Encoding and decoding V1 fMRI responses to natural images with sparse nonparametric models

TL;DR

This paper develops a sparse nonparametric encoding model for fMRI responses in the human visual cortex, improving prediction accuracy and image decoding performance by capturing nonlinearities in brain responses to natural images.

Contribution

It introduces a novel sparse nonparametric method combined with correlation screening for modeling nonlinear fMRI responses, outperforming previous models in accuracy.

Findings

Predicts 25% more accurately than previous models

Improves image decoding accuracy by 12% among 11,500 images

Reveals systematic voxel nonlinearities with biological plausibility

Abstract

Functional MRI (fMRI) has become the most common method for investigating the human brain. However, fMRI data present some complications for statistical analysis and modeling. One recently developed approach to these data focuses on estimation of computational encoding models that describe how stimuli are transformed into brain activity measured in individual voxels. Here we aim at building encoding models for fMRI signals recorded in the primary visual cortex of the human brain. We use residual analyses to reveal systematic nonlinearity across voxels not taken into account by previous models. We then show how a sparse nonparametric method [J. Roy. Statist. Soc. Ser. B 71 (2009b) 1009-1030] can be used together with correlation screening to estimate nonlinear encoding models effectively. Our approach produces encoding models that predict about 25% more accurately than models estimated using other methods [Nature 452 (2008a) 352--355]. The estimated nonlinearity impacts the inferred properties of individual voxels, and it has a plausible biological interpretation. One benefit of quantitative encoding models is that estimated models can be used to decode brain activity, in order to identify which specific image was seen by an observer. Encoding models estimated by our approach also improve such image identification by about 12% when the correct image is one of 11,500 possible images.