Two-Sample Testing for Multivariate Cross-Correlation Functions with Applications to Gut-Brain Reward Learning

TL;DR

This paper develops a statistical hypothesis testing framework for comparing multivariate cross-correlation functions in time series data, with applications to gut-brain reward learning in mice.

Contribution

It introduces integrated and maximum-type global statistics for functional data, enabling formal inference on differences in CCFs across groups.

Findings

Detected significant differences in dopamine-locomotion coupling across brain regions and sexes.

Applied methods reveal local and broad differences in CCFs in experimental datasets.

Provided a flexible FDA-based approach for analyzing dynamic dependence structures.

Abstract

Cross-correlation functions (CCFs) are classical tools for studying lead-lag relationships between paired time series, but they are most often used descriptively rather than inferentially. Motivated by mouse experiments on gut-brain interactions in reward learning, we carry out a two-sample hypothesis test for formal statistical inference on collections of subject-specific CCF curves. In our application, each experimental session yields two related CCFs describing the temporal association of dopamine activity with locomotor velocity and acceleration, which leads naturally to a multivariate functional data formulation. We treat each empirical CCF as a functional observation indexed by lag and test equality of mean multivariate CCF functions across groups using integrated and maximum-type global statistics, \(F_{\mathrm{int}}\) and \(F_{\max}\), constructed from pointwise Hotelling \(T^2\) statistics. The integrated test targets broad differences across the lag domain, whereas the maximum test is sensitive to local differences. Applied to free-feeding and intragastric infusion datasets, the proposed methods detect substantial differences in dopamine-locomotion coupling across brain region and biological sex in the free-feeding experiment, with more selective effects in the infusion setting. The proposed framework provides a flexible and rigorous FDA-based approach for comparing dynamic dependence structures across experimental conditions.