Imaging Genetics Center, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA; Laboratory of Neuro Imaging, Institute for Neuroimaging & Informatics, University of Southern California, Los Angeles, CA, USA.
Department of Neurology, University of California Los Angeles, CA, USA.
Neuroimage. 2014 Aug 15;97:284-95. doi: 10.1016/j.neuroimage.2014.04.033. Epub 2014 Apr 18.
We introduce a framework for population analysis of white matter tracts based on diffusion-weighted images of the brain. The framework enables extraction of fibers from high angular resolution diffusion images (HARDI); clustering of the fibers based partly on prior knowledge from an atlas; representation of the fiber bundles compactly using a path following points of highest density (maximum density path; MDP); and registration of these paths together using geodesic curve matching to find local correspondences across a population. We demonstrate our method on 4-Tesla HARDI scans from 565 young adults to compute localized statistics across 50 white matter tracts based on fractional anisotropy (FA). Experimental results show increased sensitivity in the determination of genetic influences on principal fiber tracts compared to the tract-based spatial statistics (TBSS) method. Our results show that the MDP representation reveals important parts of the white matter structure and considerably reduces the dimensionality over comparable fiber matching approaches.
我们介绍了一种基于大脑弥散加权图像的白质束群体分析框架。该框架能够从高角分辨率弥散图像（HARDI）中提取纤维；根据图谱中的先验知识对纤维进行聚类；使用基于最高密度点的路径（最大密度路径；MDP）紧凑地表示纤维束；使用测地线曲线匹配将这些路径一起注册，以在群体中找到局部对应关系。我们在 565 名年轻成年人的 4-Tesla HARDI 扫描上演示了我们的方法，以根据分数各向异性（FA）计算 50 条白质束的局部统计信息。实验结果表明，与基于束的空间统计学（TBSS）方法相比，该方法在确定主要纤维束的遗传影响方面具有更高的灵敏度。我们的结果表明，MDP 表示法揭示了白质结构的重要部分，并大大降低了与可比纤维匹配方法相比的维数。
