Encountering another’s suffering can elicit both empathic distress and empathic care—the warm desire to affiliate. It remains unclear whether these two feelings can be accurately and differentially predicted from neural activity and to what extent their neural substrates can be distinguished. We developed fMRI markers predicting moment-by-moment intensity levels of care and distress intensity while participants (N=66) listened to true biographies describing human suffering. Both markers’ predictions correlated strongly with self-report in out-of-sample participants (r = .59 and r = .63, ps<.00001), and both markers predicted later trial-by-trial charitable donation amounts (ps<.05). Empathic care was preferentially associated with nucleus accumbens and medial orbitofrontal cortex activity, while distress was preferentially associated with premotor and somatosensory cortical activity. In tests of marker specificity with an independent behavioral sample (N=200), the empathic care marker was associated with a mixed-valence feeling state while the empathic distress marker was specific to negative emotion.

Placebos are sham medical treatments. Nonetheless, they can have substantial effects on clinical outcomes. Placebos depend on a person's psychological and brain responses to the treatment context, which influence appraisals of future well-being. Appraisals are flexible cognitive evaluations of the personal meaning of events and situations that can directly impact symptoms and physiology. They also shape associative learning processes by guiding what is learned from experience. Appraisals are supported by a core network of brain regions associated with the default mode network involved in self-generated emotion, self-evaluation, thinking about the future, social cognition, and valuation of rewards and punishment. Placebo treatments for acute pain and a range of clinical conditions engage this same network of regions, suggesting that placebos affect behavior and physiology by changing how a person evaluates their future well-being and the personal significance of their symptoms.

People learn about their self from social information, and recent work suggests that healthy adults show a positive bias for learning self-related information. In contrast, social anxiety disorder (SAD) is characterized by a negative view of the self, yet what causes and maintains this negative self-view is not well understood. Here we employ a novel experimental paradigm and computational model to test the hypothesis that biased social learning regarding self-evaluation and self-feelings represents a core feature that distinguishes adults with SAD from healthy controls. Twenty-one adults with SAD and 35 healthy controls (HC) performed a speech in front of three judges. They subsequently evaluated themselves and received performance feedback from the judges, and then rated how they felt about themselves and the judges. Affective updating (i.e., change in feelings about the self over time, in response to feedback from the judges) was modeled using an adapted Rescorla-Wagner learning model. HC demonstrated a positivity bias in affective updating, which was absent in SAD. Further, self-performance ratings revealed group differences in learning from positive feedback—a difference that endured at an average of 1 year follow up. These findings demonstrate the presence and long-term endurance of positively biased social learning about the self among healthy adults, a bias that is absent or reversed among socially anxious adults.

IMPORTANCE Chronic back pain (CBP) is a leading cause of disability, and treatment is often ineffective. Approximately 85% of cases are primary CBP, for which peripheral etiology cannot be identified, and maintenance factors include fear, avoidance, and beliefs that pain indicates injury.OBJECTIVE To test whether a psychological treatment (pain reprocessing therapy [PRT]) aiming to shift patients' beliefs about the causes and threat value of pain provides substantial and durable pain relief from primary CBP and to investigate treatment mechanisms. DESIGN, SETTING, AND PARTICIPANTS This randomized clinical trial with longitudinal functional magnetic resonance imaging (fMRI) and 1-year follow-up assessment was conducted in a university research setting from November 2017 to August 2018, with 1-year follow-up completed by November 2019. Clinical and fMRI data were analyzed from January 2019 to August 2020. The study compared PRT with an open-label placebo treatment and with usual care in a community sample. INTERVENTIONS Participants randomized to PRT participated in 1 telehealth session with a physician and 8 psychological treatment sessions over 4 weeks. Treatment aimed to help patients reconceptualize their pain as due to nondangerous brain activity rather than peripheral tissue injury, using a combination of cognitive, somatic, and exposure-based techniques. Participants randomized to placebo received an open-label subcutaneous saline injection in the back; participants randomized to usual care continued their routine, ongoing care. MAIN OUTCOMES AND MEASURES One-week mean back pain intensity score (0 to 10) at posttreatment, pain beliefs, and fMRI measures of evoked pain and resting connectivity. RESULTS At baseline, 151 adults (54% female; mean [SD] age, 41.1 [15.6] years) reported mean (SD) pain of low to moderate severity (mean [SD] pain intensity, 4.10 [1.26] of 10; mean [SD] disability, 23.34 [10.12] of 100) and mean (SD) pain duration of 10.0 (8.9) years. Large group differences in pain were observed at posttreatment, with a mean (SD) pain score of 1.18 (1.24) in the PRT group, 2.84 (1.64) in the placebo group, and 3.13 (1.45) in the usual care group. Hedges g was −1.14 for PRT vs placebo and −1.74 for PRT vs usual care (P < .001). Of 151 total participants, 33 of 50 participants (66%) randomized to PRT were pain-free or nearly pain-free at posttreatment (reporting a pain intensity score of 0 or 1 of 10), compared with 10 of 51 participants (20%) randomized to placebo and 5 of 50 participants (10%) randomized to usual care. Treatment effects were maintained at 1-year follow-up, with a mean (SD) pain score of 1.51 (1.59) in the PRT group, 2.79 (1.78) in the placebo group, and 3.00 (1.77) in the usual care group. Hedges g was −0.70 for PRT vs placebo (P = .001) and −1.05 for PRT vs usual care (P < .001) at 1-year follow-up. Longitudinal fMRI showed (1) reduced responses to evoked back pain in the anterior midcingulate and the anterior prefrontal cortex for PRT vs placebo;(2) reduced responses in the anterio...

Compassion is critical for societal wellbeing. Yet, it remains unclear how specific thoughts and feelings motivate compassionate behavior, and we lack a scientific understanding of how to effectively cultivate compassion. Here, we conducted 2 studies designed to a) develop a psychological model predicting compassionate behavior, and b) test this model as a mediator of a Compassion Meditation (CM) intervention and identify the "active ingredients" of CM. In Study 1, we developed a model predicting compassionate behavior, operationalized as real-money charitable donation, from a linear combination of self-reported tenderness, personal distress, perceived blamelessness, and perceived instrumental value of helping with high cross-validated accuracy, r = .67, p < .0001. Perceived similarity to suffering others did not predict charitable donation when controlling for other feelings and attributions. In Study 2, a randomized controlled trial, we tested the Study 1 model as a mediator of CM and investigated active ingredients. We compared a smartphone-based CM program to 2 conditions-placebo oxytocin and a Familiarity intervention-to control for expectancy effects, demand characteristics, and familiarity effects. Relative to control conditions, CM increased charitable donations, and changes in the Study 1 model of feelings and attributions mediated this effect (pab = .002). The Familiarity intervention led to decreases in primary outcomes, while placebo oxytocin had no significant effects on primary outcomes. Overall, this work contributes a quantitative model of compassionate behavior, and informs our understanding of the change processes and intervention components of CM. (PsycINFO Database Record

Multivariate pattern analysis (MVPA) has become an important tool for identifying brain representations of psychological processes and clinical outcomes using fMRI and related methods. Such methods can be used to predict or ‘decode’ psychological states in individual subjects. Single-subject MVPA approaches, however, are limited by the amount and quality of individual-subject data. In spite of higher spatial resolution, predictive accuracy from single-subject data often does not exceed what can be accomplished using coarser, group-level maps, because single-subject patterns are trained on limited amounts of often-noisy data. Here, we present a method that combines population-level priors, in the form of biomarker patterns developed on prior samples, with single-subject MVPA maps to improve single-subject prediction. Theoretical results and simulations motivate a weighting based on the relative variances of biomarker-based prediction—based on population-level predictive maps from prior groups—and individual-subject, cross-validated prediction. Empirical results predicting pain using brain activity on a trial-by-trial basis (single-trial prediction) across 6 studies (N = 180 participants) confirm the theoretical predictions. Regularization based on a population-level biomarker—in this case, the Neurologic Pain Signature (NPS)—improved single-subject prediction accuracy compared with idiographic maps based on the individuals’ data alone. The regularization scheme that we propose, which we term group-regularized individual prediction (GRIP), can be applied broadly to within-person MVPA-based prediction. We also show how GRIP can be used to evaluate data quality and provide benchmarks for the appropriateness of population-level maps like the NPS for a given individual or study.

Caregiving and other interpersonal interactions often require accurate perception of others' pain from nonverbal cues, but perceivers may be subject to systematic biases based on gender, race, and other contextual factors. Such biases could contribute to systematic under-recognition and undertreatment of pain. In 2 experiments, we studied the impact of perceived patient sex on lay perceivers' pain estimates and treatment recommendations. In Experiment 1 (N = 50), perceivers viewed facial video clips of female and male patients in chronic shoulder pain and estimated patients' pain intensity. Multi-level linear modeling revealed that perceivers under-estimated female patients' pain compared with male patients, after controlling for patients' self-reported pain and pain facial expressiveness. Experiment 2 (N = 200) replicated these findings, and additionally found that 1) perceivers' pain-related gender stereotypes, specifically beliefs about typical women's vs. men's willingness to express pain, predicted pain estimation biases; and 2) perceivers judged female patients as relatively more likely to benefit from psychotherapy, whereas male patients were judged to benefit more from pain medicine. In both experiments, the gender bias effect size was on average 2.45 points on a 0−100 pain scale. Gender biases in pain estimation may be an obstacle to effective pain care, and experimental approaches to characterizing biases, such as the one we tested here, could inform the development of interventions to reduce such biases. Perspective: This study identifies a bias towards underestimation of pain in female patients, which is related to gender stereotypes. The findings suggest caregivers' or even clinicians' pain stereotypes are a potential target for intervention.

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