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Major Depressive Episodes and Random Mood
Siebren Y. van der Werf, PhD;
Kirsten I. Kaptein, MD;
Peter de Jonge, PhD;
Jan Spijker, PhD;
Ron de Graaf, PhD;
Jakob Korf, PhD
Arch Gen Psychiatry. 2006;63:509-518.
Context Mathematical models describing changes in mood in affective disorders may assist in the identification of underlying pathologic and neurobiologic mechanisms and in differentiating between alternative interpretations of psychiatric data.
Objective Using time-to-event data from a large epidemiologic survey on recovery from major depression, to model the survival probability, in terms of an underlying process, with parameters which might be recognized and influenced in clinical practice.
Design We present a sequential-phase model for survival analysis, which describes depression as a state with or without an additional incubation phase. Recovery is seen as the transition to a nondepressive state. We show that this sequential-phase model finds a microscopic realization in a dynamic description, the random-mood model, which depicts mood as governed by an Ornstein-Uhlenbeck type of stochastic process, driven by intermittent gaussian noise.
Results For reversible depression (80%), the fractional probability of recovery is remarkably independent of the history of the depression. Analysis with the sequential-phase model suggests single exponential decay in this group, possibly with a short incubation phase. Within the random-mood model, the data for this reversibly depressed cohort are compatible with an intermittent noise pattern of stimuli with average spacing of 4 months and incompatible with nonintermittent noise.
Conclusions Time-to-event data from psychiatric epidemiologic studies can be conceptualized through modeling as intrasubject processes. The proposed random-mood model reproduces the time-to-event data and explains the incubation phase as an artifact due to the inclusion criterion of 14 days in most current psychiatric diagnostic systems. Depression is found to result more often from pileup of negative stimuli than from single life events. Time sequences, generated using the random-mood model, produce power plots, phase-space trajectories, and pair-correlation sums, similar to recent results for individual patients. This suggests possible clinical relevance along with the model's use as a tool in survival analysis.
Author Affiliations: Kernfysisch Versneller Instituut of the University of Groningen (Dr van der Werf); Discipline Group Psychiatry, University Hospital of Groningen (Drs Kaptein, de Jonge, and Korf); Netherlands Institute of Mental Health and Addiction, Utrecht (Drs Spijker and de Graaf), the Netherlands.
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