Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling

December 02, 2016 · Entered Twilight · 🏛 arXiv.org

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Repo contents: .gitignore, .travis.yml, README.md, __init__.py, assets, config, requirements.txt, scripts, setup.py, tests, unittest, vneat-compare_statistical_maps.py, vneat-compute_fitting.py, vneat-compute_latent_maps.py, vneat-compute_statistical_maps.py, vneat-generate_user_parameters.py, vneat-prova.py, vneat-search_hyperparameters.py, vneat-show_curves.py, vneat-show_data_distribution.py, vneat-show_latent_curves.py, vneat-show_latent_visualizer.py, vneat-show_visualizer.py, vneat

Authors Santi Puch, Asier Aduriz, Adrià Casamitjana, Veronica Vilaplana, Paula Petrone, Grégory Operto, Raffaele Cacciaglia, Stavros Skouras, Carles Falcon, José Luis Molinuevo, Juan Domingo Gispert arXiv ID 1612.00667 Category stat.ML: Machine Learning (Stat) Cross-listed cs.CV, cs.LG, q-bio.NC, stat.AP Citations 1 Venue arXiv.org Repository https://github.com/imatge-upc/VNeAT ⭐ 5 Last Checked 4 months ago

Abstract

This paper describes a new neuroimaging analysis toolbox that allows for the modeling of nonlinear effects at the voxel level, overcoming limitations of methods based on linear models like the GLM. We illustrate its features using a relevant example in which distinct nonlinear trajectories of Alzheimer's disease related brain atrophy patterns were found across the full biological spectrum of the disease. The open-source toolbox presented in this paper is available at https://github.com/imatge-upc/VNeAT.