Mechanisms of Positive and Minimizing Reappraisal Bruce P. Doré Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Graduate School of Arts and Sciences COLUMBIA UNIVERSITY 2016 © 2016 Bruce Doré All rights reserved Abstract Mechanisms of Positive and Minimizing Reappraisal Bruce P. Doré The ability to find positive meaning and in turn generate positive emotions in the face of negative life circumstances is a protective factor against the harmful effects of stress, and a critical pathway to resilience and growth. Despite its clear importance, little is known about the brain mechanisms that support this ability, the processes that underlie decisions to implement it, or the long-term effects it has on memories of negative life experiences. Study 1 shows that finding positive meaning in negative experiences engages the brain’s system for reward valuation, whereas minimizing negative emotions dampens activity in a region involved in generating emotional arousal. Study 2 shows that spontaneous brain responses to aversive stimuli can be used to prospectively predict decisions to regulate emotion, and the predictive value of these responses is comparable across finding positive meaning and minimizing negative emotions. Study 3 shows that finding positive meaning and minimizing negative feelings can bring about distinct lasting effects on the content and affective impact of memories of negative experiences. Table of Contents List of Figures …………..….….….….….….….….….….….….….….…...…..........ii Chapter 1: Introduction…..….….….….….….….….….….….….….….…...….........1 Chapter 2 : Finding positive meaning in negative experiences engages ventral striatal and ventromedial prefrontal regions associated with reward valuation ..….….….….….….….….….….….….….……….….….….….……3 Chapter 3: Neural predictors of decisions to regulate emotion………..………...27 Chapter 4: Reappraisal causes lasting changes in the content and emotional impact of negative autobiographical memories…………….…………..56 References ...…………………………………………………………………………..83 i List of Figures Figure 2.1 Trial sequence for fMRI instructed reappraisal task……………………7 Figure 2.2 A) Effects of experimental condition on self-reported valence of affective experience. B) Effects of experimental condition on use of words from negative emotion (negemo) and positive emotion (posemo) categories. C) Comparison cloud visualizing individual words that show the highest relative frequencies of use for natural response (red), minimizing reappraisal (grey), and positive reappraisal (blue) conditions. Means ±SE………………………………...13 Figure 2.3 A) Common regions of activation for minimizing and positive reappraisal, identified with a conjunction of reappraisal contrasts. B) Mean betas (±SE) extracted from an anatomically and functionally constrained amygdala ROI indicate more negative trial-to-trial modulation of the amygdala for minimizing reappraisal than positive reappraisal. C) Mean betas (±SE) extracted from an anatomical nucleus accumbens ROI and a meta-analytically defined vmPFC ROI indicate more positive trial-to-trial modulation for positive reappraisal than minimizing reappraisal………………………………………………………………...16 Figure 2.4 Regions tracking trial-to-trial affect for positive reappraisal condition more so than minimizing reappraisal or natural response, identified with a whole- brain contrast of parametric maps corrected at FWE p<.05. Mean betas shown for descriptive visualization -- because these betas were derived from clusters derived from a whole-brain search, we omit error bars and significance tests….18 Figure 2.5 A) Connectivity between vmPFC seed region and amygdala is enhanced for positive reappraisal, relative to natural response condition. B) More positive connectivity between vmPFC seed region and amygdala, nucleus accumbens, dmPFC, and dlPFC regions predicts greater positive reappraisal success. Activations within amygdala and ventral striatum ROIs displayed at p<.005 uncorrected, and positive reappraisal success by connectivity beta scatterplots shown for descriptive visualization…………………………………….20 Figure 3.1 A) Negative image viewing task, and B) emotion regulation choice task. Predictive models use brain responses in the negative image viewing task to predict participant choice behaviors in the emotion regulation choice task.....31 Figure 3.2 Scatterplots and generalized linear model fits reflecting predictive relationships between person-to-person variability in brain activity in the negative image viewing task and subsequent reappraisal choice frequencies, for a priori A) anatomically-defined amygdala, and B) meta-analytically defined dmPFC, dlPFC, and vlPFC bilateral regions of interest……………………………………...39 Figure 3.3 A) Plot of regression coefficients (with 95%CI) for the full model (model 2) predicting reappraisal choice frequencies from brain and self-report ii variables (entered as simultaneous predictors). B) Model comparison metrics for full model (model 2), and reduced model including only negative affect ratings and positive affect ratings (model 1). Model fit is summarized by leave-one-out cross-validated error (LOOIC), AIC, and BIC information criterion scores (i.e., adjusted model deviance, a measure of the relative quality of the models for these data). A lower number indicates better fit……………………………………40 Figure 3.4 Greater average amygdala activity in the negative image viewing task predicts subsequently choosing to regulate emotion more frequently, and this relationship is mediated by greater prefrontal cortex activity during the image viewing task……….……….……….……….……….……….……….……….………41 Figure 3.5 Regions of the brain for which person-to-person variability in activity during the negative image viewing task is most correlated with subsequent reappraisal choices (whole-brain FWE p<.05)……………………………………...43 Figure 3.6 A) Meta-analytically derived whole-brain pattern associated with regulating emotion via reappraisal (display is thresholded at z>2.6, k=20, but all voxels were used in analyses). B) Trial-to-trial variability in expression of this pattern (z-transformed) is predictive of subsequently choosing to use reappraisal for particular images…..……….……….……….……….……….……….…………..45 Figure 3.7 A) Plot of regression coefficients (with standard error and 95%CI) for the full model (model 4) predicting reappraisal choices from brain, self-reports of affect, and normative ratings of the images (entered as simultaneous predictors) B) Model comparison metrics for full model (model 4), and reduced models with only ROIs, affect, and image norms (model 3), affect and image norms (model 2), and image norms only (model 1) C) ROC curves depicting prediction accuracy for models 1,2,3, and 4 in terms of sensitivity (correct prediction of reappraisal choices) and specificity (correct prediction of natural response choices), across a range of possible prediction thresholds….……….……….……….……….……….47 Figure 3.8 A) Plot of regression coefficients (with 95%CI) for the full model (model 2) predicting reappraisal choices from brain and self-report variables (entered as simultaneous predictors), fit to all reappraisal choices (in black), as well as only positive reappraisal choices (in blue) and only minimizing reappraisal choices (in grey). B) Plot of regression coefficients (with standard error and 95%CI) for the full model (model 4) predicting reappraisal choices from brain and self-report variables (entered as simultaneous predictors), fit to all reappraisal choices (in black), as well as only positive reappraisal choice (in blue) and only minimizing reappraisal choices (in grey)………………………………………….…49 Figure 4.1 Experimental design. Participants recalled three memories on study day 1, used positive reappraisal, minimizing reappraisal, or again recalled these three memories on day 2, and then recalled the three memories on study day 9. At all time points, participants typed their thoughts about the remembered iii experience – that is, they typed out everything that came to mind when recalling the memory.…………………………………………………………………………….62 Figure 4.2 Effects of study day and experimental group on ratings of negative affect, positive affect, memory vividness, and psychological distance when recalling and typing the content of negative autobiographical memories………..68 Figure 4.3 Effects of study day and experimental group on use of negative emotion words, positive emotion words, first-person singular pronouns, and psychological distance words when recalling and typing the content of negative autobiographical memories. Example words in context are paraphrased from memory texts selected at random……………………………………………………70 Figure 4.4 Factors predicting A) lasting change in negative affect, B) lasting change in positive affect, and C) lasting change in psychological distance experienced when recalling and typing the content of negative autobiographical memories……………………………………………………………………………….73 iv Acknowledgments This dissertation would not have been possible without the support of my family, friends, and co-workers, as well as countless others who have guided and inspired me. A giant thank you to my advisor Kevin Ochsner for believing in me and helping me discover my voice in science. Thanks also to my undergraduate research advisors Alan Kingstone and Linda Parker. Thank you to the full-time and student researchers who worked to make this research happen, especially Chelsea Boccagno, Daisy Burr, Giselle Doepker, Alexa Hubbard, Kan Long, and Jochen Weber. Thanks to my fellow SCAN lab members