Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain.
QMENTA, Barcelona, Spain.
Neuroinformatics. 2020 Oct;18(4):517-530. doi: 10.1007/s12021-020-09456-w.
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学分析工具包，用于对线性和非线性效应进行建模，克服了仅基于线性模型的标准神经影像学方法的局限性。NeAT 提供了广泛的统计和机器学习非线性方法用于模型估计、基于曲线拟合和复杂性的多种指标用于模型推断，以及用于结果可视化的图形用户界面 (GUI)。我们在两个先前已确定存在非线性效应的研究案例中说明了其有用性。首先，我们研究了阿尔茨海默病对脑形态（体积和皮质厚度）的非线性影响。其次，我们分析了载脂蛋白 APOE-ε4 基因型对大脑老化的影响及其与年龄的相互作用。NeAT 有完整的文档记录，并在 https://imatge-upc.github.io/neat-tool/ 上公开发布。
