Perens Johanna, Salinas Casper Gravesen, Roostalu Urmas, Skytte Jacob Lercke, Gundlach Carsten, Hecksher-Sørensen Jacob, Dahl Anders Bjorholm, Dyrby Tim B
Gubra ApS, Hørsholm, Denmark.
Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark.
Neuroinformatics. 2023 Apr;21(2):269-286. doi: 10.1007/s12021-023-09623-9. Epub 2023 Feb 21.
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.
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侵入性三维成像的技术。一般来说,将这两种模式的互补信息结合起来,对于研究神经科学、疾病进展和药物疗效是很有必要的。尽管这两种技术都依赖图谱映射进行定量分析,但由于组织透明化造成的形态变化以及原始数据集的巨大规模,将LSFM记录的数据转换为MRI模板变得很复杂。因此,迫切需要能够促进将LSFM记录的大脑快速准确地转换为体内无畸变模板的工具。在本研究中,我们开发了一个双向多模态图谱框架,该框架包括基于两种成像模式的脑模板、来自艾伦通用坐标框架的区域划分,以及一个源自颅骨的立体定向坐标系。该框架还提供了用于双向转换使用MR或LSFM(iDISCO透明化)小鼠脑成像获得的结果的算法,而该坐标系使用户能够轻松地在不同的脑模板之间分配体内坐标。