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Sheet Probability Index (SPI): Characterizing the geometrical organization of the white matter with diffusion MRI

机译:纸片概率指数(Sheet Probability IndexSPI):利用扩散MRI表征白质的几何组织

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摘要

The question whether our brain pathways adhere to a geometric grid structure has been a popular topic of debate in the diffusion imaging and neuroscience society. proposed that the brain’s white matter is organized like parallel sheets of interwoven pathways. concluded that this grid pattern is most likely an artifact, resulting from methodological biases that cause the tractography pathways to cross in orthogonal angles. To date, ambiguities in the mathematical conditions for a sheet structure to exist (e.g. its relation to orthogonal angles) combined with the lack of extensive quantitative evidence have prevented wide acceptance of the hypothesis. In this work, we formalize the relevant terminology and recapitulate the condition for a sheet structure to exist. Note that this condition is not related to the presence or absence of orthogonal crossing fibers, and that sheet structure is defined formally as a surface formed by two sets of interwoven pathways intersecting at arbitrary angles within the surface. To quantify the existence of sheet structure, we present a novel framework to compute the sheet probability index (SPI), which reflects the presence of sheet structure in discrete orientation data (e.g. fiber peaks derived from diffusion MRI). With simulation experiments we investigate the effect of spatial resolution, curvature of the fiber pathways, and measurement noise on the ability to detect sheet structure. In real diffusion MRI data experiments we can identify various regions where the data supports sheet structure (high SPI values), but also areas where the data does not support sheet structure (low SPI values) or where no reliable conclusion can be drawn. Several areas with high SPI values were found to be consistent across subjects, across multiple data sets obtained with different scanners, resolutions, and degrees of diffusion weighting, and across various modeling techniques. Under the strong assumption that the diffusion MRI peaks reflect true axons, our results would therefore indicate that pathways do not form sheet structures at every crossing fiber region but instead at well-defined locations in the brain. With this framework, sheet structure location, extent, and orientation could potentially serve as new structural features of brain tissue. The proposed method can be extended to quantify sheet structure in directional data obtained with techniques other than diffusion MRI, which is essential for further validation.
机译:在扩散成像和神经科学社会中,我们的大脑通路是否遵循几何网格结构一直是一个热门话题。他提出,大脑的白质的组织方式类似于相互交织的平行通道。得出的结论是,这种网格图案很可能是伪影,这是由于方法学上的偏见引起的,而这些偏见导致了物镜检查路径以正交角度交叉​​。迄今为止,片状结构存在的数学条件中的模棱两可(例如,其与正交角的关系),加上缺乏广泛的定量证据,阻止了对该假设的广泛接受。在这项工作中,我们对相关术语进行了形式化,并概括了存在薄板结构的条件。注意,该条件与正交纤维的存在与否无关,并且片状结构形式上被定义为由在表面内以任意角度相交的两组交织路径形成的表面。为了量化薄片结构的存在,我们提出了一种计算薄片概率指数(SPI)的新颖框架,该指数反映了离散方向数据中薄片结构的存在(例如,来自扩散MRI的纤维峰)。通过模拟实验,我们研究了空间分辨率,光纤路径的曲率和测量噪声对检测薄板结构的影响。在实际扩散MRI数据实验中,我们可以识别出数据支持图纸结构(高SPI值)的各个区域,还可以识别出数据不支持图纸结构(低SPI值)或无法得出可靠结论的区域。发现多个具有较高SPI值的区域在对象之间,在使用不同扫描仪,分辨率和扩散加权程度获得的多个数据集之间以及在各种建模技术之间是一致的。在强有力的假设下,弥散MRI峰反映了真实的轴突,因此我们的结果将表明,该通路并非在每个交叉纤维区域形成薄片结构,而是在大脑中定义明确的位置形成。在这种框架下,片状结构的位置,范围和方向可能会成为脑组织的新结构特征。所提出的方法可以扩展以量化通过扩散MRI以外的技术获得的定向数据中的薄片结构,这对于进一步验证至关重要。

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