UCL Discovery
UCL home » Library Services » Electronic resources » UCL Discovery

NeAT: a Nonlinear Analysis Toolbox for Neuroimaging

Casamitjana, A; Vilaplana, V; Puch, S; Aduriz, A; López, C; Operto, G; Cacciaglia, R; ... Alzheimer’s Disease Neuroimaging Initiative; + view all (2020) NeAT: a Nonlinear Analysis Toolbox for Neuroimaging. Neuroinformatics 10.1007/s12021-020-09456-w. (In press). Green open access

[thumbnail of Casamitjana2020_Article_NeATANonlinearAnalysisToolboxF.pdf]
Casamitjana2020_Article_NeATANonlinearAnalysisToolboxF.pdf - Published Version

Download (7MB) | Preview


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/.

Type: Article
Title: NeAT: a Nonlinear Analysis Toolbox for Neuroimaging
Location: United States
Open access status: An open access version is available from UCL Discovery
DOI: 10.1007/s12021-020-09456-w
Publisher version: https://doi.org/10.1007/s12021-020-09456-w
Language: English
Additional information: This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Keywords: APOE, Alzheimer's disease, GAM, GLM, SVR, inference, neuroimaging, nonlinear
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Med Phys and Biomedical Eng
URI: https://discovery.ucl.ac.uk/id/eprint/10094394
Downloads since deposit
Download activity - last month
Download activity - last 12 months
Downloads by country - last 12 months

Archive Staff Only

View Item View Item