P114 Modelling the bistable cortical dynamics of the sleep-onset period
Zhenxing Hu*1, Manaoj Aravind1, Nathan Kutz2, Jean-Julien Aucouturier1
1Universit´e Marie et Louis Pasteur, SUPMICROTECH, CNRS, institut FEMTO-ST, F-25000 Besancon, France
2Department of Applied Mathematics and Electrical and Computer Engineering, University of Washington, Seattle USA
*Email: zhenxing.hu@femto-st.fr
Introduction
The sleep-onset period (SOP) exhibits dynamic and non-monotonous changes of electroencephalogram (EEG) with high, and so far poorly understood, inter-individual variability. Computational models of the sleep regulation network have suggested that the transition to sleep can be viewed as a noisy bifurcation [1], at a saddle point which is determined by an underlying control signal or ‘sleep drive‘. However, such models do not describe how internal control signals in the SOP can produce repeated switches between stable wake and sleep states. Hence, we proposed a minimal parameterized stochastic dynamic model (Fig. 1) inspired by the modelling of C. Elegan's backward and forward motion.
Methods
We apply a data-driven embedding strategy for high-dimensional EEG time-frequency signals via interpolating the first SVD mode in wake and sleep states, paird with a parsimonious stochastic dynamical model with a quartic potential function, in which one slowly-varying control parameter drives the wake-to-sleep transition while exhibiting noise-driven bistability. Also, we provide a procedure based on Markov Chain Monte Carlo (MCMC) for estimating the parameters of the model given single observations of experimental sleep EEG data.
Results
In simulation, we found the interactions between the rate of landscape change and noise-leve could reproduce a wide-variety of SOP phenomenology. Besides, using the model to analyze a pre-existing sleep EEG dataset, we found that the estimated model parameters correlate with both subjective sleep reports and objective hypnogram metrics, suggesting that the bistable characteristics of the SOP influence the characteristics of subsequent sleep.
Discussion
Our findings extend and integrate several threads of prior research on SOP dynamics and modeling. Early mechanistic frameworks of sleep-wake regulation (e.g. the two-process model [2] and “flip-flop” switching circuits [3] ) established the concept of a bistable control of sleep and wake states, but these models usually involve many variables and parameters, making them difficult to fit directly to EEG data. Further, our model explicitly captures the SOP dynamics through stochastic dynamical systems, which effectively characterizes the continuous and stochastic nature of sleep-onset phenomena observed empirically, including intermittent reversals or ”flickering” between wake-like and sleep-like states.
Figure 1. Fig 1. Study overview. The sleep-onset period (SOP) has a strongly bistable phenomenology, marked by a non-monotonous decrease of the EEG frequency and high inter-individual variability, seen here in three illustrative spectrograms (top). We model the bistable cortical dynamics of the SOP with a minimally-parameterized stochastic dynamical system.
Acknowledgements
This work is supported by the Marie Skłodowska-Curie Actions (MSCA) Doctoral Networks ( Lullabyte).
References
[1] Yang, D. P., McKenzie-Sell, L., Karanjai, A., & Robinson, P. A. (2016). Wake-sleep transition as a noisy bifurcation.Physical Review E,94(2), 022412.https://doi.org/10.1103/PhysRevE.94.022412
[2]Borbély, A. A., Daan, S., Wirz‐Justice, A., & Deboer, T. (2016). The two‐process model of sleep regulation: a reappraisal.Journal of sleep research,25(2), 131-143.https://doi.org/10.1111/jsr.12371
[3] Lu, J., Sherman, D., Devor, M., & Saper, C. B. (2006). A putative flip–flop switch for control of REM sleep.Nature,441(7093), 589-594.https://doi.org/10.1038/nature04767
