Front Psychol. 2015 Oct 21;6:1604. doi: 10.3389/fpsyg.2015.01604. eCollection 2015.
It is widely reported that expressive writing can improve mental and physical health. However, to date, the neural correlates of expressive writing have not been reported. The current study examined the neural electrical correlates of expressive writing in a reappraisal approach. Three groups of participants were required to give a public speech. Before speaking, the reappraisal writing group was asked to write about the current stressful task in a reappraisal manner. The irrelevant writing group was asked to write about their weekly plan, and the non-writing group did not write anything. It was found that following the experimental writing manipulation, both reappraisal and irrelevant writing conditions decreased self-reported anxiety levels. But when re-exposed to the stressful situation, participants in the irrelevant writing group showed increased anxiety levels, while anxiety levels remained lower in the reappraisal group. During the experimental writing manipulation period, participants in the reappraisal group had lower frontal alpha asymmetry scores than those in the irrelevant writing group. However, following re-exposure to stress, participants in the reappraisal group showed higher frontal alpha asymmetry scores than those in the irrelevant writing group. Self-reported anxiety and frontal alpha asymmetry of the non-writing condition did not change significantly across these different stages. It is noteworthy that expressive writing in a reappraisal style seems not to be a fast-acting treatment but may instead take effect in the long run.
Soc Cogn Affect Neurosci. 2017 Sep 1;12(9):1437-1447. doi: 10.1093/scan/nsx084.
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.