发表机构
University of Göttingen; Friedrich-Alexander University Erlangen-Nürnberg; Universitätsklinikum Erlangen（哥廷根大学; 埃尔朗根-纽伦堡大学; 埃尔朗根大学附属医院）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出一个基于回归的统一框架，利用Wendland径向基函数和梯度提升直接建模皮层表面活动，提供空间分辨的效应估计，并在模拟和实验数据中优于聚类方法。
AI 中文摘要
基于聚类的置换检验广泛用于分析MEG数据，尽管其仅限于聚类级推断，且无法提供空间分辨的效应估计。我们提出一个基于回归的框架，直接对皮层表面的大脑活动进行建模。空间效应使用Wendland径向基函数表示，并采用基于模型的梯度提升进行数据驱动的局部激活模式选择与估计。这产生了可解释的、空间分辨的效应估计，同时减轻了对大量多重检验校正的需求。在一项具有异质信号结构的模拟研究中，所提方法能够恢复使用基于聚类的方法难以检测的局部效应。对实验性MEG数据的应用进一步展示了其揭示空间特定激活模式的能力。总体而言，该统一框架在统一的统计设置中为皮层表面数据建模提供了一种灵活且可解释的方法。
英文摘要
Cluster-based permutation tests are widely used for analyzing MEG data, even though they are limited to cluster-level inference and do not provide spatially resolved effect estimates. We propose a regression-based framework for modeling brain activity directly on the cortical surface. Spatial effects are represented using Wendland radial basis functions, and model-based gradient boosting is employed for data-driven selection and estimation of localized activation patterns. This yields interpretable, spatially resolved effect estimates while mitigating the need for extensive multiple testing correction. In a simulation study with heterogeneous signal structures, the proposed approach recovers localized effects that are difficult to detect using cluster-based methods. An application to experimental MEG data further illustrates its ability to reveal spatially specific activation patterns. Overall, this unified framework provides a flexible and interpretable approach for modeling cortical surface data within a unified statistical setting.
