Improved brain pattern recovery through ranking approaches

TL;DR

This paper introduces a ranking-based approach to improve the recovery of brain activity patterns from fMRI data, outperforming traditional linear models by capturing non-linear relationships.

Contribution

The paper presents a novel ranking approach for brain pattern recovery that accounts for non-linearities, enhancing decoding accuracy over standard linear methods.

Findings

Ranking approach outperforms linear models in simulations

Superiority demonstrated on real fMRI data

Improved recovery of brain activity patterns

Abstract

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.