Moyer Daniel, Gutman Boris A, Faskowitz Joshua, Jahanshad Neda, Thompson Paul M
Imaging Genetics Center, University of Southern California.
Med Image Comput Comput Assist Interv. 2016 Oct;9900:157-165. doi: 10.1007/978-3-319-46720-7_19. Epub 2016 Oct 2.
We present a continuous model for structural brain connectivity based on the Poisson point process. The model treats each stream-line curve in a tractography as an observed event in connectome space, here a product space of cortical white matter boundaries. We approximate the model parameter via kernel density estimation. To deal with the heavy computational burden, we develop a fast parameter estimation method by pre-computing associated Legendre products of the data, leveraging properties of the spherical heat kernel. We show how our approach can be used to assess the quality of cortical parcellations with respect to connectivty. We further present empirical results that suggest the "discrete" connectomes derived from our model have substantially higher test-retest reliability compared to standard methods.
我们提出了一种基于泊松点过程的大脑结构连通性连续模型。该模型将纤维束成像中的每条流线曲线视为连接组空间中的一个观测事件，这里的连接组空间是皮质白质边界的乘积空间。我们通过核密度估计来近似模型参数。为了应对繁重的计算负担，我们利用球面热核的性质，通过预先计算数据的相关勒让德积，开发了一种快速参数估计方法。我们展示了如何使用我们的方法来评估皮质分割在连通性方面的质量。我们还给出了实证结果，表明与标准方法相比，从我们的模型中导出的“离散”连接组具有显著更高的重测信度。
