title: On mathematical modeling of circadian rhythms, performance, and alertness.
abstract: Mathematical models of neurobehavioral performance and alertness have both basic science and practical applications. These models can be especially useful in predicting the effect of different sleep-wake schedules on human neurobehavioral objective performance and subjective alertness under many conditions. Several relevant models currently exist in the literature. In principle, the development and refinement of any mathematical model should be based on an explicit modeling methodology, such as the Box modeling paradigm, that formally defines the model structure and calculates the set of parameters. While most mathematical models of neurobehavioral performance and alertness include homeostatic, circadian, and sleep inertia components and their interactions, there may be fundamental differences in the equations included in these models. In part, these may be due to differences in the assumptions of the underlying physiology. Because the choice of model equations can have a dramatic influence on the results, it is necessary to consider these differences in assumptions when examining the results from a model and when comparing results across models. This article presents principles of mathematical modeling and examples of how such procedures can be applied to the development and refinement of mathematical models of neurobehavioral performance and alertness. This article also presents several methods of testing and comparing these models, suggests different uses of the models, and discusses problems with current models.
authors: Division of Sleep Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA. ebklerman@hms.harvard.edu
authors: Elizabeth B
authors: EB
authors: Klerman
authors: Melissa
authors: M
authors: St Hilaire
mesh_terms: D001143:Arousal / Q000502:physiology*; D002940:Circadian Rhythm / Q000502:physiology*; D006801:Humans; D008954:Models, Biological; D008960:Models, Psychological; D015233:Models, Statistical; D013647:Task Performance and Analysis