高斯图模型在功能连接分析中的应用：一项关于阿尔茨海默病的统计回顾
Gaussian Graphical Models for Functional Connectivity Analysis: A Statistical Review with Applications to Alzheimer's Disease
AI总结：
本文回顾了高斯图模型在功能连接分析中的应用，评估了多种方法在神经影像学中的性能，并应用于阿尔茨海默病研究，提供R包spice以提高研究可重复性。
摘要：
功能连接分析是表征脑区相互作用的重要工具，尤其在神经退行性疾病如阿尔茨海默病的研究中。高斯图模型（GGMs）提供了一种估计功能连接的统计框架，通过捕捉脑区间的条件依赖关系。尽管已提出多种正则化精度矩阵估计器来估计稀疏条件依赖结构，但它们的相对性能和在神经影像学研究中的实际意义尚不明确。本文综述了广泛使用的GGM估计方法，包括图形Lasso（glasso）、基于岭的glasso、图形弹性网、自适应glasso、平滑剪切绝对偏差（SCAD）、最小最大凹惩罚（MCP）、约束ℓ1最小化逆矩阵估计（CLIME）以及无调参图估计与回归（TIGER）。通过设计反映真实神经影像学设置的大量数据驱动模拟，以及应用于阿尔茨海默病队列研究，以展示方法学差异及其对下游网络分析的影响。此外，提供了一个用户友好的R包spice，以促进实现并提高实证研究的可重复性。
英文摘要：
Functional connectivity analysis is an important tool for characterizing interactions among brain regions, particularly in studies of neurodegenerative disorders such as Alzheimer's disease (AD). Gaussian graphical models (GGMs) provide a promising statistical framework for estimating functional connectivity by capturing conditional dependence relationships among brain regions. Although a variety of regularized precision matrix estimators have been proposed to estimate sparse conditional dependency structures for GGMs, their comparative performance and practical implications for neuroimaging studies are not well understood. In this work, we present a comprehensive statistical review and empirical evaluation of widely used GGM estimation methods, including the graphical lasso (glasso), ridge-based glasso, graphical elastic net, adaptive glasso, smoothly clipped absolute deviation (SCAD), minimax concave penalty (MCP), constrained $\ell_1$ minimization for inverse matrix estimation (CLIME), and tuning-insensitive graph estimation and regression (TIGER). Their performance is evaluated through extensive data-driven simulations designed to reflect realistic neuroimaging settings, along with an application to an AD cohort study to illustrate methodological differences and their impact on downstream network analysis. In addition, a user-friendly R package, spice, is provided to facilitate implementation and enhance the reproducibility of empirical studies.
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