Interpretability of Multivariate Brain Maps in Brain Decoding: Definition and Quantification

TL;DR

This paper introduces a formal definition and quantification method for interpretability in multivariate brain maps used in neuroimaging, focusing on reproducibility and representativeness to improve brain decoding models.

Contribution

It provides the first theoretical framework for interpretability in brain decoding, including a heuristic for quantification and a multi-objective model selection criterion.

Findings

Optimizing hyper-parameters improves interpretability of brain maps.

The proposed criterion enhances the informativeness of multivariate brain maps.

The definition enables quantitative evaluation of interpretability in neuroimaging.

Abstract

Brain decoding is a popular multivariate approach for hypothesis testing in neuroimaging. It is well known that the brain maps derived from weights of linear classifiers are hard to interpret because of high correlations between predictors, low signal to noise ratios, and the high dimensionality of neuroimaging data. Therefore, improving the interpretability of brain decoding approaches is of primary interest in many neuroimaging studies. Despite extensive studies of this type, at present, there is no formal definition for interpretability of multivariate brain maps. As a consequence, there is no quantitative measure for evaluating the interpretability of different brain decoding methods. In this paper, first, we present a theoretical definition of interpretability in brain decoding; we show that the interpretability of multivariate brain maps can be decomposed into their reproducibility and representativeness. Second, as an application of the proposed theoretical definition, we formalize a heuristic method for approximating the interpretability of multivariate brain maps in a binary magnetoencephalography (MEG) decoding scenario. Third, we propose to combine the approximated interpretability and the performance of the brain decoding model into a new multi-objective criterion for model selection. Our results for the MEG data show that optimizing the hyper-parameters of the regularized linear classifier based on the proposed criterion results in more informative multivariate brain maps. More importantly, the presented definition provides the theoretical background for quantitative evaluation of interpretability, and hence, facilitates the development of more effective brain decoding algorithms in the future.