基于颅骨坐标系统的多模态三维小鼠脑图谱框架。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2023-04-01 DOI:10.1007/s12021-023-09623-9
Johanna Perens, Casper Gravesen Salinas, Urmas Roostalu, Jacob Lercke Skytte, Carsten Gundlach, Jacob Hecksher-Sørensen, Anders Bjorholm Dahl, Tim B Dyrby
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引用次数: 2

摘要

磁共振成像(MRI)和光片荧光显微镜(LSFM)是能够对整个小鼠大脑进行非破坏性三维成像的技术。两种方式的互补信息的组合对于研究神经科学的总体、疾病进展和药物疗效是可取的。尽管这两种技术都依赖于图谱绘制来进行定量分析,但由于组织清理造成的形态学变化和原始数据集的巨大规模,LSFM记录数据到MRI模板的翻译一直很复杂。因此,对于将LSFM记录的大脑快速准确地翻译为体内非扭曲模板的工具的需求尚未得到满足。在这项研究中,我们开发了一个双向多模态图谱框架,其中包括基于两种成像模式的大脑模板,来自艾伦共同坐标框架的区域描绘,以及头骨衍生的立体坐标系统。该框架还提供了使用MR或LSFM (iDISCO清除)小鼠脑成像获得的结果的双向转换算法,而坐标系统使用户能够轻松地在不同的脑模板之间分配体内坐标。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

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Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System.

Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.

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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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