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Multiscale and multimodal fusion of tract-tracing and DTI-derived fibers in macaque brains

机译:猕猴脑中束线和DTI衍生纤维的多尺度和多峰融合

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Assessment of structural connectivity patterns of macaque brains may hold the key to understanding mechanism of cortical convolution and brain function. Therefore, lots of interests have been attracted to analyze axonal pathways via up-to-date techniques, such as tract-tracing data, which is taken as `gold standard' to estimate trustworthy meso-scale pathways and diffusion MRI (e.g., DTI), from which macroscale brain connectivity map can be reconstructed. In this paper, we for the first time propose a framework to take advantages of the two modalities to identify cross-validated connections and construct corresponding dMRI fiber bundle atlas. This framework is conducted on a whole-brain-connectivity base by fusing information from dMRI derived connectivity maps and tract-tracing connectivity maps derived from CoCoMac database, in which tract-tracing reports are collated across a large research community and the tract-tracing connectivity maps are inferred in a meta-analysis fashion. We demonstrate the effectiveness of the framework by a variety of experiments.
机译:猕猴大脑的结构连通性模式的评估可能是理解皮质回旋和大脑功能机制的关键。因此,通过最新技术(例如,道追踪数据)被用来作为“金标准”来估计可信赖的中尺度路径和弥散MRI(例如DTI),吸引了很多兴趣来分析轴突路径。 ,从中可以重建宏观大脑连接图。在本文中,我们首次提出了一个框架,以利用两种方法的优势来识别交叉验证的连接并构建相应的dMRI纤维束图集。该框架通过融合来自dMRI的连通图和来自CoCoMac数据库的道追踪连接图的信息,在全脑连通性基础上进行,其中,在一个大型研究社区和道追踪连接中整理道追踪报告。通过荟萃分析推断出地图。我们通过各种实验证明了该框架的有效性。

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