Neuroimaging-based pain biomarkers: definitions, clinical and research applications, and evaluation frameworks to achieve personalized pain medicine

What has brain diffusion magnetic resonance imaging taught us about chronic primary pain: a narrative review

ABSTRACT

Chronic pain is a pervasive and debilitating condition with increasing implications for public health, affecting millions of individuals worldwide. Despite its high prevalence, the underlying neural mechanisms and pathophysiology remain only partly understood. Since its introduction 35 years ago, brain diffusion magnetic resonance imaging (MRI) has emerged as a powerful tool to investigate changes in white matter microstructure and connectivity associated with chronic pain. This review synthesizes findings from 58 articles that constitute the current research landscape, covering methods and key discoveries. We discuss the evidence supporting the role of altered white matter microstructure and connectivity in chronic primary pain conditions, highlighting the importance of studying multiple chronic pain syndromes to identify common neurobiological pathways. We also explore the prospective clinical utility of diffusion MRI, such as its role in identifying diagnostic, prognostic, and therapeutic biomarkers. Furthermore, we address shortcomings and challenges associated with brain diffusion MRI in chronic primary pain studies, emphasizing the need for the harmonization of data acquisition and analysis methods. We conclude by highlighting emerging approaches and prospective avenues in the field that may provide new insights into the pathophysiology of chronic pain and potential new therapeutic targets. Because of the limited current body of research and unidentified targeted therapeutic strategies, we are forced to conclude that further research is required. However, we believe that brain diffusion MRI presents a promising opportunity for enhancing our understanding of chronic pain and improving clinical outcomes.

Comprehensive overview of the anesthesiology research landscape: A machine Learning Analysis of 737 NIH-funded anesthesiology primary Investigator's publication trends

Background

Anesthesiology plays a crucial role in perioperative care, critical care, and pain management, impacting patient experiences and clinical outcomes. However, our understanding of the anesthesiology research landscape is limited. Accordingly, we initiated a data-driven analysis through topic modeling to uncover research trends, enabling informed decision-making and fostering progress within the field.

Methods

The easyPubMed R package was used to collect 32,300 PubMed abstracts spanning from 2000 to 2022. These abstracts were authored by 737 Anesthesiology Principal Investigators (PIs) who were recipients of National Institute of Health (NIH) funding from 2010 to 2022. Abstracts were preprocessed, vectorized, and analyzed with the state-of-the-art BERTopic algorithm to identify pillar topics and trending subtopics within anesthesiology research. Temporal trends were assessed using the Mann-Kendall test.

Results

The publishing journals with most abstracts in this dataset were Anesthesia & Analgesia 1133, Anesthesiology 992, and Pain 671. Eight pillar topics were identified and categorized as basic or clinical sciences based on a hierarchical clustering analysis. Amongst the pillar topics, "Cells & Proteomics" had both the highest annual and total number of abstracts. Interestingly, there was an overall upward trend for all topics spanning the years 2000-2022. However, when focusing on the period from 2015 to 2022, topics "Cells & Proteomics" and "Pulmonology" exhibit a downward trajectory. Additionally, various subtopics were identified, with notable increasing trends in "Aneurysms", "Covid 19 Pandemic", and "Artificial intelligence & Machine Learning".

Conclusion

Our work offers a comprehensive analysis of the anesthesiology research landscape by providing insights into pillar topics, and trending subtopics. These findings contribute to a better understanding of anesthesiology research and can guide future directions.

Resting-state electroencephalography delta and theta bands as compensatory oscillations in chronic neuropathic pain: a secondary data analysis

Chronic neuropathic pain (CNP) remains a significant clinical challenge, with complex neurophysiological underpinnings that are not fully understood. Identifying specific neural oscillatory patterns related to pain perception and interference can enhance our understanding and management of CNP. To analyze resting electroencephalography data from individuals with chronic neuropathic pain to explore the possible neural signatures associated with pain intensity, pain interference, and specific neuropathic pain characteristics. We conducted a secondary analysis from a cross-sectional study using electroencephalography data from a previous study, and Brief Pain Inventory from 36 patients with chronic neuropathic pain. For statistical analysis, we modeled a linear or logistic regression by dependent variable for each model. As independent variables, we used electroencephalography data with such brain oscillations: as delta, theta, alpha, and beta, as well as the oscillations low alpha, high alpha, low beta, and high beta, for the central, frontal, and parietal regions. All models tested for confounding factors such as age and medication. There were no significant models for Pain interference in general activity, walking, work, relationships, sleep, and enjoyment of life. However, the model for pain intensity during the past four weeks showed decreased alpha oscillations, and increased delta and theta oscillations were associated with decreased levels of pain, especially in the central area. In terms of pain interference in mood, the model showed high oscillatory Alpha signals in the frontal and central regions correlated with mood impairment due to pain. Our models confirm recent findings proposing that lower oscillatory frequencies, likely related to subcortical pain sources, may be associated with brain compensatory mechanisms and thus may be associated with decreased pain levels. On the other hand, higher frequencies, including alpha oscillations, may disrupt top-down compensatory mechanisms.

Pain mechanisms in the transgender individual: a review

Specific Aim

Provide an overview of the literature addressing major areas pertinent to pain in transgender persons and to identify areas of primary relevance for future research.

Methods

A team of scholars that have previously published on different areas of related research met periodically though zoom conferencing between April 2021 and February 2023 to discuss relevant literature with the goal of providing an overview on the incidence, phenotype, and mechanisms of pain in transgender patients. Review sections were written after gathering information from systematic literature searches of published or publicly available electronic literature to be compiled for publication as part of a topical series on gender and pain in the Frontiers in Pain Research.

Results

While transgender individuals represent a significant and increasingly visible component of the population, many researchers and clinicians are not well informed about the diversity in gender identity, physiology, hormonal status, and gender-affirming medical procedures utilized by transgender and other gender diverse patients. Transgender and cisgender people present with many of the same medical concerns, but research and treatment of these medical needs must reflect an appreciation of how differences in sex, gender, gender-affirming medical procedures, and minoritized status impact pain.

Conclusions

While significant advances have occurred in our appreciation of pain, the review indicates the need to support more targeted research on treatment and prevention of pain in transgender individuals. This is particularly relevant both for gender-affirming medical interventions and related medical care. Of particular importance is the need for large long-term follow-up studies to ascertain best practices for such procedures. A multi-disciplinary approach with personalized interventions is of particular importance to move forward.

Artificial Intelligence and Pain Medicine: An Introduction

Artificial intelligence was introduced 60 years ago and has evolved immensely since that time. While artificial intelligence is found in nearly all aspects of our life, the use of artificial intelligence in the healthcare industry has only recently become apparent and more widely discussed. It is expected that artificial intelligence will allow improved disease recognition, treatment optimization, cost and time savings, product development, decision making, and marketing. For pain medicine specifically, these same benefits will be translatable and we can expect better disease recognition and treatment selection. As adoption occurs with this impressive technology, it will be imperative for the pain medicine community to be informed on proper definitions and expected use cases for artificial intelligence. Our objective was to provide pain medicine physicians an overview of artificial intelligence, including important definitions to aid understanding, and to offer potential clinical applications pertinent to the specialty.

Recent developments and future avenues for human corticospinal neuroimaging

Non-invasive neuroimaging serves as a valuable tool for investigating the mechanisms within the central nervous system (CNS) related to somatosensory and motor processing, emotions, memory, cognition, and other functions. Despite the extensive use of brain imaging, spinal cord imaging has received relatively less attention, regardless of its potential to study peripheral communications with the brain and the descending corticospinal systems. To comprehensively understand the neural mechanisms underlying human sensory and motor functions, particularly in pathological conditions, simultaneous examination of neuronal activity in both the brain and spinal cord becomes imperative. Although technically demanding in terms of data acquisition and analysis, a growing but limited number of studies have successfully utilized specialized acquisition protocols for corticospinal imaging. These studies have effectively assessed sensorimotor, autonomic, and interneuronal signaling within the spinal cord, revealing interactions with cortical processes in the brain. In this mini-review, we aim to examine the expanding body of literature that employs cutting-edge corticospinal imaging to investigate the flow of sensorimotor information between the brain and spinal cord. Additionally, we will provide a concise overview of recent advancements in functional magnetic resonance imaging (fMRI) techniques. Furthermore, we will discuss potential future perspectives aimed at enhancing our comprehension of large-scale neuronal networks in the CNS and their disruptions in clinical disorders. This collective knowledge will aid in refining combined corticospinal fMRI methodologies, leading to the development of clinically relevant biomarkers for conditions affecting sensorimotor processing in the CNS.

Pain and the Non-Pharmacological Management of Pain in People with Parkinson's Disease

Pain is a distressing and universal experience, yet everyone's pain experience is influenced by a complex array of biological, psychological, and social factors. For people with Parkinson's disease (PwP), these biopsychosocial factors include neurodegeneration and the psychological and social factors that accompany living with a chronic, neurodegenerative condition in addition to the factors experienced by those in the general population (e.g., living with co-morbidities such as osteoarthritis). The way these factors influence each individual is likely to determine which pain management strategies are optimal for them. This review first describes pain and the biopsychosocial model of pain. It explores how pain is classified in Parkinson's disease (PD) and describes the three main types of pain: nociceptive, neuropathic, and nociplastic pain. This background provides context for a discussion of non-pharmacological pain management strategies that may aid in the management of pain in PwP; exercise, psychological strategies, acupuncture and massage. While there is little PD-specific research to inform the non-pharmacological management of pain, findings from current PD research are combined with that from chronic pain research to present recommendations for clinical practice. Recommendations include assessment that incorporates potential biopsychosocial contributors to pain that will then guide a holistic, multi-modal approach to management. As exercise provides overall benefits for PwP, those with chronic pain should be carefully monitored with exercise prescribed and adjusted accordingly. Research is needed to develop and evaluate multi-modal approaches to pain management that are delivered in a biopsychosocial framework.

Microstructural Changes in the Spinothalamic Tract of CPSS Patients: Preliminary Results from a Single-Center Diffusion-Weighted Magnetic Resonance Imaging Study

INTRODUCTION

Chronic pain after spinal surgery (CPSS), formerly known as failed back surgery syndrome, encompasses a variety of highly incapacitating chronic pain syndromes emerging after spinal surgery. The intractability of CPSS makes objective parameters that could aid classification and treatment essential. In this study, we investigated the use of cerebral diffusion-weighted magnetic resonance imaging.

METHODS

Cerebral 3T diffusion-weighted (DW-) MRI data from adult CPSS patients were assessed and compared with those of healthy controls matched by age and gender. Only imaging data without relevant artefacts or significant pathologies were included. Apparent diffusion coefficient (ADC) maps were calculated from the b0 and b1000 values using nonlinear regression. After skull stripping and affine registration of all imaging data, ADC values for fifteen anatomical regions were calculated and analyzed with independent samples T-tests.

RESULTS

A total of 32 subjects were included (sixteen CPSS patients and sixteen controls). The mean ADC value of the spinothalamic tract was found to be significantly higher in CPSS patients compared with in healthy controls (p = 0.013). The other anatomical regions did not show statistically different ADC values between the two groups.

CONCLUSION

Our results suggest that patients suffering from CPSS are subject to microstructural changes, predominantly within the cerebral spinothalamic tract. Additional research could possibly lead to imaging biomarkers derived from ADC values in CPSS patients.

Translation and validation of urdu version short form-mcgill pain questionnaire-2

BACKGROUND

Low back pain is one of the most common complaints affecting many individuals. The McGill Pain Questionnaire is used in various clinical settings to assess different types of pain and one of the most extensively used outcomes measures for pain in the world. The purpose of this study was to translate and validate the original English version of the SF-MPQ-2 into Urdu (SF-MPQ-2-U).

METHODS

For this study, Mapi Research Trust protocols were followed for the forward and backward translation. Test-retest reliability was used to assess the reliability. Cronbach's alpha and Omega was used to determine internal consistency. Pearson's correlation was used to evaluate convergent validity. Confirmatory factor analysis was also conducted.

RESULTS

The Cronbach's alpha for SF-MPQ-2-U was 0.73 to 0.79, indicating acceptable internal consistency. Omega score for the SF-MPQ-U were 0.918. The ICC varied from 0.799 to 0.878 for domains of SF-MPQ-2-U. The CFA of the SF-MPQ-2-U met model fit indices with GFI and NFI > 0.90. The inter-scale correlation between baseline and re-test data was from 0.63 to 0.71, indicating a positive and strong correlation. The SF-MPQ-2-U and ODI-U had a baseline correlation of 0.547. The correlation of SF-MPQ-2-U & VAS at baseline data was 0.558. Pearson's correlation between subscales was r = 0.253 with p 0.01, which was statistically significant.

CONCLUSION

The SF-MPQ-2-U is considered to have good convergent validity at inter scale and between two scale levels. Reliability was checked by test-retest reliability, Internal consistency was checked using Cronbach's alpha and Omega that showed good internal consistency for measuring different types of pain in patients with low back pain who speak Urdu. To make the questionnaire more valid and reliable, it is recommended for the researchers to do in-depth research on larger sample size.

Exploring Psychophysical and Neurophysiological Responses to Intra-Epidermal Electrical Stimuli in Patients With Persistent Spinal Pain Syndrome Type 2 with a Spinal Cord Stimulator

There is a lack of measures that provide insights into how spinal cord stimulation (SCS) modulates nociceptive function in patients with persistent spinal pain syndrome type 2 (PSPS-T2). Recently, we observed altered nociceptive detection thresholds (NDTs) in response to intra-epidermal electrical stimulation (IES) on the feet of PSPS-T2 patients when dorsal root ganglion stimulation was turned on. Furthermore, we observed altered NDTs and evoked potentials (EPs) in response to IES on the hands of PSPS-T2 patients. To explore whether EPs were obstructed by SCS artifacts, we applied IES twice to the hands of patients with SCS turned on (SCS-ON/ON group). To explore possible confounding effects of SCS outside the stimulated area, we repeated IES on the hands of these patients, once with SCS turned off and subsequently once with SCS turned on (SCS-OFF/ON group). The results demonstrated that EPs were not obstructed by SCS artifacts. Additionally, NDTs and EPs did not significantly change between measurements in the SCS-ON/ON and the SCS-OFF/ON groups. Therefore, the results suggested that possible confounding effects of SCS outside the nociceptive system did not interfere with the detection task performance. This work warrants further exploration of NDT-EP phenomena in response to IES at the painful feet of patients.Clinical Relevance-This work contributes to developing a clinical tool to explore psychophysical and neurophysiological biomarkers for observing modulating effects of SCS in patients with PSPS-T2.

Resting-state electroencephalography and magnetoencephalography as biomarkers of chronic pain: a systematic review

ABSTRACT

Reliable and objective biomarkers promise to improve the assessment and treatment of chronic pain. Resting-state electroencephalography (EEG) is broadly available, easy to use, and cost efficient and, therefore, appealing as a potential biomarker of chronic pain. However, results of EEG studies are heterogeneous. Therefore, we conducted a systematic review (PROSPERO CRD42021272622) of quantitative resting-state EEG and magnetoencephalography (MEG) studies in adult patients with different types of chronic pain. We excluded populations with severe psychiatric or neurologic comorbidity. Risk of bias was assessed using a modified Newcastle-Ottawa Scale. Semiquantitative data synthesis was conducted using modified albatross plots. We included 76 studies after searching MEDLINE, Web of Science Core Collection, Cochrane Central Register of Controlled Trials, and EMBASE. For cross-sectional studies that can serve to develop diagnostic biomarkers, we found higher theta and beta power in patients with chronic pain than in healthy participants. For longitudinal studies, which can yield monitoring and/or predictive biomarkers, we found no clear associations of pain relief with M/EEG measures. Similarly, descriptive studies that can yield diagnostic or monitoring biomarkers showed no clear correlations of pain intensity with M/EEG measures. Risk of bias was high in many studies and domains. Together, this systematic review synthesizes evidence on how resting-state M/EEG might serve as a diagnostic biomarker of chronic pain. Beyond, this review might help to guide future M/EEG studies on the development of pain biomarkers.

Psychophysical pain encoding in the cingulate cortex predicts responsiveness of electrical stimulation

Background

The anterior cingulate cortex (ACC) plays an important role in the cognitive and emotional processing of pain. Prior studies have used deep brain stimulation (DBS) to treat chronic pain, but results have been inconsistent. This may be due to network adaptation over time and variable causes of chronic pain. Identifying patient-specific pain network features may be necessary to determine patient candidacy for DBS.

Hypothesis

Cingulate stimulation would increase patients' hot pain thresholds if non-stimulation 70-150 Hz activity encoded psychophysical pain responses.

Methods

In this study, four patients who underwent intracranial monitoring for epilepsy monitoring participated in a pain task. They placed their hand on a device capable of eliciting thermal pain for five seconds and rated their pain. We used these results to determine the individual's thermal pain threshold with and without electrical stimulation. Two different types of generalized linear mixed-effects models (GLME) were employed to assess the neural representations underlying binary and graded pain psychophysics.

Results

The pain threshold for each patient was determined from the psychometric probability density function. Two patients had a higher pain threshold with stimulation than without, while the other two patients had no difference. We also evaluated the relationship between neural activity and pain responses. We found that patients who responded to stimulation had specific time windows where high-frequency activity was associated with increased pain ratings.

Conclusion

Stimulation of cingulate regions with increased pain-related neural activity was more effective at modulating pain perception than stimulating non-responsive areas. Personalized evaluation of neural activity biomarkers could help identify the best target for stimulation and predict its effectiveness in future studies evaluating DBS.

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

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.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

Maximizing treatment efficacy through patient stratification in neuropathic pain trials

Treatment of neuropathic pain remains inadequate despite the elucidation of multiple pathophysiological mechanisms and the development of promising therapeutic compounds. The lack of success in translating knowledge into clinical practice has discouraged pharmaceutical companies from investing in pain medicine; however, new patient stratification approaches could help bridge the translation gap and develop individualized therapeutic approaches. As we highlight in this article, subgrouping of patients according to sensory profiles and other baseline characteristics could aid the prediction of treatment success. Furthermore, novel outcome measures have been developed for patients with neuropathic pain. The extent to which sensory profiles and outcome measures can be employed in routine clinical practice and clinical trials and across distinct neuropathic pain aetiologies is yet to be determined. Improvements in animal models, drawing on our knowledge of human pain, and robust public-private partnerships will be needed to pave the way to innovative and effective pain medicine in the future.

Precision Medicine in Neuropathic Pain

Neuropathic pain is a common chronic pain condition that is caused by a lesion or disease of the somatosensory nervous system. The multitude of sensory negative and positive sensations and associated comorbidities have a major impact on quality of life of affected patients. Current treatment options often only lead to a partial pain relief or are even completely ineffective. In addition, many clinical trials for the development of new drugs have not met the primary endpoint. Therefore, there is still an unmet clinical need in neuropathic pain syndromes. One reason for this therapeutic dilemma could be the heterogeneity of neuropathic pain with a variety of pathophysiological pain mechanisms that are expressed differently in each patient regardless of the underlying disease etiology. Reclassification of neuropathic pain syndromes therefore focuses on the underlying mechanisms of pain development rather than the disease etiology. A priori stratification of patients based on these individual mechanisms could allow the identification of potential treatment responders and thus realize the concept of a mechanism-based treatment. As no biomarkers for pain mechanisms have been discovered yet, one has to rely on surrogate markers that are thought to be closely related to these mechanisms. In this chapter, we present promising predictive biomarkers, focusing in particular on sensory symptoms and signs assessed by patient-reported outcome measures and sensory testing, and discuss how these tools might be used in clinical trials in the future.

Biomarkers for Chronic Pain: Significance and Summary of Recent Advances

Chronic pain can be difficult to predict and a challenge to treat. Biomarkers for chronic pain signal an opportunity for advancements in both management and prevention, and through their research and development offer new insights into the complex processes at play. This review considers the latest research in chronic pain biomarker development and considers how close we are to bringing these from bench to bedside. While some headway has been made that offers efficiencies in patient selection, it is unlikely that a single test will encompass the variety of chronic pain phenotypes. We offer some insights for the near future in biomarker development and areas of continued unmet need.

Relief of chronic pain associated with increase in midline frontal theta power

Unique electroencephalogram signatures of relief from chronic pain demonstrate theta power increase in the midline frontal cortex.

Introduction:

Objectives:

Methods:

Results:

Conclusion:

Dissociation between individual differences in self-reported pain intensity and underlying fMRI brain activation

Pain is an individual experience. Previous studies have highlighted changes in brain activation and morphology associated with within- and interindividual pain perception. In this study we sought to characterize brain mechanisms associated with between-individual differences in pain in a sample of healthy adolescent and adult participants (N = 101). Here we show that pain ratings varied widely across individuals and that individuals reported changes in pain evoked by small differences in stimulus intensity in a manner congruent with their pain sensitivity, further supporting the utility of subjective reporting as a measure of the true individual experience. Furthermore, brain activation related to interindividual differences in pain was not detected, despite clear sensitivity of the Blood Oxygenation Level-Dependent (BOLD) signal to small differences in noxious stimulus intensities within individuals. These findings suggest fMRI may not be a useful objective measure to infer reported pain intensity.

Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review

Diagnosis and management of chronic neuropathic pain are challenging, leading to current efforts to characterize 'objective' biomarkers of pain using imaging or neurophysiological techniques, such as electroencephalography (EEG). A systematic literature review was conducted in PubMed-Medline and Web-of-Science until October 2021 to identify EEG biomarkers of chronic neuropathic pain in humans. The risk of bias was assessed by the Newcastle-Ottawa-Scale. Experimental, provoked, or chronic non-neuropathic pain studies were excluded. We identified 14 studies, in which resting-state EEG spectral analysis was compared between patients with pain related to a neurological disease and patients with the same disease but without pain or healthy controls. From these heterogeneous exploratory studies, some conclusions can be drawn, even if they must be weighted by the fact that confounding factors, such as medication and association with anxio-depressive disorders, are generally not taken into account. Overall, EEG signal power was increased in the θ band (4-7Hz) and possibly in the high-β band (20-30Hz), but decreased in the high-α-low-β band (10-20Hz) in the presence of ongoing neuropathic pain, while increased γ band oscillations were not evidenced, unlike in experimental pain. Consequently, the dominant peak frequency was decreased in the θ-α band and increased in the whole-β band in neuropathic pain patients. Disappointingly, pain intensity correlated with various EEG changes across studies, with no consistent trend. This review also discusses the location of regional pain-related EEG changes in the pain connectome, as the perspectives offered by advanced techniques of EEG signal analysis (source location, connectivity, or classification methods based on artificial intelligence). The biomarkers provided by resting-state EEG are of particular interest for optimizing the treatment of chronic neuropathic pain by neuromodulation techniques, such as transcranial alternating current stimulation or neurofeedback procedures.

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.

Patient Selection for Spinal Cord Stimulation in Treatment of Pain: Sequential Decision-Making Model - A Narrative Review

Despite the well-known efficacy of spinal cord stimulation (SCS) in chronic pain management, patient selection in clinical practice remains challenging. The aim of this review is to provide an overview of the factors that can influence the process of patient selection for SCS treatment. A sequential decision-making model is presented within a tier system that operates in clinical practice. The first level incorporates the underlying disease as a primary indication for SCS, country-related reimbursement rules, and SCS screening-trial criteria in combination with underlying psychological factors as initial selection criteria in evaluating patient eligibility for SCS. The second tier is aligned with the individualized approach within precision pain medicine, whereby individual goals and expectations and the potential need for preoperative optimizations are emphasized. Additionally, this tier relies on results from prediction models to provide an estimate of the efficacy of SCS in the long term. In the third tier, selection bias, MRI compatibility, and ethical beliefs are included, together with recent technological innovations, superiority of specific stimulation paradigms, and new feedback systems that could indirectly influence the decision-making of the physician. Both patients and physicians should be aware of the different aspects that influence patient selection in relation to SCS for pain management to make an independent decision on whether or not to initiate a treatment trajectory with SCS.

Chronic Musculoskeletal Pain Moderates the Association between Sleep Quality and Dorsostriatal-Sensorimotor Resting State Functional Connectivity in Community-Dwelling Older Adults

Aging is associated with poor sleep quality and greater chronic pain prevalence, with age-related changes in brain function as potential underlying mechanisms. Objective. The following cross-sectional study aimed to determine whether self-reported chronic musculoskeletal pain in community-dwelling older adults moderates the association between sleep quality and resting state functional brain connectivity (rsFC). Methods. Community-dwelling older individuals (mean age = 73.29 years) part of the NEPAL study who completed the Pittsburg Sleep Quality Index (PSQI) and a rsFC scan were included (n = 48) in the present investigation. To that end, we tested the effect of chronic pain-by-PSQI interaction on rsFC among atlas-based brain regions-of-interest, controlling for age and sex. Results and Discussion. A significant network connecting the bilateral putamen and left caudate with bilateral precentral gyrus, postcentral gyrus, and juxtapositional lobule cortex, survived global multiple comparisons (FDR; q < 0.05) and threshold-free network-based-statistics. Greater PSQI scores were significantly associated with greater dorsostriatal-sensorimotor rsFC in the no-pain group, suggesting that a state of somatomotor hyperarousal may be associated with poorer sleep quality in this group. However, in the pain group, greater PSQI scores were associated with less dorsostriatal-sensorimotor rsFC, possibly due to a shift of striatal functions toward regulation sensorimotor aspects of the pain experience, and/or aberrant cortico-striatal loops in the presence of chronic pain. This preliminary investigation advances knowledge about the neurobiology underlying the associations between chronic pain and sleep in community-dwelling older adults that may contribute to the development of effective therapies to decrease disability in geriatric populations.

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.

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.

A candidate neuroimaging biomarker for detection of neurotransmission-related functional alterations and prediction of pharmacological analgesic response in chronic pain

Chronic pain is a world-wide clinical challenge. Response to analgesic treatment is limited and difficult to predict. Functional MRI has been suggested as a potential solution. However, while most analgesics target specific neurotransmission pathways, functional MRI-based biomarkers are not specific for any neurotransmitter system, limiting our understanding of how they might contribute to predict treatment response. Here, we sought to bridge this gap by applying Receptor-Enriched Analysis of Functional Connectivity by Targets to investigate whether neurotransmission-enriched functional connectivity mapping can provide insights into the brain mechanisms underlying chronic pain and inter-individual differences in analgesic response after a placebo or duloxetine. We performed secondary analyses of two openly available resting-state functional MRI data sets of 56 patients with chronic knee osteoarthritis pain who underwent pre-treatment brain scans in two clinical trials. Study 1 (n = 17) was a 2-week single-blinded placebo pill trial. Study 2 (n = 39) was a 3-month double-blinded randomized trial comparing placebo to duloxetine, a dual serotonin–noradrenaline reuptake inhibitor. Across two independent studies, we found that patients with chronic pain present alterations in the functional circuit related to the serotonin transporter, when compared with age-matched healthy controls. Placebo responders in Study 1 presented with higher pre-treatment functional connectivity enriched by the dopamine transporter compared to non-responders. Duloxetine responders presented with higher pre-treatment functional connectivity enriched by the serotonin and noradrenaline transporters when compared with non-responders. Neurotransmission-enriched functional connectivity mapping might hold promise as a new mechanistic-informed biomarker for functional brain alterations and prediction of response to pharmacological analgesia in chronic pain.

Brain circuits for pain and its treatment

[Figure: see text].

Neuroimaging enters the pain biomarker arena

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

Classifying chronic pain using multidimensional pain-agnostic symptom assessments and clustering analysis

Chronic pain conditions present in various forms, yet all feature symptomatic impairments in physical, mental, and social domains. Rather than assessing symptoms as manifestations of illness, we used them to develop a chronic pain classification system. A cohort of real-world treatment-seeking patients completed a multidimensional patient-reported registry as part of a routine initial evaluation in a multidisciplinary academic pain clinic. We applied hierarchical clustering on a training subset of 11,448 patients using nine pain-agnostic symptoms. We then validated a three-cluster solution reflecting a graded scale of severity across all symptoms and eight independent pain-specific measures in additional subsets of 3817 and 1273 patients. Negative affect–related factors were key determinants of cluster assignment. The smallest subset included follow-up assessments that were predicted by baseline cluster assignment. Findings provide a cost-effective classification system that promises to improve clinical care and alleviate suffering by providing putative markers for personalized diagnosis and prognosis.

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.

Orofacial musculoskeletal pain: An evidence-based bio-psycho-social matrix model

Pain is a multidimensional experience comprising sensory-discriminative, affective-motivational, and cognitive-evaluative dimensions. Clinical and research findings have demonstrated a complex interplay between social burdens, individual coping strategies, mood states, psychological disorders, sleep disturbances, masticatory muscle tone, and orofacial musculoskeletal pain. Accordingly, current classification systems for orofacial pain require psychosocial assessments to be an integral part of the multidimensional diagnostic process. Here, we review evidence on how psychosocial and biological factors may generate and perpetuate musculoskeletal orofacial pain. Specifically, we discuss studies investigating a putative causal relationship between stress, bruxism, and pain in the masticatory system. We present findings that attribute brain structures various roles in modulating pain perception and pain-related behavior. We also examine studies investigating how the nervous and immune system on cellular and molecular levels may account for orofacial nociceptive signaling. Furthermore, we review evidence pointing towards associations between orofacial musculoskeletal pain and neuroendocrine imbalances, sleep disturbances, and alterations of the circadian timing system. We conclude with several proposals that may help to alleviate orofacial pain in the future.

Brainstem trigeminal fiber microstructural abnormalities are associated with treatment response across subtypes of trigeminal neuralgia

ABSTRACT

Neurosurgical treatments for trigeminal neuralgia (TN) can provide long-lasting pain relief; however, some patients fail to respond and undergo multiple, repeat procedures. Surgical outcomes can vary depending on the type of TN, but the reasons for this are not well understood. Neuroimaging studies of TN point to abnormalities in the brainstem trigeminal fibers; however, whether this is a common characteristic of treatment nonresponse across different subtypes of TN is unknown. Here, we used diffusion tensor imaging (DTI) to determine whether the brainstem trigeminal fiber microstructure is a common biomarker of surgical response in TN and whether the extent of these abnormalities is associated with the likelihood of response across subtypes of TN. We studied 98 patients with TN (61 classical TN, 26 TN secondary to multiple sclerosis, and 11 TN associated with a solitary pontine lesion) who underwent neurosurgical treatment and 50 healthy controls. We assessed treatment response using pain intensity measures and examined microstructural features by extracting pretreatment DTI metrics from the proximal pontine segment of the trigeminal nerves. We found that microstructural abnormalities in the affected pontine trigeminal fibers (notably, lower fractional anisotropy and higher radial diffusivity) highlight treatment nonresponders (n = 47) compared with responders (n = 51) and controls, and that the degree of abnormalities is associated with the likelihood of surgical response across subtypes of TN. These novel findings demonstrate the value of DTI as an objective, noninvasive tool for the prediction of treatment response and elucidate the features that distinguish treatment responders from nonresponders in the TN population.

Pain Biomarkers in Cancer: An Overview

BACKGROUND

Pain is a common symptom in oncologic patients and its management is generally guided with reference to pain individually perceived by patients and expressed through self-reported scales. However, the utility of these tools is limited as it strongly depends on patients' opinions. For this reason, more objective instruments are desirable.

OBJECTIVE

In this overview, scientific articles indicating potential markers to be used for pain management in cancer were collected and discussed.

METHODS

Research was performed on principal electronic scientific databases by using the words "pain", "cancer", "markers" and "biomarkers" as the main keywords, and findings describing potential biomarkers for the management of cancer pain were reported.

RESULTS

Studies on pain markers not specific for cancer typology (inflammatory, genetic markers predicting response to analgesic drugs, neuroimaging markers) and pain markers for specific types of cancer (bone cancer, breast cancer, lung cancer, head and neck cancer, prostate cancer, cancer in pediatrics) have been presented and commented on.

CONCLUSION

This overview supports the view of the involvement of inflammatory mediators in the mechanisms underlying cancer pain. Only a small amount of data from research up till today is available on markers that can help in the management of pain, except for pro-inflammatory cytokines and other inflammatory indexes such as C-reactive protein (CRP). However, biomarkers are a promising strategy useful to predict pain intensity and to objectively quantify analgesic response in guiding decisions regarding individual-tailored treatments for cancer patients.

Dynamics of neuronal oscillations underlying nociceptive response in the mouse primary somatosensory cortex

Pain is caused by tissue injury, inflammatory disease, pathogen invasion, or neuropathy. The perception of pain is attributed to the neuronal activity in the brain. However, the dynamics of neuronal activity underlying pain perception are not fully known. Herein, we examined theta-oscillation dynamics of local field potentials in the primary somatosensory cortex of a mouse model of formalin-induced pain, which usually shows a bimodal behavioral response interposed between pain-free periods. We found that formalin injection exerted a reversible shift in the theta-peak frequency toward a slower frequency. This shift was observed during nociceptive phases but not during the pain-free period and was inversely correlated with instantaneous pain intensity. Furthermore, instantaneous oscillatory analysis indicated that the probability of slow theta oscillations increased during nociceptive phases with an association of augmented slow theta power. Finally, cross-frequency coupling between theta and gamma oscillations indicated that the coupling peak frequency of theta oscillations was also shifted toward slower oscillations without affecting coupling strength or gamma power. Together, these results suggest that the dynamic changes in theta oscillations in the mouse primary somatosensory cortex represent the ongoing status of pain sensation.

Preliminary study of the impact of elevated circulating plasma levels of catecholamines on opioid requirements for acute surgical pain

Introduction:

The objective of this study is to determine whether elevated circulating plasma catecholamine levels significantly impact opioid requirements during the first 24 hours postoperative period in individuals with acute surgical pain.

Methods:

We retrospectively reviewed 15 electronic medical records (EMRs) from adults 18 years and older, with confirmed elevated plasma catecholamine levels (experimental) and 15 electronic health records (EHRs) from matched-controls for age, gender, race and type of surgery, with a follow up of 24 hours postoperatively.

Results:

The total morphine milligram equivalents (MMEs) requirements from the experimental group were not statistically different when compared with controls [44.1 (13 to 163) mg versus 47.5 (13 to 151) mg respectively; p 0.4965]. However, the intraoperative MMEs showed a significant difference, among the two groups; [(experimental) 32.5 (13. to 130) mg, (control) 15 (6.5 to 130) mg; p 0.0734]. The intraoperative dosage of midazolam showed a highly significant positive correlation to the total MMEs (p 0.0005). The subjects with both elevated plasma catecholamines and hypertension used significantly higher intraoperative MMEs compared to controls [34.1 (13 to 130) mg versus 15 (6.5 to 130) mg, respectively; p 0.0292)]. Those 51 years and younger, with elevated circulating levels of catecholamines, required significantly higher levels of both the postoperative MMEs [29.1 (0 to 45) mg versus 12 (0 to 71.5) mg; (p 0.0553)] and total MMEs [544.05 (13 to 81) mg versus 29.42 (13 to 92.5) mg; (p 0.00018), when compared to controls with history of nicotine and alcohol use.

Conclusion:

This preliminary study evaluated a biologic factor, which have promising clinical usefulness for predicting analgesic requirements that can drive clinical decisions on acute surgical pain.

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.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Objective Pain Assessment: a Key for the Management of Chronic Pain

The individual and social burdens associated with chronic pain have been escalating globally. Accurate pain measurement facilitates early diagnosis, disease progression monitoring and therapeutic efficacy evaluation, thus is a key for the management of chronic pain. Although the "golden standards" of pain measurement are self-reported scales in clinical practice, the reliability of these subjective methods could be easily affected by patients' physiological and psychological status, as well as the assessors' predispositions. Therefore, objective pain assessment has attracted substantial attention recently. Previous studies of functional magnetic resonance imaging (fMRI) revealed that certain cortices and subcortical areas are commonly activated in subjects suffering from pain. Dynamic pain connectome analysis also found various alterations of neural network connectivity that are correlated with the severity of clinical pain symptoms. Electroencephalograph (EEG) demonstrated suppressed spontaneous oscillations during pain experience. Spectral power and coherence analysis of EEG also identified signatures of different types of chronic pain. Furthermore, fMRI and EEG can visualize objective brain activities modulated by analgesics in a mechanism-based way, thus bridge the gaps between animal studies and clinical trials. Using fMRI and EEG, researchers are able to predict therapeutic efficacy and identify personalized optimal first-line regimens. In the future, the emergence of magnetic resonance spectroscopy and cell labelling in MRI would encourage the investigation on metabolic and cellular pain biomarkers. The incorporation of machine learning algorithms with neuroimaging or behavior analysis could further enhance the specificity and accuracy of objective pain assessments.