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Nonlinear time-series approaches in characterizing mood stability and mood instability in bipolar disorder.

机译：非线性时间序列方法用于表征躁郁症的情绪稳定和情绪不稳定。

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摘要

Bipolar disorder is a psychiatric condition characterized by episodes of elevated mood interspersed with episodes of depression. While treatment developments and understanding the disruptive nature of this illness have focused on these episodes, it is also evident that some patients may have chronic week-to-week mood instability. This is also a major morbidity. The longitudinal pattern of this mood instability is poorly understood as it has, until recently, been difficult to quantify. We propose that understanding this mood variability is critical for the development of cognitive neuroscience-based treatments. In this study, we develop a time-series approach to capture mood variability in two groups of patients with bipolar disorder who appear on the basis of clinical judgement to show relatively stable or unstable illness courses. Using weekly mood scores based on a self-rated scale (quick inventory of depressive symptomatology-self-rated; QIDS-SR) from 23 patients over a 220-week period, we show that the observed mood variability is nonlinear and that the stable and unstable patient groups are described by different nonlinear time-series processes. We emphasize the necessity in combining both appropriate measures of the underlying deterministic processes (the QIDS-SR score) and noise (uncharacterized temporal variation) in understanding dynamical patterns of mood variability associated with bipolar disorder.
机译：躁郁症是一种精神疾病，其特征是情绪高涨发作时散布着抑郁症。尽管治疗的发展和对这种疾病的破坏性的理解集中在这些发作上，但也很明显，有些患者可能会出现每周几周的慢性情绪不稳定。这也是主要的发病率。直到最近，这种情绪不稳定的纵向模式还很难被量化。我们建议理解这种情绪变化对于基于认知神经科学的治疗方法的发展至关重要。在这项研究中，我们开发了一种时间序列方法来捕获两组躁郁症患者的情绪变化，这些患者根据临床判断出现表现出相对稳定或不稳定的病程。使用基于220周内自评量表（抑郁症状自评量表； QIDS-SR）的每周情绪评分，我们发现观察到的情绪变化是非线性的，并且稳定且稳定。不稳定的患者群体通过不同的非线性时间序列过程进行描述。我们强调必须结合潜在的确定性过程（QIDS-SR评分）和噪声（无特征的时间变化）的适当度量，以了解与躁郁症相关的情绪变化的动态模式。

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