Improved brain pattern recovery through ranking approaches
This work addresses the challenge of brain mapping for neuroscience researchers, offering an incremental improvement over existing decoding methods.
The paper tackled the problem of inferring functional specificity of brain regions from fMRI data by proposing a ranking approach to account for non-linearities, showing superiority over linear models in simulation and on a real fMRI dataset.
Inferring the functional specificity of brain regions from functional Magnetic Resonance Images (fMRI) data is a challenging statistical problem. While the General Linear Model (GLM) remains the standard approach for brain mapping, supervised learning techniques (a.k.a.} decoding) have proven to be useful to capture multivariate statistical effects distributed across voxels and brain regions. Up to now, much effort has been made to improve decoding by incorporating prior knowledge in the form of a particular regularization term. In this paper we demonstrate that further improvement can be made by accounting for non-linearities using a ranking approach rather than the commonly used least-square regression. Through simulation, we compare the recovery properties of our approach to linear models commonly used in fMRI based decoding. We demonstrate the superiority of ranking with a real fMRI dataset.