A novel time-varying multivariate autoregressive model of EDA and HRV revealed a parasympathetic-driven interaction at rest and dynamic changes in directionality during stressful tasks.
A novel time-varying multivariate autoregressive model using HRV and EDA signals can effectively quantify the causal interactions between the parasympathetic and sympathetic nervous systems during different stress tasks.
OBJECTIVE: Most of the bodily functions are regulated by multiple interactions between the parasympathetic (PNS) and sympathetic (SNS) nervous system. In this study, we propose a novel framework to quantify the causal flow of information between PNS and SNS through the analysis of heart rate variability (HRV) and electrodermal activity (EDA) signals. METHODS: Our method is based on a time-varying (TV) multivariate autoregressive model of EDA and HRV time-series and incorporates physiologically inspired assumptions by estimating the Directed Coherence in a specific frequency range. The statistical significance of the observed interactions is assessed by a bootstrap procedure purposely developed to infer causalities in the presence of both TV model coefficients and TV model residuals (i.e., heteroskedasticity). We tested our method on two different experiments designed to trigger a sympathetic response, i.e., a hand-grip task (HG) and a mental-computation task (MC). RESULTS: Our results show a parasympathetic driven interaction in the resting state, which is consistent across different studies. The onset of the stressful stimulation triggers a cascade of events characterized by the presence or absence of the PNS-SNS interaction and changes in the directionality. Despite similarities between the results related to the two tasks, we reveal differences in the dynamics of the PNS-SNS interaction, which might reflect different regulatory mechanisms associated with different stressors. CONCLUSION: We estimate causal coupling between PNS and SNS through MVAR modeling of EDA and HRV time-series. SIGNIFICANCE: Our results suggest promising future applicability to investigate more complex contexts such as affective and pathological scenarios.
Callara et al. (Mon,) reported a other. Hand-grip and mental-computation tasks vs. Resting state was evaluated on Causal flow of information between parasympathetic and sympathetic nervous systems. A novel time-varying multivariate autoregressive model of EDA and HRV revealed a parasympathetic-driven interaction at rest and dynamic changes in directionality during stressful tasks.
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