Multitaper estimates of phase-amplitude coupling

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 PAC with commonly used statistical measures have been well-documented. The limitations of these measures include: (1) response to non-oscillatory, high-frequency, broad-band activity, (2) response to high-frequency components of the low-frequency oscillation, (3) adhoc selection of analysis frequency-intervals, and (4) reliance upon data shuffling to assess statistical significance. Objective. To address issues (1)-( 4) by introducing a nonparametric multitaper estimator of PAC. Approach. In this work, a multitaper PAC estimator is proposed that addresses these issues. Specifically, issue (1) 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 (2) is addressed by replacing coherence between the low-frequency and high-frequency components in a standard PAC estimator with multitaper partial coherence, while issue (3) is addressed with a physical argument regarding meaningful neural oscillation. Finally, asymptotic statistical assessment of the multitaper estimator is introduced to address issue (4). Main results. Multitaper estimates of PAC 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 PAC, a phenomena exhibited by many neural systems, and provides a basis upon which further nonparametric multitaper-related methods can be developed.

BOARD-FTD-PACC: a graphical user interface for the synaptic and cross-frequency analysis derived from neural signals

“…In addition, that tool is focused only in PAC detection at the difference of BOARD-FTD-PACC, which also includes PC and PSD analysis. In a recent development, a nonparametric multitaper estimator of PAC was proposed to solve some pitfalls in cross-frequency statistical analysis [ 59 ].…”

Unsupervised Multitaper Spectral Method for Identifying REM Sleep in Intracranial EEG Recordings Lacking EOG/EMG Data

“…This information, if it exists, may further divide the REM cluster and necessitate an otherwise unnecessary cluster-merging step. Similarly, one might consider incorporating phase-phase and phase-amplitude spectral features (e.g., [ 51 ]). We seek to capture known average dynamics that occur in REM and, furthermore, the across-brain region timing of alpha oscillations.…”

“…The norm of any DPSS is equal to 1. With this specification, out-of-band bias due to spectral leakage is limited by the optimal in-band energy concentration properties of the DPSSs [ 34 , 49 , 50 , 51 ]. The resulting K is equal to 22 ( ).…”