Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
Machine Learning
2017-04-20 v3 Computer Vision and Pattern Recognition
Machine Learning
Neurons and Cognition
Applications
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.
Keywords
Cite
@article{arxiv.1612.00667,
title = {Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling},
author = {Santi Puch and Asier Aduriz and Adrià Casamitjana and Veronica Vilaplana and Paula Petrone and Grégory Operto and Raffaele Cacciaglia and Stavros Skouras and Carles Falcon and José Luis Molinuevo and Juan Domingo Gispert},
journal= {arXiv preprint arXiv:1612.00667},
year = {2017}
}
Comments
4 pages + 1 page for acknowledgements and references. NIPS 2016 Workshop on Machine Learning for Health (NIPS ML4HC)