2023 article
A nonparametric test of group distributional differences for hierarchically clustered functional data
Long, A. S., Reich, B. J., Staicu, A.-M., & Meitzen, J. (2023, March 13). BIOMETRICS.
author keywords: bivariate functional data; functional data analysis; hierarchically clustered data; multivariate testing
TL;DR:
This paper proposes a method to test for differences in the electrophysiological properties across estrous cycle phase without first defining a metric of interest by modeling membrane potential data in the frequency domain as realizations of a bivariate process by adopting existing methods for longitudinal functional data.
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Source: Web Of Science
Added: March 27, 2023
Abstract Biological sex and gender are critical variables in biomedical research, but are complicated by the presence of sex-specific natural hormone cycles, such as the estrous cycle in female rodents, typically divided into phases. A common feature of these cycles are fluctuating hormone levels that induce sex differences in many behaviors controlled by the electrophysiology of neurons, such as neuronal membrane potential in response to electrical stimulus, typically summarized using a priori defined metrics. In this paper, we propose a method to test for differences in the electrophysiological properties across estrous cycle phase without first defining a metric of interest. We do this by modeling membrane potential data in the frequency domain as realizations of a bivariate process, also depending on the electrical stimulus, by adopting existing methods for longitudinal functional data. We are then able to extract the main features of the bivariate signals through a set of basis function coefficients. We use these coefficients for testing, adapting methods for multivariate data to account for an induced hierarchical structure that is a product of the experimental design. We illustrate the performance of the proposed approach in simulations and then apply the method to experimental data.