Statistical Inference for Modulation Index in Phase-Amplitude Coupling

TL;DR

This paper introduces a statistical framework for assessing the significance of the modulation index in phase-amplitude coupling, providing a reliable, efficient method to determine true coupling beyond chance levels without extensive simulations.

Contribution

We derived a closed-form null distribution for the modulation index, enabling straightforward significance testing in phase-amplitude coupling analysis.

Findings

The null distribution is a scaled beta distribution.

Monte Carlo simulations validate the method's accuracy.

The approach is computationally efficient and reliable.

Abstract

Phase-amplitude coupling is a phenomenon observed in several neurological processes, where the phase of one signal modulates the amplitude of another signal with a distinct frequency. The modulation index (MI) is a common technique used to quantify this interaction by assessing the Kullback-Leibler divergence between a uniform distribution and the empirical conditional distribution of amplitudes with respect to the phases of the observed signals. The uniform distribution is an ideal representation that is expected to appear under the absence of coupling. However, it does not reflect the statistical properties of coupling values caused by random chance. In this paper, we propose a statistical framework for evaluating the significance of an observed MI value based on a null hypothesis that a MI value can be entirely explained by chance. Significance is obtained by comparing the value with a reference distribution derived under the null hypothesis of independence (i.e., no coupling) between signals. We derived a closed-form distribution of this null model, resulting in a scaled beta distribution. To validate the efficacy of our proposed framework, we conducted comprehensive Monte Carlo simulations, assessing the significance of MI values under various experimental scenarios, including amplitude modulation, trains of spikes, and sequences of high-frequency oscillations. Furthermore, we corroborated the reliability of our model by comparing its statistical significance thresholds with reported values from other research studies conducted under different experimental settings. Our method offers several advantages such as meta-analysis reliability, simplicity and computational efficiency, as it provides p-values and significance levels without resorting to generating surrogate data through sampling procedures.