Dynamics of data-driven microstates in bipolar disorder.

Dynamics of data-driven microstates in bipolar disorder.
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双相情感障碍中数据驱动微观状态的动力学。

DOI:
10.1016/j.jpsychires.2021.07.021
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发表时间:
2021-09
影响因子:
4.8
通讯作者:
Cochran AL
Cochran AL
中科院分区:
医学2区
文献类型:
--
作者:
Yee MA;Yocum AK;McInnis MG;Cochran AL

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许多现有的双相情感障碍的情绪模型在很大程度上可以分为两个阵营,跟踪情绪作为一个离散或连续变量。两组都依赖于某些假设,大多数只考虑临床工具的总评分。在这项研究中,我们提出了一个新的框架,该框架结合了离散和连续情绪模型的元素,使用机器学习管道来检测个体之间的微妙模式。潜在因素是从项目级别的评估中构建的,然后聚类成被称为微观状态的组。微观状态之间的转换通过离散时间马尔可夫链捕获,允许情绪的动态性质的表征。主要发现包括一个严重映射到易怒和攻击性的因素,以及抑郁症和躁狂症中微观状态的层次模式。这些结果的有效性通过在来自单独受试者队列的不可见数据集中再现来证实。
Many of the existing models of mood in bipolar disorder can largely be divided into two camps, tracking mood as either a discrete or continuous variable. Both groups rely upon certain assumptions, with most considering only aggregate scores on clinical instruments. In this study, we propose a novel framework that combines elements from both discrete and continuous mood models, using a machine learning pipeline to detect subtle patterns across individuals. Latent factors are constructed from assessments at the item level, then clustered into groups referred to as microstates. Transitions between microstates are captured via a discrete-time Markov chain, allowing for characterization of mood’s dynamic nature. Key findings include a factor mapping heavily onto irritability and aggression, as well as a hierarchical pattern of microstates within depression and mania. Validity of these results is confirmed by reproduction in an unseen data set from a separate subject cohort.
DOI: 10.1017/s0033291714000439
发表时间: 2014-10
影响因子: 6.9
作者:
Burdick KE;Russo M;Frangou S;Mahon K;Braga RJ;Shanahan M;Malhotra AK
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