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).
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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/.
| 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 |
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