Extraction of Hierarchical Functional Connectivity Components in human brain using Adversarial Learning

TL;DR

This paper introduces an adversarial learning approach to extract robust, hierarchical functional connectivity components from rsfMRI data, improving reproducibility and interpretability for understanding brain organization and biomarkers.

Contribution

It proposes a novel adversarial learning framework for hierarchical brain network estimation that is resilient to scanner variations, advancing neuroimaging analysis methods.

Findings

High reproducibility of components in simulations and real data

Outperforms existing methods in robustness to inter-scanner variations

Successfully captures hierarchical brain connectivity patterns

Abstract

The estimation of sparse hierarchical components reflecting patterns of the brain's functional connectivity from rsfMRI data can contribute to our understanding of the brain's functional organization, and can lead to biomarkers of diseases. However, inter-scanner variations and other confounding factors pose a challenge to the robust and reproducible estimation of functionally-interpretable brain networks, and especially to reproducible biomarkers. Moreover, the brain is believed to be organized hierarchically, and hence single-scale decompositions miss this hierarchy. The paper aims to use current advancements in adversarial learning to estimate interpretable hierarchical patterns in the human brain using rsfMRI data, which are robust to "adversarial effects" such as inter-scanner variations. We write the estimation problem as a minimization problem and solve it using alternating updates. Extensive experiments on simulation and a real-world dataset show high reproducibility of the components compared to other well-known methods.