Inferring distinct mechanisms in the absence of subjective differences: Placebo and centrally acting analgesic underlie unique brain adaptations

Investigating mortality salience as a potential causal influence and moderator of responses to laboratory pain

Background

Because pain can have profound ramifications for quality of life and daily functioning, understanding nuances in the interplay of psychosocial experiences with pain perception is vital for effective pain management. In separate lines of research, pain resilience and mortality salience have emerged as potentially important psychological correlates of reduced pain severity and increased tolerance of pain. However, to date, there has been a paucity of research examining potentially interactive effects of these factors on pain perception. To address this gap, the present experiment investigated mortality salience as a causal influence on tolerance of laboratory pain and a moderator of associations between pain resilience and pain tolerance within a Chinese sample.

Methods

Participants were healthy young Chinese adults (86 women, 84 men) who first completed a brief initial cold pressor test (CPT) followed by measures of demographics and pain resilience. Subsequently, participants randomly assigned to a mortality salience (MS) condition completed two open-ended essay questions in which they wrote about their death as well as a death anxiety scale while those randomly assigned to a control condition completed analogous tasks about watching television. Finally, all participants engaged in a delay task and a second CPT designed to measure post-manipulation pain tolerance and subjective pain intensity levels.

Results

MS condition cohorts showed greater pain tolerance than controls on the post-manipulation CPT, though pain intensity levels did not differ between groups. Moderator analyses indicated that the relationship between the behavior perseverance facet of pain resilience and pain tolerance was significantly stronger among MS condition participants than controls.

Conclusions

This experiment is the first to document potential causal effects of MS on pain tolerance and Ms as a moderator of the association between self-reported behavior perseverance and behavioral pain tolerance. Findings provide foundations for extensions within clinical pain samples.

Neurobiology of osteoarthritis: a systematic review and activation likelihood estimation meta-analysis

Osteoarthritis (OA) affects 240 million people worldwide. Neuroimaging has been increasingly used to investigate brain changes in OA, however, there is considerable heterogeneity in reported results. The goal of this systematic review and meta-analysis was to synthesise existing literature and identify consistent brain alterations in OA. Six databases were searched from inception up to June, 2022. Full-texts of original human studies were included if they had: (i) neuroimaging data by site of OA (e.g. hand, knee, hip); (ii) data in healthy controls (HC); (iii) > 10 participants. Activation likelihood estimation (ALE) was conducted using GingerALE software on studies that reported peak activation coordinates and sample size. Our search strategy identified 6250 articles. Twenty-eight studies fulfilled the eligibility criteria, of which 18 were included in the meta-analysis. There were no significant differences in brain structure or function between OA and healthy control contrasts. In exploratory analysis, the right insula was associated with OA vs healthy controls, with less activity, connectivity and brain volume in OA. This region was implicated in both knee and hip OA, with an additional cluster in the medial prefrontal cortex observed only in the contrast between healthy controls and the hip OA subgroup, suggesting a possible distinction between the neural correlates of OA subtypes. Despite the limitations associated with heterogeneity and poor study quality, this synthesis identified neurobiological outcomes associated with OA, providing insight for future research. PROSPERO registration number: CRD42021238735.

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Toward Composite Pain Biomarkers of Neuropathic Pain—Focus on Peripheral Neuropathic Pain

Chronic pain affects ~10–20% of the U.S. population with an estimated annual cost of $600 billion, the most significant economic cost of any disease to-date. Neuropathic pain is a type of chronic pain that is particularly difficult to manage and leads to significant disability and poor quality of life. Pain biomarkers offer the possibility to develop objective pain-related indicators that may help diagnose, treat, and improve the understanding of neuropathic pain pathophysiology. We review neuropathic pain mechanisms related to opiates, inflammation, and endocannabinoids with the objective of identifying composite biomarkers of neuropathic pain. In the literature, pain biomarkers typically are divided into physiological non-imaging pain biomarkers and brain imaging pain biomarkers. We review both types of biomarker types with the goal of identifying composite pain biomarkers that may improve recognition and treatment of neuropathic pain.

Validating a biosignature-predicting placebo pill response in chronic pain in the settings of a randomized controlled trial

ABSTRACT

The objective of this study is to validate a placebo pill response predictive model-a biosignature-that classifies chronic pain patients into placebo responders (predicted-PTxResp) and nonresponders (predicted-PTxNonR) and test whether it can dissociate placebo and active treatment responses. The model, based on psychological and brain functional connectivity, was derived in our previous study and blindly applied to current trial participants. Ninety-four chronic low back pain (CLBP) patients were classified into predicted-PTxResp or predicted-PTxNonR and randomized into no treatment, placebo treatment, or naproxen treatment. To monitor analgesia, back pain intensity was collected twice a day: 3 weeks baseline, 6 weeks of treatment, and 3 weeks of washout. Eighty-nine CLBP patients were included in the intent-to-treat analyses and 77 CLBP patients in the per-protocol analyses. Both analyses showed similar results. At the group level, the predictive model performed remarkably well, dissociating the separate effect sizes of pure placebo response and pure active treatment response and demonstrating that these effects interacted additively. Pain relief was about 15% stronger in the predicted-PTxResp compared with the predicted-PTxNonR receiving either placebo or naproxen, and the predicted-PTxNonR successfully isolated the active drug effect. At a single subject level, the biosignature better predicted placebo nonresponders, with poor accuracy. One component of the biosignature (dorsolateral prefrontal cortex-precentral gyrus functional connectivity) could be generalized across 3 placebo studies and in 2 different cohorts-CLBP and osteoarthritis pain patients. This study shows that a biosignature can predict placebo response at a group level in the setting of a randomized controlled trial.

Effects of Chronic Pain Treatment on Altered Functional and Metabolic Activities in the Brain: A Systematic Review and Meta-Analysis of Functional Neuroimaging Studies

Previous studies have identified altered brain changes in chronic pain patients, however, it remains unclear whether these changes are reversible. We summarized the neural and molecular changes in patients with chronic pain and employed a meta-analysis approach to quantify the changes. We included 75 studies and 11 of these 75 studies were included in the activation likelihood estimation (ALE) analysis. In the 62 functional magnetic resonance imaging (fMRI) studies, the primary somatosensory and motor cortex (SI and MI), thalamus, insula, and anterior cingulate cortex (ACC) showed significantly decreased activity after the treatments compared to baseline. In the 13 positron emission tomography (PET) studies, the SI, MI, thalamus, and insula showed significantly increased glucose uptake, blood flow, and opioid-receptor binding potentials after the treatments compared to baseline. A meta-analysis of fMRI studies in patients with chronic pain, during pain-related tasks, showed a significant deactivation likelihood cluster in the left medial posterior thalamus. Further studies are warranted to understand brain reorganization in patients with chronic pain compared to the normal state, in terms of its relationship with symptom reduction and baseline conditions.

The cerebral mechanism of the specific and nonspecific effects of acupuncture based on knee osteoarthritis: study protocol for a randomized controlled trial

Background

Research on the effect of acupuncture has been limited. Whether the effect of acupuncture is equivalent to placebo has been the focus of debate in this field. This study will explore the specific and non-specific effects of acupuncture for knee osteoarthritis (KOA) by functional magnetic resonance imaging (fMRI).

Methods and design

Ninety participants diagnosed with KOA will be randomly divided into the acupuncture group, sham acupuncture group, and waiting list group in a ratio of 1:1:1. Except for the waiting list group, the other participants will receive acupuncture or sham acupuncture three sessions per week for 4 weeks respectively. The primary outcome will be the response rate which is defined on an individual basis as at least a 2-point decrease in the numerical rating scale (NRS) of pain at the end of intervention period compared with the baseline. fMRI scans will be performed at baseline and the end of the intervention period to examine the response of various brain regions. The secondary outcomes will include the Western Ontario and McMaster Osteoarthritis Index (WOMAC), State-Trait Anxiety Scale-State Anxiety Subscale (STAI-S), and Stanford Expectations of Treatment Scale (SETS). Pearson’s correlation coefficient will be performed to investigate the changes in brain activity and clinical variables.

Discussion

The results of our study will help to evaluate the specific and nonspecific effects of acupuncture combined with clinical and brain function changes based on KOA.

Trial registration

Chinese Clinical Trial Registry ChiCTR1900025799. Registered on 9 September 2019.

A deeper look at pain variability and its relationship with the placebo response: results from a randomized, double-blind, placebo-controlled clinical trial of naproxen in osteoarthritis of the knee

Previous studies have shown a robust correlation between variability of clinical pain scores and responsiveness to placebo (but not active drug) in pain studies, but explanations for these relationships are lacking. We investigated this further by assessing relationship between the Focused Analgesia Selection Test (FAST), a psychophysical method that quantifies pain reporting variability in response to experimental stimuli, variability of daily clinical pain scores as captured using diary, and response to treatment in the context of a randomized controlled crossover trial of naproxen vs placebo in knee osteoarthritis. Evoked pain using the Staircase-Evoked Pain Procedure served as the primary efficacy endpoint. Variability of daily pain scores and the FAST were assessed at baseline. Fifty-five subjects completed the study and were included in the analyses. Our results indicated a statistically significant, moderate linear relationship between variability of clinical and experimental pain reports (r = -0.416, P = 0.004). Both correlated with the placebo response (r = 0.393, P = 0.004; r =-0.371, P = 0.009; respectively), but only the FAST predicted the treatment difference between naproxen and placebo, as demonstrated both in a regression model (P = 0.002, Beta = 0.456, t = 3.342) and in a receiver operating characteristic curve (0.721) analysis. Our results extend previous findings to include a correlation between experimental pain variability and the placebo response and suggest that experimental pain variability is a better predictor of patients who respond preferentially to drug over placebo. A theoretical model unifying these observations is proposed, and practical implications are discussed.

Deconstructing biomarkers for chronic pain: context- and hypothesis-dependent biomarker types in relation to chronic pain

This review expounds on types and properties of biomarkers for chronic pain, given a mechanistic model of processes underlying development of chronic pain. It covers advances in the field of developing biomarkers for chronic pain, while outlining the general principles of categorizing types of biomarkers driven by specific hypotheses regarding underlying mechanisms. Within this theoretical construct, example biomarkers are described and their properties expounded. We conclude that the field is advancing in important directions and the developed biomarkers have the potential of impacting both the science and the clinical practice regarding chronic pain.

Composite Pain Biomarker Signatures for Objective Assessment and Effective Treatment

Pain is a subjective sensory experience that can, mostly, be reported but cannot be directly measured or quantified. Nevertheless, a suite of biomarkers related to mechanisms, neural activity, and susceptibility offer the possibility-especially when used in combination-to produce objective pain-related indicators with the specificity and sensitivity required for diagnosis and for evaluation of risk of developing pain and of analgesic efficacy. Such composite biomarkers will also provide improved understanding of pain pathophysiology.

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Reduced anterior cingulate grey matter volume in painful hand osteoarthritis

Objective

Increasing evidence supports the role of central sensitisation in osteoarthritis (OA) pain. In this study, we used neuroimaging to compare pain-processing regions of the brain in participants with and without hand OA. We then assessed for volumetric changes in these brain regions following treatment with centrally acting analgesics.

Methods

Participants with hand OA (n = 28) underwent T1-weighted MRI of the brain before and after 12 weeks of treatment with pregabalin, duloxetine or placebo. Grey matter volume in the anterior cingulate cortex (ACC), insular cortex and thalamus was compared to non-OA control subjects (n = 11) using FreeSurfer regional volumetric analysis and voxel-based morphometry, and evaluated for differences pre- and post-treatment.

Results

Relative to non-OA controls, hand OA participants had areas of reduced grey matter volume in the ACC at baseline (p = 0.007). Regional volumetric differences in the ACC persisted after 13 weeks’ treatment with pregabalin or duloxetine (p = 0.004) with no significant differences between treatment cohorts, despite improvements in NRS pain scores for pregabalin (p = 0.005) and duloxetine (p = 0.050). The ACC grey matter changes persisted despite a significant improvement in pain in the pregabalin and duloxetine groups vs. placebo. No structural differences were observed in the insular cortex or thalamus at baseline or following treatment.

Conclusion

Our study found evidence of reduced ACC grey matter volume in participants with hand arthritis that persisted after treatment with centrally acting analgesics pregabalin and duloxetine, respectively. The sustained changes observed in the ACC in our study could reflect the relatively short duration of treatment, or that the differences observed are irreversible volume changes due to chronic pain that are established over time.

Electronic supplementary material

The online version of this article (10.1007/s00296-018-4085-2) contains supplementary material, which is available to authorized users.

Inferring distinct mechanisms in the absence of subjective differences: Placebo and centrally acting analgesic underlie unique brain adaptations

Development and maintenance of chronic pain is associated with structural and functional brain reorganization. However, few studies have explored the impact of drug treatments on such changes. The extent to which long-term analgesia is related to brain adaptations and its effects on the reversibility of brain reorganization remain unclear. In a randomized placebo-controlled clinical trial, we contrasted pain relief (3-month treatment period), and anatomical (gray matter density [GMD], assessed by voxel-based morphometry) and functional connectivity (resting state fMRI nodal degree count [DC]) adaptations, in 39 knee osteoarthritis (OA) patients (22 females), randomized to duloxetine (DLX, 60 mg once daily) or placebo. Pain relief was equivalent between treatment types. However, distinct circuitry (GMD and DC) could explain pain relief in each group: up to 85% of variance for placebo analgesia and 49% of variance for DLX analgesia. No behavioral measures (collected at entry into the study) could independently explain observed analgesia. Identified circuitry were outside of nociceptive circuitry and minimally overlapped with OA-abnormal or placebo response predictive brain regions. Mediation analysis revealed that changes in GMD and DC can influence each other across remote brain regions to explain observed analgesia. Therefore, we can conclude that distinct brain mechanisms underlie DLX and placebo analgesia in OA. The results demonstrate that even in the absence of differences in subjective pain relief, pharmacological treatments can be differentiated from placebo based on objective brain biomarkers. This is a crucial step to untangling mechanisms and advancing personalized therapy approaches for chronic pain.