NeAT: A nonlinear neuroimaging analysis toolbox.
Authors: Adrià Casamitjana, Santi Puch, Asier Aduriz, Verónica Vilaplana, Juando Gispert, José Luis Molinuevo

We implemented a new neuroimaging analysis toolbox for modeling linear and nonlinear effects overcoming the limitations of the standard neuroimaging processing tools that perform statistical inference based only on linear models. NeAT is a modular, exible and user-friendly tool via command line interface that provides a range of statistical and machine learning non-linear methods for model estimation. Several metrics based on curve fitting and complexity are used for model inference. The toolbox includes a graphical user interface (GUI) for visualization of results.

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