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

Published in NIPS Workshop Machine Learning for Health (ML4H), 2017

Authors: Santi Puch, Asier Aduriz, Adrià Casamitjana, Verónica Vilaplana, Paula Petrone, Grégory Operto, Raffaele Cacciaglia, Stavros Skouras, Carles Falcón, José Luis Molinuevo, and Juan Domingo Gispert



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 is available in GitHub: https://github.com/imatge-upc/VNeAT.