학술논문
Direct modulation index: A measure of phase amplitude coupling for neurophysiology data
Document Type
Report
Author
Source
Human Brain Mapping. March 2023, Vol. 44 Issue 5, p1862, 6 p.
Subject
Language
English
ISSN
1065-9471
Abstract
Abbreviations INTRODUCTION The investigation of the communication between structures across different spatial and temporal scales has been a major area of interest in the field of cognitive and motor neuroscience [...]
: Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase‐amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is (1) bound to an absolute interval between 0 and +1, (2) resistant against noise, and (3) reliable even for small amounts of data. To highlight the properties of this newly‐proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase‐locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN.
: Neural communication across different spatial and temporal scales is a topic of great interest in clinical and basic science. Phase‐amplitude coupling (PAC) has attracted particular interest due to its functional role in a wide range of cognitive and motor functions. Here, we introduce a novel measure termed the direct modulation index (dMI). Based on the classical modulation index, dMI provides an estimate of PAC that is (1) bound to an absolute interval between 0 and +1, (2) resistant against noise, and (3) reliable even for small amounts of data. To highlight the properties of this newly‐proposed measure, we evaluated dMI by comparing it to the classical modulation index, mean vector length, and phase‐locking value using simulated data. We ascertained that dMI provides a more accurate estimate of PAC than the existing methods and that is resilient to varying noise levels and signal lengths. As such, dMI permits a reliable investigation of PAC, which may reveal insights crucial to our understanding of functional brain architecture in key contexts such as behaviour and cognition. A Python toolbox that implements dMI and other measures of PAC is freely available at https://github.com/neurophysiological-analysis/FiNN.