通过交互式数字模型探索白质动态和形态:白质生成器

IF 2.5 4区 医学 Q2 MATHEMATICAL & COMPUTATIONAL BIOLOGY Frontiers in Neuroinformatics Pub Date : 2024-07-31 DOI:10.3389/fninf.2024.1354708
Sidsel Winther, Oscar Peulicke, Mariam Andersson, Hans M. Kjer, Jakob A. Bærentzen, Tim B. Dyrby
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引用次数: 0

摘要

脑白质是一个动态的环境,会随着刺激和病理变化而不断适应和重组。尤其是神经胶质细胞,在组织修复、炎症调节和神经恢复中发挥着关键作用。神经胶质细胞的运动及其浓度变化会影响周围轴突的形态。我们引入了白质生成器(WMG)工具,以研究轴突形态如何受到此类动态过程的影响,以及这反过来又如何影响扩散加权磁共振成像信号。通过在整个优化过程中对模型生成的配置进行交互式更改,使这一研究成为可能。模型可由髓鞘轴突、无髓鞘轴突和细胞簇组成,并由细胞外空间分隔。由于形态上的灵活性和优化过程中的计算优势,该工具使用椭圆体作为所有结构的构建模块;轴突使用椭圆体链,细胞簇使用单个椭圆体。优化后,椭圆体表示法可转换为网格表示法,用于蒙特卡洛扩散模拟。这为在受控的生物模拟白质环境中评估用于扩散加权磁共振成像的组织微结构模型提供了一种有效的方法。因此,WMG 为了解白质的适应性及其对扩散加权磁共振成像微结构模型的影响提供了宝贵的见解,从而有望推动各种神经系统疾病和损伤的临床诊断、治疗和康复策略。
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Exploring white matter dynamics and morphology through interactive numerical phantoms: the White Matter Generator
Brain white matter is a dynamic environment that continuously adapts and reorganizes in response to stimuli and pathological changes. Glial cells, especially, play a key role in tissue repair, inflammation modulation, and neural recovery. The movements of glial cells and changes in their concentrations can influence the surrounding axon morphology. We introduce the White Matter Generator (WMG) tool to enable the study of how axon morphology is influenced through such dynamical processes, and how this, in turn, influences the diffusion-weighted MRI signal. This is made possible by allowing interactive changes to the configuration of the phantom generation throughout the optimization process. The phantoms can consist of myelinated axons, unmyelinated axons, and cell clusters, separated by extra-cellular space. Due to morphological flexibility and computational advantages during the optimization, the tool uses ellipsoids as building blocks for all structures; chains of ellipsoids for axons, and individual ellipsoids for cell clusters. After optimization, the ellipsoid representation can be converted to a mesh representation which can be employed in Monte-Carlo diffusion simulations. This offers an effective method for evaluating tissue microstructure models for diffusion-weighted MRI in controlled bio-mimicking white matter environments. Hence, the WMG offers valuable insights into white matter's adaptive nature and implications for diffusion-weighted MRI microstructure models, and thereby holds the potential to advance clinical diagnosis, treatment, and rehabilitation strategies for various neurological disorders and injuries.
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来源期刊
Frontiers in Neuroinformatics
Frontiers in Neuroinformatics MATHEMATICAL & COMPUTATIONAL BIOLOGY-NEUROSCIENCES
CiteScore
4.80
自引率
5.70%
发文量
132
审稿时长
14 weeks
期刊介绍: Frontiers in Neuroinformatics publishes rigorously peer-reviewed research on the development and implementation of numerical/computational models and analytical tools used to share, integrate and analyze experimental data and advance theories of the nervous system functions. Specialty Chief Editors Jan G. Bjaalie at the University of Oslo and Sean L. Hill at the École Polytechnique Fédérale de Lausanne are supported by an outstanding Editorial Board of international experts. This multidisciplinary open-access journal is at the forefront of disseminating and communicating scientific knowledge and impactful discoveries to researchers, academics and the public worldwide. Neuroscience is being propelled into the information age as the volume of information explodes, demanding organization and synthesis. Novel synthesis approaches are opening up a new dimension for the exploration of the components of brain elements and systems and the vast number of variables that underlie their functions. Neural data is highly heterogeneous with complex inter-relations across multiple levels, driving the need for innovative organizing and synthesizing approaches from genes to cognition, and covering a range of species and disease states. Frontiers in Neuroinformatics therefore welcomes submissions on existing neuroscience databases, development of data and knowledge bases for all levels of neuroscience, applications and technologies that can facilitate data sharing (interoperability, formats, terminologies, and ontologies), and novel tools for data acquisition, analyses, visualization, and dissemination of nervous system data. Our journal welcomes submissions on new tools (software and hardware) that support brain modeling, and the merging of neuroscience databases with brain models used for simulation and visualization.
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