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Localization and imaging of white matter fiber crossings in whole mouse brains using diffusion MRI and serial blockface OCT

机译:使用扩散MRI和连续块面血管面大脑在整个鼠脑中的白质纤维交叉的定位和成像

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To obtain an accurate representation of a brain structural connectivity, diffusion MRI and fiber tracking depend on a good understanding of white matter fiber structures. Although the tracking methods work well when performed in single orientation fiber bundles, most methods arc limited in more- complex rases, especially to take into account crossing, fanning, and kissing fibers. A recent international fiber tracking challenge concluded that most tracking algorithms generated 4-5 times more false positive tracks than true tracks on average. This was attributed in large part to a lack of knowledge about the fiber crossing geometry. There is thus a dire need to study more complex fiber geometries to improve the tractography algorithms, for example by classifying those geometries into characteristic crossing topologies (e.g., fanning, curving, bottleneck, pure crossing, …). Here, we propose a multimodal neuroimaging pipeline to identify and acquire fiber crossing areas in whole mouse brains. Our method uses the Allen Mouse Brain connectivity atlas and tractogram analysis using diffusion MRI techniques to identify candidate regions of interests containing fiber crossings based on two predetermined retrograde viral injection site locations. Based on serial OCT acquisitions, we confirmed the location of crossings. Further experiments will validate in detail the structural nature of crossings using retrograde injections of fluorescent tracers and whole mouse brain serial blockface histology. We believe that this new methodological approach will provide indispensable data for the development of a new generation of tractography algorithms that better resolve complex fiber geometries.
机译:为了获得脑结构连通性的准确表示,扩散MRI和纤维跟踪取决于对白质纤维结构的良好理解。尽管跟踪方法在单方向光纤束中执行时工作良好,但大多数方法在更复杂的rases中受到限制,尤其是考虑到交叉,扇动和亲吻纤维。最近的国际纤维跟踪挑战结论认为,大多数跟踪算法通常会产生4-5倍的假轨道,而不是平均真实轨道。这在很大程度上归因于对光纤交叉几何形状的缺乏知识。因此,脚步需要研究更复杂的纤维几何形状以改善牵引算法,例如通过将这些几何形状分类为特征交叉拓扑(例如,扇动,弯曲,瓶颈,纯支,......)。在这里,我们提出了一种多模式神经影像道,以识别和获取全鼠大脑中的光纤交叉区域。我们的方法使用扩散MRI技术使用艾伦小鼠脑连接地图集和牵引分析,以识别基于两个预定的逆行病毒注射部位的含有纤维交叉的候选物的候选地区。基于串行OCT收购,我们确认了过境点的位置。进一步的实验将详细验证交叉的结构性质使用逆行注射荧光示踪剂和整个小鼠脑连杆表面组织学。我们认为,这种新的方法方法将为开发新一代牵引算法提供不可或缺的数据,以更好地解决复杂的纤维几何形状。

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