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Updated: Jan 10, 2026

A Chronic Immobilization Stress Protocol for Inducing Depression-Like Behavior in Mice
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Major depressive episodes and random mood.

Siebren Y van der Werf1, Kirsten I Kaptein, Peter de Jonge

  • 1Kernfysisch Versneller Instituut of the University of Groningen, The Netherlands. s.y.van.der.werf@rug.nl

Archives of General Psychiatry
|May 3, 2006
PubMed
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Mathematical modeling of depression recovery reveals that mood fluctuations follow a random-mood process. Recovery probability is independent of depression history, suggesting intermittent negative stimuli, not single events, trigger major depression.

Area of Science:

  • Psychiatry and Mathematical Biology
  • Computational Psychiatry
  • Stochastic Processes in Medicine

Background:

  • Mathematical models can elucidate mechanisms in affective disorders.
  • Understanding mood dynamics aids in interpreting psychiatric data and identifying neurobiological underpinnings.

Purpose of the Study:

  • To model the survival probability of recovery from major depression using time-to-event data.
  • To develop a model with parameters relevant for clinical practice and understanding depression.

Main Methods:

  • A sequential-phase model for survival analysis was developed, conceptualizing depression with a potential incubation phase.
  • This model was realized as a random-mood model, using an Ornstein-Uhlenbeck stochastic process with intermittent Gaussian noise to describe mood dynamics.

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Last Updated: Jan 10, 2026

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Main Results:

  • For reversible depression (80% of cases), recovery probability was largely independent of depression history, suggesting a short incubation phase.
  • The random-mood model indicated that recovery is associated with intermittent stimuli, with an average spacing of 4 months, rather than continuous noise.

Conclusions:

  • Time-to-event data in psychiatric epidemiology can be modeled as intrasubject processes.
  • The random-mood model explains the incubation phase as a diagnostic artifact and suggests depression arises from accumulated negative stimuli.
  • Model-generated time sequences mimic individual patient data, indicating clinical relevance for survival analysis in psychiatry.