Neural activity during affect labeling predicts expressive writing effects on well-being: GLM and SVM approaches

Abstract

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.

Keywords: affect labeling; expressive writing; functional magnetic resonance imaging (fMRI); supervised learning; support vector machines.

Fig. 1.

Task completed by the subjects in the fMRI scanner. The paradigm for this study included two conditions: (A) affect label, (B) gender label. During the affect label condition (A), participants were shown a series of human face images showing various emotions. For each image, the participant was asked to choose one of two given label options presented on the screen that best described the facial emotion in the image (e.g. angry, fearful and happy). A non-emotional GL task (B) was also included as a control condition indexing simple cognitive responses. Functional peaks were determined based on group-level analysis of data, using the affect label–gender label contrast, and 5% significance level (FDR P < 0.05).

Fig. 2.

System overview of the machine learning-based prediction protocol.

Fig. 3.

RVLPFC (shown in green) and left amygdala (shown in brown) were top predictors of the four outcome measures of psychological and physical health, i.e. BDI, SWLS, PILL and BSI_ANX (axial view). Moreover, right ventral striatum (shown in purple), as well as right interior frontal gyrus pars triangularis, and left amygdala showed significant differential predictiveness as a function of condition (expressive writing or control).

Fig. 4.

Performance comparison between the conventional GLM method and the SVM approach. The reported values are percentage of average prediction errors (RMSE %) calculated based on a LOOCV scheme. A Mann–Whitney U-test between prediction error samples (113 samples for 113 subjects) showed significantly lower error (superior performance) for the SVM method compared with the GLM method, in all cases (P < 0.05).

Fig. 5.

Percentage of average prediction error (RMSE %) for GLM and SVM-rbf predictive models based on inclusion of each feature and its higher ranking features (as listed in Table 3) for (a) BDI, (b) SWLS, (c) PILL and (d) BSI_ANX.

Fig. 6.

Contribution of the neural predictors as selected by the feature selection algorithm in predicting the four outcomes (BDI, Beck Depression Inventory score; SWLS, Satisfaction with Life Scale; PILL, Pennebaker Inventory of Limbic Languidness score) BSI_ANX, Brief Symptom Inventory Anxiety score. Plus sign represents positive correlations between the activity of the brain region and improvement on the outcome, whereas a minus sign represents a negative correlation between the feature and the outcome improvement.