对重度抑郁障碍患者早起影响的主成分分析

Julia R. Higdon, Jonghoon Kang
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引用次数: 0

摘要

睡眠障碍是与重度抑郁症(MDD)相关的最普遍症状之一。最近的一篇文章(Wu 等人,2022 年,《多尺度神经科学杂志》1,133-139)探讨了早醒(EMA)这种睡眠障碍类型与 MDD 患者康复之间的重要关系。在论文中,作者用 12 项神经心理参数研究了 EMA 与 MDD 治疗之间的关系。作者采用了两种单变量统计技术--学生 t 检验和方差分析来分析数据。虽然他们的分析得出了一个有意义的结论,即 EMA 可能会在统计学和临床上导致显著的康复延迟,但我们发现,多变量统计技术--主成分分析(PCA)--从他们的研究中提取了更多的定量信息。在本文中,我们将介绍从 PCA 中获得的 EMA 与 MDD 治疗之间相互作用的定量特征。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Principal component analysis on the effect of early morning awakening in major depressive disorder
Sleep disturbance is one of the most prevalent symptoms associated with Major Depressive Disorder (MDD). A recent article (Wu et al., 2022, Journal of Multiscale Neuroscience 1, 133-139) explored the significant relationship between early morning awakening (EMA), a type of sleep disturbance, and recovery in MDD patients. In the paper, the authors examined the relationship between EMA and the treatment of MDD with twelve neuropsychological parameters. The authors employed two univariate statistical techniques, students’ t-test and ANOVA, to analyze their data. While their analysis derived a meaningful conclusion that EMA may result in a statistically and clinically significant delay in recovery, we found that a multivariate statistical technique, principal component analysis (PCA), extracted additional quantitative information from their study. In this paper, we present quantitative features in the interaction between EMA and the treatment of MDD obtained from PCA.
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