Pain: A Precision Signal for Reinforcement Learning and Control

Success-efficient/failure-safe strategy for hierarchical reinforcement motor learning

Our study explores how ecological aspects of motor learning enhance survival by improving movement efficiency and mitigating injury risks during task failures. Traditional motor control theories mainly address isolated body movements and often overlook these ecological factors. We introduce a novel computational motor control approach, incorporating ecological fitness and a strategy that alternates between success-driven movement efficiency and failure-driven safety, akin to win-stay/lose-shift tactics. In our experiments, participants performed squat-to-stand movements under novel force perturbations. They adapted effectively through various adaptive motor control mechanisms to avoid falls, reducing failure rates rapidly. The results indicate a high-level ecological controller in human motor learning that switches objectives between safety and movement efficiency, depending on failure or success. This approach is supported by policy learning, internal model adaptation, and adaptive feedback control. Our findings offer a comprehensive perspective on human motor control, integrating risk management in a hierarchical reinforcement learning framework for real-world environments.

The Interplay Between Experimental Heat Pain and Noninvasive Stimulation of the Medial Prefrontal Cortex on Reinforcement Learning With Manipulated Outcome Contingencies

Pain negatively affects several cognitive abilities, but knowledge about its effect on reinforcement learning (RL) is limited. During RL, instrumental choices can be influenced by heuristic tendencies to approach rewards or inhibit actions when facing potentially aversive events, introducing "Pavlovian bias" in behavior. Recent studies suggest that compromised outcome controllability enhances Pavlovian bias, a phenomenon that may be linked to suboptimal decision-making in learned helplessness (LH). Since LH is common in chronic pain syndromes, this study sought to establish a link between experimental heat pain (EHP), disrupted reward/loss contingencies, and RL performance in healthy adults. In addition, we investigated if intermittent theta burst stimulation (iTBS) above the medial prefrontal/dorsal anterior cingulate cortex (mPFC/dACC) alleviates the deleterious effects of EHP on choice behavior. In a preregistered, 2 × 2 between-group, double-blind study (N = 100), healthy adult participants underwent three blocks of an orthogonalized Go/NoGo task with two interleaved bouts of active or sham iTBS, and either EHP or warm skin stimulation combined with manipulated response-outcome contingency during the task. Although EHP did not impact response accuracy, it invigorated actions for rewards, reflecting enhanced Pavlovian bias. Whereas two bouts of iTBS attenuated Pavlovian tendencies, this effect was counteracted by EHP, indicating antagonistic effects of pain and iTBS-modulated mPFC activity on Pavlovian-instrumental interactions. Surprisingly, EHP and iTBS exerted largely similar effects on other latent parameters of RL (go-bias, learning rate, and exploration) in a manner that resembled LH. These findings shed light on the role of experimental pain and mPFC/dACC activity in LH-like choice behavior.

Bayesian surprise intensifies pain in a novel visual-noxious association

Pain perception is not solely determined by noxious stimuli, but also varies due to other factors, such as beliefs about pain and its uncertainty. A widely accepted theory posits that the brain integrates prediction of pain with noxious stimuli, to estimate pain intensity. This theory assumes that the estimated pain value is adjusted to minimize surprise, mathematically defined as errors between predictions and outcomes. However, it is still unclear whether the represented surprise directly influences pain perception or merely serves to update this estimate. In this study, we empirically examined this question using virtual reality. In the task, participants reported felt pain via VAS after their arm was stimulated by noxious heat and thrusted into by a virtual knife actively. To manipulate surprise level, the visual threat suddenly disappeared randomly, and noxious heat was presented in the on- or post-action phases. We observed that a transphysical surprising event, created by sudden disappearance of a visual threat cue combined with delayed noxious heat, amplified pain intensity. Subsequent model-based analysis using Bayesian theory revealed significant modulation of pain by the Bayesian surprise value. These results illustrated a real-time computational process for pain perception during a single task trial, suggesting that the brain anticipates pain using an efference copy of actions, integrates it with multimodal stimuli, and perceives it as a surprise.

Unveiling the Truth in Pain: Neural and Behavioral Distinctions Between Genuine and Deceptive Pain

Background/Objectives: Fake pain expressions are more intense, prolonged, and include non-pain-related actions compared to genuine ones. Despite these differences, individuals struggle to detect deception in direct tasks (i.e., when asked to detect liars). Regarding neural correlates, while pain observation has been extensively studied, little is known about the neural distinctions between processing genuine, fake, and suppressed pain facial expressions. This study seeks to address this gap using authentic pain stimuli and an implicit emotional processing task. Methods: Twenty-four healthy women underwent an fMRI study, during which they were instructed to complete an implicit gender discrimination task. Stimuli were video clips showing genuine, fake, suppressed pain, and neutral facial expressions. After the scanning session, participants reviewed the stimuli and rated them indirectly according to the intensity of the facial expression (IE) and the intensity of the pain (IP). Results: Mean scores of IE and IP were significantly different for each category. A greater BOLD response for the observation of genuine pain compared to fake pain was observed in the pregenual anterior cingulate cortex (pACC). A parametric analysis showed a correlation between brain activity in the mid-cingulate cortex (aMCC) and the IP ratings. Conclusions: Higher IP ratings for genuine pain expressions and higher IE ratings for fake ones suggest that participants were indirectly able to recognize authenticity in facial expressions. At the neural level, pACC and aMCC appear to be involved in unveiling the genuine vs. fake pain and in coding the intensity of the perceived pain, respectively.

Magnetoencephalography studies in migraine and headache disorders: A systematic review

BACKGROUND

Understanding the neural mechanisms underlying migraine and other primary headache disorders is critical for the development of long-term cures. Magnetoencephalography (MEG), an imaging modality that measures neuronal currents and cortical excitability with high temporal and superior spatial resolution, has been increasingly used in neurological research. Initial MEG studies showed promise in directly recording cortical spreading depression-a cortical correlate of migraine with aura. However, lately MEG technology has highly evolved with greater potential to reveal underlying pathophysiology of migraine and primary headache disorders, and aid in the identification of biomarkers.

OBJECTIVE

To systematically review the use of MEG in migraine and other primary headache disorders and summarize findings.

METHODS

We conducted a systematic search and selection of MEG studies in migraine and primary headache disorders from inception until June 8, 2023, in Medline, Embase, Cochrane, and Scopus databases. Peer-reviewed English articles reporting the use of MEG for clinical or research purposes in migraine and primary headache disorders were selected.

RESULTS

We found 560 articles and included 38 in this review after screening. Twelve studies investigated resting-state, while others investigated a sensory modality using an evoked or event-related paradigm with a total of 35 cohort and 3 case studies. Thirty-two studies focused exclusively on migraine, while the rest reported other primary headache disorders.

CONCLUSION

The findings show an evolution of MEG from a 7- to a 306-channel system and analysis evolving from sensor-level evoked responses to more advanced source-level connectivity measures. A relatively few MEG studies portrayed migraine and primary headache disorders as a sensory abnormality, especially of the visual system. We found heterogeneity in the datasets, data reporting standards (due to constantly evolving MEG technology and analysis methods), and patient characteristics. Studies were inadequately powered and there was no evidence of blinding procedures to avoid selection bias in case-control studies, which could have led to false-positive findings. More studies are needed to investigate the affective-cognitive aspects that exacerbate pain and disability in migraine and primary headache disorders.

Exploring when to exploit: the cognitive underpinnings of foraging-type decisions in relation to psychopathy

Impairments in reinforcement learning (RL) might underlie the tendency of individuals with elevated psychopathic traits to behave exploitatively, as they fail to learn from their mistakes. Most studies on the topic have focused on binary choices, while everyday functioning requires us to learn the value of multiple options. In this study, we evaluated the cognitive correlates of naturalistic foraging-type decision-making and their electrophysiological signatures in a community sample (n = 108) with varying degrees of psychopathic traits. Reinforcers with different salience were included in a foraging-type decision-making task. Recruitment of various cognitive processes was estimated with a computational model and electrophysiology, and the relationships to psychopathic traits were assessed. Higher Antisocial traits were associated with a bias towards expecting more volatility in the environment when high-salience reinforcers were used. Additionally, higher levels of Interpersonal traits were associated with reduced learning from personalized rewards, as evidenced by reductions in the prediction errors (PEs) about rate of change. Higher Affective traits were associated with lower PEs and aberrant learning from painful punishments. Lastly, the PEs about rate of change were reflected in the trial-wise trajectories of Feedback-Related Negativity event-related potentials. Together, our results point to the importance of volatility processing in understanding aberrant decision-making in relation to psychopathy, demonstrate the relationships between psychopathic traits and learning through reward and punishment, and emphasise the potentially more beneficial effect of personalized rewards and punishment for improving reinforcement-based decision-making in individuals with elevated psychopathic traits.

Mechanisms of increased pain discrimination by contingent reinforcement: a perceptual decision-making and instrumental learning account

ABSTRACT

Recent evidence highlights that monetary rewards can increase the precision at which healthy human volunteers can detect small changes in the intensity of thermal noxious stimuli, contradicting the idea that rewards exert a broad inhibiting influence on pain perception. This effect was stronger with contingent rewards compared with noncontingent rewards, suggesting a successful learning process. In the present study, we implemented a model comparison approach that aimed to improve our understanding of the mechanisms that underlie thermal noxious discrimination in humans. In a between-subject design, 54 healthy human volunteers took part in a pain discrimination task with monetary rewards either contingent or noncontingent on successful discrimination of small changes in the intensity painful heat stimulation. We used models from 2 traditions in decision-making research, perceptual decision-making, and instrumental learning. Replicating the previous findings, only rewards contingent on behavior enhanced pain discrimination. Drift diffusion modelling revealed increased sensory signal strength and decreased response caution and nondecision times as mechanisms underlying this effect of contingent rewards on pain discrimination. In addition, reinforcement learning models indicated a temporal evolution of discriminative abilities reflected by a trial-by-trial increase of perceived signal strength only with contingent rewards but not with noncontingent rewards. Modelling of separate learning rates for positive and negative prediction errors indicated that this temporal evolution of discriminative abilities was driven by positive reward prediction errors. These results might indicate increased sensitivity towards better-than-expected outcomes in the temporal adaptation of pain discrimination abilities to a rewarding context in humans.

How control modulates pain

Pain, an indicator of potential tissue damage, ideally falls under individual control. Although previous work shows a trend towards reduced pain in contexts where pain is controllable, there is a large variability across studies that is probably related to different aspects of control. We therefore outline a taxonomy of different aspects of control relevant to pain, sketch how control over pain can be integrated into a Bayesian pain model, and suggest changes in expectations and their precision as potential mechanisms. We also highlight confounding cognitive factors, particularly predictability, that emphasize the necessity for careful experimental designs. Finally, we describe the neurobiological underpinnings of how control affects pain processing in studies using different types of control, and highlight the roles of the anterior insula, middle frontal gyrus (MFG), and anterior cingulate cortex (ACC).

Application of the grading system for "nociplastic pain" in chronic primary and chronic secondary pain conditions: a field study

ABSTRACT

The concept "nociplastic pain" has been developed for patients with features of nociceptive system sensitization that are not explained as nociceptive or neuropathic. Here, we tested how well the recently published grading system differentiates between chronic primary and secondary pain conditions. We recruited patients with fibromyalgia (FMS, n = 41), complex regional pain syndrome (CRPS, n = 11), osteoarthritis (OA, n = 21), or peripheral nerve injury (PNI, n = 8). We used clinical history, pain drawings, quantitative sensory testing (QST), and questionnaires to classify their pains as possibly or probably "nociplastic." All patients with chronic primary pain exhibited widespread/regional pain not explainable by either nociceptive or neuropathic mechanisms. Widespread pain occurred in 12 patients with OA but was identified as nociceptive in 11 of 12. Regional pain occurred in 4 patients with PNI but was identified as neuropathic in 3 of 4. At this step, the grading system had 100% sensitivity and 93% specificity. Clinical evidence for pain hypersensitivity by QST, and history of hypersensitivity and mental comorbidities did not differentiate between chronic primary pain (QST: 36/52 = 69%, history: 43/52 = 83%) and secondary pain conditions (QST: 20/29 = 69%, history: 24/29 83%). Based on these data, specificity remained excellent (93%), but sensitivity dropped substantially (60%) due to lacking evidence for pain hypersensitivity in many patients with FMS. This low sensitivity suggests that the published grading system is not suitable for screening purposes. We suggest structural and content modifications to improve sensitivity, including placement of patient history before clinical examination and addition of a high tender point count as evidence for widespread pain hypersensitivity.

Topographically selective motor inhibition under threat of pain

ABSTRACT

Pain-related motor adaptations may be enacted predictively at the mere threat of pain, before pain occurrence. Yet, in humans, the neurophysiological mechanisms underlying motor adaptations in anticipation of pain remain poorly understood. We tracked the evolution of changes in corticospinal excitability (CSE) as healthy adults learned to anticipate the occurrence of lateralized, muscle-specific pain to the upper limb. Using a Pavlovian threat conditioning task, different visual stimuli predicted pain to the right or left forearm (experiment 1) or hand (experiment 2). During stimuli presentation before pain occurrence, single-pulse transcranial magnetic stimulation was applied over the left primary motor cortex to probe CSE and elicit motor evoked potentials from target right forearm and hand muscles. The correlation between participants' trait anxiety and CSE was also assessed. Results showed that threat of pain triggered corticospinal inhibition specifically in the limb where pain was expected. In addition, corticospinal inhibition was modulated relative to the threatened muscle, with threat of pain to the forearm inhibiting the forearm and hand muscles, whereas threat of pain to the hand inhibited the hand muscle only. Finally, stronger corticospinal inhibition correlated with greater trait anxiety. These results advance the mechanistic understanding of pain processes showing that pain-related motor adaptations are enacted at the mere threat of pain, as sets of anticipatory, topographically organized motor changes that are associated with the expected pain and are shaped by individual anxiety levels. Including such anticipatory motor changes into models of pain may lead to new treatments for pain-related disorders.

A Computational Framework for Understanding the Impact of Prior Experiences on Pain Perception and Neuropathic Pain

Pain perception is influenced not only by sensory input from afferent neurons but also by cognitive factors such as prior expectations. It has been suggested that overly precise priors may be a key contributing factor to chronic pain states such as neuropathic pain. However, it remains an open question how overly precise priors in favor of pain might arise. Here, we first verify that a Bayesian approach can describe how statistical integration of prior expectations and sensory input results in pain phenomena such as placebo hypoalgesia, nocebo hyperalgesia, chronic pain, and spontaneous neuropathic pain. Our results indicate that the value of the prior, which is determined by the internal model parameters, may be a key contributor to these phenomena. Next, we apply a hierarchical Bayesian approach to update the parameters of the internal model based on the difference between the predicted and the perceived pain, to reflect that people integrate prior experiences in their future expectations. In contrast with simpler approaches, this hierarchical model structure is able to show for placebo hypoalgesia and nocebo hyperalgesia how these phenomena can arise from prior experiences in the form of a classical conditioning procedure. We also demonstrate the phenomenon of offset analgesia, in which a disproportionally large pain decrease is obtained following a minor reduction in noxious stimulus intensity. Finally, we turn to simulations of neuropathic pain, where our hierarchical model corroborates that persistent non-neuropathic pain is a risk factor for developing neuropathic pain following denervation, and additionally offers an interesting prediction that complete absence of informative painful experiences could be a similar risk factor. Taken together, these results provide insight to how prior experiences may contribute to pain perception, in both experimental and neuropathic pain, which in turn might be informative for improving strategies of pain prevention and relief.

Pain reflects the informational value of nociceptive inputs

ABSTRACT

Pain perception and its modulation are fundamental to human learning and adaptive behavior. This study investigated the hypothesis that pain perception is tied to pain's learning function. Thirty-one participants performed a threat conditioning task where certain cues were associated with a possibility of receiving a painful electric shock. The cues that signaled potential pain or safety were regularly changed, requiring participants to continually establish new associations. Using computational models, we quantified participants' pain expectations and prediction errors throughout the task and assessed their relationship with pain perception and electrophysiological responses. Our findings suggest that subjective pain perception increases with prediction error, that is, when pain was unexpected. Prediction errors were also related to physiological nociceptive responses, including the amplitude of nociceptive flexion reflex and electroencephalography markers of cortical nociceptive processing (N1-P2-evoked potential and gamma-band power). In addition, higher pain expectations were related to increased late event-related potential responses and alpha/beta decreases in amplitude during cue presentation. These results further strengthen the idea of a crucial link between pain and learning and suggest that understanding the influence of learning mechanisms in pain modulation could help us understand when and why pain perception is modulated in health and disease.

Diminished pain sensitivity mediates the relationship between psychopathic traits and reduced learning from pain

Individuals with elevated psychopathic traits exhibit decision-making deficits linked to a failure to learn from negative outcomes. We investigated how reduced pain sensitivity affects reinforcement-based decision-making in individuals with varying levels of psychopathic traits, as measured by the Self-Report Psychopathy Scale-Short Form. Using computational modelling, we estimated the latent cognitive processes in a community non-offender sample (n = 111) that completed a task with choices leading to painful and non-painful outcomes. Higher psychopathic traits were associated with reduced pain sensitivity and disturbances in reinforcement learning from painful outcomes. In a Structural Equation Model, a superordinate psychopathy factor was associated with a faster return to original stimulus-outcome associations as pain tolerance increased. This provides evidence directly linking reduced pain sensitivity and learning from painful outcomes with elevated psychopathic traits. Our results offer insights into the computational mechanisms of maladaptive decision-making in psychopathy and antisocial behavior.

Expectation of pain and relief: A dynamical model of the neural basis for pain-trauma co-morbidity

Posttraumatic Stress Disorder (PTSD) is highly co-morbid with chronic pain conditions. When present, PTSD significantly worsens chronic pain outcomes. Likewise, pain contributes to a more severe PTSD as evidenced by greater disability, more frequent use of harmful opioid analgesics and increased pain severity. The biomechanism behind this comorbidity is incompletely understood, however recent work strongly supports the widely-accepted role of expectation, in the entanglement of chronic pain and trauma symptoms. This work has shown that those with trauma have a maladaptive brain response while expecting stress and pain, whereas those with chronic pain may have a notable impairment in brain response while expecting pain relief. This dynamical expectation model of the interaction between neural systems underlying expectation of pain onset (traumatic stress) and pain offset (chronic pain) is biologically viable and may provide a biomechanistic insight into pain-trauma comorbidity. These predictive mechanisms work through interoceptive pathways in the brain critically the insula cortex. Here we highlight how the neural expectation-related mechanisms augment the existing models of pain and trauma to better understand the dynamics of pain and trauma comorbidity. These ideas will point to targeted complementary clinical approaches, based on mechanistically separable neural biophenotypes for the entanglement of chronic pain and trauma symptoms.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Drug treatments for pain often do not outperform placebo, and a better understanding of placebo mechanisms is needed to improve treatment development and clinical practice. In a large-scale fMRI study (N = 392) with pre-registered analyses, we tested whether placebo analgesic treatment modulates nociceptive processes, and whether its effects generalize from conditioned to unconditioned pain modalities. Placebo treatment caused robust analgesia in conditioned thermal pain that generalized to unconditioned mechanical pain. However, placebo did not decrease pain-related fMRI activity in brain measures linked to nociceptive pain, including the Neurologic Pain Signature (NPS) and spinothalamic pathway regions, with strong support for null effects in Bayes Factor analyses. In addition, surprisingly, placebo increased activity in some spinothalamic regions for unconditioned mechanical pain. In contrast, placebo reduced activity in a neuromarker associated with higher-level contributions to pain, the Stimulus Intensity Independent Pain Signature (SIIPS), and affected activity in brain regions related to motivation and value, in both pain modalities. Individual differences in behavioral analgesia were correlated with neural changes in both modalities. Our results indicate that cognitive and affective processes primarily drive placebo analgesia, and show the potential of neuromarkers for separating treatment influences on nociception from influences on evaluative processes.

An externally validated resting-state brain connectivity signature of pain-related learning

Pain can be conceptualized as a precision signal for reinforcement learning in the brain and alterations in these processes are a hallmark of chronic pain conditions. Investigating individual differences in pain-related learning therefore holds important clinical and translational relevance. Here, we developed and externally validated a novel resting-state brain connectivity-based predictive model of pain-related learning. The pre-registered external validation indicates that the proposed model explains 8-12% of the inter-individual variance in pain-related learning. Model predictions are driven by connections of the amygdala, posterior insula, sensorimotor, frontoparietal, and cerebellar regions, outlining a network commonly described in aversive learning and pain. We propose the resulting model as a robust and highly accessible biomarker candidate for clinical and translational pain research, with promising implications for personalized treatment approaches and with a high potential to advance our understanding of the neural mechanisms of pain-related learning.

Statistical learning shapes pain perception and prediction independently of external cues

The placebo and nocebo effects highlight the importance of expectations in modulating pain perception, but in everyday life we don't need an external source of information to form expectations about pain. The brain can learn to predict pain in a more fundamental way, simply by experiencing fluctuating, non-random streams of noxious inputs, and extracting their temporal regularities. This process is called statistical learning. Here, we address a key open question: does statistical learning modulate pain perception? We asked 27 participants to both rate and predict pain intensity levels in sequences of fluctuating heat pain. Using a computational approach, we show that probabilistic expectations and confidence were used to weigh pain perception and prediction. As such, this study goes beyond well-established conditioning paradigms associating non-pain cues with pain outcomes, and shows that statistical learning itself shapes pain experience. This finding opens a new path of research into the brain mechanisms of pain regulation, with relevance to chronic pain where it may be dysfunctional.

[Retrospective evaluation of electronically recorded patient questionnaires of a university outpatient pain clinic with the painDETECT® questionnaire]

BACKGROUND AND OBJECTIVE

Psychometric tests can provide important information for diagnostics and progression in chronic pain patients. Between 2008 and 2018, the electronic system painDETECT® was used in the outpatient pain clinic of the Hannover Medical School (MHH). The aim of this retrospective study was to evaluate the pain symptomatology data recorded using painDETECT® and the treatment procedures used in the patient cohort examined over a period of 15 months.

MATERIAL AND METHODS

A statistical analysis of baseline and follow-up data was performed. The analysis comprised pain-related parameters recorded by use of the painDETECT® system as well as outpatient records.

RESULTS

Baseline data of 459 patients (66% women) could be evaluated. The most common clinical pictures were spinal pain, headache, facial pain, and somatoform disorders, mostly with many years of previous treatment. Approximately 40% showed evidence of neuropathic pain components or central sensitization. With a mean pain intensity of VAS 6 (0-10), a predominantly high degree of chronicity was present. Approximately one third showed a high degree of pain-related functional impairment. Slightly more than half showed evidence of clinically relevant depression. Approximately 80% showed clinically relevant sleep disturbances. Follow-up data were available for 145 patients (31.6%). The proportion of patients receiving a nonpharmacological form of treatment increased by 44.1% (physical therapy) and by 24.1% (psychotherapeutic procedures) during the observation period. The use of co-analgesics increased by approximately 30% over the course.

CONCLUSION

In the outpatient setting, an extension of treatment can be successful for high-grade chronic pain patients. Close structural networking with the clinics for rehabilitation medicine and for psychosomatics and psychotherapy at the MHH can be a favorable prerequisite for this.

Pain perception as hierarchical Bayesian inference: A test case for the theory of constructed emotion

An intriguing perspective about human emotion, the theory of constructed emotion considers emotions as generative models according to the Bayesian brain hypothesis. This theory brings fresh insight to existing findings, but its complexity renders it challenging to test experimentally. We argue that laboratory studies of pain could support the theory because although some may not consider pain to be a genuine emotion, the theory must at minimum be able to explain pain perception and its dysfunction in pathology. We review emerging evidence that bear on this question. We cover behavioral and neural laboratory findings, computational models, placebo hyperalgesia, and chronic pain. We conclude that there is substantial evidence for a predictive processing account of painful experience, paving the way for a better understanding of neuronal and computational mechanisms of other emotions.

Patient Expectations and Therapeutic Alliance Affect Pain Reduction Following Lidocaine Infusion in an Interdisciplinary Chronic Pain Clinic

Pain experience is affected by both ascending nociceptive signals and descending modulation. Expectations can affect pain experience and augment treatment-induced analgesia through descending inhibitory modulation of pain. This open-label, prospective cohort study examined the association between participant expectation ratings and pain reduction in adult participants with chronic pain receiving an intravenous lidocaine infusion. We aimed to explore whether: 1) participants' expectations of treatment efficacy were associated with pain reduction over 8 weeks after infusion; and 2) participants' therapeutic alliance was associated with expectations and/or pain reduction. We recruited 70 participants with chronic pain scheduled for lidocaine infusion. Study measures included pain intensity (pre-treatment, post-treatment, and daily for 8 weeks), treatment expectations (EXPECT), and therapeutic alliance (Trust in Physician and Working Alliance Inventory-Short Revised). Baseline treatment expectations were significantly correlated with pain reduction (r = .42, P < .01). Therapeutic alliance was significantly correlated with expectations (r = .27, P < .05) and pain reduction (r = .38, P < .01). This study quantifies the contribution of: 1) treatment expectations; and 2) therapeutic alliance to the magnitude of lidocaine-induced pain reduction. Results generate the hypothesis that focused efforts to augment treatment expectations and therapeutic alliance could serve to improve pain treatment outcomes. PERSPECTIVE: This study evaluates the relationship between pain reduction and ratings of: 1) treatment expectations; and 2) therapeutic alliance following an intravenous lidocaine infusion. Results generate the hypothesis that focused efforts to augment treatment expectations and therapeutic alliance could serve to improve pain treatment outcomes.

Mimicking opioid analgesia in cortical pain circuits

The anterior cingulate cortex plays a pivotal role in the cognitive and affective aspects of pain perception. Both endogenous and exogenous opioid signaling within the cingulate mitigate cortical nociception, reducing pain unpleasantness. However, the specific functional and molecular identities of cells mediating opioid analgesia in the cingulate remain elusive. Given the complexity of pain as a sensory and emotional experience, and the richness of ethological pain-related behaviors, we developed a standardized, deep-learning platform for deconstructing the behavior dynamics associated with the affective component of pain in mice-LUPE (Light aUtomated Pain Evaluator). LUPE removes human bias in behavior quantification and accelerated analysis from weeks to hours, which we leveraged to discover that morphine altered attentional and motivational pain behaviors akin to affective analgesia in humans. Through activity-dependent genetics and single-nuclei RNA sequencing, we identified specific ensembles of nociceptive cingulate neuron-types expressing mu-opioid receptors. Tuning receptor expression in these cells bidirectionally modulated morphine analgesia. Moreover, we employed a synthetic opioid receptor promoter-driven approach for cell-type specific optical and chemical genetic viral therapies to mimic morphine's pain-relieving effects in the cingulate, without reinforcement. This approach offers a novel strategy for precision pain management by targeting a key nociceptive cortical circuit with on-demand, non-addictive, and effective analgesia.

A neural circuit associated with anxiety-like behaviors induced by chronic inflammatory pain and the anxiolytic effects of electroacupuncture

AIMS

Negative emotions induced by chronic pain are a serious clinical problem. Electroacupuncture (EA) is a clinically proven safe and effective method to manage pain-related negative emotions. However, the circuit mechanisms underlying the effect of EA treatment on negative emotions remain unclear.

METHODS

Plantar injection of complete Freund's adjuvant (CFA) was performed to establish a rat model of chronic inflammatory pain-induced anxiety-like behaviors. Adeno-associated virus (AAV) tracing was used to identify excitatory synaptic transmission from the rostral anterior cingulate cortex (rACC) to the dorsal raphe nucleus (DRN). Employing chemogenetic approaches, we examined the role of the rACC-DRN circuit in chronic pain-induced anxiety-like behaviors and investigated whether EA could reverse chronic pain-induced dysfunctions of the rACC-DRN circuit and anxiety-like behaviors.

RESULTS

We found that chemogenetic activation of the rACC-DRN circuit alleviated CFA-induced anxiety-like behaviors, while chemogenetic inhibition of the rACC-DRN circuit resulted in short-term CFA-induced anxiety-like behaviors. Further research revealed that the development of CFA-induced anxiety-like behaviors was attributed to the dysfunction of rACC CaMKII neurons projecting to DRN serotonergic neurons (rACCCaMKII-DRN5-HT neurons) but not rACC CaMKII neurons projecting to DRN GABAergic neurons (rACCCaMKII-DRNGABA neurons). This is supported by the findings that chemogenetic activation of the rACCCaMKII-DRN5-HT circuit alleviates anxiety-like behaviors in rats with chronic pain, whereas neither chemogenetic inhibition nor chemogenetic activation of the rACCCaMKII-DRNGABA circuit altered CFA chronic pain-evoked anxiety-like behaviors in rats. More importantly, we found that EA could reverse chronic pain-induced changes in the activity of rACC CaMKII neurons and DRN 5-HTergic neurons and that chemogenetic inhibition of the rACCCaMKII-DRN5-HT circuit blocked the therapeutic effects of EA on chronic pain-induced anxiety-like behaviors.

CONCLUSIONS

Our data suggest that the reversal of rACCCaMKII-DRN5-HT circuit dysfunction may be a mechanism underlying the therapeutic effect of EA on chronic pain-induced anxiety-like behaviors.

Fear Learning: An Evolving Picture for Plasticity at Synaptic Afferents to the Amygdala

Unraveling the neuronal mechanisms of fear learning might allow neuroscientists to make links between a learned behavior and the underlying plasticity at specific synaptic connections. In fear learning, an innocuous sensory event such as a tone (called the conditioned stimulus, CS) acquires an emotional value when paired with an aversive outcome (unconditioned stimulus, US). Here, we review earlier studies that have shown that synaptic plasticity at thalamic and cortical afferents to the lateral amygdala (LA) is critical for the formation of auditory-cued fear memories. Despite the early progress, it has remained unclear whether there are separate synaptic inputs that carry US information to the LA to act as a teaching signal for plasticity at CS-coding synapses. Recent findings have begun to fill this gap by showing, first, that thalamic and cortical auditory afferents can also carry US information; second, that the release of neuromodulators contributes to US-driven teaching signals; and third, that synaptic plasticity additionally happens at connections up- and downstream of the LA. Together, a picture emerges in which coordinated synaptic plasticity in serial and parallel circuits enables the formation of a finely regulated fear memory.

Naturalistic reinforcement learning

Humans possess a remarkable ability to make decisions within real-world environments that are expansive, complex, and multidimensional. Human cognitive computational neuroscience has sought to exploit reinforcement learning (RL) as a framework within which to explain human decision-making, often focusing on constrained, artificial experimental tasks. In this article, we review recent efforts that use naturalistic approaches to determine how humans make decisions in complex environments that better approximate the real world, providing a clearer picture of how humans navigate the challenges posed by real-world decisions. These studies purposely embed elements of naturalistic complexity within experimental paradigms, rather than focusing on simplification, generating insights into the processes that likely underpin humans' ability to navigate complex, multidimensional real-world environments so successfully.

Cognitive fatigue weakens the effects of distraction on pain

ABSTRACT

Perceived pain can be viewed because of a competition between nociceptive inputs and other competing goals, such as performing a demanding cognitive task. Task performance, however, suffers when cognitively fatigued. We therefore predicted that cognitive fatigue would weaken the pain-reducing effects of performing a concurrent cognitive task, which would indicate a causal link between fatigue and heightened pain sensitivity. In this study, 2 groups of pain-free adults performed cognitive tasks while receiving painful heat stimuli. In 1 group, we induced cognitive fatigue before performing the tasks. We found that fatigue led to more pain and worse performance when the task was demanding, suggesting that fatigue weakens one's ability to distract from pain. These findings show that cognitive fatigue can impair performance on subsequent tasks and that this impairment can lower a person's ability to distract from and reduce their pain.

Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions, including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

Placebo treatment affects brain systems related to affective and cognitive processes, but not nociceptive pain

Placebo analgesia is a replicable and well-studied phenomenon, yet it remains unclear to what degree it includes modulation of nociceptive processes. Some studies find effects consistent with nociceptive effects, but meta-analyses show that these effects are often small. We analyzed placebo analgesia in a large fMRI study (N = 392), including placebo effects on brain responses to noxious stimuli. Placebo treatment caused robust analgesia in both conditioned thermal and unconditioned mechanical pain. Placebo did not decrease fMRI activity in nociceptive pain regions, including the Neurologic Pain Signature (NPS) and pre-registered spinothalamic pathway regions, with strong support from Bayes Factor analyses. However, placebo treatment affected activity in pre-registered analyses of a second neuromarker, the Stimulus Intensity Independent Pain Signature (SIIPS), and several associated a priori brain regions related to motivation and value, in both thermal and mechanical pain. Individual differences in behavioral analgesia were correlated with neural changes in both thermal and mechanical pain. Our results indicate that processes related to affective and cognitive aspects of pain primarily drive placebo analgesia.

Words hurt: common and distinct neural substrates underlying nociceptive and semantic pain

Introduction

Recent studies have shown that processing semantic pain, such as words associated with physical pain, modulates pain perception and enhances activity in regions of the pain matrix. A direct comparison between activations due to noxious stimulation and processing of words conveying physical pain may clarify whether and to what extent the neural substrates of nociceptive pain are shared by semantic pain. Pain is triggered also by experiences of social exclusion, rejection or loss of significant others (the so-called social pain), therefore words expressing social pain may modulate pain perception similarly to what happens with words associated with physical pain. This event-related fMRI study aims to compare the brain activity related to perceiving nociceptive pain and that emerging from processing semantic pain, i.e., words related to either physical or social pain, in order to identify common and distinct neural substrates.

Methods

Thirty-four healthy women underwent two fMRI sessions each. In the Semantic session, participants were presented with positive words, negative pain-unrelated words, physical pain-related words, and social pain-related words. In the Nociceptive session, participants received cutaneous mechanical stimulations that could be either painful or not. During both sessions, participants were asked to rate the unpleasantness of each stimulus. Linguistic stimuli were also rated in terms of valence, arousal, pain relatedness, and pain intensity, immediately after the Semantic session.

Results

In the Nociceptive session, the 'nociceptive stimuli' vs. 'non-nociceptive stimuli' contrast revealed extensive activations in SI, SII, insula, cingulate cortex, thalamus, and dorsolateral prefrontal cortex. In the Semantic session, words associated with social pain, compared to negative pain-unrelated words, showed increased activity in most of the same areas, whereas words associated with physical pain, compared to negative pain-unrelated words, only activated the left supramarginal gyrus and partly the postcentral gyrus.

Discussion

Our results confirm that semantic pain partly shares the neural substrates of nociceptive pain. Specifically, social pain-related words activate a wide network of regions, mostly overlapping with those pertaining to the affective-motivational aspects of nociception, whereas physical pain-related words overlap with a small cluster including regions related to the sensory-discriminative aspects of nociception. However, most regions of overlap are differentially activated in different conditions.

Lifeworlds in pain: a principled method for investigation and intervention

The experience of pain spans biological, psychological and sociocultural realms, both basic and complex, it is by turns necessary and devastating. Despite an extensive knowledge of the constituents of pain, the ability to translate this into effective intervention remains limited. It is suggested that current, multiscale, medical approaches, largely informed by the biopsychosocial (BPS) model, attempt to integrate knowledge but are undermined by an epistemological obligation, one that necessitates a prior isolation of the constituent parts. To overcome this impasse, we propose that an anthropological stance needs to be taken, underpinned by a Bayesian apparatus situated in computational psychiatry. Here, pain is presented within the context of lifeworlds, where attention is shifted away from the constituents of experience (e.g. nociception, reward processing and fear-avoidance), towards the dynamic affiliation that occurs between these processes over time. We argue that one can derive a principled method of investigation and intervention for pain from modelling approaches in computational psychiatry. We suggest that these modelling methods provide the necessary apparatus to navigate multiscale ontology and epistemology of pain. Finally, a unified approach to the experience of pain is presented, where the relational, inter-subjective phenomenology of pain is brought into contact with a principled method of translation; in so doing, revealing the conditions and possibilities of lifeworlds in pain.

Pain catastrophizing is associated with reduced neural response to monetary reward

Introduction

Pain catastrophizing, a measure of an individual's negative emotional and cognitive appraisals of pain, has been included as a key treatment target in many psychological interventions for pain. However, the neural correlates of pain catastrophizing have been understudied. Prior neuroimaging evidence suggests that adults with pain show altered reward processing throughout the mesocorticolimbic reward circuitry.

Methods

In this study, we tested the association between Pain Catastrophizing Scale (PCS) scores and neural activation to the Monetary Incentive Delay (MID) reward neuroimaging task in 94 adults reporting a range of pain, insomnia, and mood symptoms.

Results

Results indicated that PCS score but not pain intensity was significantly associated with blunted activation in the caudate and putamen in response to feedback of successful vs. unsuccessful trials on the MID task. Mediation analyses indicated that PCS score fully mediated the relationship between depression symptoms and reward activation.

Discussion

These findings provide evidence that pain catastrophizing is independently associated with altered striatal function apart from depression symptoms and pain intensity. Thus, in individuals experiencing pain and/or co- morbid conditions, reward dysfunction is directly related to pain catastrophizing.

"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.

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.

Reinforcement learning establishes a minimal metacognitive process to monitor and control motor learning performance

Humans and animals develop learning-to-learn strategies throughout their lives to accelerate learning. One theory suggests that this is achieved by a metacognitive process of controlling and monitoring learning. Although such learning-to-learn is also observed in motor learning, the metacognitive aspect of learning regulation has not been considered in classical theories of motor learning. Here, we formulated a minimal mechanism of this process as reinforcement learning of motor learning properties, which regulates a policy for memory update in response to sensory prediction error while monitoring its performance. This theory was confirmed in human motor learning experiments, in which the subjective sense of learning-outcome association determined the direction of up- and down-regulation of both learning speed and memory retention. Thus, it provides a simple, unifying account for variations in learning speeds, where the reinforcement learning mechanism monitors and controls the motor learning process.

Post-injury pain and behaviour: a control theory perspective

Injuries of various types occur commonly in the lives of humans and other animals and lead to a pattern of persistent pain and recuperative behaviour that allows safe and effective recovery. In this Perspective, we propose a control-theoretic framework to explain the adaptive processes in the brain that drive physiological post-injury behaviour. We set out an evolutionary and ethological view on how animals respond to injury, illustrating how the behavioural state associated with persistent pain and recuperation may be just as important as phasic pain in ensuring survival. Adopting a normative approach, we suggest that the brain implements a continuous optimal inference of the current state of injury from diverse sensory and physiological signals. This drives the various effector control mechanisms of behavioural homeostasis, which span the modulation of ongoing motivation and perception to drive rest and hyper-protective behaviours. However, an inherent problem with this is that these protective behaviours may partially obscure information about whether injury has resolved. Such information restriction may seed a tendency to aberrantly or persistently infer injury, and may thus promote the transition to pathological chronic pain states.

Acute exercise as active inference in chronic musculoskeletal pain, effects on gait kinematics and muscular activity in patients and healthy participants: a study protocol for a randomised controlled laboratory trial

INTRODUCTION

Chronic musculoskeletal pain is a highly prevalent, complex and distressing condition that may negatively affect all domains of life. In view of an active inference framework, and resting on the concept of allostasis, human movement per se becomes a prerequisite for health and well-being while chronic pain becomes a sign of a system unable to attenuate an allostatic load. Previous studies on different subgroups of chronic pain conditions have demonstrated alterations in gait kinematics and muscle activity, indicating shared disturbances in the motor system from long-term allostatic load. We hypothesise that such alterations exist in heterogenous populations with chronic musculoskeletal pain, and that exposure to acute and controlled exercise may attenuate these alterations. Therefore, the main aim of this study is to investigate the acute effects of exercise on gait kinematics and activity of the back and neck muscles during diverse walking conditions in patients with chronic musculoskeletal pain compared with a reference sample consisting of healthy participants.

METHODS AND ANALYSIS

This two-sample two-armed parallel randomised controlled laboratory trial will include 40 participants with chronic musculoskeletal pain (>3 months) and 40 healthy participants. Participants will be randomly allocated to either 30 min of aerobic exercise or rest. Primary outcomes are gait kinematics (walking speed, step frequency, stride length, lumbar rotation, gait stability) and muscular activity (spatial and temporal) of the back and neck during diverse walking conditions. Secondary outcomes are variability of gait kinematics and muscle activity and subjective pain ratings assessed regularly during the trial.

ETHICS AND DISSEMINATION

The study has been approved by the Regional Ethics Review Board in Uppsala, Sweden (#2018/307). Findings will be disseminated via conference presentations, publications in peer-reviewed journals and engagement with patient support groups and clinicians.

TRIAL REGISTRATION NUMBER

NCT03882333.

Cognitive exertion affects the appraisal of one's own and other people's pain

Correctly evaluating others' pain is a crucial prosocial ability. In both clinical and private settings, caregivers assess their other people's pain, sometimes under the effect of poor sleep and high workload and fatigue. However, the effect played by such cognitive strain in the appraisal of others' pain remains unclear. Fifty participants underwent one of two demanding tasks, involving either working memory (Experiment 1: N-Back task) or cognitive interference (Experiment 2: Stroop task). After each task, participants were exposed to painful laser stimulations at three intensity levels (low, medium, high), or video-clips of patients experiencing three intensity levels of pain (low, medium, high). Participants rated the intensity of each pain event on a visual analogue scale. We found that the two tasks influenced rating of both one's own and others' pain, by decreasing the sensitivity to medium and high events. This was observed either when comparing the demanding condition to a control (Stroop), or when modelling linearly the difficulty/performance of each depleting task (N-Back). We provide converging evidence that cognitive exertion affects the subsequent appraisal of one's own and likewise others' pain.

The complex syndrome of functional neurological disorder

BACKGROUND

Patients with functional neurological disorders (FND) often present with multiple motor, sensory, psychological and cognitive symptoms. In order to explore the relationship between these common symptoms, we performed a detailed clinical assessment of motor, non-motor symptoms, health-related quality of life (HRQoL) and disability in a large cohort of patients with motor FND. To understand the clinical heterogeneity, cluster analysis was used to search for subgroups within the cohort.

METHODS

One hundred fifty-two patients with a clinically established diagnosis of motor FND were assessed for motor symptom severity using the Simplified Functional Movement Disorder Rating Scale (S-FMDRS), the number of different motor phenotypes (i.e. tremor, dystonia, gait disorder, myoclonus, and weakness), gait severity and postural instability. All patients then evaluated each motor symptom type severity on a Likert scale and completed questionnaires for depression, anxiety, pain, fatigue, cognitive complaints and HRQoL.

RESULTS

Significant correlations were found among the self-reported and all objective motor symptoms severity measures. All self-reported measures including HRQoL correlated strongly with each other. S-FMDRS weakly correlated with HRQoL. Hierarchical cluster analysis supplemented with gap statistics revealed a homogenous patient sample which could not be separated into subgroups.

CONCLUSIONS

We interpret the lack of evidence of clusters along with a high degree of correlation between all self-reported and objective measures of motor or non-motor symptoms and HRQoL within current neurobiological models as evidence to support a unified pathophysiology of 'functional' symptoms. Our results support the unification of functional and somatic syndromes in classification schemes and for future mechanistic and therapeutic research.

Role of Immersive Virtual Reality in Motor Behaviour Decision-Making in Chronic Pain Patients

Primary chronic pain is a major contributor to disability worldwide, with an estimated prevalence of 20-33% of the world's population. The high socio-economic impact of musculoskeletal pain justifies seeking an appropriate therapeutic strategy. Immersive virtual reality (VR) has been proposed as a first-line intervention for chronic musculoskeletal pain. However, the growing literature has not been accompanied by substantial progress in understanding how VR exerts its impact on the pain experience and what neurophysiological mechanisms might be involved in the clinical effectiveness of virtual reality interventions in chronic pain patients. The aim of this review is: (i) to establish the state of the art on the effects of VR on patients with chronic pain; (ii) to identify neuroplastic changes associated with chronic pain that may be targeted by VR intervention; and (iii) to propose a hypothesis on how immersive virtual reality could modify motor behavioral decision-making through an interactive experience in patients with chronic pain.

The role of expectations, control and reward in the development of pain persistence based on a unified model

Chronic, or persistent pain affects more than 10% of adults in the general population. This makes it one of the major physical and mental health care problems. Although pain is an important acute warning signal that allows the organism to take action before tissue damage occurs, it can become persistent and its role as a warning signal thereby inadequate. Although per definition, pain can only be labeled as persistent after 3 months, the trajectory from acute to persistent pain is likely to be determined very early and might even start at the time of injury. The biopsychosocial model has revolutionized our understanding of chronic pain and paved the way for psychological treatments for persistent pain, which routinely outperform other forms of treatment. This suggests that psychological processes could also be important in shaping the very early trajectory from acute to persistent pain and that targeting these processes could prevent the development of persistent pain. In this review, we develop an integrative model and suggest novel interventions during early pain trajectories, based on predictions from this model.

Characterization of spatiotemporal dynamics of binary and graded tonic pain in humans using intracranial recordings

Pain is a complex experience involving sensory, emotional, and cognitive aspects, and multiple networks manage its processing in the brain. Examining how pain transforms into a behavioral response can shed light on the networks' relationships and facilitate interventions to treat chronic pain. However, studies using high spatial and temporal resolution methods to investigate the neural encoding of pain and its psychophysical correlates have been limited. We recorded from intracranial stereo-EEG (sEEG) electrodes implanted in sixteen different brain regions of twenty patients who underwent psychophysical pain testing consisting of a tonic thermal stimulus to the hand. Broadband high-frequency local field potential amplitude (HFA; 70-150 Hz) was isolated to investigate the relationship between the ongoing neural activity and the resulting psychophysical pain evaluations. Two different generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics. The first model examined the relationship between HFA and whether the patient responded "yes" or "no" to whether the trial was painful. The second model investigated the relationship between HFA and how painful the stimulus was rated on a visual analog scale. GLMEs revealed that HFA in the inferior temporal gyrus (ITG), superior frontal gyrus (SFG), and superior temporal gyrus (STG) predicted painful responses at stimulus onset. An increase in HFA in the orbitofrontal cortex (OFC), SFG, and striatum predicted pain responses at stimulus offset. Numerous regions including the anterior cingulate cortex, hippocampus, IFG, MTG, OFC, and striatum, predicted the pain rating at stimulus onset. However, only the amygdala and fusiform gyrus predicted increased pain ratings at stimulus offset. We characterized the spatiotemporal representations of binary and graded painful responses during tonic pain stimuli. Our study provides evidence from intracranial recordings that the neural encoding of psychophysical pain changes over time during a tonic thermal stimulus, with different brain regions being predictive of pain at the beginning and end of the stimulus.

Hierarchical predictive coding in distributed pain circuits

Predictive coding is a computational theory on describing how the brain perceives and acts, which has been widely adopted in sensory processing and motor control. Nociceptive and pain processing involves a large and distributed network of circuits. However, it is still unknown whether this distributed network is completely decentralized or requires networkwide coordination. Multiple lines of evidence from human and animal studies have suggested that the cingulate cortex and insula cortex (cingulate-insula network) are two major hubs in mediating information from sensory afferents and spinothalamic inputs, whereas subregions of cingulate and insula cortices have distinct projections and functional roles. In this mini-review, we propose an updated hierarchical predictive coding framework for pain perception and discuss its related computational, algorithmic, and implementation issues. We suggest active inference as a generalized predictive coding algorithm, and hierarchically organized traveling waves of independent neural oscillations as a plausible brain mechanism to integrate bottom-up and top-down information across distributed pain circuits.

A novel computational approach to pain perception modelling within a Bayesian framework using quantitative sensory testing

Pain perception can be studied as an inferential process in which prior information influences the perception of nociceptive input. To date, there are no suitable psychophysical paradigms to measure this at an individual level. We developed a quantitative sensory testing paradigm allowing for quantification of the influence of prior expectations versus current nociceptive input during perception. Using a Pavlovian-learning task, we investigated the influence of prior expectations on the belief about the varying strength of association between a painful electrical cutaneous stimulus and a visual cue in healthy subjects (N = 70). The belief in cue-pain associations was examined with computational modelling using a Hierarchical Gaussian Filter (HGF). Prior weighting estimates in the HGF model were compared with the established measures of conditioned pain modulation (CPM) and temporal summation of pain (TSP) assessed by cuff algometry. Subsequent HGF-modelling and estimation of the influence of prior beliefs on perception showed that 70% of subjects had a higher reliance on nociceptive input during perception of acute pain stimuli, whereas 30% showed a stronger weighting of prior expectations over sensory evidence. There was no association between prior weighting estimates and CPM or TSP. The data demonstrates relevant individual differences in prior weighting and suggests an importance of top-down cognitive processes on pain perception. Our new psychophysical testing paradigm provides a method to identify individuals with traits suggesting greater reliance on prior expectations in pain perception, which may be a risk factor for developing chronic pain and may be differentially responsive to learning-based interventions.

Evidence for dopaminergic involvement in endogenous modulation of pain relief

Relief of ongoing pain is a potent motivator of behavior, directing actions to escape from or reduce potentially harmful stimuli. Whereas endogenous modulation of pain events is well characterized, relatively little is known about the modulation of pain relief and its corresponding neurochemical basis. Here, we studied pain modulation during a probabilistic relief-seeking task (a 'wheel of fortune' gambling task), in which people actively or passively received reduction of a tonic thermal pain stimulus. We found that relief perception was enhanced by active decisions and unpredictability, and greater in high novelty-seeking trait individuals, consistent with a model in which relief is tuned by its informational content. We then probed the roles of dopaminergic and opioidergic signaling, both of which are implicated in relief processing, by embedding the task in a double-blinded cross-over design with administration of the dopamine precursor levodopa and the opioid receptor antagonist naltrexone. We found that levodopa enhanced each of these information-specific aspects of relief modulation but no significant effects of the opioidergic manipulation. These results show that dopaminergic signaling has a key role in modulating the perception of pain relief to optimize motivation and behavior.

Pain, from perception to action: A computational perspective

Pain is driven by sensation and emotion, and in turn, it motivates decisions and actions. To fully appreciate the multidimensional nature of pain, we formulate the study of pain within a closed-loop framework of sensory-motor prediction. In this closed-loop cycle, prediction plays an important role, as the interaction between prediction and actual sensory experience shapes pain perception and subsequently, action. In this Perspective, we describe the roles of two prominent computational theories-Bayesian inference and reinforcement learning-in modeling adaptive pain behaviors. We show that prediction serves as a common theme between these two theories, and that each of these theories can explain unique aspects of the pain perception-action cycle. We discuss how these computational theories and models can improve our mechanistic understandings of pain-centered processes such as anticipation, attention, placebo hypoalgesia, and pain chronification.

The association of spouse interactions and emotional learning in interference related to chronic back pain

Social interactions affect individual behaviours, preferences, and attitudes. This is also critical in the context of experiencing pain and expressing pain behaviours, and may relate to learned emotional responses. In this respect, individual variability in the medial prefrontal cortex (mPFC), which is involved in adjusting an organism's behaviour to its environment by evaluating and interpreting information within the context of past experiences, is important. It is critical for selecting suitable behavioural responses within a social environment and may reinforce maladaptation in chronic pain. In our study, we used brain imaging during appetitive and aversive pavlovian conditioning in persons with chronic back pain (CBP), subacute back pain (SABP), and healthy controls (HC), together with information on spouse responses to pain behaviours. We also examined the relationship of these responses with pain-related interference in the patients. Our findings yielded a significant negative association between mPFC responses to appetitive and aversive learning in CBP. We also observed a significant negative association for mPFC responses during aversive learning and distracting spouse responses, and a significant positive association between mPFC responses during appetitive learning and solicitous spouse responses in CBP. Both significantly predicted pain-related interference in the CBP group (explained variance up to 53%). Significant associations were not found for SABP or HC. Our findings support an association between appetitive and aversive pavlovian learning, related brain circuits and spouse responses to pain in CBP, where appetitive and aversive learning processes seem to be differentially involved. This can inform prevention and early intervention in a mechanistic approach.

Computational and neural mechanisms of statistical pain learning

Pain invariably changes over time. These fluctuations contain statistical regularities which, in theory, could be learned by the brain to generate expectations and control responses. We demonstrate that humans learn to extract these regularities and explicitly predict the likelihood of forthcoming pain intensities in a manner consistent with optimal Bayesian inference with dynamic update of beliefs. Healthy participants received probabilistic, volatile sequences of low and high-intensity electrical stimuli to the hand during brain fMRI. The inferred frequency of pain correlated with activity in sensorimotor cortical regions and dorsal striatum, whereas the uncertainty of these inferences was encoded in the right superior parietal cortex. Unexpected changes in stimulus frequencies drove the update of internal models by engaging premotor, prefrontal and posterior parietal regions. This study extends our understanding of sensory processing of pain to include the generation of Bayesian internal models of the temporal statistics of pain.

Top-down threat bias in pain perception is predicted by intrinsic structural and functional connections of the brain

Top-down processes such as expectations play a key role in pain perception. In specific contexts, inferred threat of impending pain can affect perceived pain more than the noxious intensity. This biasing effect of top-down threats can affect some individuals more strongly than others due to differences in fear of pain. The specific characteristics of intrinsic brain characteristics that mediate the effects of top-down threat bias are mainly unknown. In this study, we examined whether threat bias is associated with structural and functional brain connectivity. The variability in the top-down bias was mapped to the microstructure of white matter in diffusion weighted images (DWI) using MRTrix3. Mean functional connectivity of five canonical resting state networks was tested for association with bias scores and with the identified DWI metrics. We found that the fiber density of the splenium of the corpus callosum was significantly low in individuals with high top-down threat bias (FWE corrected with 5000 permutations, p < 0.05). The mean functional connectivity within the language/memory and between language/memory and default mode networks predicted the bias scores. Functional connectivity within language memory networks predicted the splenium fiber density, higher pain catastrophizing and lower mindful awareness. Probabilistic tractography showed that the identified region in the splenium connected several sensory regions and high-order parietal regions between the two hemispheres, indicating the splenium's role in sensory integration. These findings demonstrate that individuals who show more change in pain with changes in the threat of receiving a stronger noxious stimulus have lower structural connectivity in the pathway necessary for integrating top-down cue information with bottom-up sensory information. Conversely, systems involved in memory recall, semantic and self-referential processing are more strongly connected in people with top-down threat bias.

Asymmetric representation of aversive prediction errors in Pavlovian threat conditioning

Survival in biological environments requires learning associations between predictive sensory cues and threatening outcomes. Such aversive learning may be implemented through reinforcement learning algorithms that are driven by the signed difference between expected and encountered outcomes, termed prediction errors (PEs). While PE-based learning is well established for reward learning, the role of putative PE signals in aversive learning is less clear. Here, we used functional magnetic resonance imaging in humans (21 healthy men and women) to investigate the neural representation of PEs during maintenance of learned aversive associations. Four visual cues, each with a different probability (0, 33, 66, 100%) of being followed by an aversive outcome (electric shock), were repeatedly presented to participants. We found that neural activity at omission (US-) but not occurrence of the aversive outcome (US+) encoded PEs in the medial prefrontal cortex. More expected omission of aversive outcome was associated with lower neural activity. No neural signals fulfilled axiomatic criteria, which specify necessary and sufficient components of PE signals, for signed PE representation in a whole-brain search or in a-priori regions of interest. Our results might suggest that, different from reward learning, aversive learning does not involve signed PE signals that are represented within the same brain region for all conditions.

The neural signature of the decision value of future pain

SignificanceWe often willingly experience pain to reach a goal. However, potential pain can also prevent reckless action. How do we consider future pain when deciding on the best course of action? To date, the precise neural mechanisms underlying the valuation of future pain remain unknown. Using functional MRI, we derive a whole-brain signature of the value of future pain capable of predicting participants' choices to accept pain in exchange for a reward. We show that this signature is characterized by a distributed pattern of activity with clear contributions from structures encoding reward and salience, notably the ventral and dorsal striatum. These findings highlight how the brain assigns value to future pain when choosing the best course of action.