0114 A PRINCIPLED QUANTITATIVE CHARACTERIZATION OF CONTINUOUS EEG DYNAMICS IN SLEEP CONTINUOUS EEG DYNAMICS IN SLEEP

Abstract Introduction: Sleep has been shown to be a continuous and dynamic process in every physiological and behavioral system studied thus far. The ability to accurately describe these dynamics is therefore essential to understanding the way in which healthy and pathological brain activity evolves during sleep. Although current clinical staging has been instrumental in important advances in sleep medicine, it artificially discretizes the continuum of sleep into 30-second epochs of fixed sleep stages. As such, this discretization disagrees with our understanding of sleep circuitry dynamics, and also fails to account for activity that does not into a single stage definition. Additionally, quantitative sleep electroencephalogram (EEG) analysis relying on spectral estimation is highly prone to “spectral bleeding”, as an oscillation may not fall fully within a fixed canonical band or unrelated oscillations may enter. It is therefore vital to progress in our understanding of sleep and related pathologies that we develop accurate, objective methods to capture the full dynamic nature of sleep neurophysiology. Methods: We describe a novel framework for more accurately characterizing the dynamics of multiple simultaneously-occurring oscillations within the sleep EEG. Given the time-frequency spectral representation of the sleep EEG, we estimate the peak frequency, power, and bandwidth of multiple oscillations (e.g. alpha, delta, sigma, theta) at each point in time. This is achieved by decomposing the EEG spectrogram into a series of time-varying parametric spectral basis functions. Results: We present applications to simulated and experimental sleep EEG data, as well as to depth recordings from anesthetized rodents. In each case, the model robustly estimates the peak frequency, bandwidth, and power of each constituent oscillation more accurately than traditional bandpass methods. We also illustrate the ability to perform rigorous Bayesian statistical inference and goodness-of-fit analyses, not possible with traditional methods. Conclusion: By developing a fully Bayesian framework for modeling EEG oscillation dynamics, we provide a pathway towards a statistically-principled, robust, flexible, and continuous characterization of brain dynamics during sleep, which is essential to characterizing the vast heterogeneity observed across both healthy and pathological populations. Support (If Any): NINDS R01 NS-096177 (M.J.P.).