MBV-Pipe:用于跨尺度研究的小鼠脑形态变化评估一站式工具箱

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Wentao Jiang, Xinyi Liu, Ming Song, Zhengyi Yang, Lan Sun, Tianzi Jiang
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引用次数: 0

摘要

小鼠模型对神经科学研究至关重要,但由于样本制备会改变大脑形态,因此宏观和中观尺度之间存在差异。磁共振成像(MRI)数据处理工具箱的缺乏给评估小鼠大脑形态变化带来了挑战。为了解决这个问题,我们开发了 MBV-Pipe(小鼠脑容量统计管道)工具箱,它整合了通过幂级数列代数(DARTEL)进行的差形解剖学注册-基于体素的形态测量(VBM)和基于瓣膜的空间统计(TBSS)方法,用于评估脑组织体积和白质完整性。为了验证 MBV-Pipe 的可靠性,我们使用 9.4T 超高磁共振成像系统采集了七只小鼠在三个时间点(体内、灌注后和固定后)的脑磁共振成像数据。利用 MBV-Pipe 工具箱,我们发现灌注后小鼠大脑的体积相对于体内状态发生了很大变化,而固定过程引起的变化可以忽略不计。重要的是,在整个样本制备过程中,白质的完整性基本保持稳定。MBV-Pipe 的源代码是公开的,包括一个用户友好的图形用户界面,便于质量控制和实验方案优化,有望在未来推动小鼠大脑研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies

MBV-Pipe: A One-Stop Toolbox for Assessing Mouse Brain Morphological Changes for Cross-Scale Studies

Mouse models are crucial for neuroscience research, yet discrepancies arise between macro- and meso-scales due to sample preparation altering brain morphology. The absence of an accessible toolbox for magnetic resonance imaging (MRI) data processing presents a challenge for assessing morphological changes in the mouse brain. To address this, we developed the MBV-Pipe (Mouse Brain Volumetric Statistics-Pipeline) toolbox, integrating the methods of Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL)-Voxel-based morphometry (VBM) and Tract-Based Spatial Statistics (TBSS) to evaluate brain tissue volume and white matter integrity. To validate the reliability of MBV-Pipe, brain MRI data from seven mice at three time points (in vivo, post-perfusion, and post-fixation) were acquired using a 9.4T ultra-high MRI system. Employing the MBV-Pipe toolbox, we discerned substantial volumetric changes in the mouse brain following perfusion relative to the in vivo condition, with the fixation process inducing only negligible variations. Importantly, the white matter integrity was found to be largely stable throughout the sample preparation procedures. The MBV-Pipe source code is publicly available and includes a user-friendly GUI for facilitating quality control and experimental protocol optimization, which holds promise for advancing mouse brain research in the future.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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