Inter-regional correlation estimators for functional magnetic resonance imaging

TL;DR

This paper compares nine different estimators for measuring functional connectivity in fMRI data, analyzing their properties, robustness, and impact on correlation estimates across synthetic, animal, and human datasets.

Contribution

It systematically evaluates and introduces six novel estimators for inter-regional correlation, considering spatial structure and noise robustness in fMRI data.

Findings

Estimator choice significantly affects correlation values.

Six new estimators demonstrate improved robustness and properties.

Empirical analysis across diverse datasets validates estimator performance.

Abstract

Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values.