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Abstract：
Analysis and interpretation of neuroimaging datasets has become a multidisciplinary endeavor, relying not only on statistical methods, but increasingly on associations with respect to other brain-derived features such as gene expression, histological data, and functional as well as cognitive architectures. Here, we introduce BrainStat - a toolbox for (i) univariate and multivariate linear models in volumetric and surface-based brain imaging datasets, and (ii) multidomain feature association of results with respect to spatial maps of post-mortem gene expression and histology, task-based fMRI meta-analysis, as well as resting-state fMRI motifs across several common surface templates. The combination of statistics and feature associations into a turnkey toolbox streamlines analytical processes and accelerates cross-modal research. The toolbox is implemented in both Python and MATLAB, two widely used programming languages in the neuroimaging and neuroinformatics communities. BrainStat is openly available and complemented by an expandable documentation.
摘要：
神经影像数据集的分析和解释已成为多学科的努力，不仅依靠统计方法，但越来越多地与其他脑源性特征如基因表达相关，组织学数据,和功能以及认知架构。这里,我们引入BrainStat-一个工具箱，用于（i）基于体积和表面的脑成像数据集中的单变量和多变量线性模型，和（ii）关于死后基因表达和组织学的空间图的结果的多域特征关联，基于任务的功能磁共振成像荟萃分析，以及几种常见表面模板上的静息状态功能磁共振成像基序。将统计数据和特征关联结合到交钥匙工具箱中，简化了分析过程并加速了跨模式研究。工具箱在Python和MATLAB中实现，在神经成像和神经信息学领域广泛使用的两种编程语言。BrainStat是公开可用的，并由可扩展的文档补充。
