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Abstract:
Disease dynamics can be characterized by features of complex systems such as critical phase transitions, but in the biomedical field little evidence has been provided for this concept so far. Technological advancements are now making it possible to measure the intensive longitudinal data (ILD) necessary to capture pathologically-relevant signal components exhibiting the multiscale complexity of disease dynamics. Using a well-established model of alcohol relapse in rats as an example of disease onset and progression, we applied a multiscale computational approach to extract dynamical characteristics of massive high-resolution measurements of rat drinking behavior and locomotor activity. We show a stage-by-stage dynamical phase transition into relapse behavior preceded by early warning signals such as instability of drinking behavior and circadian rhythms, and a resultant increase in low frequency, ultradian rhythms. This study provides a blueprint for processing ILD from clinical studies and will help to predict disease dynamics in general.
