Clustering-Based Inter-Regional Correlation Estimation

TL;DR

This paper introduces a non-parametric clustering-based method for accurately estimating inter-regional correlations in noisy, grouped data, especially applied to brain fMRI analysis, outperforming existing approaches.

Contribution

It presents a novel hierarchical clustering approach that effectively handles noise and intra-regional correlation in correlation estimation, with proven consistency and empirical superiority.

Findings

Outperforms existing methods in correlation estimation quality.

Proven consistency of the proposed estimator.

Effective application demonstrated on real-world fMRI data.

Abstract

A novel non-parametric estimator of the correlation between grouped measurements of a quantity is proposed in the presence of noise. This work is primarily motivated by functional brain network construction from fMRI data, where brain regions correspond to groups of spatial units, and correlation between region pairs defines the network. The challenge resides in the fact that both noise and intra-regional correlation lead to inconsistent inter-regional correlation estimation using classical approaches. While some existing methods handle either one of these issues, no non-parametric approaches tackle both simultaneously. To address this problem, we propose a trade-off between two procedures: correlating regional averages, which is not robust to intra-regional correlation; and averaging pairwise inter-regional correlations, which is not robust to noise. To that end, we project the data onto a space where Euclidean distance is used as a proxy for sample correlation. We then propose to leverage hierarchical clustering to gather together highly correlated variables within each region prior to inter-regional correlation estimation. We provide consistency results, and empirically show our approach surpasses several other popular methods in terms of quality. We also provide illustrations on real-world datasets that further demonstrate its effectiveness.