van der Werf Siebren Y, Kaptein Kirsten I, de Jonge Peter, Spijker Jan, de Graaf Ron, Korf Jakob
Kernfysisch Versneller Instituut of the University of Groningen, The Netherlands.
Arch Gen Psychiatry. 2006 May;63(5):509-18. doi: 10.1001/archpsyc.63.5.509.
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.
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.
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.
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.
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.
描述情感障碍中情绪变化的数学模型可能有助于识别潜在的病理和神经生物学机制，并区分对精神科数据的不同解释。
利用一项关于重度抑郁症康复的大型流行病学调查中的事件发生时间数据，根据一个潜在过程对生存概率进行建模，该过程的参数在临床实践中可能是可识别和可影响的。
我们提出了一种用于生存分析的序贯阶段模型，该模型将抑郁症描述为一种有或没有额外潜伏期的状态。康复被视为向非抑郁状态的转变。我们表明，这种序贯阶段模型在一种动态描述即随机情绪模型中有微观实现，该模型将情绪描述为由间歇性高斯噪声驱动的奥恩斯坦 - 乌伦贝克类型的随机过程所支配。
对于可逆性抑郁症（80%），康复的分数概率显著独立于抑郁症的病史。序贯阶段模型分析表明该组呈单指数衰减，可能有一个短潜伏期。在随机情绪模型中，该可逆性抑郁队列的数据与平均间隔为4个月的间歇性刺激噪声模式相符，而与非间歇性噪声不相符。
精神科流行病学研究中的事件发生时间数据可以通过建模概念化为个体内部过程。所提出的随机情绪模型再现了事件发生时间数据，并将潜伏期解释为由于当前大多数精神科诊断系统中14天的纳入标准而产生的一种假象。发现抑郁症更多是由负面刺激的堆积而非单一生活事件导致的。使用随机情绪模型生成的时间序列产生了功率图、相空间轨迹和对关联和，类似于最近针对个体患者的结果。这表明该模型在生存分析中作为一种工具具有潜在的临床相关性。
