De-Individualizing fMRI Signals via Mahalanobis Whitening and Bures Geometry

TL;DR

This paper introduces a novel approach combining Mahalanobis whitening and Bures geometry to de-individualize fMRI signals, enhancing the analysis of brain connectivity and potentially improving early Alzheimer's diagnosis.

Contribution

It proposes a new data whitening method and interprets it through Bures geometry, linking quantum mechanics concepts to neuroimaging data analysis.

Findings

Mahalanobis whitening distills meaningful subject-specific information from fMRI signals.

The Bures distance provides a theoretical foundation for de-individualizing neuroimaging data.

Potential to improve early Alzheimer's diagnosis accuracy.

Abstract

Functional connectivity has been widely investigated to understand brain disease in clinical studies and imaging-based neuroscience, and analyzing changes in functional connectivity has proven to be valuable for understanding and computationally evaluating the effects on brain function caused by diseases or experimental stimuli. By using Mahalanobis data whitening prior to the use of dimensionality reduction algorithms, we are able to distill meaningful information from fMRI signals about subjects and the experimental stimuli used to prompt them. Furthermore, we offer an interpretation of Mahalanobis whitening as a two-stage de-individualization of data which is motivated by similarity as captured by the Bures distance, which is connected to quantum mechanics. These methods have potential to aid discoveries about the mechanisms that link brain function with cognition and behavior and may improve the accuracy and consistency of Alzheimer's diagnosis, especially in the preclinical stage of disease progression.