The Past, Present, and Future of the Brain Imaging Data Structure (BIDS).

The Brain Imaging Data Structure (BIDS) is a community-driven standard for the organization of data and metadata from a growing range of neuroscience modalities. This paper is meant as a history of how the standard has developed and grown over time. We outline the principles behind the project, the mechanisms by which it has been extended, and some of the challenges being addressed as it evolves. We also discuss the lessons learned through the project, with the aim of enabling researchers in other domains to learn from the success of BIDS.

Application of a Clinical Approach to Diagnosing Primary Pain: Prevalence and Correlates of Primary Back and Neck Pain in a Community Physiatry Clinic.

Chronic back or neck pain (CBNP) can be primary (nociplastic or neuroplastic; without clear peripheral etiology) or secondary (to nociceptive or neuropathic causes). Expanding on available models of nociplastic pain, we developed a clinic-ready approach to diagnose primary/nociplastic pain: first, a standard physical exam and review of imaging to rule out secondary pain; and second, a detailed history of symptom presentation to rule in primary pain. We trained a physician who evaluated 222 patients (73.9% female, age M = 59.6) with CBNP; patients separately completed pain and psychosocial questionnaires. We estimated the prevalence of primary CBNP and explored biomedical, imaging, and psychological correlates of primary CBNP. Although almost all patients (97.7%) had at least 1 spinal anomaly on imaging, the diagnostic approach estimated that 88.3% of patients had primary pain, 5.0% had secondary pain, and 6.8% had mixed pain. Patients with primary pain were more likely than the other 2 groups of patients (combined as "non-primary pain") to report certain functional conditions, central sensitization, and features such as sensitivity to light touch, spreading pain, and pain worsening with stress; however, no difference was detected in depression, anxiety, and pain catastrophizing between those with primary and nonprimary pain. These findings are consistent with prior estimates that 85 to 90% of CBNP is "nonspecific." Further research is needed to validate and perhaps refine this diagnostic approach, which holds the potential for better outcomes if patients are offered treatments targeted to primary pain, such as pain neuroscience education and several emerging psychological therapies. PERSPECTIVE: We developed an approach to diagnose chronic primary pain, which was applied in a physiatry clinic to 222 patients with CBNP. Most patients (88.3%) had primary pain, despite almost universal anomalies on spinal imaging. This diagnostic approach can guide educational and psychological treatments tailored for primary pain.

Reattribution to Mind-Brain Processes and Recovery From Chronic Back Pain: A Secondary Analysis of a Randomized Clinical Trial.

Importance: In primary chronic back pain (CBP), the belief that pain indicates tissue damage is both inaccurate and unhelpful. Reattributing pain to mind or brain processes may support recovery. Objectives: To test whether the reattribution of pain to mind or brain processes was associated with pain relief in pain reprocessing therapy (PRT) and to validate natural language-based tools for measuring patients' symptom attributions. Design, Setting, and Participants: This secondary analysis of clinical trial data analyzed natural language data from patients with primary CBP randomized to PRT, placebo injection control, or usual care control groups and treated in a US university research setting. Eligible participants were adults aged 21 to 70 years with CBP recruited from the community. Enrollment extended from 2017 to 2018, with the current analyses conducted from 2020 to 2022. Interventions: PRT included cognitive, behavioral, and somatic techniques to support reattributing pain to nondangerous, reversible mind or brain causes. Subcutaneous placebo injection and usual care were hypothesized not to affect pain attributions. Main Outcomes and Measures: At pretreatment and posttreatment, participants listed their top 3 perceived causes of pain in their own words (eg, football injury, bad posture, stress); pain intensity was measured as last-week average pain (0 to 10 rating, with 0 indicating no pain and 10 indicating greatest pain). The number of attributions categorized by masked coders as reflecting mind or brain processes were summed to yield mind-brain attribution scores (range, 0-3). An automated scoring algorithm was developed and benchmarked against human coder-derived scores. A data-driven natural language processing (NLP) algorithm identified the dimensional structure of pain attributions. Results: We enrolled 151 adults (81 female [54%], 134 White [89%], mean [SD] age, 41.1 [15.6] years) reporting moderate severity CBP (mean [SD] intensity, 4.10 [1.26]; mean [SD] duration, 10.0 [8.9] years). At pretreatment, 41 attributions (10%) were categorized as mind- or brain-related across intervention conditions. PRT led to significant increases in mind- or brain-related attributions, with 71 posttreatment attributions (51%) in the PRT condition categorized as mind- or brain-related, as compared with 22 (8%) in control conditions (mind-brain attribution scores: PRT vs placebo, g = 1.95 [95% CI, 1.45-2.47]; PRT vs usual care, g = 2.06 [95% CI, 1.57-2.60]). Consistent with hypothesized PRT mechanisms, increases in mind-brain attribution score were associated with reductions in pain intensity at posttreatment (standardized ß = -0.25; t127 = -2.06; P = .04) and mediated the effects of PRT vs control on 1-year follow-up pain intensity (ß = -0.35 [95% CI, -0.07 to -0.63]; P = .05). The automated word-counting algorithm and human coder-derived scores achieved moderate and substantial agreement at pretreatment and posttreatment (Cohen κ = 0.42 and 0.68, respectively). The data-driven NLP algorithm identified a principal dimension of mind and brain vs biomechanical attributions, converging with hypothesis-driven analyses. Conclusions and Relevance: In this secondary analysis of a randomized trial, PRT increased attribution of primary CBP to mind- or brain-related causes. Increased mind-brain attribution was associated with reductions in pain intensity.

"I don't have chronic back pain anymore": Patient Experiences in Pain Reprocessing Therapy for Chronic Back Pain.

In a recently published randomized controlled trial, two-thirds of the patients receiving a novel psychological treatment, pain reprocessing therapy (PRT), reported elimination or near-elimination of chronic back pain. The mechanisms of PRT and related treatments remain poorly understood but are hypothesized to center on pain reappraisal, fear reduction, and exposure-potentiated extinction. Here, we investigated treatment mechanisms from the participants' perspective. A sample of 32 adults with chronic back pain who received PRT completed semi-structured posttreatment interviews about their treatment experiences. The interviews were analyzed with multiphase thematic analysis. The analyses identified 3 major themes reflecting participants' understanding of how PRT led to pain relief: 1) reappraisal to reduce fear of pain, which included guiding participants to relate to pain as a helpful indicator, overcoming pain-related fear and avoidance, and reconceptualizing pain as a "sensation;" 2) the link between pain, emotions, and, stress, which included gaining insight into these connections and resolving difficult emotions; and 3) social connections, which included patient-provider alliance, therapist belief in the treatment model, and peer models of recovery from chronic pain. Our findings support the hypothesized mechanisms of PRT centered on pain reappraisal and fear reduction, but also highlight additional processes from the participants' perspective, including a focus on emotions and relationships. This study underscores the value of qualitative research methods in illuminating the mechanisms of novel pain therapies. PERSPECTIVE: This article presents participants' perspectives on their experience engaging in a novel psychotherapy for chronic pain, PRT. Through pain reappraisal, linking pain, emotions, and stress, and connecting with their therapist and peers, many participants reported an elimination or near-elimination of their chronic back pain with therapy.

Confounds in neuroimaging: A clear case of sex as a confound in brain-based prediction.

Muscle weakness is common in many neurological, neuromuscular, and musculoskeletal conditions. Muscle size only partially explains muscle strength as adaptions within the nervous system also contribute to strength. Brain-based biomarkers of neuromuscular function could provide diagnostic, prognostic, and predictive value in treating these disorders. Therefore, we sought to characterize and quantify the brain's contribution to strength by developing multimodal MRI pipelines to predict grip strength. However, the prediction of strength was not straightforward, and we present a case of sex being a clear confound in brain decoding analyses. While each MRI modality-structural MRI (i.e., gray matter morphometry), diffusion MRI (i.e., white matter fractional anisotropy), resting state functional MRI (i.e., functional connectivity), and task-evoked functional MRI (i.e., left or right hand motor task activation)-and a multimodal prediction pipeline demonstrated significant predictive power for strength (R 2 = 0.108-0.536, p ≤ 0.001), after correcting for sex, the predictive power was substantially reduced (R 2 = -0.038-0.075). Next, we flipped the analysis and demonstrated that each MRI modality and a multimodal prediction pipeline could significantly predict sex (accuracy = 68.0%-93.3%, AUC = 0.780-0.982, p < 0.001). However, correcting the brain features for strength reduced the accuracy for predicting sex (accuracy = 57.3%-69.3%, AUC = 0.615-0.780). Here we demonstrate the effects of sex-correlated confounds in brain-based predictive models across multiple brain MRI modalities for both regression and classification models. We discuss implications of confounds in predictive modeling and the development of brain-based MRI biomarkers, as well as possible strategies to overcome these barriers.

Effect of Pain Reprocessing Therapy vs Placebo and Usual Care for Patients With Chronic Back Pain: A Randomized Clinical Trial.

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 anterior insula for PRT vs usual care; (3) increased resting connectivity from the anterior prefrontal cortex and the anterior insula to the primary somatosensory cortex for PRT vs both control groups; and (4) increased connectivity from the anterior midcingulate to the precuneus for PRT vs usual care. Conclusions and Relevance: Psychological treatment centered on changing patients' beliefs about the causes and threat value of pain may provide substantial and durable pain relief for people with CBP. Trial Registration: ClinicalTrials.gov Identifier: NCT03294148.

Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature.

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

Effects of compassion training on brain responses to suffering others.

Compassion meditation (CM) is a promising intervention for enhancing compassion, although its active ingredients and neurobiological mechanisms are not well-understood. To investigate these, we conducted a three-armed placebo-controlled randomized trial (N = 57) with longitudinal functional magnetic resonance imaging (fMRI). We compared a 4-week CM program delivered by smartphone application with (i) a placebo condition, presented to participants as the compassion-enhancing hormone oxytocin, and (ii) a condition designed to control for increased familiarity with suffering others, an element of CM which may promote compassion. At pre- and post-intervention, participants listened to compassion-eliciting narratives describing suffering others during fMRI. CM increased brain responses to suffering others in the medial orbitofrontal cortex (mOFC) relative to the familiarity condition, p < 0.05 family-wise error rate corrected. Among CM participants, individual differences in increased mOFC responses positively correlated with increased compassion-related feelings and attributions, r = 0.50, p = 0.04. Relative to placebo, the CM group exhibited a similar increase in mOFC activity at an uncorrected threshold of P < 0.001 and 10 contiguous voxels. We conclude that the mOFC, a region closely related to affiliative affect and motivation, is an important brain mechanism of CM. Effects of CM on mOFC function are not explained by familiarity effects and are partly explained by placebo effects.

Brain markers predicting response to cognitive-behavioral therapy for social anxiety disorder: an independent replication of Whitfield-Gabrieli et al. 2015.

Predictive brain markers promise a number of important scientific, clinical, and societal applications. Over 600 predictive brain markers have been described in published reports, but very few have been tested in independent replication attempts. Here, we conducted an independent replication of a previously published marker predicting treatment response to cognitive-behavioral therapy for social anxiety disorder from patterns of resting-state fMRI amygdala connectivity1. The replication attempt was conducted in an existing dataset similar to the dataset used in the original report, by a team of independent investigators in consultation with the original authors. The precise model described in the original report positively predicted treatment outcomes in the replication dataset, but with marginal statistical significance, permutation test p = 0.1. The effect size was substantially smaller in the replication dataset, with the model explaining 2% of the variance in treatment outcomes, as compared to 21% in the original report. Several lines of evidence, including the current replication attempt, suggest that features of amygdala function or structure may be able to predict treatment response in anxiety disorders. However, predictive models that explain a substantial amount of variance in independent datasets will be needed for scientific and clinical applications.

Gender Biases in Estimation of Others' Pain.

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.

Empathic Care and Distress: Predictive Brain Markers and Dissociable Brain Systems.

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 = 0.59 and r = 0.63, p < 0.00001), and both markers predicted later trial-by-trial charitable donation amounts (p < 0.05). Empathic care was preferentially associated with nucleus accumbens and medial orbitofrontal cortex activity, whereas 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, whereas the empathic distress marker was specific to negative emotion.

Brain Mechanisms of the Placebo Effect: An Affective Appraisal Account.

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.

Social anxiety is characterized by biased learning about performance and the self.

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 the authors use 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 (HCs) performed a speech in front of 3 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. HCs 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. (PsycINFO Database Record

Group-regularized individual prediction: theory and application to pain.

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.

Effects of compassion meditation on a psychological model of charitable donation.

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

Interactions between donor Agreeableness and recipient characteristics in predicting charitable donation and positive social evaluation.

Agreeable people are more likely to display prosocial attitudes and helpful behavior in a broad range of situations. Here we show that this tendency interacts with the personal characteristics of interaction partners. In an online study (n = 284), participants were given the opportunity to report attitudes toward and make monetary donations to needy individuals who were described in dynamically generated biographies. Using a machine learning and multilevel modeling framework, we tested three potential explanations for the facilitatory influence of Agreeableness on charitable behavior. We find that Agreeableness preferentially increased donations and prosocial attitudes toward targets normatively rated as being more deserving. Our results advance understanding of person-by-situation interactions in the context of charitable behavior and prosocial attitudes.

The challenges of forecasting resilience.

Developing prospective models of resilience using the translational and transdiagnostic framework proposed in the target article is a challenging endeavor and will require large-scale data sets with dense intraindividual temporal sampling and innovative analytic methods.