J Neural Eng. 2021 Aug 16. doi: 10.1088/1741-2552/ac1deb. Online ahead of print.
ABSTRACT
Phase-amplitude coupling (PAC) is the association of the amplitude of a high-frequency oscillation with the phase of a low-frequency oscillation. In neuroscience, this relationship provides a mechanism by which neural activity might be coordinated between distant regions. The dangers and pitfalls of assessing phase-amplitude coupling with commonly used statistical measures have been well-documented. The limitations of these measures include: (i) response to non-oscillatory, high-frequency, broad-band activity, (ii) response to high-frequency components of the low-frequency oscillation, (iii) adhoc selection of analysis frequency-intervals, and (iv) reliance upon data shuffling to assess statistical significance.
OBJECTIVE: To address issues (i)-(iv) by introducing a nonparametric multitaper estimator of phase-amplitude coupling.
APPROACH: In this work, a multitaper phase-amplitude coupling estimator is proposed that addresses these issues. Specifically, issue (i) is addressed by replacing the analytic signal envelope estimator computed using the Hilbert transform with a multitaper estimator that down-weights non-sinusoidal activity using a classical, multitaper super-resolution technique. Issue (ii) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (iii) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (iv).
MAIN RESULTS: Multitaper estimates of phase-amplitude coupling are introduced. Their efficacy is demonstrated in simulation and on human intracranial recordings obtained from epileptic patients.
SIGNIFICANCE: This work facilitates a more informative statistical assessment of phase-amplitude coupling, a phenomena exhibited by many neural systems, and provides a basis upon which further nonparametric multitaper-related methods can be developed.
PMID:34399415 | DOI:10.1088/1741-2552/ac1deb
