Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.
Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches.
复制标题
情感标记期间的神经活动预测表达性写作对幸福感的影响：GLM 和 SVM 方法。
DOI：
10.1093/scan/nsx084
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发表时间：
2017
影响因子：
4.2
通讯作者：
Lieberman,MatthewD
中科院分区：
文献类型：
--
作者：
Memarian,Negar;Torre,JaredB;Haltom,KateE;Stanton,AnnetteL;Lieberman,MatthewD
情感标签是一种附带的情绪调节形式，可以支持表现性写作的一些好处(即写关于负面经历的文章)。在这里，我们表明，神经反应的影响标签预测变化的心理和身体健康的结果指标3个月后 。此外，特定额叶区域和杏仁核的神经活动作为表情写作的函数预测了这些结果。使用有监督学习(支持向量机回归)，对表现性写作干预后的4项心身健康指标(躯体症状、抑郁、焦虑和生活满意度)的改善进行预测，平均预测误差为0.85%[均方根误差(RMSE)%]。机器学习的预测精度明显高于传统的广义线性模型方法(平均RMSE：1.3%)。与情感标签研究一致，右腹外侧前额叶皮质(RVLPFC)和杏仁核是四种结果改善的最高预测因素。此外，RVLPFC和左侧杏仁核分别预测了表达写作对生活满意度和抑郁结果测量的好处。这项研究证明了有监督的机器学习在社会和情感神经科学中用于现实世界结果预测的实质性优点。
Affect labeling (putting feelings into words) is a form of incidental emotion regulation that could underpin some benefits of expressive writing (i.e. writing about negative experiences). Here, we show that neural responses during affect labeling predicted changes in psychological and physical well-being outcome measures 3 months later. Furthermore, neural activity of specific frontal regions and amygdala predicted those outcomes as a function of expressive writing. Using supervised learning (support vector machines regression), improvements in four measures of psychological and physical health (physical symptoms, depression, anxiety and life satisfaction) after an expressive writing intervention were predicted with an average of 0.85% prediction error [root mean square error (RMSE) %]. The predictions were significantly more accurate with machine learning than with the conventional generalized linear model method (average RMSE: 1.3%). Consistent with affect labeling research, right ventrolateral prefrontal cortex (RVLPFC) and amygdalae were top predictors of improvement in the four outcomes. Moreover, RVLPFC and left amygdala predicted benefits due to expressive writing in satisfaction with life and depression outcome measures, respectively. This study demonstrates the substantial merit of supervised machine learning for real-world outcome prediction in social and affective neuroscience.