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NeAT: a nonlinear analysis toolbox for neuroimaging

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Casamitjana Díaz, AdriàMés informacióMés informacióMés informació
Vilaplana Besler, VerónicaMés informacióMés informacióMés informació
Puch Giner, Santi
Aduriz Saiz, Asier
Operto, Grégory
Cacciaglia, Raffaele
Falcón, Carlos
Molinuevo, José Luis
Gispert, Juan Domingo
López Molina, Carlos AlejandroMés informacióMés informació
Document typeArticle
Defense date2020-03-25
Rights accessOpen Access
Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain
Abstract
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/.
CitationCasamitjana, A. [et al.]. NeAT: a nonlinear analysis toolbox for neuroimaging. "Neuroinformatics", 25 Març 2020, p. 1-14. 
URIhttp://hdl.handle.net/2117/192478
DOI10.1007/s12021-020-09456-w
ISSN1539-2791
Publisher versionhttp://link.springer.com/article/10.1007/s12021-020-09456-w
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