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00925njm 22002777a 4500 dc Casamitjana Díaz, Adrià author Vilaplana Besler, Verónica author Puch Giner, Santi author Aduriz Saiz, Asier author Operto, Grégory author Cacciaglia, Raffaele author Falcón, Carlos author Molinuevo, José Luis author Gispert, Juan Domingo author López Molina, Carlos Alejandro author 2020-03-25 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-e4 genotype on brain aging and its interaction with age. NeAT is fully documented and publicly distributed at https://imatge-upc.github.io/neat-tool/. This work has been partially supported by the project MALEGRA TEC2016-75976-R financed by the Spanish Ministerio de Economía y Competitividad and the European Regional Development Fund (ERDF). Adrià Casamitjana is supported by the Spanish “Ministerio de Educación, Cultura y Deporte” FPU Research Fellowship. Juan D. Gispert holds a “‘Ramón y Cajal’” fellowship (RYC-2013-13054). Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). As such, the investigators within the ADNI contributed to the design and implementation of ADNI and/or provided data but did not participate in analysis or writing of this report. A complete listing of ADNI investigators can be found at: http://adni.loni.usc.edu/wpcontent/uploads/how to apply/ADNI Acknowledgement List.pdf. Peer Reviewed Postprint (published version) Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial Àrees temàtiques de la UPC::Ciències de la salut::Medicina::Neurologia Machine learning Neurology Nonlinear Neuroimaging GLM GAM SVR Alzheimer's disease Inference APOE Aprenentatge automàtic Neurologia NeAT: a nonlinear analysis toolbox for neuroimaging