献

Morphogenesis in the central nervous system has received intensive attention as elucidating fundamental mechanisms of morphogenesis will shed light on the physiology and pathophysiology of the developing central nervous system. Morphogenesis of the central nervous system is of a vast topic that includes important morphogenetic events such as neurulation and cortical folding. Here we review three types of methods used to improve our understanding of morphogenesis of the central nervous system: in vivo experiments, organoids (in vivo), and computational models (in silico). The in vivo experiments are used to explore cellular- and tissue-level mechanics and interpret them on the roles of neurulation morphogenesis. Recent advances in human brain organoids have provided new opportunities to study morphogenesis and neurogenesis to compensate for the limitations of in vivo experiments, as organoid models are able to recapitulate some critical neural morphogenetic processes during early human brain development. Due to the complexity and costs of in vivo and in vitro studies, a variety of computational models have been developed and used to explain the formation and morphogenesis of brain structures. We review and discuss the advantages and disadvantages of these methods and their usage in the studies on morphogenesis of the central nervous system. Notably, none of these methods alone is sufficient to unveil the biophysical mechanisms of morphogenesis, thus calling for the interdisciplinary approaches using a combination of these methods in order to test hypotheses and generate new insights on both normal and abnormal development of the central nervous system.

Statement of Significance: The understanding of the central nervous system is essential to provide supports to treat and prevent neurological conditions. Mechanisms of morphogenesis therein can be elucidated from multiple unique perspectives via multidisciplinary approaches. The in vivo experiments, organoid models, and computational modeling are three most effective ways to study brain morphogenesis. In vivo experiments on live animals provide important evidence for studying the roles of mechanical forces in morphogenetic events. The human brain organoid models can greatly assist to study early human brain development and closely simulate the in-vivo counterpart. Moreover, computational models based on physical principles can test hypotheses in conjunctions with experiments to facilitate understanding of the spatial and temporal evolution of these complex structures. The combination of these approaches can complement each other to unveil fundamental mechanisms of the neural morphogenesis and shed light on the development, prevention, and treatment of neurological disorders.

An aneurysm is a medical condition where a section of the artery bulges out under high pressure. Patients suffering from aneurysm rupture have a mortality rate of around 20% and a morbidity rate of up to 40%. The present imaging methods, such as MRI and CT scans, only offer geometrical information on the aneurysm and cannot predict the risk of rupture associated with its progression. To address this gap, a novel computational modeling framework was developed to describe aneurysm growth and analyze the rupture risk under varying pressure loading conditions. The aneurysms were modeled at the vulnerable posterior cerebral artery (PCA) and posterior communicating artery (PCoA), extracted using image segmentation. Five different aneurysm diameters and two wall thicknesses were considered to simulate different phases of aneurysm progression. The realistic pressure loadings on the posterior cerebral arteries were described using three pressures (diastolic, systolic, and hypertensive), and the stress distributions across all models were evaluated to estimate the rupture risk. For PCA, the value of max. von-Mises stress varied between 5.334 MPa and 13.324 MPa for different models with wall thickness of 0.05 mm and from 2.579 MPa to 7.582 MPa for 0.1 mm wall thickness models. For PCoA, the value of max. von-Mises stress ranged from 2.073 MPa to 11.383 MPa for artery-aneurysm models with 0.075 mm thickness and from 2.817 MPa to 10.779 MPa for artery-aneurysm models with 0.15 mm thickness. It was found that the stress values on the aneurysm walls significantly varies with change in blood pressure and aneurysm diameter. An aneurysm with a large diameter and thin wall was also observed to pose a significant risk of rupture, particularly at high blood pressures. These results are expected to provide valuable information to the medical practitioners and help in the prediction of rupture risks using image analysis of aneurysm size and in making timely treatment decisions.

Statement of Significance

The points of significance of our work are:

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  A novel computational modeling framework to evaluate the aneurysm growth and analyze the rupture risk.

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  The realistic pressure loadings conditions (i.e., diastolic, systolic, and hypertensive) of the cardiac cycle were considered and the stress distributions were evaluated to estimate the rupture risk.

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  To date, such extensive research on cerebral aneurysms has not been reported. The results are anticipated to provide valuable information to the medical practitioners in predicting the rupture risks using structural parameters of the aneurysm.

Traumatic brain injury (TBI) is an alarming global public health issue with high morbidity and mortality rates. Although the causal link between external insults and consequent brain injury remains largely elusive, both strain and strain rate are generally recognized as crucial factors for TBI onsets. With respect to the flourishment of strain-based investigation, ambiguity and inconsistency are noted in the scheme for strain rate calculation within the TBI research community. Furthermore, there is no experimental data that can be used to validate the strain rate responses of finite element (FE) models of the human brain. The current work presented a theoretical clarification of two commonly used strain rate computational schemes: the strain rate was either calculated as the time derivative of strain or derived from the rate of deformation tensor. To further substantiate the theoretical disparity, these two schemes were respectively implemented to estimate the strain rate responses from a previous-published cadaveric experiment and an FE head model secondary to a concussive impact. The results clearly showed scheme-dependent responses, both in the experimentally determined principal strain rate and model-derived principal and tract-oriented strain rates. The results highlight that cross-scheme comparison of strain rate responses is inappropriate, and the utilized strain rate computational scheme needs to be reported in future studies. The newly calculated experimental strain rate curves in the supplementary material can be used for strain rate validation of FE head models.

Statement of significance

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  Delineates a theoretical clarification of two algorithms for strain rate computation.

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  Highlights the strain rate responses directly depends on the computational schemes.

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  Presents experimental strain rate curves, serving as references for strain rate validation of finite element head models.

Finite element (FE) brain models have revolutionised research on the biomechanics of traumatic brain injury (TBI). The accuracy and reliability of results arising from brain models depend equally on their geometric accuracy as the quality of the material properties used to describe the mechanical behaviour of brain. However, much of the literature on human brain tissues’ material properties have been performed at low strain rates and strains. This is particularly striking considering a large portion of the brain tissue mechanical characterisation literature is presented with a motivation of understanding brain tissues’ behaviour during TBI which occurs due to brain tissues’ exposure to large strains at high strain rates. Therefore, the aim of this review is to collate the mechanical characterisation studies on human brain tissue under conditions suitable for modelling TBI. We first review injury threshold studies and show that ≥20% strain at ≥10/s strain rate is a reasonable minimum threshold for producing injury to the brain. Using this threshold, we show that there are only five studies on the mechanical characterisation of human brain tissue under strains at strain rates relevant to TBI. These studies, provide material properties of human brain tissue at moderate and high rate loading, with only a recent study showing its region dependent characteristics. This review acts as a reference for scientists and engineers to select suitable material data when modelling human TBI. It also calls for more research to provide high fidelity material properties for modelling of TBI.

Statement of significance

The significance of this work is underscored by the reporting of brain tissues’ material properties in the context of traumatic brain injury (TBI) despite these properties having been characterised under strains and strain rates that are not relevant to TBI. This can result in inaccurate results if implemented in finite element brain models. Here, we address this problem by performing a review on the mechanical characterisation of human brain tissue under conditions that are suitable for modelling human TBI. Our findings show that there are only five studies on the mechanical characterisation of human brain tissue under strains at strain rate levels relevant to TBI. These results will allow researchers to select appropriate material properties for modelling human TBI providing more realistic behaviour of brain tissue in simulations. These results also provide minimum strain and strain rate values for mechanical characterisation experiments on brain tissue for TBI applications. Furthermore, our findings highlight the lack of suitable material properties of human brain tissue for modelling TBI and calls for more research into mechanical characterisation of human brain tissue under large strain at high strain rates.

This paper introduces an orthogonal moment pre-processing method to enhance convolutional neural network outcomes for whole brain image segmentation in magnetic resonance images. The method implements kernel windows based on orthogonal moments to transform the original image into a modified version with orthogonal moment properties. The transformed image contains the optimal representation of the coefficients of the Legendre, Tchebichef and Pseudo-Zernike moments. The approach was evaluated on three distinct datasets; NFBS, OASIS, and TCIA, and obtained an improvement of 4.12%, 1.91%, and 1.05%, respectively. A further investigation employing transfer learning using orthogonal moments of various orders and repetitions, achieved an improvement of 9.86% and 7.76% on the NFBS and OASIS datasets, respectively, when trained using the TCIA dataset. In addition, the best image representations were used to compare different convolutional neural network architectures, including U-Net, U-Net++, and U-Net3+. U-Net3+ demonstrated a slight improvement over U-Net in an overall accuracy of 0.64 % for the original image and 0.33 % for the modified orthogonal moment image.

Statement of Significance

This manuscript introduces a method to initialize convolutional neural network using orthogonal moment filters for whole brain image segmentation in magnetic resonance images. Three orthogonal moments were selected and tests were performed in three distinct datasets. Also, the comparison of three different convolutional neural network (U-Net, U-Net++, and U-Net3+) were conducted. The use of an initial orthogonal moment filter for convolutional neural network in brain segmentation in magnetic resonance imaging achieved an improvement over conventional method. The findings in this study contribute to the long-standing search for the development of a pre-processing technique for whole brain segmentation in MRI.

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 $0.05\text{ms}$ for STN neurons and $0.035\text{ms}$ 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.