发表机构
Duke University; University of South Carolina; University of Wisconsin-Madison; University of North Carolina at Chapel Hill（杜克大学; 南卡罗来纳大学; 威斯康星大学麦迪逊分校; 北卡罗来纳大学教堂山分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出基于对数高斯 Cox 过程的贝叶斯框架，用于连续脑连接的不确定性量化估计，其预测保留流线的效果优于核平滑。
AI 中文摘要
连续结构连接通过强度函数描述皮层表面任意两点间的连接关系。现有方法基于纤维束成像流线的端点，大多采用核平滑估计该函数，仅能给出点估计。本文提出一种用于连续连接估计的贝叶斯框架，可量化不确定性：将映射到两个球体的纤维束成像流线端点建模为球体乘积空间上的对数高斯 Cox 过程，采用 Matérn 高斯过程先验。借助先验的有限元表示，后验的拉普拉斯近似可实现精细网格上的计算，每对半球含超过 40 万个潜在变量。数值实验验证了该方法点估计的准确性及可信区间的覆盖率；在青少年脑认知发展研究中，该方法对保留流线的预测效果优于核平滑。
英文摘要
Continuous structural connectivity describes the connection between any two points of the cortical surface by an intensity function. Based on the endpoints of tractography streamlines, existing methods estimate this function mostly by kernel smoothing, which only gives a point estimate. We propose a Bayesian framework that estimates continuous connectivity with uncertainty quantification. The endpoints of tractography streamlines, mapped to two spheres, are modeled by a log-Gaussian Cox process on the product of the spheres with a Matérn Gaussian process prior. With the finite element representation of the prior, the Laplace approximation of the posterior enables the computation on a fine mesh with over 400,000 latent variables per hemisphere pair. Numerical experiments show the accuracy of the point estimate of this approach and the coverage of its credible intervals. In the Adolescent Brain Cognitive Development Study, the proposed method predicts held-out streamlines better than kernel smoothing.
Comments28 pages, 2 figures
