论文标题
将大脑结构连通性分析为连续的随机函数
Analyzing Brain Structural Connectivity as Continuous Random Functions
论文作者
论文摘要
这项工作考虑了一个连续的框架，以表征结构连通性的人群级别的可变性。我们的框架假设观察到的白色物质纤维道终点是由在产品歧管域上定义的潜在随机函数驱动的。为了克服分析这种复杂潜在功能的计算挑战，我们开发了一种有效的算法来构建数据驱动的减少级别功能空间，以表示潜在的连续连接。使用人类Connectome项目中的真实数据，我们表明我们的方法优于采用传统ATLAS结构连通性矩阵的最先进方法，这些方法在连接性分析任务上。我们还展示了如何使用我们的方法来识别与显着组差异相关的皮质表面上的局部区域和连通性模式。代码将在https://github.com/sbci-brain上提供。
This work considers a continuous framework to characterize the population-level variability of structural connectivity. Our framework assumes the observed white matter fiber tract endpoints are driven by a latent random function defined over a product manifold domain. To overcome the computational challenges of analyzing such complex latent functions, we develop an efficient algorithm to construct a data-driven reduced-rank function space to represent the latent continuous connectivity. Using real data from the Human Connectome Project, we show that our method outperforms state-of-the-art approaches applied to the traditional atlas-based structural connectivity matrices on connectivity analysis tasks of interest. We also demonstrate how our method can be used to identify localized regions and connectivity patterns on the cortical surface associated with significant group differences. Code will be made available at https://github.com/sbci-brain.
