Abstract
Judiciously classifying phase-A subtypes in cyclic alternating pattern (CAP) is critical for investigating sleep dynamics. Phase-amplitude coupling (PAC), one of the representative forms of neural rhythmic interaction, is defined as the amplitude of high-frequency activities modulated by the phase of low-frequency oscillations. To examine PACs under more or less synchronized conditions, we propose a nonlinear approach, named the masking phase-amplitude coupling (MPAC), to quantify physiological interactions between high (α/lowβ) and low (δ) frequency bands. The results reveal that the coupling intensity is generally the highest in subtype A1 and lowest in A3. MPACs among various physiological conditions/disorders (p < 0.0001) and sleep stages (p < 0.0001 except S4) are tested. MPACs are found significantly stronger in light sleep than deep sleep (p < 0.0001). Physiological conditions/disorders show similar order in MPACs. Phase-amplitude dependence between δ and α/lowβ oscillations are examined as well. δ phase tent to phase-locked to α/lowβ amplitude in subtype A1 more than the rest. These results suggest that an elevated δ-α/lowβ MPACs can reflect some synchronization in CAP. Therefore, MPAC can be a potential tool to investigate neural interactions between different time scales, and δ-α/lowβ MPAC can serve as a feasible biomarker for sleep microstructure.
Highlights
The most straightforward cyclic alternating pattern (CAP)-scoring method is visual recognition of the EEG waveform
We aim to develop a reliable method in quantifying Phase-amplitude coupling (PAC) of phasic events through the non-invasive measures EEGs: (1) a novel method masking phase-amplitude coupling (MPAC) is proposed to handle with the disorganized neurons and artifacts which may erase the observation of significant PACs in EEGs. (2) Only the large spatial scale of synchronization is considered in which these PACs may still reliable despite of the attenuation and desynchronization in layers
Evidences hint that pronounced changes in PACs may exist during sleep since sleep macrostructure was shown to be associated with the mechanisms of different frequency modulations[13]
Summary
The most straightforward CAP-scoring method is visual recognition of the EEG waveform. Mostly rely on the spectral assessments from the EEGs with the application of machine-learning algorithms, for the detection of CAP4,5. None of these methods are applied in clinics since they either require certain clinical intervention and/or large data volumes to provide the physiological information in need. In an attempt to understand whether coupling is different in more or less synchronized phase-A subtypes, we focus purely on the physiological interaction between high-frequency (α and lowβ activities: 10–17 Hz) and low-frequency (δ activities: 0.25–2.5 Hz) bands with the consideration of interferences from sleep stages and pathophysiological factors. MPACs among different sleep stages and pathophysiological conditions are carefully examined and compared under various phase-A subtypes as well
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