Kondo Fumika, Whitehead Jocelyne C, Corbalán Fernando, Beaulieu Serge, Armony Jorge L
Douglas Mental Health University Institute, Verdun, QC, Canada.
Integrated Program in Neuroscience, McGill University, Montreal, QC, Canada.
Int J Bipolar Disord. 2023 Mar 25;11(1):12. doi: 10.1186/s40345-023-00292-w.
Bipolar disorder type-I (BD-I) patients are known to show emotion regulation abnormalities. In a previous fMRI study using an explicit emotion regulation paradigm, we compared responses from 19 BD-I patients and 17 matched healthy controls (HC). A standard general linear model-based univariate analysis revealed that BD patients showed increased activations in inferior frontal gyrus when instructed to decrease their emotional response as elicited by neutral images. We implemented multivariate pattern recognition analyses on the same data to examine if we could classify conditions within-group as well as HC versus BD.
We reanalyzed explicit emotion regulation data using a multivariate pattern recognition approach, as implemented in PRONTO software. The original experimental paradigm consisted of a full 2 × 2 factorial design, with valence (Negative/Neutral) and instruction (Look/Decrease) as within subject factors.
The multivariate models were able to accurately classify different task conditions when HC and BD were analyzed separately (63.24%-75.00%, p = 0.001-0.012). In addition, the models were able to correctly classify HC versus BD with significant accuracy in conditions where subjects were instructed to downregulate their felt emotion (59.60%-60.84%, p = 0.014-0.018). The results for HC versus BD classification demonstrated contributions from the salience network, several occipital and frontal regions, inferior parietal lobes, as well as other cortical regions, to achieve above-chance classifications.
Our multivariate analysis successfully reproduced some of the main results obtained in the previous univariate analysis, confirming that these findings are not dependent on the analysis approach. In particular, both types of analyses suggest that there is a significant difference of neural patterns between conditions within each subject group. The multivariate approach also revealed that reappraisal conditions provide the most informative activity for differentiating HC versus BD, irrespective of emotional valence (negative or neutral). The current results illustrate the importance of investigating the cognitive control of emotion in BD. We also propose a set of candidate regions for further study of emotional control in BD.
已知I型双相情感障碍（BD-I）患者存在情绪调节异常。在之前一项使用明确情绪调节范式的功能磁共振成像（fMRI）研究中，我们比较了19名BD-I患者和17名匹配的健康对照者（HC）的反应。基于标准一般线性模型的单变量分析显示，当被指示减少由中性图像引发的情绪反应时，BD患者额下回的激活增加。我们对相同数据进行多变量模式识别分析，以检查我们是否能够在组内以及HC与BD之间对条件进行分类。
我们使用PRONTO软件中实现的多变量模式识别方法重新分析了明确情绪调节数据。原始实验范式包括一个完整的2×2析因设计，以内在主题因素为效价（负性/中性）和指令（观看/减少）。
当分别分析HC和BD时，多变量模型能够准确地对不同任务条件进行分类（63.24%-75.00%，p = 0.001-0.012）。此外，在指示受试者下调其感受情绪的条件下，模型能够以显著的准确性正确区分HC与BD（59.60%-60.84%，p = 0.014-0.018）。HC与BD分类的结果表明，显著性网络、几个枕叶和额叶区域、顶下小叶以及其他皮质区域对实现高于机会水平的分类有贡献。
我们的多变量分析成功再现了先前单变量分析中获得的一些主要结果，证实这些发现不依赖于分析方法。特别是，两种类型的分析都表明每个受试者组内不同条件之间存在神经模式的显著差异。多变量方法还表明，无论情绪效价（负性或中性）如何，重新评估条件为区分HC与BD提供了最具信息性的活动。当前结果说明了研究BD中情绪认知控制的重要性。我们还提出了一组候选区域，用于进一步研究BD中的情绪控制。
