ArticleHuman brain mapping2023
Direct modulation index: A measure of phase amplitude coupling for neurophysiology data.
Article in Human brain mapping, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Inconsistent subthalamic local field potential beta activity amid in- and antiphasic neuronal bursts.NPJ Parkinson's disease · 2026Article
- Multimodal EEG and explainable machine learning characterize neuroticism-related neurodynamic heterogeneity in mild cognitive impairment during visuospatial working memory.Frontiers in aging neuroscience · 2026Article
- Modulation of Cerebellar Nuclear Activity by Transcranial AC Stimulation in Awake Rats.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2024Article
- Direct modulation index: A measure of phase amplitude coupling for neurophysiology data.Human brain mapping · 2023Article
- FiNN: A toolbox for neurophysiological network analysis.Network neuroscience (Cambridge, Mass.) · 2022Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.