Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling

TL;DR

This paper introduces a new probabilistic model for comparing brain connectivity matrices in fMRI data, enabling detection of differences between post-stroke patients and healthy controls with higher sensitivity.

Contribution

The authors develop a matrix-variate probabilistic model on the SPD manifold for inter-subject comparison of functional connectivity, applied to post-stroke diagnosis.

Findings

Identified neurologically relevant connectivity differences in post-stroke patients.

Model outperforms standard procedures in sensitivity.

First report of single-patient vs. group connectivity comparison.

Abstract

Functional brain connectivity, as revealed through distant correlations in the signals measured by functional Magnetic Resonance Imaging (fMRI), is a promising source of biomarkers of brain pathologies. However, establishing and using diagnostic markers requires probabilistic inter-subject comparisons. Principled comparison of functional-connectivity structures is still a challenging issue. We give a new matrix-variate probabilistic model suitable for inter-subject comparison of functional connectivity matrices on the manifold of Symmetric Positive Definite (SPD) matrices. We show that this model leads to a new algorithm for principled comparison of connectivity coefficients between pairs of regions. We apply this model to comparing separately post-stroke patients to a group of healthy controls. We find neurologically-relevant connection differences and show that our model is more sensitive that the standard procedure. To the best of our knowledge, these results are the first report of functional connectivity differences between a single-patient and a group and thus establish an important step toward using functional connectivity as a diagnostic tool.