Dynamical state transitions into addictive behaviour and their early-warning signals

Area of Science:

• Complex Systems Theory
• Biomedical Science
• Neuroscience

Background:

• Critical transition theory offers potential for predicting abrupt changes in complex systems.
• Early-warning signals (EWS) from fluctuation analysis are key to this theory.
• The biomedical field requires robust demonstrations of EWS for disease dynamics.

Purpose of the Study:

• To demonstrate the applicability of critical transition theory and EWS in a biomedical context.
• To identify EWS preceding the onset of excessive alcohol consumption in an animal model.
• To develop a framework for analyzing intensive longitudinal data (ILD) in disease dynamics.

Main Methods:

• Utilized a validated animal model of alcohol-induced excessive drinking.
• Acquired intensive longitudinal data (ILD) on rat drinking behavior and locomotor activity over 14 weeks using automated drinkometers.
• Applied a multi-scale computational approach to extract dynamical characteristics from ILD.

Main Results:

• Observed early-warning signals preceding the transition to addictive behavior, including instability in drinking patterns and locomotor circadian rhythms.
• Detected an increase in low-frequency, ultradian rhythms during the initial deprivation week.
• Provided evidence for a critical transition towards excessive alcohol consumption during prolonged deprivation.

Conclusions:

• This study successfully demonstrates critical transitions and EWS in a model of alcohol addiction.
• The findings suggest that changes in behavioral and physiological rhythms can serve as early indicators of disease onset.
• The developed framework is adaptable for analyzing clinical ILD and advancing the study of disease dynamics and EWS in biomedicine.