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Journal Articles Computational Statistics and Data Analysis Year : 2024

Clustering-Based Inter-Regional Correlation Estimation


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 intraregional correlation lead to inconsistent interregional 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 interregional 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 interregional 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.
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hal-04269760 , version 1 (03-11-2023)




Hanâ Lbath, Alexander Petersen, Wendy Meiring, Sophie Achard. Clustering-Based Inter-Regional Correlation Estimation. Computational Statistics and Data Analysis, 2024, 191, pp.107876:1-32. ⟨10.1016/j.csda.2023.107876⟩. ⟨hal-04269760⟩
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