Investigation of electro-vascular phase-amplitude coupling during an auditory task

Area of Science:

• Neuroscience
• Biomedical Engineering
• Cognitive Science

Background:

• Multimodal neuroimaging combines electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) for comprehensive cortical activity assessment.
• Auditory processing research benefits from integrating electrical and hemodynamic signal data.
• Nonlinear interactions between EEG and fNIRS signals, specifically electro-vascular coupling, remain underexplored.

Purpose of the Study:

• To investigate electro-vascular phase-amplitude coupling (PAC) between hemodynamic (fNIRS) and electrical (EEG) signals during an auditory task.
• To apply a novel correction method to mitigate the influence of systemic physiology on fNIRS data.
• To characterize PAC patterns in auditory regions and their modulation by task conditions.

Main Methods:

• Utilized simultaneous EEG and fNIRS recordings during a controlled auditory task.
• Employed electro-vascular phase-amplitude coupling (PAC) analysis to quantify cross-frequency interactions.
• Implemented a temporally embedded canonical correlation analysis (tCCA)-general linear model (GLM)-based approach for physiological artifact correction in fNIRS signals.

Main Results:

• Significant pre-correction PAC was observed between fNIRS and EEG across frontal, left auditory (LA), and right auditory (RA) regions in various frequency bands (broadband, β, γ).
• Task-specific differences in PAC were identified in LA and RA γ sub-bands and frontal low-frequency activity (5-20 Hz).
• Post-correction, significant PAC persisted in frontal and LA regions (γ-band), and RA (α and β bands), with task-related decreases in frontal γ-band and increases in β-band PAC.

Conclusions:

• This study presents the first characterization of electro-vascular PAC between fNIRS and EEG during an auditory task.
• Findings demonstrate significant electro-vascular coupling in auditory processing, modulated by task engagement.
• The results highlight the utility of multimodal neuroimaging and advanced analytical techniques for uncovering complex neural dynamics.