A Comparison of ChatGPT and Fine-Tuned Open Pre-Trained Transformers (OPT) Against Widely Used Sentiment Analysis Tools: Sentiment Analysis of COVID-19 Survey Data.

BACKGROUND: Health care providers and health-related researchers face significant challenges when applying sentiment analysis tools to health-related free-text survey data. Most state-of-the-art applications were developed in domains such as social media, and their performance in the health care context remains relatively unknown. Moreover, existing studies indicate that these tools often lack accuracy and produce inconsistent results. OBJECTIVE: This study aims to address the lack of comparative analysis on sentiment analysis tools applied to health-related free-text survey data in the context of COVID-19. The objective was to automatically predict sentence sentiment for 2 independent COVID-19 survey data sets from the National Institutes of Health and Stanford University. METHODS: Gold standard labels were created for a subset of each data set using a panel of human raters. We compared 8 state-of-the-art sentiment analysis tools on both data sets to evaluate variability and disagreement across tools. In addition, few-shot learning was explored by fine-tuning Open Pre-Trained Transformers (OPT; a large language model [LLM] with publicly available weights) using a small annotated subset and zero-shot learning using ChatGPT (an LLM without available weights). RESULTS: The comparison of sentiment analysis tools revealed high variability and disagreement across the evaluated tools when applied to health-related survey data. OPT and ChatGPT demonstrated superior performance, outperforming all other sentiment analysis tools. Moreover, ChatGPT outperformed OPT, exhibited higher accuracy by 6% and higher F-measure by 4% to 7%. CONCLUSIONS: This study demonstrates the effectiveness of LLMs, particularly the few-shot learning and zero-shot learning approaches, in the sentiment analysis of health-related survey data. These results have implications for saving human labor and improving efficiency in sentiment analysis tasks, contributing to advancements in the field of automated sentiment analysis.

Human ventromedial prefrontal cortex lesions enhance the effect of expectations on pain perception.

Pain is strongly modulated by expectations and beliefs. Across species, subregions of ventromedial prefrontal cortex (VMPFC) are implicated in a variety of functions germane to pain, predictions, and learning. Human fMRI studies show that VMPFC activity tracks expectations about pain and mediates expectancy effects on pain-related activity in other brain regions. Prior lesion studies suggest that VMPFC may instead play a more general role in generating affective responses to painful stimuli. To test whether VMPFC is required to generate affective responses to pain or is more specifically involved in expectancy-based pain modulation, we studied responses to heat stimuli in five adults with bilateral surgical lesions of VMPFC and twenty healthy adults without brain damage. All participants underwent a quantitative sensory testing procedure followed by a pain expectancy task in which cues predicting either low or high pain were followed by intermittent medium intensity heat stimuli. Compared to adults without brain damage, individuals with VMPFC lesions reported larger differences in expected pain based on predictive cues and failed to update expectations following the covert introduction of unexpected medium temperature stimuli. Consistent with observed expectancy differences, subjective pain unpleasantness ratings in the VMPFC lesion group were more strongly modulated by cue during thermal stimulation. We found no group differences in overall pain sensitivity, nor in relationships between pain and autonomic arousal, suggesting that VMPFC damage specifically enhances the effect of expectations on pain processing, likely driven by impaired integration of new sensory feedback to update expectations about pain. These results provide essential new data regarding the specific functional contribution of VMPFC to pain modulation.

How Pain-Related Facial Expressions Are Evaluated in Relation to Gender, Race, and Emotion.

Inequities in pain assessment are well-documented; however, the psychological mechanisms underlying such biases are poorly understood. We investigated potential perceptual biases in the judgments of faces displaying pain-related movements. Across five online studies, 956 adult participants viewed images of computer-generated faces ("targets") that varied in features related to race (Black and White) and gender (women and men). Target identity was manipulated across participants, and each target had equivalent facial movements that displayed varying intensities of movement in facial action-units related to pain (Studies 1-4) or pain and emotion (Study 5). On each trial, participants provided categorical judgments as to whether a target was in pain (Studies 1-4) or which expression the target displayed (Study 5) and then rated the perceived intensity of the expression. Meta-analyses of Studies 1-4 revealed that movement intensity was positively associated with both categorizing a trial as painful and perceived pain intensity. Target race and gender did not consistently affect pain-related judgments, contrary to well-documented clinical inequities. In Study 5, in which pain was equally likely relative to other emotions, pain was the least frequently selected emotion (5%). Our results suggest that perceivers can utilize facial movements to evaluate pain in other individuals, but perceiving pain may depend on contextual factors. Furthermore, assessments of computer-generated, pain-related facial movements online do not replicate sociocultural biases observed in the clinic. These findings provide a foundation for future studies comparing CGI and real images of pain and emphasize the need for further work on the relationship between pain and emotion. Supplementary Information: The online version contains supplementary material available at 10.1007/s42761-023-00181-6.

How Instructions, Learning, and Expectations Shape Pain and Neurobiological Responses.

Treatment outcomes are strongly influenced by expectations, as evidenced by the placebo effect. Meta-analyses of clinical trials reveal that placebo effects are strongest in pain, indicating that psychosocial factors directly influence pain. In this review, I focus on the neural and psychological mechanisms by which instructions, learning, and expectations shape subjective pain. I address new experimental designs that help researchers tease apart the impact of these distinct processes and evaluate the evidence regarding the neural mechanisms by which these cognitive factors shape subjective pain. Studies reveal that expectations modulate pain through parallel circuits that include both pain-specific and domain-general circuits such as those involved in affect and learning. I then review how expectations, learning, and verbal instructions impact clinical outcomes, including placebo analgesia and responses to pharmacological treatments, and discuss implications for future work.

The Effects of Expectations and Worries on the Experience of COVID-19 Symptoms.

Introduction: The COVID-19 pandemic has been shown to have profound effects on both mental and physical health. Distress and widespread uncertainty about global events and personal risk are associated with increased worry and negative expectations that impact physical health. Thus, the current pandemic poses a possibility for the experience of nocebo effects. Objective: To evaluate the likelihood of nocebo-induced COVID-19 symptoms in a US sample. Methods: An online study on the mental health impact of COVID-19 asked participants to complete a set of biweekly surveys over a 6-month period between April 2020 and May 2021. We focus on responses from 3,027 individuals who reported never testing positive for COVID-19. We assessed the association between two types of worry and self-reported symptoms of COVID-19. We used multi-level models to examine variations across and within participants over time. We further investigated the effects of pre-existing health conditions and mental health status. Results: There was a positive association between symptoms and both general (b= 2.56, p<0.01) and personal worry (b=2.77, p<0.01). However, worry reported at one timepoint was not specifically associated with symptoms reported two weeks later (p = 0.63, p=0.56). We also found that a greater number of prior clinical comorbidities and greater mental health burden were significant predictors of symptom reporting. Conclusions: These results suggest that increased worries during the COVID-19 pandemic were associated with greater symptoms. Further studies investigating worry and symptoms in populations with confirmed negative COVID-19 tests or isolated populations will be needed to isolate the occurrence of true nocebo effects during the pandemic.

Trends in Language Use During the COVID-19 Pandemic and Relationship Between Language Use and Mental Health: Text Analysis Based on Free Responses From a Longitudinal Study.

BACKGROUND: The COVID-19 pandemic and its associated restrictions have been a major stressor that has exacerbated mental health worldwide. Qualitative data play a unique role in documenting mental states through both language features and content. Text analysis methods can provide insights into the associations between language use and mental health and reveal relevant themes that emerge organically in open-ended responses. OBJECTIVE: The aim of this web-based longitudinal study on mental health during the early COVID-19 pandemic was to use text analysis methods to analyze free responses to the question, "Is there anything else you would like to tell us that might be important that we did not ask about?" Our goals were to determine whether individuals who responded to the item differed from nonresponders, to determine whether there were associations between language use and psychological status, and to characterize the content of responses and how responses changed over time. METHODS: A total of 3655 individuals enrolled in the study were asked to complete self-reported measures of mental health and COVID-19 pandemic-related questions every 2 weeks for 6 months. Of these 3655 participants, 2497 (68.32%) provided at least 1 free response (9741 total responses). We used various text analysis methods to measure the links between language use and mental health and to characterize response themes over the first year of the pandemic. RESULTS: Response likelihood was influenced by demographic factors and health status: those who were male, Asian, Black, or Hispanic were less likely to respond, and the odds of responding increased with age and education as well as with a history of physical health conditions. Although mental health treatment history did not influence the overall likelihood of responding, it was associated with more negative sentiment, negative word use, and higher use of first-person singular pronouns. Responses were dynamically influenced by psychological status such that distress and loneliness were positively associated with an individual's likelihood to respond at a given time point and were associated with more negativity. Finally, the responses were negative in valence overall and exhibited fluctuations linked with external events. The responses covered a variety of topics, with the most common being mental health and emotion, social or physical distancing, and policy and government. CONCLUSIONS: Our results identify trends in language use during the first year of the pandemic and suggest that both the content of responses and overall sentiments are linked to mental health.

Instructions and experiential learning have similar impacts on pain and pain-related brain responses but produce dissociations in value-based reversal learning.

Recent data suggest that interactions between systems involved in higher order knowledge and associative learning drive responses during value-based learning. However, it is unknown how these systems impact subjective responses, such as pain. We tested how instructions and reversal learning influence pain and pain-evoked brain activation. Healthy volunteers (n=40) were either instructed about contingencies between cues and aversive outcomes or learned through experience in a paradigm where contingencies reversed three times. We measured predictive cue effects on pain and heat-evoked brain responses using functional magnetic resonance imaging. Predictive cues dynamically modulated pain perception as contingencies changed, regardless of whether participants received contingency instructions. Heat-evoked responses in the insula, anterior cingulate, and other regions updated as contingencies changed, and responses in the prefrontal cortex mediated dynamic cue effects on pain, whereas responses in the brainstem's rostroventral medulla (RVM) were shaped by initial contingencies throughout the task. Quantitative modeling revealed that expected value was shaped purely by instructions in the Instructed Group, whereas expected value updated dynamically in the Uninstructed Group as a function of error-based learning. These differences were accompanied by dissociations in the neural correlates of value-based learning in the rostral anterior cingulate, thalamus, and posterior insula, among other regions. These results show how predictions dynamically impact subjective pain. Moreover, imaging data delineate three types of networks involved in pain generation and value-based learning: those that respond to initial contingencies, those that update dynamically during feedback-driven learning as contingencies change, and those that are sensitive to instruction. Together, these findings provide multiple points of entry for therapies designs to impact pain.

Individual variability in brain representations of pain.

Characterizing cerebral contributions to individual variability in pain processing is crucial for personalized pain medicine, but has yet to be done. In the present study, we address this problem by identifying brain regions with high versus low interindividual variability in their relationship with pain. We trained idiographic pain-predictive models with 13 single-trial functional MRI datasets (n = 404, discovery set) and quantified voxel-level importance for individualized pain prediction. With 21 regions identified as important pain predictors, we examined the interindividual variability of local pain-predictive weights in these regions. Higher-order transmodal regions, such as ventromedial and ventrolateral prefrontal cortices, showed larger individual variability, whereas unimodal regions, such as somatomotor cortices, showed more stable pain representations across individuals. We replicated this result in an independent dataset (n = 124). Overall, our study identifies cerebral sources of individual differences in pain processing, providing potential targets for personalized assessment and treatment of pain.

A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems.

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.

Test-Retest Reliability of an Adaptive Thermal Pain Calibration Procedure in Healthy Volunteers.

Quantitative sensory testing (QST) allows researchers to evaluate associations between noxious stimuli and acute pain in clinical populations and healthy participants. Despite its widespread use, our understanding of QST's reliability is limited, as reliability studies have used small samples and restricted time windows. We examined the reliability of pain ratings in response to noxious thermal stimulation in 171 healthy volunteers (n = 99 female, n = 72 male) who completed QST on multiple visits ranging from 1 day to 952 days between visits. On each visit, participants underwent an adaptive pain calibration in which they experienced 24 heat trials and rated pain intensity after stimulus offset on a 0 to 10 Visual Analog Scale. We used linear regression to determine pain threshold, pain tolerance, and the correlation between temperature and pain for each session and examined the reliability of these measures. Threshold and tolerance were moderately reliable (Intra-class correlation = .66 and .67, respectively; P < .001), whereas temperature-pain correlations had low reliability (Intra-class correlation = .23). In addition, pain tolerance was significantly more reliable in female participants than male participants, and we observed similar trends for other pain sensitive measures. Our findings indicate that threshold and tolerance are largely consistent across visits, whereas sensitivity to changes in temperature vary over time and may be influenced by contextual factors. PERSPECTIVE: This article assesses the reliability of an adaptive thermal pain calibration procedure. We find that pain threshold and tolerance are moderately reliable whereas the correlation between pain rating and stimulus temperature has low reliability. Female participants were more reliable than male participants on all pain sensitivity measures.

Threat imminence reveals links among unfolding of anticipatory physiological response, cortical-subcortical intrinsic functional connectivity, and anxiety.

Excessive expression of fear responses in anticipation of threat occurs in anxiety, but understanding of underlying pathophysiological mechanisms is limited. Animal research indicates that threat-anticipatory defensive responses are dynamically organized by threat imminence and rely on conserved circuitry. Insight from basic neuroscience research in animals on threat imminence could guide mechanistic research in humans mapping abnormal function in this circuitry to aberrant defensive responses in pathological anxiety. 50 pediatric anxiety patients and healthy-comparisons (33 females) completed an instructed threat-anticipation task whereby cues signaled delivery of painful (threat) or non-painful (safety) thermal stimulation. Temporal changes in skin-conductance indexed anxiety effects on anticipatory responding as function of threat imminence. Multivariate network analyses of resting-state functional connectivity data from a subsample were used to identify intrinsic-function correlates of anticipatory-response dynamics, within a specific, distributed network derived from translational research on defensive responding. By considering threat imminence, analyses revealed specific anxiety effects. Importantly, pathological anxiety was associated with excessive deployment of anticipatory physiological response as threat, but not safety, outcomes became more imminent. Magnitude of increase in threat-anticipatory physiological responses corresponded with magnitude of intrinsic connectivity within a cortical-subcortical circuit. Moreover, more severe anxiety was associated with stronger associations between anticipatory physiological responding and connectivity that ventromedial prefrontal cortex showed with hippocampus and basolateral amygdala, regions implicated in animal models of anxiety. These findings link basic and clinical research, highlighting variations in intrinsic function in conserved defensive circuitry as a potential pathophysiological mechanism in anxiety.

Rating expectations can slow aversive reversal learning.

The process of learning allows organisms to develop predictions about outcomes in the environment, and learning is sensitive to both simple associations and higher order knowledge. However, it is unknown whether consciously attending to expectations shapes the learning process itself. Here, we directly tested whether rating expectations shapes arousal during classical conditioning. Participants performed an aversive learning paradigm wherein one image (CS+) was paired with shock on 50% of trials, while a second image (CS-) was never paired with shock. Halfway through the task, contingencies reversed. One group of participants rated the probability of upcoming shock on each trial, while the other group made no online ratings. We measured skin conductance response (SCR) evoked in response to the CS and used traditional analyses as well as quantitative models of reinforcement learning to test whether rating expectations influenced arousal and aversive reversal learning. Participants who provided online expectancy ratings displayed slower learning based on a hybrid model of adaptive learning and reduced reversal of SCR relative to those who did not rate expectations. Mediation analysis revealed that the effect of associative learning on SCR could be fully explained through its effects on subjective expectancy within the group who provided ratings. This suggests that the act of rating expectations reduces the speed of learning, likely through changes in attention, and that expectations directly influence arousal. Our findings indicate that higher order expectancy judgments can alter associative learning.

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.

COVID-19 and Mental Health: Predicted Mental Health Status is Associated with Clinical Symptoms and Pandemic-Related Psychological and Behavioral Responses.

BACKGROUND: The COVID-19 pandemic led to dramatic threats to health and social life. Study objectives - develop a prediction model leveraging subsample of known Patient/Controls and evaluate the relationship of predicted mental health status to clinical outcome measures and pandemic-related psychological and behavioral responses during lockdown (spring/summer 2020). METHODS: Online cohort study conducted by National Institute of Mental Health Intramural Research Program. Convenience sample of English-speaking adults (enrolled 4/4-5/16/20; n=1,992). Enrollment measures: demographics, clinical history, functional status, psychiatric and family history, alcohol/drug use. Outcome measures (enrollment and q2 weeks/6 months): distress, loneliness, mental health symptoms, and COVID-19 survey. NIMH IRP Patient/Controls survey responses informed assignment of Patient Probability Scores (PPS) for all participants. Regression models analyzed the relationship between PPS and outcome measures. OUTCOMES: Mean age 46.0 (±14.7), female (82.4%), white (88.9 %). PPS correlated with distress, loneliness, depression, and mental health factors. PPS associated with negative psychological responses to COVID-19. Worry about mental health (OR 1.46) exceeded worry about physical health (OR 1.13). PPS not associated with adherence to social distancing guidelines but was with stress related to social distancing and worries about infection of self/others. INTERPRETATION: Mental health status (PPS) was associated with concurrent clinical ratings and COVID-specific negative responses. A focus on mental health during the pandemic is warranted, especially among those with mental health vulnerabilities. We will include PPS when conducting longitudinal analyses of mental health trajectories and risk and resilience factors that may account for differing clinical outcomes. FUNDING: NIMH (ZIAMH002922); NCCIH (ZIAAT000030).

A social affective neuroscience lens on placebo analgesia.

Pain is a fundamental experience that promotes survival. In humans, pain stands at the intersection of multiple health crises: chronic pain, the opioid epidemic, and health disparities. The study of placebo analgesia highlights how social, cognitive, and affective processes can directly shape pain, and identifies potential paths for mitigating these crises. This review examines recent progress in the study of placebo analgesia through affective science. It focuses on how placebo effects are shaped by expectations, affect, and the social context surrounding treatment, and discusses neurobiological mechanisms of placebo, highlighting unanswered questions and implications for health. Collaborations between clinicians and social and affective scientists can address outstanding questions and leverage placebo to reduce pain and improve human health.

Dispositional Mindfulness and Acute Heat Pain: Comparing Stimulus-Evoked Pain With Summary Pain Assessment.

OBJECTIVE: Dispositional mindfulness is associated with reduced pain in clinical and experimental settings. However, researchers have neglected the type of pain assessment, as dispositional mindfulness may have unique benefits for reduced pain sensitivity when relying on summary pain assessments, in contrast to assessing the pain of each noxious stimulus. Here, we test the association between dispositional mindfulness and pain using both trial-by-trial pain assessments and overall summary ratings after acute pain tasks. METHODS: One hundred thirty-one healthy adult volunteers (mean age = 29.09 [8.00] years, 55.7% female) underwent two experimental thermal pain paradigms. We tested whether dispositional mindfulness measured with the Mindful Attention Awareness Scale was related to a) heat-evoked pain sensitivity, as measured by pain threshold, pain tolerance, average pain, trial-by-trial ratings, and heat-evoked skin conductance response, and b) summary judgments of sensory and affective pain assessed using the McGill Pain Questionnaire (MPQ). RESULTS: Mindful Attention Awareness Scale ratings were associated with decreased pain on the MPQ sensory (B = -0.18, SE = 0.05, 95% confidence interval = -0.29 to -0.07, t = -3.28, p = .001) and affective (B = -0.11, SE = 0.03, 95% confidence interval = -0.18 to -0.05, t = -3.32, p = .001) dimensions but not with experimental thermal pain assessments, including threshold, tolerance, heat-evoked pain, or skin conductance response (p values ≥ .29). CONCLUSIONS: In this study, dispositional mindfulness mitigated acute thermal pain only when pain was assessed using the MPQ. These findings may reflect differences in immediate versus retrospective judgments or the type of pain assessed by each measure. Future research should examine regulation processes that may explain these differential analgesic benefits, such as attention, rumination, or reappraisal.

Expectations about pain and analgesic treatment are shaped by medical providers' facial appearances: Evidence from five online clinical simulation experiments.

RATIONALE: There is a robust link between patients' expectations and clinical outcomes, as evidenced by the placebo effect. Expectations depend in large part on the context surrounding treatment, including the patient-provider interaction. Prior work indicates that providers' behavior and characteristics, including warmth and competence, can shape patient outcomes. Yet humans rapidly form trait impressions of others before any in-person interaction. It is unknown whether these first impressions influence subsequent health care choices and expectations. OBJECTIVE: Our goal was to test whether trait impressions of hypothetical medical providers, based exclusively on facial images, influence the choice of medical providers and expectations about pain and analgesia following hypothetical painful medical procedures. METHOD: Across five online experiments, participants (total N = 1108) viewed and made judgments about hypothetical healthcare providers. Experiments 1-4 included computer-generated faces that varied in features associated with competence, while experiment 5 included real faces. We measured how apparent competence affected expectations about pain and anticipated analgesic use in all studies. We also measured warmth and similarity. RESULTS: Across five online studies, participants selected providers who appeared more competent, based on facial visual information alone. Further, providers' apparent competence predicted participants' expectations about post-procedural pain and medication use. Participants' perception of their similarity to providers also shaped expectations about pain and treatment outcomes. CONCLUSIONS: Results from our experimental simulations suggest that humans develop expectations about pain and health outcomes before even setting foot in the clinic, based exclusively on first impressions. These findings have strong implications for health care, as individuals increasingly rely on digital services to select healthcare providers and even receive treatment, a trend that is exacerbated as the world embraces telemedicine.

Threat-anticipatory psychophysiological response is enhanced in youth with anxiety disorders and correlates with prefrontal cortex neuroanatomy.

Background: Threat anticipation engages neural circuitry that has evolved to promote defensive behaviours; perturbations in this circuitry could generate excessive threat-anticipation response, a key characteristic of pathological anxiety. Research into such mechanisms in youth faces ethical and practical limitations. Here, we use thermal stimulation to elicit pain-anticipatory psychophysiological response and map its correlates to brain structure among youth with anxiety and healthy youth. Methods: Youth with anxiety (n = 25) and healthy youth (n = 25) completed an instructed threat-anticipation task in which cues predicted nonpainful or painful thermal stimulation; we indexed psychophysiological response during the anticipation and experience of pain using skin conductance response. High-resolution brain-structure imaging data collected in another visit were available for 41 participants. Analyses tested whether the 2 groups differed in their psychophysiological cue-based pain-anticipatory and pain-experience responses. Analyses then mapped psychophysiological response magnitude to brain structure. Results: Youth with anxiety showed enhanced psychophysiological response specifically during anticipation of painful stimulation (b = 0.52, p = 0.003). Across the sample, the magnitude of psychophysiological anticipatory response correlated negatively with the thickness of the dorsolateral prefrontal cortex (pFWE < 0.05); psychophysiological response to the thermal stimulation correlated positively with the thickness of the posterior insula (pFWE < 0.05). Limitations: Limitations included the modest sample size and the cross-sectional design. Conclusion: These findings show that threat-anticipatory psychophysiological response differentiates youth with anxiety from healthy youth, and they link brain structure to psychophysiological response during pain anticipation and experience. A focus on threat anticipation in research on anxiety could delineate relevant neural circuitry.