NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
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ORIGINAL ARTICLE
NeAT: a Nonlinear Analysis Toolbox for Neuroimaging Adrià Casamitjana 1 & Verónica Vilaplana 1 & Santi Puch 2 & Asier Aduriz 3 & Carlos López 1 & Grégory Operto 4 & Raffaele Cacciaglia 4 & Carles Falcón 4,5 & José Luis Molinuevo 4,6,7,8 & Juan Domingo Gispert 4,7,8,9 & for the Alzheimer’s Disease Neuroimaging Initiative
# The Author(s) 2020
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-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/. Keywords nonlinear . neuroimaging . GLM . GAM . SVR . Alzheimer's disease . inference . APOE
Introduction The increase of computational power and advances in neuroimaging acquisition that enable faster scans and provide multiple image contrasts and modalities has motivated the
development of complex modeling techniques for imaging data. An armoury of neuroimaging analysis tools is available to the neuroscientific community, whose ultimate goal is to conduct statistical tests to identify significant effects in the images without any a priori hypothesis on the location or
Alzheimer’s Disease Neuroimaging Initiative (ADNI) is a Group/ Institutional Author. Data used in preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.ucla.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.ucla.edu/ wpcontent/uploads/how_to_apply/ADN I_Acknowledgement_List.pdf * Verónica Vilaplana [email protected]
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Alzheimer’s Disease and Other Cognitive Disorders Unit, Hospital Clínic, Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
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CIBER Fragilidad y Envejecimiento Saludable (CIBERFES), Madrid, Spain
Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), Barcelona, Spain
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Universitat Pompeu Fabra, Barcelona, Spain
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QMENTA, Barcelona, Spain
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Vilynx, Barcelona, Spain
IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain
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BarcelonaBeta Brain Research Center (BBRC), Pasqual Maragall Foundation, Barcelona
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