CRCNS: Model Based Data Assimilation & Control of Sleep-Wake Regulation in Epilepsy

Project: Research project

Description

DESCRIPTION (provided by applicant): Sleep is a fundamental biological cycle that is coupled into every aspect of body function from behavior and information processing to metabolic storage and release. Sleep-wake patterns correlate with, and sleep disruptions are comorbid with, many neurological and mental health disease dynamics including epilepsy. Abnormal sleep can be disruptive to quality of life and further exacerbate the primary disorders. Within the past decade a number of groups have developed mathematical and computational dynamical models for the network of brain nuclei and cell groups that regulate sleep-wake dynamics. But their validation to date has been substantially limited to reproduction of statistic of cycle time and dwell time durations, and their application to understanding and control of diseases limited. The first objective of this project is to validate and optimize these models for reconstruction, forecasting, and control of sleep-wake regulation. This involves experimentally recording activity from select cell groups of the sleep-wake regulatory system (SWRS) along with cortical, hippocampal, and behavioral activity. The mathematical models will be incorporated into model-based data assimilation (DA). The parameters and models will be optimized for reconstruction and forecasting, and performance will be used to establish the 'best' model. Experimental perturbation of sleep state and sleep cycle dynamics will be done with both sensory and direct neural stimulation. The models will then be modified to account for and predict changed dynamics from such perturbations. The second objective of this project is to apply these models and framework to understand and control sleep-cycle dis-regulation in a model of temporal lobe epilepsy. This involves experimentally recording activity from the SWRS in epileptic animals, modifying and optimizing the models to reconstruct and forecast the observed sleep cycle dynamics. The models will then be used in closed feedback form to prescribe control perturbations to regularize the sleep cycles of the epileptic animals. The project embodies a paradigm shift for neuroscience and neural-engineering in which computational models are validated and optimized through their capacity to reconstruct and forecast detailed time series from real neurological measurements, that such model-based reconstruction is used to observe detailed state dynamics from less costly (invasive or damaging) measurements, and in which such biologically based models are used to control neurological systems and treat neurological disorders. The approach of this proposed research will have a major impact in diagnosing, monitoring, and controlling neurological disorders by both incorporating detailed biologically based models into the measurement or observation process, and by allowing remote observation through measurement of identified less costly measurements. The specific validation and improvement of computational models and observation methodology of the sleep-wake regulatory system will allow detailed investigation of its role in a host of neurological diseases in which sleep regulatin is implicated either as a cause or consequence, such as epilepsy and schizophrenia, and thereby the development of interventions or therapy. In addition to the theoretical and experimental advances, educational and outreach will be served through this project, including development of new course materials and enhancing underrepresented participation in research.
StatusFinished
Effective start/end date9/17/145/31/19

Funding

  • National Institutes of Health: $298,488.00
  • National Institutes of Health: $305,633.00
  • National Institutes of Health: $307,895.00
  • National Institutes of Health: $295,550.00

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Epilepsy
Sleep
Observation
Nervous System Diseases
Temporal Lobe Epilepsy
Neurosciences
Cell Nucleus
Automatic Data Processing
Research
Reproduction
Schizophrenia
Mental Health
Theoretical Models
Quality of Life