The cerebral cortex is fundamental to the functioning of the mind and body. In vivo cortical morphology can be studied through magnetic resonance imaging in several ways, including reconstructing surface-based models of the cortex. However, existing software for surface-based statistical analyses cannot accommodate “big data” or commonly-used statistical methods such as the imputation of missing data, extensive bias correction, and non-linear modeling. To address these shortcomings, we developed the QDECR package, a flexible and extensible R package for group-level statistical analysis of cortical morphology. QDECR was written with large population-based epidemiological studies in mind and was designed to fully utilize the extensive modeling options in R. QDECR currently supports vertex-wise linear regression. Design matrix generation can be done through simple, familiar R formula specification, and includes user-friendly extensions for R options such as polynomials, splines, interactions and other terms. QDECR can handle unimputed and imputed datasets with thousands of participants. QDECR has a modular design, and new statistical models can be implemented which utilize several aspects from other generic modules which comprise QDECR. In summary, QDECR provides a framework for vertex-wise surface-based analyses that enables flexible statistical modeling and features commonly used in population-based and clinical studies, which have until now been largely absent from neuroimaging research.

中文翻译：

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QDECR：R 中灵活、可扩展的顶点分析框架

大脑皮层是身心功能的基础。体内皮质形态可以通过磁共振成像以多种方式进行研究，包括重建基于表面的皮质模型。然而，现有的基于表面的统计分析软件无法适应“大数据”或常用的统计方法，例如缺失数据的插补、广泛的偏差校正和非线性建模。为了解决这些缺点，我们开发了 QDECR 包，这是一个灵活且可扩展的 R 包，用于皮层形态的组级统计分析。QDECR 是在编写时考虑到基于大规模人群的流行病学研究，旨在充分利用 R 中的广泛建模选项。QDECR 目前支持逐点线性回归。设计矩阵生成可以通过简单、熟悉的 R 公式规范来完成，并包括 R 选项的用户友好扩展，例如多项式、样条曲线、交互作用和其他术语。QDECR 可以处理具有数千名参与者的未插补和插补数据集。QDECR 采用模块化设计，可以利用构成 QDECR 的其他通用模块的多个方面来实现新的统计模型。总之，QDECR 提供了基于顶点的基于表面的分析框架，可实现基于人群和临床研究中常用的灵活统计建模和功能，而这些功能迄今为止在神经影像学研究中基本上不存在。