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Inter-Brain Synchrony in Open-Ended Collaborative Learning: An fNIRS-Hyperscanning Study

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Published on: July 21, 2021

Untangling cross-regional cross-frequency coupling in dynamic neural oscillations.

Soroush Niketeghad¹, Koorosh Mirpour², Mahsa Malekmohammadi³

¹Department of Bioengineering, University of California, Los Angeles, CA, United States of America.

Journal of Neural Engineering

|November 21, 2025

Summary

A new partial modulation index (PMI) method accurately distinguishes true cross-regional coupling from biased measurements in brain networks. This advances understanding of neural communication and neurological disorders.

Keywords:

causality cross-frequency coupling neural oscillation phase coupling thalamocortical

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Area of Science:

Neuroscience
Computational Neuroscience
Signal Processing

Background:

Brain networks utilize long-range phase coupling of low-frequency oscillations (LFOs) for communication.
Cross-frequency coupling (CFC), where LFO phase modulates high-frequency power, is crucial for neural activity regulation.
The mechanisms of cross-regional CFC and methods to differentiate local from remote LFO influences are not well understood.

Purpose of the Study:

To develop and validate a novel method, the partial modulation index (PMI), to accurately measure cross-regional CFC.
To differentiate true cross-regional CFC from apparent coupling caused by simultaneous phase coupling.
To assess the PMI's effectiveness in simulated data and human intracranial recordings.

Main Methods:

Developed the partial modulation index (PMI) based on Pearl's do-calculus, a derivation of the modulation index (MI).
Tested PMI on simulated datasets to assess its ability to distinguish biased from unbiased CFC.
Evaluated PMI using intracranial local field potentials from thalamus and cortex in a patient with essential tremor.

Main Results:

PMI successfully differentiated between genuine focal CFC and biased cross-regional phase coupling in simulated data, unlike conventional MI.
Analysis of human thalamocortical data indicated that observed CFC was partially biased, as revealed by PMI.
Simulated data results validated PMI's capability to remove mathematical bias inherent in CFC measurements.

Conclusions:

The novel PMI method provides a mathematically rigorous approach to characterize residual CFC.
This facilitates accurate investigation of brain-wide LFO contributions to CFC.
Improved understanding of CFC mechanisms can enhance insights into neurological disorders.