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Mood Oscillations and Coupling Between Mood and Weather in Patients with Rapid Cycling Bipolar Disorder

机译:快速骑自行车双相情感障碍患者的情绪波动与情绪与天气的耦合

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摘要

Rapid Cycling Bipolar Disorder (RCBD) outpatients completed twice-daily mood self-ratings for 3 consecutive months. These ratings were matched with local measurements of atmospheric pressure, cloud cover, and temperature. Several alternative second order differential equation models were fit to the data in which mood oscillations in RCBD were allowed to be linearly coupled with daily weather patterns. The modeling results were consistent with an account of mood regulation that included intrinsic homeostatic regulation as well as coupling between weather and mood. Models were tested first in a nomothetic method where models were fit over all individuals and fit statistics of each model compared to one another. Since substantial individual differences in intrinsic dynamics were observed, the models were next fit using an ideographic method where each individual’s data were fit separately and best-fitting models identified. The best-fitting within-individual model for the largest number of individuals was also the best-fitting nomothetic model: temperature and the first derivative of temperature coupled to mood and no effect of barometric pressure or cloud cover. But this model was not the best-fitting model for all individuals, suggesting that there may be substantial individual differences in the dynamic association between weather and mood in RCBD patients. Heterogeneity in the parameters of the differential equation model of homeostatic equilibrium as well as the coupling of mood to an inherently unpredictable (i.e., nonstationary) process such as weather provide an alternative account for reported broadband frequency spectra of daily mood in RCBD.
机译:快速自行车双相情感障碍(RCBD)门诊患者连续3个月完成了每天两次的情绪自评。这些额定值与大气压,云量和温度的局部测量值相匹配。几种替代的二阶微分方程模型适合于数据,其中RCBD中的情绪波动与日常天气模式线性关联。建模结果与包括内在的体内稳态调节以及天气和情绪之间的耦合在内的情绪调节有关。首先以非自然方法对模型进行测试,其中模型适用于所有个体,并且每个模型之间的拟合统计量相互比较。由于观察到内在动力学存在很大的个体差异,因此接下来使用表意方法对模型进行拟合,其中将每个人的数据分别拟合并确定最佳拟合模型。最适合个体的最适合个体内部模型也是最适合的代名词模型:温度和温度的一阶导数与情绪耦合,并且不受气压或云层覆盖的影响。但是,该模型并不是适用于所有个体的最佳模型,这表明RCBD患者的天气和情绪之间的动态关联可能存在实质性个体差异。稳态平衡​​微分方程模型参数的异质性以及情绪与诸如天气之类的固有不可预测(即非平稳)过程的耦合为RCBD中每日情绪的宽带频谱提供了另一种解释。

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