The objective of this research is to develop statistical models of multi-scale sleep EEG dynamics, which will form a framework for identifying biomarkers of psychological and neurological disorders. Sleep is a natural, restorative, altered state of consciousness common to every living human being. Neurophysiologically, sleep is a continuous, dynamic process involving the complex interaction of cortical and sub-cortical networks within the brain operating on multiple time scales. Study of the sleep electroencephalogram (EEG) is therefore an ideal, natural means of simultaneously observing the correlates of activity from numerous brain regions. Fur- thermore, numerous psychological (e.g., schizophrenia, depression, and anxiety) and neurological (e.g., Alz- heimer's disease and Parkinson's disease) disorders are associated with disrupted sleep dynamics, affecting millions of people worldwide. Although ~20 million Americans suffer with disorders of sleep dynamics, such as chronic and/or severe insomnia, current clinical practice does not objectively quantify sleep EEG dynamics. This is because current methods, though instrumental our present understanding of sleep, limit the degree to which sleep dynamics can be described-discretizing the sleep in time and state through subjective, visual scoring practices. Furthermore, the sleep field has yet to adopt powerful spectral estimation techniques, which could greatly improve the characterization of sleep EEG dynamics. Therefore, there would be great benefits in developing objective methods incorporating the state-of-the-art in dynamic modeling and signal processing for sleep dynamics. In order to approach this problem, we have recently developed a signal processing and mod- eling framework to characterize the dynamics of multiple simultaneous neural processes evolving on different time scales. We have shown that these dynamic models significantly outperform traditional stage-based meth- ods in accurately characterizing the behavioral and physiological dynamics of sleep onset. Also, our prelimi- nary studies have shown that optimized spectral estimation methods vastly improve the analysis of sleep EEG dynamics in healthy subjects, and reveal vivid signatures of pathological sleep in Alzheimer's and schizophre- nia patients. In this proposal, we will develop a novel signal processing and dynamic modeling framework to characterize sleep EEG dynamics from a set of large databases (~20,000 records) of sleep recordings from healthy and pathological subjects. By characterizing variability in healthy sleep, we will develop a novel anoma- ly detection approach for identifying and quantifying differences in the sleep EEG dynamics of pathological populations.