Voxelwise nonlinear regression toolbox for neuroimage analysis: Application to aging and neurodegenerative disease modeling
This provides a tool for researchers in neuroimaging to analyze complex nonlinear patterns in brain data, particularly for aging and neurodegenerative diseases, but it is incremental as it builds on existing regression methods.
The authors developed a neuroimaging toolbox for voxelwise nonlinear regression to model nonlinear effects in brain data, overcoming linear model limitations, and demonstrated its application by identifying distinct nonlinear trajectories of Alzheimer's disease-related brain atrophy across the disease spectrum.
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.