Improving clinical interpretability of linear neuroimaging models through feature whitening

TL;DR

This paper introduces a neuroanatomically informed whitening method to improve the interpretability of linear neuroimaging models by disentangling correlated brain region signals without losing predictive power.

Contribution

It presents a novel whitening approach tailored for neuroimaging data that enhances interpretability of linear models while maintaining classification accuracy.

Findings

Whitening improves interpretability of model weights.

The method preserves predictive performance.

It effectively disentangles correlated neuroanatomical signals.

Abstract

Linear models are widely used in computational neuroimaging to identify biomarkers associated with brain pathologies. However, interpreting the learned weights remains challenging, as they do not always yield clinically meaningful insights. This difficulty arises in part from the inherent correlation between brain regions, which causes linear weights to reflect shared rather than region-specific contributions. In particular, some groups of regions, including homologous structures in the left and right hemispheres, are known to exhibit strong anatomical correlations. In this work, we leverage this prior neuroanatomical knowledge to introduce a whitening approach applied to groups of regions with known shared variance, designed to disentangle overlapping information across correlated brain measures. We additionally propose a regularized variant that allows controlled tuning of the degree of decorrelation. We evaluate this method using region-of-interest features in two psychiatric classification tasks, distinguishing individuals with bipolar disorder or schizophrenia from healthy controls. Importantly, unlike PCA or ICA which use whitening as a dimensionality reduction step, our approach decorrelates anatomically informed pairs of neuroanatomical regions while retaining the full input signal, making it specifically suited for feature interpretation rather than feature selection. Our findings demonstrate that whitening improves the interpretability of model weights while preserving predictive performance, providing a robust framework for linking linear model outputs to neurobiological mechanisms.