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NeAT: a nonlinear analysis toolbox for neuroimaging

Author
Casamitjana, A.; Vilaplana, V.; Puch, S.; Aduriz, A.; Lopez, C.; Operto, G.; Cacciaglia, R.; Falcón, C.; Molinuevo, J.; Gispert, J. D.
Type of activity
Journal article
Journal
Neuroinformatics
Date of publication
2020-03-25
First page
1
Last page
14
DOI
10.1007/s12021-020-09456-w
Project funding
Multimodal Signal Processing and Machine Learning on Graphs
Repository
http://hdl.handle.net/2117/192478 Open in new window
URL
http://link.springer.com/article/10.1007/s12021-020-09456-w Open in new window
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 ...
Citation
Casamitjana, A. [et al.]. NeAT: a nonlinear analysis toolbox for neuroimaging. "Neuroinformatics", 25 Març 2020, p. 1-14.
Keywords
APOE, Alzheimer's disease, GAM, GLM, Inference, Neuroimaging, Nonlinear, SVR
Group of research
GPI - Image and Video Processing Group
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center

Participants

  • Casamitjana Díaz, Adrià  (author)
  • Vilaplana Besler, Veronica  (author)
  • Puch Giner, Santi  (author)
  • Aduriz Saiz, Asier  (author)
  • Lopez Molina, Carlos Alejandro  (author)
  • Operto, Grégory  (author)
  • Cacciaglia, Raffaele  (author)
  • Falcón, Carlos  (author)
  • Molinuevo, José Luis  (author)
  • Gispert, Juan Domingo  (author)

Attachments