NeAT. A nonlinear analysis toolbox for neuroimaging

Published in Neuroinformatics, 2020

Authors: Adrià Casamitjana, Verónica Vilaplana, Santi Puch, Asier Aduriz, Carlos López, Grégory Operto, Raffaele Cacciaglia, Carles Falcón, José Luis Molinuevo, Juan Domingo Gispert



NeAT is a modular, flexible and user-friendly neuroimaging analysis toolbox for modeling linear and nonlinear effects over-coming the limitations of the standard neuroimaging methods which are solely based on linear models. NeAT provides a widerange of statistical and machine learning non-linear methods for model estimation, several metrics based on curve fitting andcomplexity for model inference and a graphical user interface (GUI) for visualization of results. We illustrate its usefulness on twostudy cases where non-linear effects have been previously established. Firstly, we study the nonlinear effects of Alzheimer’sdisease on brain morphology (volume and cortical thickness). Secondly, we analyze the effect of the apolipoprotein APOE-ε4genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at