Measuring phase-amplitude coupling opposition in neurophysiological signals with the Mean Opposition Vector Index (MOVI)

BackgroundThe phase-amplitude coupling (PAC) opposition between distinct neural oscillations is critical to understanding brain functions. Available methods to assess phase-preference differences between conditions rely on density of occurrences. Other methods like the Kullback-Leibler Divergence (DKL) assess the distance between two conditions by transforming neurophysiological data into probabilistic distributions of phase-preference and assessing the distance between them. However, these methods have limitations such as susceptibility to noise and bias. New MethodWe propose the "Mean Opposition Vector Index" (MOVI), a parameter-free, data-driven algorithm for unbiased estimation of PAC opposition. MOVI establishes a unified framework that integrates the strength of PAC to account for reliable unimodal differences in phase-specific amplitude coupling between neurophysiological datasets. ResultsWe found that MOVI accurately detected phase opposition, was resistant to noise and gave consistent results with low or asymmetrical number of trials, therefore in conditions more similar to experimental studies. Comparison with existing methodsMOVI outperformed Jensen-Shannon Divergence (JSD), an adaptation of the DKL, in terms of sensitivity, specificity, and accuracy to detect phase opposition. ConclusionsMOVI provides a novel and useful approach to study of phase-preference opposition in neurophysiological datasets.