Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

Neuroscientific evidence for pain being a classically conditioned response to trauma- and pain-related cues in humans

ABSTRACT

Psychological trauma is typically accompanied by physical pain, and posttraumatic stress disorder (PTSD) often co-occurs with chronic pain. Clinical reports suggest that pain after trauma may be part of a re-experiencing symptomatology. Classical conditioning can underlie visual re-experiencing since intrusions can occur as conditioned responses (CRs) to trauma-related cues. If individuals also experience pain to cues previously paired with, but not anymore inflicting nociceptive stimulation (CSs), conditioning could also explain re-experiencing of pain. Sixty-five participants underwent classical conditioning, where painful electrocutaneous stimulation and aversive film-clips served as unconditioned stimuli (USs) in a 2(pain/no pain)×2(aversive/neutral film) design. CSs were neutral pictures depicting contextual details from the films. One day later, participants were re-exposed to CSs during a memory-triggering-task (MTT). We assessed pain-CRs by self-report and an fMRI-based marker of nociceptive pain, the neurologic pain signature (NPS); and recorded spontaneous daily-life pain-intrusions with an e-diary. During conditioning, pain-signaling CSs elicited more self-reported-pain and NPS-responses than no-pain-signaling CSs. Possibly because the aversive-film masked differences in participants' responses to pain-signaling vs. no-pain-signaling CSs, pain-CRs during acquisition only emerged within the neutral-film condition. When participants were re-exposed to CSs during MTT, self-reported-pain-CRs during the neutral-film condition and, though more uncertain, NPS-CRs during the aversive-film condition persisted. Importantly, participants with stronger pain-CRs showed a greater probability and severity of experiencing spontaneous pain intrusions during daily-life. Our data support that pain can emerge as a CR with emotional and sensory components. Classical conditioning presents a possible mechanism explaining pain-intrusions, and more broadly, pain experienced without nociceptive input.

Supraspinal Mechanisms Underlying Ocular Pain

Supraspinal mechanisms of pain are increasingly understood to underlie neuropathic ocular conditions previously thought to be exclusively peripheral in nature. Isolating individual causes of centralized chronic conditions and differentiating them is critical to understanding the mechanisms underlying neuropathic eye pain and ultimately its treatment. Though few functional imaging studies have focused on the eye as an end-organ for the transduction of noxious stimuli, the brain networks related to pain processing have been extensively studied with functional neuroimaging over the past 20 years. This article will review the supraspinal mechanisms that underlie pain as they relate to the eye.

The neurologic pain signature responds to nonsteroidal anti-inflammatory treatment vs placebo in knee osteoarthritis

Supplemental Digital Content is Available in the Text.

fMRI-based measures, validated for nociceptive pain, respond to acute osteoarthritis pain, are not sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Introduction:

Many drug trials for chronic pain fail because of high placebo response rates in primary endpoints. Neurophysiological measures can help identify pain-linked pathophysiology and treatment mechanisms. They can also help guide early stop/go decisions, particularly if they respond to verum treatment but not placebo. The neurologic pain signature (NPS), an fMRI-based measure that tracks evoked pain in 40 published samples and is insensitive to placebo in healthy adults, provides a potentially useful neurophysiological measure linked to nociceptive pain.

Objectives:

This study aims to validate the NPS in knee osteoarthritis (OA) patients and test the effects of naproxen on this signature.

Methods:

In 2 studies (50 patients, 64.6 years, 75% females), we (1) test the NPS and other control signatures related to negative emotion in knee OA pain patients; (2) test the effect of placebo treatments; and (3) test the effect of naproxen, a routinely prescribed nonsteroidal anti-inflammatory drug in OA.

Results:

The NPS was activated during knee pain in OA (d = 1.51, P < 0.001) and did not respond to placebo (d = 0.12, P = 0.23). A single dose of naproxen reduced NPS responses (vs placebo, NPS d = 0.34, P = 0.03 and pronociceptive NPS component d = 0.38, P = 0.02). Naproxen effects were specific for the NPS and did not appear in other control signatures.

Conclusion:

This study provides preliminary evidence that fMRI-based measures, validated for nociceptive pain, respond to acute OA pain, do not appear sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Scalp EEG-based Pain Detection using Convolutional Neural Network

Pain is an integrative phenomenon coupled with dynamic interactions between sensory and contextual processes in the brain, often associated with detectable neurophysiological changes. Recent advances in brain activity recording tools and machine learning technologies have intrigued research and development of neurocomputing techniques for objective and neurophysiology-based pain detection. This paper proposes a pain detection framework based on Electroencephalogram (EEG) and deep convolutional neural networks (CNN). The feasibility of CNN is investigated for distinguishing induced pain state from resting state in the recruitment of 10 chronic back pain patients. The experimental study recorded EEG signals in two phases: 1. movement stimulation (MS), where induces back pain by executing predefined movement tasks; 2. video stimulation (VS), where induces back pain perception by watching a set of video clips. A multi-layer CNN classifies the EEG segments during the resting state and the pain state. The novel approach offers high and robust performance and hence is significant in building a powerful pain detection algorithm. The area under the receiver operating characteristic curve (AUC) of our approach is 0.83±0.09 and 0.81±0.15, in MS and VS, respectively, higher than the state-of-the-art approaches. The sub-brain-areas are also analyzed, to examine distinct brain topographies relevant for pain detection. The results indicate that MS-induced pain tends to evoke a generalized brain area, while the evoked area is relatively partial under VS-induced pain. This work may provide a new solution for researchers and clinical practitioners on pain detection.

Brain Imaging Biomarkers for Chronic Pain

The prevalence of chronic pain has reached epidemic levels. In addition to personal suffering chronic pain is associated with psychiatric and medical co-morbidities, notably substance misuse, and a huge a societal cost amounting to hundreds of billions of dollars annually in medical cost, lost wages, and productivity. Chronic pain does not have a cure or quantitative diagnostic or prognostic tools. In this manuscript we provide evidence that this situation is about to change. We first start by summarizing our current understanding of the role of the brain in the pathogenesis of chronic pain. We particularly focus on the concept of learning in the emergence of chronic pain, and the implication of the limbic brain circuitry and dopaminergic signaling, which underly emotional learning and decision making, in this process. Next, we summarize data from our labs and from other groups on the latest brain imaging findings in different chronic pain conditions focusing on results with significant potential for translation into clinical applications. The gaps in the study of chronic pain and brain imaging are highlighted in throughout the overview. Finally, we conclude by discussing the costs and benefits of using brain biomarkers of chronic pain and compare to other potential markers.

Features of the superficial white matter as biomarkers for the detection of Alzheimer's disease and mild cognitive impairment: A diffusion tensor imaging study

BACKGROUND

With the development of medical imaging and processing tools, accurate diagnosis of diseases has been made possible by intelligent systems. Owing to the remarkable ability of support vector machines (SVMs) for diseases diagnosis, extensive research has been conducted using the SVM algorithm for the classification of Alzheimer's disease (AD) and mild cognitive impairment (MCI).

OBJECTIVES

In this study, we applied an automated method to classify patients with AD and MCI and healthy control (HC) subjects based on the diffusion tensor imaging (DTI) features in the superficial white matter (SWM).

PARTICIPANTS

For this purpose, DTI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI). This method employed DTI data from 72 subjects: 24 subjects as HC, 24 subjects with MCI, and 24 subjects with AD.

MEASURE

ments: DTI processing was performed using DSI Studio software and all machine learning analyses were performed using MATLAB software.

RESULTS

The linear kernel of SVM was the best classifier, with an accuracy of 95.8% between the AD and HC groups, followed by the quadratic kernel of SVM with an accuracy of 83.3% between the MCI and HC groups and the Gaussian kernel of SVM with an accuracy of 83.3% between the AD and MCI groups.

CONCLUSIONS

Given the importance of diagnosing AD and MCI as well as the role of superficial white matter in the diagnosis of neurodegenerative diseases, in this study, the features of different DTI methods of the SWM are discussed, which could be a useful tool to assist in the diagnosis of AD and MCI.

Pancreatic QST Differentiates Chronic Pancreatitis Patients into Distinct Pain Phenotypes Independent of Psychiatric Comorbidities

BACKGROUND & AIMS

Quantitative sensory testing (QST) has been previously used to study pain in chronic pancreatitis (CP) but included methods that are not suitable for clinical purposes. The aims of this study were to determine if pancreatic QST (P-QST) can differentiate patients into distinct pain phenotypes and to determine the association of these with their clinical pain and psychiatric comorbidities.

METHODS

A multicenter cross-sectional study was conducted where patients completed validated questionnaires assessing quality of life (QoL), depression and anxiety scores as well as clinical pain symptoms followed by P-QST which included a cold pressor test, repetitive pinprick stimuli and pressure stimulation of the upper abdominal (T10) and control dermatomes. P-QST categorized patients into pain phenotypes based on a previously established nomogram. QoL, clinical pain and psychiatric assessment scores were compared across these groups.

RESULTS

A total of 179 patients were enrolled with a mean age of 54.1±13.6 years among whom 59% were males and 42% had an alcoholic etiology. P-QST showed no hyperalgesia in 91 (51%), segmental hyperalgesia in 50 (28%) and widespread hyperalgesia in 38 (21%) patients. Patients with widespread hyperalgesia had significantly higher pain intensity scores (P = .03) and rates of constant pain (P = .002) as well as decreased QoL (P < .001) and physical functioning (P =.03) in comparison with the other two pain phenotypes. In contrast, psychiatric comorbidities were similar across all groups.

CONCLUSIONS

P-QST may serve as a novel unbiased pain assessment tool in CP as it categorizes patients into distinct pain phenotypes independent of their psychiatric comorbidities.

Developmental Mechanisms of CPSP: Clinical Observations and Translational Laboratory Evaluations

Understanding mechanisms that underly the transition from acute to chronic pain and identifying potential targets for preventing or minimizing this progression have specific relevance for chronic postsurgical pain (CPSP). Though it is clear that multiple psychosocial, family, and environmental factors may influence CPSP, this review will focus on parallels between clinical observations and translational laboratory studies investigating the acute and long-term effects of surgical injury on nociceptive pathways. This includes data related to alterations in sensitivity at different points along nociceptive pathways from the periphery to the brain; age- and sex-dependent mechanisms underlying the transition from acute to persistent pain; potential targets for preventive interventions; and the impact of prior surgical injury. Ongoing preclinical studies evaluating age- and sex-dependent mechanisms will also inform comparative efficacy and preclinical safety assessments of potential preventive pharmacological interventions aimed at reducing the risk of CPSP. In future clinical studies, more detailed and longitudinal peri-operative phenotyping with patient- and parent-reported chronic pain core outcomes, alongside more specialized evaluations of somatosensory function, modulation, and circuitry, may enhance understanding of individual variability in postsurgical pain trajectories and improve recognition and management of CPSP.

Stability of spectral estimates in resting-state magnetoencephalography: Recommendations for minimal data duration with neuroanatomical specificity

The principle of resting-state paradigms is appealing and practical for collecting data from impaired patients and special populations, especially if data collection times can be minimized. To achieve this goal, researchers need to ensure estimated signal features of interest are robust. In electro- and magnetoencephalography (EEG, MEG) we are not aware of any studies of the minimal length of data required to yield a robust one-session snapshot of the frequency-spectrum derivatives that are typically used to characterize the complex dynamics of the brain’s resting-state. We aimed to fill this knowledge gap by studying the stability of common spectra measures of resting-state MEG source time series obtained from large samples of single-session recordings from shared data repositories featuring different recording conditions and instrument technologies (OMEGA: N = 107; Cam-CAN: N = 50). We discovered that the rhythmic and arrhythmic spectral properties of intrinsic brain activity can be robustly estimated in most cortical regions when derived from relatively short segments of 30-s to 120-s of resting-state data, regardless of instrument technology and resting-state paradigm. Using an adapted leave-one-out approach and Bayesian analysis, we also provide evidence that the stability of spectral features over time is unaffected by age, sex, handedness, and general cognitive function. In summary, short MEG sessions are sufficient to yield robust estimates of frequency-defined brain activity during resting-state. This study may help guide future empirical designs in the field, particularly when recording times need to be minimized, such as with patient or special populations.

Asymmetric Lateralization during Pain Processing

Pain is defined as “an unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage”. This complex perception arises from the coordinated activity of several brain areas processing either sensory–discriminative or affective–motivational components. Functional studies performed in healthy volunteers revealed that affective–emotional components of pain are processed bilaterally but present a clear lateralization towards the right hemisphere, regardless of the site of stimulation. Studies at the cellular level performed in experimental animal models of pain have shown that neuronal activity in the right amygdala is clearly pronociceptive, whilst activation of neurons in the left amygdala might even exert antinociceptive effects. A shift in lateralization becomes evident during the development of chronic pain; thus, in patients with neuropathic pain symptoms, there is increased activity in ipsilateral brain areas related with pain. These observations extend the asymmetrical left–right lateralization within the nervous system and provide a new hypothesis for the pathophysiology of chronic forms of pain. In this article, we will review experimental data from preclinical and human studies on functional lateralization in the brain during pain processing, which will help to explain the affective disorders associated with persistent, chronic pain.

Brain circuits for pain and its treatment

[Figure: see text].

Neurophysiological characteristics in the periventricular/periaqueductal gray correlate with pain perception, sensation, and affect in neuropathic pain patients

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The PAG/PVG carries out its biology function by oscillatory network.

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Three distinct local networks of oscillations involved in pain perception, sensory and affective.

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The delta oscillation is a key hub for coding pain perception.

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The high-gamma oscillation is a key hub for coding sensory pain.

The periventricular/periaqueductal gray (PAG/PVG) is critical for pain perception and is associated with the emotional feelings caused by pain. However, the electrophysiological characteristics of the PAG/PVG have been little investigated in humans with chronic pain. The present study analyzed the oscillatory characteristics of local field potentials (LFPs) in the PAG/PVG of eighteen neuropathic pain patients. Power spectrum analysis and neural state analysis were applied to the PAG/PVG LFPs. Neural state analysis is based on a dynamic neural state identification approach and discriminates the LFPs into different neural states, including a single neural state based on one oscillation and a combinational neural state based on two paired oscillations. The durations and occurrence rates were used to quantify the dynamic features of the neural state. The results show that the combined neural state forms three local networks based on neural oscillations that are responsible for the perceptive, sensory, and affective components of pain. The first network is formed by the interaction of the delta oscillation with other oscillations and is responsible for the coding of pain perception. The second network is responsible for the coding of sensory pain information, uses high gamma as the main node, and is widely connected with other neural oscillations. The third network is responsible for the coding of affective pain information, and beta oscillations play an important role in it. This study suggested that the combination of two neural oscillations in the PAG/PVG is essential for encoding perceptive, sensory, and affective measures of pain.

Neuroimaging enters the pain biomarker arena

Neuroimaging methods can be used to identify biomarkers of acute and chronic pain.

Uncovering associations between data-driven learned qMRI biomarkers and chronic pain

Knee pain is the most common and debilitating symptom of knee osteoarthritis (OA). While there is a perceived association between OA imaging biomarkers and pain, there are weak or conflicting findings for this relationship. This study uses Deep Learning (DL) models to elucidate associations between bone shape, cartilage thickness and T₂ relaxation times extracted from Magnetic Resonance Images (MRI) and chronic knee pain. Class Activation Maps (Grad-CAM) applied on the trained chronic pain DL models are used to evaluate the locations of features associated with presence and absence of pain. For the cartilage thickness biomarker, the presence of features sensitive for pain presence were generally located in the medial side, while the features specific for pain absence were generally located in the anterior lateral side. This suggests that the association of cartilage thickness and pain varies, requiring a more personalized averaging strategy. We propose a novel DL-guided definition for cartilage thickness spatial averaging based on Grad-CAM weights. We showed a significant improvement modeling chronic knee pain with the inclusion of the novel biomarker definition: likelihood ratio test p-values of 7.01 × 10^–33 and 1.93 × 10^–14 for DL-guided cartilage thickness averaging for the femur and tibia, respectively, compared to the cartilage thickness compartment averaging.

Enhanced Temporal Coupling between Thalamus and Dorsolateral Prefrontal Cortex Mediates Chronic Low Back Pain and Depression

Numerous neuroimaging studies have demonstrated that the brain plasticity is associated with chronic low back pain (cLBP). However, there is a lack of knowledge regarding the underlying mechanisms of thalamic pathways for chronic pain and psychological effects in cLBP caused by lumbar disc herniation (LDH). Combining psychophysics and magnetic resonance imaging (MRI), we investigated the structural and functional brain plasticity in 36 patients with LDH compared with 38 age- and gender-matched healthy controls. We found that (1) LDH patients had increased psychophysical disturbs (i.e., depression and anxiety), and depression (Beck-Depression Inventory, BDI) was found to be an outstanding significant factor to predict chronic pain (short form of the McGill Pain Questionnaire, SF-MPQ); (2) the LDH group showed significantly smaller fractional anisotropy values in the region of posterior corona radiate while gray matter volumes were comparable in both groups; (3) resting state functional connectivity analysis revealed that LDH patients exhibited increased temporal coupling between the thalamus and dorsolateral prefrontal cortex (DLPFC), which further mediate the relationship from chronic pain to depression. Our results emphasized that thalamic pathways underlying prefrontal cortex might play a key role in regulating chronic pain and depression of the pathophysiology of LDH.

Decoding pain from brain activity

Pain is a dynamic, complex and multidimensional experience. The identification of pain from brain activity as neural readout may effectively provide a neural code for pain, and further provide useful information for pain diagnosis and treatment. Advances in neuroimaging and large-scale electrophysiology have enabled us to examine neural activity with improved spatial and temporal resolution, providing opportunities to decode pain in humans and freely behaving animals. This topical review provides a systematical overview of state-of-the-art methods for decoding pain from brain signals, with special emphasis on electrophysiological and neuroimaging modalities. We show how pain decoding analyses can help pain diagnosis and discovery of neurobiomarkers for chronic pain. Finally, we discuss the challenges in the research field and point to several important future research directions.

Rationale for and Development of the Pancreatic Quantitative Sensory Testing Consortium to Study Pain in Chronic Pancreatitis

OBJECTIVES

Abdominal pain is the primary symptom of chronic pancreatitis (CP), but pain is difficult to assess, and objective methods for pain assessment are lacking. The characterization of the sensory component of pain as a surrogate for nociception can be achieved by sensory testing using standardized stimuli. Herein, we describe the rationale for and development of an international consortium to better understand and characterize CP pain.

METHODS

A collaboration was initially formed between the University of Aalborg, Johns Hopkins University, and the University of Pittsburgh. This group refined the protocol for pancreatic quantitative sensory testing (P-QST) and then expanded the collaboration with plans for incorporating P-QST into prospective studies.

RESULTS

The collaboration has successfully developed a P-QST nomogram. Chronic pancreatitis patients identified with P-QST as having widespread hyperalgesia had higher pain intensity scores, higher prevalence of constant pain, and decreased quality of life. Psychiatric comorbidities were independent of pain phenotypes. Multiple studies are underway to validate these findings and evaluate their utility in clinical trials.

CONCLUSIONS

Development of the P-QST Consortium will facilitate collaborative efforts to use P-QST as a means for evaluation and characterization of pain in CP patients, and optimize methods to guide individualized pain management approaches.

Cerebral current-source distribution associated with pain improvement by non-invasive painless signaling therapy in patients with failed back surgery syndrome

BACKGROUND

Non-invasive painless signaling therapy (NPST) is an electro-cutaneous treatment that converts endogenous pain information into synthetic non-pain information. This study explored whether pain improvement by NPST in failed back surgery syndrome (FBSS) patients is related to cerebral modulation.

METHODS

Electroencephalography (EEG) analysis was performed in 11 patients with FBSS. Subjects received daily NPST for 5 days. Before the first treatment, patients completed the Brief Pain Inventory (BPI) and Beck Depression Inventory and underwent baseline EEG. After the final treatment, they responded again to the BPI, reported the percent pain improvement (PPI), and then underwent post-treatment EEG. If the PPI grade was zero, they were assigned to the ineffective group, while all others were assigned to the effective group. We used standardized low-resolution brain electromagnetic tomography (sLORETA) to explore the EEG current-source distribution (CSD) associated with pain improvement by NPST.

RESULTS

The 11 participants had a median age of 67.0 years, and 63.6% were female. The sLORETA images revealed a beta-2 CSD increment in 12 voxels of the right anterior cingulate gyrus (ACG) and the right medial frontal area. The point of maximal CSD changes was in the right ACG. The alpha band CSD increased in 2 voxels of the left transverse gyrus.

CONCLUSIONS

Pain improvement by NPST in FBSS patients was associated with increased cerebral activity, mainly in the right ACG. The change in afferent information induced by NPST seems to be associated with cerebral pain perception.

Systematic Review of the Effectiveness of Machine Learning Algorithms for Classifying Pain Intensity, Phenotype or Treatment Outcomes Using Electroencephalogram Data

Recent attempts to utilize machine learning (ML) to predict pain-related outcomes from Electroencephalogram (EEG) data demonstrate promising results. The primary aim of this review was to evaluate the effectiveness of ML algorithms for predicting pain intensity, phenotypes or treatment response from EEG. Electronic databases MEDLINE, EMBASE, Web of Science, PsycINFO and The Cochrane Library were searched. A total of 44 eligible studies were identified, with 22 presenting attempts to predict pain intensity, 15 investigating the prediction of pain phenotypes and seven assessing the prediction of treatment response. A meta-analysis was not considered appropriate for this review due to heterogenos methods and reporting. Consequently, data were narratively synthesized. The results demonstrate that the best performing model of the individual studies allows for the prediction of pain intensity, phenotypes and treatment response with accuracies ranging between 62 to 100%, 57 to 99% and 65 to 95.24%, respectively. The results suggest that ML has the potential to effectively predict pain outcomes, which may eventually be used to assist clinical care. However, inadequate reporting and potential bias reduce confidence in the results. Future research should improve reporting standards and externally validate models to decrease bias, which would increase the feasibility of clinical translation. PERSPECTIVE: This systematic review explores the state-of-the-art machine learning methods for predicting pain intensity, phenotype or treatmentresponse from EEG data. Results suggest that machine learning may demonstrate clinical utility, pending further research and development. Areas for improvement, including standardized processing, reporting and the need for better methodological assessment tools, are discussed.

What have we really learned from functional connectivity in clinical populations?

Functional connectivity (FC), or the statistical interdependence of blood-oxygen dependent level (BOLD) signals between brain regions using fMRI, has emerged as a widely used tool for probing functional abnormalities in clinical populations due to the promise of the approach across conceptual, technical, and practical levels. With an already vast and steadily accumulating neuroimaging literature on neurodevelopmental, psychiatric, and neurological diseases and disorders in which FC is a primary measure, we aim here to provide a high-level synthesis of major concepts that have arisen from FC findings in a manner that cuts across different clinical conditions and sheds light on overarching principles. We highlight that FC has allowed us to discover the ubiquity of intrinsic functional networks across virtually all brains and clarify typical patterns of neurodevelopment over the lifespan. This understanding of typical FC maturation with age has provided important benchmarks against which to evaluate divergent maturation in early life and degeneration in late life. This in turn has led to the important insight that many clinical conditions are associated with complex, distributed, network-level changes in the brain, as opposed to solely focal abnormalities. We further emphasize the important role that FC studies have played in supporting a dimensional approach to studying transdiagnostic clinical symptoms and in enhancing the multimodal characterization and prediction of the trajectory of symptom progression across conditions. We highlight the unprecedented opportunity offered by FC to probe functional abnormalities in clinical conditions where brain function could not be easily studied otherwise, such as in disorders of consciousness. Lastly, we suggest high priority areas for future research and acknowledge critical barriers associated with the use of FC methods, particularly those related to artifact removal, data denoising and feasibility in clinical contexts.

Nursing Assessment of Wound-Related Pain: An Italian Learning Survey

BACKGROUND

Pain is an important symptom in wound management, and the choice of treatment directly affects the patient's quality of life. Pain assessment (PA) is essential for quality wound care and, in Italy, mandatory by law.

OBJECTIVE

To administer a dedicated learning survey to obtain a better sense of current PA practices, ensure more training, improve procedures, and reduce malpractice.

METHODS

A 16-month learning survey of nurses based on a validated questionnaire developed for this project.

RESULTS

The survey sample comprised 512 questionnaires. Of respondents, 78% were female, 56.1% were older than 40 years, 94% were RNs, and 6% were wound care specialist nurses. Participants performed a range of dressing changes per week (1-5, 46.3%; 6-20, 34.4%; >21, 19.3%). Although 93% of respondents considered PA important, only 26% recognized it as a vital parameter, and barely one-quarter (25.4%) were aware of current legislation mandating PA. The majority (95.3%) believed that PA is not consistent with pain perceived by the patient. Further, 87.3% stated that they did not have adequate knowledge to conduct a PA, 91.4% did not consider themselves up-to-date on PA, and 81% did not document PA results. However, specific wound care training leads to significantly better PA (P < .001): 71.9% of wound care specialist nurses recognized pain as a vital parameter, and 59.4% were aware of current legislation regarding PA; further, 81.3% consistently evaluated pain, 59.4% documented PA results, and 50% communicated the outcome to the physician in charge.

CONCLUSIONS

The results illustrate the lack of sensitivity, training, and education that Italian RNs have regarding PA in wound care.

Role of SIRT1 in Neuropathic Pain from the Viewpoint of Neuroimmunity

The current clinical first-line treatment of neuropathic pain still considers only the nervous system as the target, and its therapeutic effect is limited. An increasing number of studies support the opinion that neuropathic pain is a result of the combined action of the sensory nervous system and the related immune system. Under physiological conditions, both the nervous system and the immune system can maintain homeostasis by adjusting the mitochondrial function when sensing noxious stimulation. However, in the case of neuropathic pain, mitochondrial regulatory dysfunction occurs, which may result from the decreased expression of SIRT1. In this study, we review the role of SIRT1 in neuropathic pain from the viewpoint of neuroimmunity.

XGBoost based machine learning approach to predict the risk of fall in older adults using gait outcomes

This study aimed to identify the optimal features of gait parameters to predict the fall risk level in older adults. The study included 746 older adults (age: 63–89 years). Gait tests (20 m walkway) included speed modification (slower, preferred, and faster-walking) while wearing the inertial measurement unit sensors embedded in the shoe-type data loggers on both outsoles. A metric was defined to classify the fall risks, determined based on a set of questions determining the history of falls and fear of falls. The extreme gradient boosting (XGBoost) model was built from gait features to predict the factor affecting the risk of falls. Moreover, the definition of the fall levels was classified into high- and low-risk groups. At all speeds, three gait features were identified with the XGBoost (stride length, walking speed, and stance phase) that accurately classified the fall risk levels. The model accuracy in classifying fall risk levels ranged between 67–70% with 43–53% sensitivity and 77–84% specificity. Thus, we identified the optimal gait features for accurate fall risk level classification in older adults. The XGBoost model could inspire future works on fall prevention and the fall-risk assessment potential through the gait analysis of older adults.

Pain Management for Dental Medicine in 2021: Opioids, Coronavirus and Beyond

Over the past year our attention has inevitably been on the coronavirus pandemic, the health and welfare of our families, patients, and office staffs as well as the re-opening of our dental practices. In addition, the opioid crisis continues, is very likely to worsen as a result of the pandemic and continues to be a challenge to Dentistry. National public health issues and healthcare disparities continue and have created a global concern for providing evidence-based, adequate pain management in the dental setting. We have brought together a group of national thought leaders and experts in this field who will share their insights on the current state of opioid prescribing in Dentistry and describe some of the exciting work being done in advancing pain management. The learning objectives for this conference proceedings were: Describing the implications of current public health concerns for safe and effective pain management in dental medicine.Identifying risk factors and understanding the current guidelines for the use of opioid and non-opioid medications in dental medicine.Analyzing the interprofessional collaborations necessary for effective pain management in dental medicine.Recognizing the challenges and opportunities brought about by the COVID-19 pandemic for the dental profession.Applying evidence-based strategies for managing the complex pain patient in the dental setting.Appraising new and future modalities for the assessment and management of orofacial pain.

Pain induces stable, active microcircuits in the somatosensory cortex that provide a therapeutic target

Sustained neuropathic pain from injury or inflammation remains a major burden for society. Rodent pain models have informed some cellular mechanisms increasing neuronal excitability within the spinal cord and primary somatosensory cortex (S1), but how activity patterns within these circuits change during pain remains unclear. We have applied multiphoton in vivo imaging and holographic stimulation to examine single S1 neuron activity patterns and connectivity during sustained pain. Following pain induction, there is an increase in synchronized neuronal activity and connectivity within S1, indicating the formation of pain circuits. Artificially increasing neuronal activity and synchrony using DREADDs reduced pain thresholds. The expression of N-type voltage-dependent Ca²⁺ channel subunits in S1 was increased after pain induction, and locally blocking these channels reduced both the synchrony and allodynia associated with inflammatory pain. Targeting these S1 pain circuits, via inhibiting N-type Ca²⁺ channels or other approaches, may provide ways to reduce inflammatory pain.

Challenges and opportunities in translational pain research - An opinion paper of the working group on translational pain research of the European pain federation (EFIC)

For decades, basic research on the underlying mechanisms of nociception has held promise to translate into efficacious treatments for patients with pain. Despite great improvement in the understanding of pain physiology and pathophysiology, translation to novel, effective treatments for acute and chronic pain has however been limited, and they remain an unmet medical need. In this opinion paper bringing together pain researchers from very different disciplines, the opportunities and challenges of translational pain research are discussed. The many factors that may prevent the successful translation of bench observations into useful and effective clinical applications are reviewed, including interspecies differences, limited validity of currently available preclinical disease models of pain, and limitations of currently used methods to assess nociception and pain in non-human and human models of pain. Many paths are explored to address these issues, including the backward translation of observations made in patients and human volunteers into new disease models that are more clinically relevant, improved generalization by taking into account age and sex differences, and the integration of psychobiology into translational pain research. Finally, it is argued that preclinical and clinical stages of developing new treatments for pain can be improved by better preclinical models of pathological pain conditions alongside revised methods to assess treatment-induced effects on nociception in human and non-human animals. Significance: For decades, basic research of the underlying mechanisms of nociception has held promise to translate into efficacious treatments for patients with pain. Despite great improvement in the understanding of pain physiology and pathophysiology, translation to novel, effective treatments for acute and chronic pain has however been limited, and they remain an unmet medical need.

A novel method to simultaneously record spinal cord electrophysiology and electroencephalography signals

The brain and the spinal cord together make up the central nervous system (CNS). The functions of the human brain have been the focus of neuroscience research for a long time. However, the spinal cord is largely ignored, and the functional interaction of these two parts of the CNS is only partly understood. This study developed a novel method to simultaneously record spinal cord electrophysiology (SCE) and electroencephalography (EEG) signals and validated its performance using a classical resting-state study design with two experimental conditions: eyes-closed (EC) and eyes-open (EO). We recruited nine postherpetic neuralgia patients implanted with a spinal cord stimulator, which was modified to record SCE signals simultaneously with EEG signals. For both EEG and SCE, similar differences were found in delta- and alpha-band oscillations between the EC and EO conditions, and the spectral power of these frequency bands was able to predict EC/EO behaviors. Moreover, causal connectivity analysis suggested a top-down regulation in delta-band oscillations from the brain to the spinal cord. Altogether, this study demonstrates the validity of simultaneous SCE-EEG recording and shows that the novel method is a valuable tool to investigate the brain-spinal interaction. With this method, we can better unite knowledge about the brain and the spinal cord for a deeper understanding of the functions of the whole CNS.

Magnetoencephalography: physics, techniques, and applications in the basic and clinical neurosciences

Magnetoencephalography (MEG) is a technique used to measure the magnetic fields generated from neuronal activity in the brain. MEG has a high temporal resolution on the order of milliseconds and provides a more direct measure of brain activity when compared with hemodynamic-based neuroimaging methods such as magnetic resonance imaging and positron emission tomography. The current review focuses on basic features of MEG such as the instrumentation and the physics that are integral to the signals that can be measured, and the principles of source localization techniques, particularly the physics of beamforming and the techniques that are used to localize the signal of interest. In addition, we review several metrics that can be used to assess functional coupling in MEG and describe the advantages and disadvantages of each approach. Lastly, we discuss the current and future applications of MEG.

A neuroimaging biomarker for sustained experimental and clinical pain

Sustained pain is a major characteristic of clinical pain disorders, but it is difficult to assess in isolation from co-occurring cognitive and emotional features in patients. In this study, we developed a functional magnetic resonance imaging signature based on whole-brain functional connectivity that tracks experimentally induced tonic pain intensity and tested its sensitivity, specificity and generalizability to clinical pain across six studies (total n = 334). The signature displayed high sensitivity and specificity to tonic pain across three independent studies of orofacial tonic pain and aversive taste. It also predicted clinical pain severity and classified patients versus controls in two independent studies of clinical low back pain. Tonic and clinical pain showed similar network-level representations, particularly in somatomotor, frontoparietal and dorsal attention networks. These patterns were distinct from representations of experimental phasic pain. This study identified a brain biomarker for sustained pain with high potential for clinical translation.

Common and distinct neural representations of aversive somatic and visceral stimulation in healthy individuals

Different pain types may be encoded in different brain circuits. Here, we examine similarities and differences in brain processing of visceral and somatic pain. We analyze data from seven fMRI studies (N = 165) and five types of pain and discomfort (esophageal, gastric, and rectal distension, cutaneous thermal stimulation, and vulvar pressure) to establish and validate generalizable pain representations. We first evaluate an established multivariate brain measure, the Neurologic Pain Signature (NPS), as a common nociceptive pain system across pain types. Then, we develop a multivariate classifier to distinguish visceral from somatic pain. The NPS responds robustly in 98% of participants across pain types, correlates with perceived intensity of visceral pain and discomfort, and shows specificity to pain when compared with cognitive and affective conditions from twelve additional studies (N = 180). Pre-defined signatures for non-pain negative affect do not respond to visceral pain. The visceral versus the somatic classifier reliably distinguishes somatic (thermal) from visceral (rectal) stimulation in both cross-validation and independent cohorts. Other pain types reflect mixtures of somatic and visceral patterns. These results validate the NPS as measuring a common core nociceptive pain system across pain types, and provide a new classifier for visceral versus somatic pain.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

The pharmacogenetics of opioid treatment for pain management

BACKGROUND

Opioids are widely used as an analgesic for the treatment of moderate to severe pain. However, there are interindividual variabilities in opioid response. Current evidence suggests that these variabilities can be attributed to single nucleotide polymorphisms in genes involved in opioid pharmacodynamics and pharmacokinetics. Knowledge of these genetic factors through pharamacogenetic (PGx) testing can help clinicians to more consistently prescribe opioids that can provide patients with maximal clinical benefit and minimal risk of adverse effects.

AIM

The research outlined in this literature review identifies variants involved in opioid PGx, which may be an important tool to achieving the goal of personalized pain management.

RESULTS

Cytochrome P450 (CYP) 2D6, CYP3A4, CYP3A5, catechol-o-methyltransferase (COMT), adenosine triphosphate binding cassette transporter B1 (ABCB1), opioid receptor mu 1 (OPRM1), and opioid receptor delta 1 (OPRD1) are all important genes involved in opioid drug response, side effect profile and risk of dependence; these are important genetic factors that should be included in potential opioid PGx tests for pain management.

CONCLUSIONS

Employing a PGx-guided strategy for prescribing opioids can improve response rate, reduce side effects and increase adherence to treatment plans for pain; more research is needed to explore opioid-related PGx factors for the development and validation of an opioid genetic panel. Optimal prescriptions could also provide healthcare payers with beneficial savings, while reducing the risk of propagating the current opioid crisis.

Cortico-spinal imaging to study pain

Functional magnetic resonance imaging of the brain has helped to reveal mechanisms of pain perception in health and disease. Recently, imaging approaches have been developed that allow recording neural activity simultaneously in the brain and in the spinal cord. These approaches offer the possibility to examine pain perception in the entire central pain system and in addition, to investigate cortico-spinal interactions during pain processing. Although cortico-spinal imaging is a promising technique, it bears challenges concerning data acquisition and data analysis strategies. In this review, we discuss studies that applied simultaneous imaging of the brain and spinal cord to explore central pain processing. Furthermore, we describe different MR-related acquisition techniques, summarize advantages and disadvantages of approaches that have been implemented so far and present software that has been specifically developed for the analysis of spinal fMRI data to address challenges of spinal data analysis.

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients’ responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

Pain phenotypes classified by machine learning using electroencephalography features

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

The revised International Association for the Study of Pain definition of pain: concepts, challenges, and compromises

ABSTRACT

The current International Association for the Study of Pain (IASP) definition of pain as "An unpleasant sensory and emotional experience associated with actual or potential tissue damage, or described in terms of such damage" was recommended by the Subcommittee on Taxonomy and adopted by the IASP Council in 1979. This definition has become accepted widely by health care professionals and researchers in the pain field and adopted by several professional, governmental, and nongovernmental organizations, including the World Health Organization. In recent years, some in the field have reasoned that advances in our understanding of pain warrant a reevaluation of the definition and have proposed modifications. Therefore, in 2018, the IASP formed a 14-member, multinational Presidential Task Force comprising individuals with broad expertise in clinical and basic science related to pain, to evaluate the current definition and accompanying note and recommend whether they should be retained or changed. This review provides a synopsis of the critical concepts, the analysis of comments from the IASP membership and public, and the committee's final recommendations for revisions to the definition and notes, which were discussed over a 2-year period. The task force ultimately recommended that the definition of pain be revised to "An unpleasant sensory and emotional experience associated with, or resembling that associated with, actual or potential tissue damage," and that the accompanying notes be updated to a bulleted list that included the etymology. The revised definition and notes were unanimously accepted by the IASP Council early this year.

Brain dysfunction in chronic pain patients assessed by resting-state electroencephalography

Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches.

Patients with fibromyalgia show increased beta connectivity across distant networks and microstates alterations in resting-state electroencephalogram

Fibromyalgia (FM) is a chronic condition characterized by widespread pain of unknown etiology associated with alterations in the central nervous system. Although previous studies demonstrated altered patterns of brain activity during pain processing in patients with FM, alterations in spontaneous brain oscillations, in terms of functional connectivity or microstates, have been barely explored so far. Here we recorded the EEG from 43 patients with FM and 51 healthy controls during open-eyes resting-state. We analyzed the functional connectivity between different brain networks computing the phase lag index after group Independent Component Analysis, and also performed an EEG microstates analysis. Patients with FM showed increased beta band connectivity between different brain networks and alterations in some microstates parameters (specifically lower occurrence and coverage of microstate class C). We speculate that the observed alterations in spontaneous EEG may suggest the dominance of endogenous top-down influences; this could be related to limited processing of novel external events and the deterioration of flexible behavior and cognitive control frequently reported for FM. These findings provide the first evidence of alterations in long-distance phase connectivity and microstate indices at rest, and represent progress towards the understanding of the pathophysiology of fibromyalgia and the identification of novel biomarkers for its diagnosis.

Alterations of distributed neuronal network oscillations during acute pain in freely-moving mice

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Injection of capsaicine in mice causes prolonged acute pain and characteristic changes in neuronal network oscillations.

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Changes are most prominent in higher-order phenomena like interregional oscillation coherence.

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Power in standard frequency bands is largely unaltered.

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Behavioral states related to acute pain can be predicted from network activity by a logistic regression classifier.

The experience of pain involves the activation of multiple brain areas. Pain-specific activity patterns within and between these local networks remain, however, largely unknown. We measured neuronal network oscillations in different relevant regions of the mouse brain during acute pain, induced by subcutaneous injection of capsaicin into the left hind paw. Field potentials were recorded from primary somatosensory cortex, anterior cingulate cortex (ACC), posterior insula, ventral posterolateral thalamic nucleus, parietal cortex, central nucleus of the amygdala and olfactory bulb. Analysis included power spectra of local signals as well as interregional coherences and cross-frequency coupling (CFC). Capsaicin injection caused hypersensitivity to mechanical stimuli for at least one hour. At the same time, CFC between low (1−12 Hz) and fast frequencies (80−120 Hz) was increased in the ACC, as well as interregional coherence of low frequency oscillations (< 30 Hz) between several networks. However, these changes were not significant anymore after multiple comparison corrections. Using a variable selection method (elastic net) and a logistic regression classifier, however, the pain state was reliably predicted by combining parameters of power and coherence from various regions. Distinction between capsaicin and saline injection was also possible when data were restricted to frequencies <30 Hz, as used in clinical electroencephalography (EEG). Our findings indicate that changes of distributed brain oscillations may provide a functional signature of acute pain or pain-related alterations in activity.

Cortical thickness in chronic pain: A protocol for systematic review and meta-analysis

BACKGROUND

Numerous studies using a variety of non-invasive neuroimaging techniques in vivo have demonstrated that chronic pain (CP) is associated with brain alterations. Cortical thickness (CTh) via surface-based morphometry (SBM) analysis of magnetic resonance imaging data is a valid and sensitive method to investigate the structure of brain gray matter. Many studies have employed SBM to measure CTh difference between patients with CP and pain-free controls and provided important insights into the brain basis of CP. However, the findings from these studies were inconsistent and have not been quantitatively reviewed.

METHODS

Three major electronic medical databases: PubMed, Web of Science, and Embase were searched for eligible studies published in English on April 3, 2020. This protocol was prepared based on the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols. The Seed-based d Mapping with Permutation of Subject Images software package will be employed to conducted a coordinate-based meta-analysis (CBMA) to identify consistent CTh differences between patients with CP and pain-free controls. Several complementary analyses, including sensitivity analysis, heterogeneity analysis, publication bias, subgroup analysis, and meta-regression analysis, will be further conducted to test the robustness of the results.

RESULTS

This CBMA will tell us whether CP with different subtypes shares common CTh alterations and what the pattern of its characterized alterations is.

CONCLUSIONS

To the best of our knowledge, this will be the first CBMA of SBM studies that characterizes brain CTh alterations in CP. The CBMA will provide the quantitative evidence of common brain cortical morphometry of CP. The findings will help us to understand the neural basis underlying CP.

TRIAL REGISTRATION NUMBER

INPLASY202050069.

Altered local connectivity in chronic pain: A voxel-wise meta-analysis of resting-state functional magnetic resonance imaging studies

BACKGROUND

A number of studies have used regional homogeneity (ReHo) to depict local functional connectivity in chronic pain (CP). However, the findings from these studies were mixed and inconsistent.

METHODS

A computerized literature search will be performed in PubMed, Web of Science, Embase, China National Knowledge Infrastructure (CNKI), WanFang, and SinoMed databases until June 15, 2019 and updated on March 20, 2020. This protocol will follow the Preferred Reporting Items for Systematic review and Meta-Analysis Protocols (PRISMA-P). The Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software will be used for this voxel-wise meta-analysis.

RESULTS

This meta-analysis will identify the most consistent ReHo alterations in CP.

CONCLUSIONS

To our knowledge, this will be the first voxel-wise meta-analysis that integrates ReHo findings in CP. This meta-analysis will offer the quantitative evidence of ReHo alterations that characterize brain local functional connectivity of CP.

PROSPERO REGISTRATION NUMBER

CRD42019148523.

A novel cortical biomarker signature for predicting pain sensitivity: protocol for the PREDICT longitudinal analytical validation study

PREDICT will undertake analytical validation of a peak alpha frequency and corticomotor excitability biomarker, determining the sensitivity, specificity, and accuracy at predicting pain sensitivity.

Introduction:

Temporomandibular disorder is a common musculoskeletal pain condition with development of chronic symptoms in 49% of patients. Although a number of biological factors have shown an association with chronic temporomandibular disorder in cross-sectional and case control studies, there are currently no biomarkers that can predict the development of chronic symptoms. The PREDICT study aims to undertake analytical validation of a novel peak alpha frequency (PAF) and corticomotor excitability (CME) biomarker signature using a human model of the transition to sustained myofascial temporomandibular pain (masseter intramuscular injection of nerve growth factor [NGF]). This article describes, a priori, the methods and analysis plan.

Methods:

This study uses a multisite longitudinal, experimental study to follow individuals for a period of 30 days as they progressively develop and experience complete resolution of NGF-induced muscle pain. One hundred fifty healthy participants will be recruited. Participants will complete twice daily electronic pain diaries from day 0 to day 30 and undergo assessment of pressure pain thresholds, and recording of PAF and CME on days 0, 2, and 5. Intramuscular injection of NGF will be given into the right masseter muscle on days 0 and 2. The primary outcome is pain sensitivity.

Perspective:

PREDICT is the first study to undertake analytical validation of a PAF and CME biomarker signature. The study will determine the sensitivity, specificity, and accuracy of the biomarker signature to predict an individual's sensitivity to pain.

Registration details:

ClinicalTrials.gov: NCT04241562 (prospective).