NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a wide range of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting and complexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on two study cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’s disease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/.

NeAT是一个模块化，灵活且用户友好的神经影像分析工具箱，用于建模线性和非线性效应，克服了仅基于线性模型的标准神经影像方法的局限性。NeAT提供了广泛的用于模型估计的统计和机器学习非线性方法，基于曲线拟合和模型推理复杂性的多个度量标准以及用于结果可视化的图形用户界面（GUI）。我们在先前已经建立了非线性影响的两个研究案例中说明了其有效性。首先，我们研究了阿尔茨海默氏病对大脑形态（体积和皮层厚度）的非线性影响。其次，我们分析了载脂蛋白APOE-ε4基因型对脑衰老及其与年龄的相互作用的影响。NeAT已被充分记录并在以下位置公开发布https://imatge-upc.github.io/neat-tool/。