Model-based analysis and forecast of sleep-wake regulatory dynamics: Tools and Applications to Data

Extensive clinical and experimental evidence links sleep-wake regulation and state of vigilance to neurological disorders including schizophrenia and epilepsy. To understand the bidirectional coupling between disease severity and sleep disturbances, we need to investigate the underlying neurophysiological interactions of the sleep-wake regulatory system in normal and pathological brain. We utilized Unscented Kalman Filter (UKF) based data assimilation (DA) and physiologically-based mathematical models of sleep-wake regulatory network synchronized with experimental measurements to reconstruct and predict the state of sleep-wake regulatory system (SWRS) in chronically implanted animals. Critical to applying this technique to real biological systems is the need to estimate the underlying model parameters. We have developed an estimation method capable of simultaneously fitting and tracking multiple model parameters to optimize the reconstructed system state. To demonstrate application of our DA framework, we have experimentally recorded brain activity from freely behaving rodents and classified discrete state of vigilance (SOV) continuously for many-day long recordings. These discretized observations were then used as the 'noisy observables' in the implemented framework to estimate time-dependent model parameters and then to forecast future state and state transitions from out-of-sample recordings.

This work is described in the publication "Model-based analysis and forecast of sleep-wake regulatory dynamics: Tools and Applications to Data," published in the journal Chaos.

Here we provide computational examples for the algorithms developed and/or integrated together, example animal sleep data on which these algorithms were applied, and code for generating the figures from this article.