Pain measurement and brain activity: will neuroimages replace pain ratings?

Athletes and Experimental Pain: A Systematic Review and Meta-Analysis

The evidence that athletes respond to and report indices of experimental pain differently to non-athlete populations was analysed. Databases screened were SPORTDiscus, PubMED, PsycArticles, the Cochrane Library (Cochrane Database of Systematic Reviews), Web of Science, Scopus, and CINAHL. Studies that compared experimentally induced pain responses (threshold, tolerance, intensity, unpleasantness, bothersomeness, and effect on performance) in athletes and controls were included. Meta-analyses were performed where appropriate and effects were described as standardised mean differences, pooled using random effects models. Thirty-six studies (2,492 participants) met the inclusion criteria comprising 19 pain tolerance, 17 pain threshold, 21 pain intensity, 5 pain unpleasantness, 2 performance in pain and 1 bothersomeness study. Athletes demonstrated greater pain tolerance (g = .88 [95% confidence interval [CI] .65, .13]) and reported less pain intensity (g = -.80, [95% CI -1.13, -.47]) compared to controls; they also had higher pain threshold but with smaller effects (g = .41, [95% CI .08, .75]). Differences for unpleasantness did not reach statistical significance but the effects were large (g = -1.23 [95% CI -2.29, .18]). Two studies reported that performance in pain was better in contact athletes than non-athletes, and one concluded that athletes find pain less bothersome than controls. There were considerable inconsistencies in the methods employed that were reflected in the meta-analyses' findings. Sub-group analyses of tolerance and intensity were conducted between endurance, contact, and other athlete groups, but were not significant. The data suggest that athletic participation is associated with altered pain responses, but mechanisms remain unclear and more transparent methods are recommended.This study was registered on the PROSPERO site in January 2019 (ref ID: CRD42019119611). PERSPECTIVE: This review examined differences in pain outcomes (threshold, tolerance, intensity, unpleasantness, bothersomeness) and the effect of pain on performance, in athletes versus controls. Meta-analyses revealed athletes had higher threshold and tolerance and found pain less intense than controls; there was some evidence of differences in bothersomeness and performance.

SOMAScience: A Novel Platform for Multidimensional, Longitudinal Pain Assessment

Chronic pain is one of the most significant health issues in the United States, affecting more than 20% of the population. Despite its contribution to the increasing health crisis, reliable predictors of disease development, progression, or treatment outcomes are lacking. Self-report remains the most effective way to assess pain, but measures are often acquired in sparse settings over short time windows, limiting their predictive ability. In this paper, we present a new mobile health platform called SOMAScience. SOMAScience serves as an easy-to-use research tool for scientists and clinicians, enabling the collection of large-scale pain datasets in single- and multicenter studies by facilitating the acquisition, transfer, and analysis of longitudinal, multidimensional, self-report pain data. Data acquisition for SOMAScience is done through a user-friendly smartphone app, SOMA, that uses experience sampling methodology to capture momentary and daily assessments of pain intensity, unpleasantness, interference, location, mood, activities, and predictions about the next day that provide personal insights into daily pain dynamics. The visualization of data and its trends over time is meant to empower individual users' self-management of their pain. This paper outlines the scientific, clinical, technological, and user considerations involved in the development of SOMAScience and how it can be used in clinical studies or for pain self-management purposes. Our goal is for SOMAScience to provide a much-needed platform for individual users to gain insight into the multidimensional features of their pain while lowering the barrier for researchers and clinicians to obtain the type of pain data that will ultimately lead to improved prevention, diagnosis, and treatment of chronic pain.

Pediatric Neural Changes to Physical and Emotional Pain After Intensive Interdisciplinary Pain Treatment: A Pilot Study

Brain areas activated during pain can contribute to enhancing or reducing the pain experience, showing a potential connection between chronic pain and the neural response to pain in adolescents and youth. This study examined changes in brain activation associated with experiencing physical pain, and the observation of physical and emotional pain in others, by using functional magnetic resonance imaging (fMRI) before and after intensive interdisciplinary pain treatment (IIPT). Eighteen youth (age 14 to 18) with widespread chronic pain completed fMRI testing before and after IIPT to assess changes in brain activation in response to physical and emotional pain. Broadly, brain activation changes were observed in frontal, somatosensory, and limbic regions. These changes suggest improvements in descending pain modulation via thalamus and caudate, and the different pattern of brain activation after treatment suggests better discrimination between physical and emotional pain. Brain activation changes were also correlated with improvements in clinical outcomes of catastrophizing (reduced activation in right caudate, right mid-cingulate, and postcentral gyrus) and pain-related disability (increased activation in precentral gyrus, left hippocampus, right middle occipital cortex, and left superior frontal gyrus). These changes support interpretation that reduced brain protective responses to pain were associated with treatment-related improvements. This pilot study highlights the need for larger trials designed to better understand the brain mechanisms involved in pediatric widespread pain treatment.

Does Pain Acceptance Contribute to Improved Functionality through Walking in Women with Fibromyalgia? Looking at Depressive Comorbidity

In the last decade, research has pointed to physical exercise as an effective treatment in fibromyalgia patients. Some studies have highlighted the role of acceptance and commitment therapy in optimizing the benefits of exercise in patients. However, given the high comorbidity in fibromyalgia, it is necessary to value its possible influence on the effect of certain variables, such as acceptance, on the benefits of treatments, such as physical exercise. Our aim is to test the role of acceptance in the benefits of walking over functional limitation, further assessing whether this model is equally valid, considering depressive symptomatology as an additional differential diagnosis. A cross-sectional study with a convenience sample through contacting Spanish fibromyalgia associations was carried out. A total of 231 women with fibromyalgia (mean age 56.91 years) participated in the study. Data were analyzed with the Process program (Model 4, Model 58, Model 7). The results highlight the role of acceptance as a mediator between walking and functional limitation (B = -1.86, SE = 0.93, 95% CI = [-3.83, -0.15]). This model, when depression is incorporated as a moderator, is significant only in patients without depression, revealing the need for personalized treatments in fibromyalgia, considering their most prevalent comorbidity.

Effects of Walking as Physical Exercise on Functional Limitation through Pain in Patients with Fibromyalgia-How Does Catastrophic Thinking Contribute?

Background: Aerobic exercise has a beneficial impact on physical and mental health. However, patients with fibromyalgia do not always report perceiving these improvements. Objective: The aim of this study was to examine whether catastrophic thinking moderated the effects of perceived pain severity once an active and regular lifestyle had been established on functional limitation in chronic pain patients, in particular in fibromyalgia patients. Methods: The sample consisted of a total of 491 women with fibromyalgia diagnosed according to the criteria of the American College of Rheumatology. Participants completed an ad-hoc item about lifestyle related to walking pattern, the Brief Pain Inventory, the Pain Catastrophization Scale, and the Fibromyalgia Impact Questionnaire-Revised. To examine the relationship between the variables, a moderate mediation analysis was performed through the macro PROCESS (model 14). Results: The relationship between the performance of the recommended walking pattern and functional limitation was mediated by the severity of pain (B = −5.19, SE = 1.59, t = −3.25, 95% CI = [−4.06, −0.28], p < 0.001). Furthermore, it was found that the mediating effect of pain severity was moderated by catastrophic thinking (Index = −0.014, SE = 0.007, 95% CI [0.002, 0.030]). Conclusions: The positive effect of walking on functionality through the reduction of pain levels is favored when patients present low catastrophizing, which affects the relevance of including interventions focused on the reduction of catastrophizing in the prescription of physical exercise in patients with fibromyalgia as the treatment of choice.

Algometry for the assessment of central sensitisation to pain in fibromyalgia patients: a systematic review

INTRODUCTION

The pathophysiology of fibromyalgia (FM) is related to central sensitisation (CS) to pain. Algometry allows assessing CS based on dynamic evoked pain. However, current algometrýs protocols require optimising, unifying and updating.

OBJECTIVES

1) identify the dynamic pain measures used most frequently to effectively assess CS processes in FM, and 2) consider the future of the algometry assessing CS in these patients.

METHODS

Cochrane Collaboration guidelines and PRISMA statements were followed. The protocol was registered in PROSPERO database (ID: CRD42021270135). The selected articles were evaluated using the Cochrane risk of bias (ROB) assessment tool. The PubMed, Scopus, and Web of Science databases were searched.

RESULTS

Thirty-four studies were selected, including measures such as temporal summation of pain (TSP), aftersensations (AS), spatial summation of pain (SSP), the noxious flexion reflex (NFR) threshold, conditioned pain modulation (CPM), cutaneous silent period (CuSP), and slowly repeated evoked pain (SREP); and evoked pain combined with neuroimaging. Each measure offered various advantages and limitations. According to ROB, 28 studies were of low quality, 3 of moderate quality, and 3 of high quality.

CONCLUSIONS

Several pain indicators have been demonstrated to successfully examine CS involvement in FM in the last years. Algometry, especially when it involves diverse body sites and tissues, might provide further insight into (1) the evaluation of psychological factors known to influence pain experience, (2) new dynamic pain indicators, and (3) the simultaneous use of certain neuroimaging techniques. Further research clarifying the mechanisms underlying some of these measures, and homogenisation and optimisation of the algometrýs protocols, are needed. KEY MESSAGESAlgometry allows for assessing Central Sensitisation by applying dynamic evoked pain.The future of algometry could relapse in its combination with neuroimaging.Recently-emerged pain indicators should be considered for algometrýs new protocols.

What is Pain-Related Suffering? Conceptual Critiques, Key Attributes, and Outstanding Questions

Suffering holds a central place within pain research, theory, and practice. However, the construct of pain-related suffering has yet to be operationalized by the International Association for the Study of Pain and is largely underdeveloped. Eric Cassell's seminal work on suffering serves as a conceptual anchor for the limited pain research that specifically addresses this construct. Yet, important critiques of Cassell's work have not been integrated within the pain literature. This Focus Article aims to take a preliminary step towards an updated operationalization of pain-related suffering by 1) presenting key attributes of pain-related suffering derived from a synthesis of the literature and 2) highlighting key challenges associated with Cassell's conceptualization of suffering. We present 4 key attributes: 1) pain and suffering are inter-related, but distinct experiences, 2) suffering is a subjective experience, 3) the experience of suffering is characterized by a negative affective valence, and 4) disruption to one's sense of self is an integral part of suffering. A key outstanding challenge is that suffering is commonly viewed as a self-reflective and future-oriented process, which fails to validate many forms of suffering and marginalizes certain populations. Future research addressing different modes of suffering - with and without self-reflection - are discussed. PERSPECTIVE: This article offers a preliminary step toward operationalizing the construct of pain-related suffering and proposes priorities for future research. A robust operationalization of this construct is essential to developing clinical strategies that aim to better recognize and alleviate suffering among people living with pain.

Own Pain Distress Mediates the Link Between the Lifestyle Facet of Psychopathy and Estimates of Pain Distress in Others

Psychopathy is a multifaceted personality construct entailing interpersonal-affective disturbances, antisocial traits, and a tendency to lead an erratic lifestyle. Elevated levels of psychopathic traits have been linked to having an altered experience of pain, reduced responsivity to distress in others, and making poor moral choices that bring harm to others. In the context of moral decision-making, it is possible that the capacity to estimate the distress felt by others is linked to a limitation in the first-hand experience of distress, as the presence of psychopathic traits increases. We employed a model-based approach in a non-offender sample (n = 174) to investigate whether pain-related distress mediated the links between facets of psychopathy and estimates of the pain distress potentially experienced by others. Participants judged the permissibility of moral dilemmas and rated how much pain distress they would experience while making such judgements, as well as how much pain distress they believed the “victims” would feel as a result of the moral choice made by the participant. We found that ratings of own pain distress predicted beliefs about the distress others may experience, and elevated scores on the lifestyle facet of psychopathy uniquely predicted lower estimates of own pain distress. Furthermore, own pain distress mediated the relationship between the lifestyle facet and beliefs about others’ distress. Finally, exploratory zero-order correlation analyses revealed that ratings of own pain distress decreased as the scores on multiple psychopathic traits increased. Only the lifestyle facet correlated in the negative direction with beliefs about others’ distress. Taken together, our findings suggest that beliefs about how much pain distress others may experience is indeed mediated by own pain distress, and that the tendency to lead an erratic lifestyle is linked to alterations in this mechanism.

Computer mediated automatic detection of pain-related behavior: prospect, progress, perils

Pain is often characterized as a fundamentally subjective phenomenon; however, all pain assessment reduces the experience to observables, with strengths and limitations. Most evidence about pain derives from observations of pain-related behavior. There has been considerable progress in articulating the properties of behavioral indices of pain; especially, but not exclusively those based on facial expression. An abundant literature shows that a limited subset of facial actions, with homologs in several non-human species, encode pain intensity across the lifespan. Unfortunately, acquiring such measures remains prohibitively impractical in many settings because it requires trained human observers and is laborious. The advent of the field of affective computing, which applies computer vision and machine learning (CVML) techniques to the recognition of behavior, raised the prospect that advanced technology might overcome some of the constraints limiting behavioral pain assessment in clinical and research settings. Studies have shown that it is indeed possible, through CVML, to develop systems that track facial expressions of pain. There has since been an explosion of research testing models for automated pain assessment. More recently, researchers have explored the feasibility of multimodal measurement of pain-related behaviors. Commercial products that purport to enable automatic, real-time measurement of pain expression have also appeared. Though progress has been made, this field remains in its infancy and there is risk of overpromising on what can be delivered. Insufficient adherence to conventional principles for developing valid measures and drawing appropriate generalizations to identifiable populations could lead to scientifically dubious and clinically risky claims. There is a particular need for the development of databases containing samples from various settings in which pain may or may not occur, meticulously annotated according to standards that would permit sharing, subject to international privacy standards. Researchers and users need to be sensitive to the limitations of the technology (for e.g., the potential reification of biases that are irrelevant to the assessment of pain) and its potentially problematic social implications.

Give me a pain that I am used to: distinct habituation patterns to painful and non-painful stimulation

Pain habituation is associated with a decrease of activation in brain areas related to pain perception. However, little is known about the specificity of these decreases to pain, as habituation has also been described for other responses like spinal reflexes and other sensory responses. Thus, it might be hypothesized that previously reported reductions in activation are not specifically related to pain habituation. For this reason, we performed a 3 T fMRI study using either painful or non-painful electrical stimulation via an electrode attached to the back of the left hand. Contrasting painful vs. non-painful stimulation revealed significant activation clusters in regions well-known to be related to pain processing, such as bilateral anterior and posterior insula, primary/secondary sensory cortices (S1/S2) and anterior midcingulate cortex (aMCC). Importantly, our results show distinct habituation patterns for painful (in aMCC) and non-painful (contralateral claustrum) stimulation, while similar habituation for both types of stimulation was identified in bilateral inferior frontal gyrus (IFG) and contralateral S2. Our findings thus distinguish a general habituation in somatosensory processing (S2) and reduced attention (IFG) from specific pain and non-pain related habituation effects where pain-specific habituation effects within the aMCC highlight a change in affective pain perception.

Psychometric properties of the Cognitive Fusion Questionnaire in females with fibromyalgia

Cognitive fusion, or the degree to which a person is entangled with their thoughts and takes them literally, is considered a normal yet detrimental cognitive process associated with diverse negative outcomes across healthy and disordered functioning, including in fibromyalgia. Given the relevance of this cognitive process, the aim of the present study is to generate empirical evidence on the reliability and validity of the Cognitive Fusion Questionnaire (CFQ) in a sample of Spanish females with fibromyalgia. In this cross-sectional study, 230 Spanish females diagnosed with fibromyalgia (mean age 56.89 years; SD = 8.96) were assessed on cognitive fusion, pain catastrophizing, personality traits, positive and negative affect, anxiety symptoms, depressive symptoms and disease severity. Confirmatory factor analysis, reliability and validity analyses were performed. The CFQ showed a unidimensional structure, adequate temporal stability and good internal consistency. In addition, convergent and discriminant validity were found with respect to dispositional, pain-related, affective, mood and anxious measures; as well as incremental and criterion validity in the prediction of psychological symptoms or the disease severity status. The CFQ has been revealed as a solid and valid measure in the evaluation of cognitive fusion in Spanish females suffering from fibromyalgia, and may also be useful in clinical contexts due to the relationships it has shown with important outcomes. Limitations and future directions are also discussed.

Test-retest reliability of laser evoked pain perception and fMRI BOLD responses

Pain perception is a subjective experience and highly variable across time. Brain responses evoked by nociceptive stimuli are highly associated with pain perception and also showed considerable variability. To date, the test–retest reliability of laser-evoked pain perception and its associated brain responses across sessions remain unclear. Here, an experiment with a within-subject repeated-measures design was performed in 22 healthy volunteers. Radiant-heat laser stimuli were delivered on subjects’ left-hand dorsum in two sessions separated by 1–5 days. We observed that laser-evoked pain perception was significantly declined across sessions, coupled with decreased brain responses in the bilateral primary somatosensory cortex (S1), right primary motor cortex, supplementary motor area, and middle cingulate cortex. Intraclass correlation coefficients between the two sessions showed “fair” to “moderate” test–retest reliability for pain perception and brain responses. Additionally, we observed lower resting-state brain activity in the right S1 and lower resting-state functional connectivity between right S1 and dorsolateral prefrontal cortex in the second session than the first session. Altogether, being possibly influenced by changes of baseline mental state, laser-evoked pain perception and brain responses showed considerable across-session variability. This phenomenon should be considered when designing experiments for laboratory studies and evaluating pain abnormalities in clinical practice.

Structural MRI Analysis of Chronic Pain Patients Following Interdisciplinary Treatment Shows Changes in Brain Volume and Opiate-Dependent Reorganization of the Amygdala and Hippocampus

OBJECTIVE

The present study examined pre- to post-treatment changes in volumes for brain structures known to be associated with pain processing (thalamus, caudate, putamen, pallidum, hippocampus, amygdala, and accumbens) following an interdisciplinary pain management program.

DESIGN

Twenty-one patients participating in a four-week interdisciplinary pain management program completed the study. The program consisted of individual and group therapies with the following disciplines: physical therapy, occupational therapy, pain psychology, biofeedback/relaxation training, nursing lectures, and medical management. All patients underwent functional magnetic resonance imaging of the brain before the start and at completion of the program. They also completed standard outcome measures assessing pain, symptoms of central sensitization, disability, mood, coping, pain acceptance, and impressions of change.

RESULTS

Our results showed a significant increase in total brain volume, as well as increased volumes in the thalamus, hippocampus, and amygdala. As expected, we also found significant improvements in our standard outcome measures. The majority of patients rated themselves as much or very much improved. The increase in volume in the hippocampus was significantly associated with patient perceptions of change. However, the correlations were in the unexpected direction, such that greater increases in hippocampal volume were associated with perceptions of less improvement. Further exploratory analyses comparing patients by their opioid use status (use vs no use) showed differential program effects on volume increases in the hippocampus and amygdala.

CONCLUSIONS

These findings show that a four-week interdisciplinary pain management program resulted in changes in the brain, which adds objective findings further demonstrating program efficacy.

Noxious pressure stimulation demonstrates robust, reliable estimates of brain activity and self-reported pain

Functional neuroimaging techniques have provided great insight in the field of pain. Utilising these techniques, we have characterised pain-induced responses in the brain and improved our understanding of key pain-related phenomena. Despite the utility of these methods, there remains a need to assess the test retest reliability of pain modulated blood-oxygen-level-dependant (BOLD) MR signal across repeated sessions. This is especially the case for more novel yet increasingly implemented stimulation modalities, such as noxious pressure, and it is acutely important for multi-session studies considering treatment efficacy. In the present investigation, BOLD signal responses were estimated for noxious-pressure stimulation in a group of healthy participants, across two separate sessions. Test retest reliability of functional magnetic resonance imaging (fMRI) data and self-reported visual analogue scale measures were determined by the intra-class correlation coefficient. High levels of reliability were observed in several key brain regions known to underpin the pain experience, including in the thalamus, insula, somatosensory cortices, and inferior frontal regions, alongside "excellent" reliability of self-reported pain measures. These data demonstrate that BOLD-fMRI derived signals are a valuable tool for quantifying noxious responses pertaining to pressure stimulation. We further recommend the implementation of pressure as a stimulation modality in experimental applications.

Bildgebende Untersuchungen des neuronalen Schmerznetzwerks

Der vorliegende Artikel soll eine Übersicht über bildgebende Untersuchungen im Bereich chronischer Schmerzsyndrome bieten. Auf die einleitenden Worte zur allgemeinen Phänomenologie des Schmerzes folgt ein umfassender Einblick in die gegenwärtige Anwendung funktioneller und struktureller Bildgebungstechniken am Beispiel ausgewählter Schmerzsyndrome (Chronischer Rückenschmerz, Fibromyalgiesyndrom (FMS), Phantomschmerz und Komplexes regionales Schmerzsyndrom (CRPS)). In diesem Zusammenhang werden Gemeinsamkeiten und Besonderheiten der spezifischen neurologischen Korrelate verschiedener chronischer Schmerzerkrankungen diskutiert.

Associations of Cognitive Fusion and Pain Catastrophizing with Fibromyalgia Impact through Fatigue, Pain Severity, and Depression: An Exploratory Study Using Structural Equation Modeling

Differences in fibromyalgia impact on functioning exist and appear to be influenced by numerous factors, including symptomatology severity, as well as the cognitive profile of the individual. The contribution of these elements, however, tends to be explored in a fragmented manner. To address this issue, we tested a comprehensive structural equation model in which associations of cognitive fusion and pain catastrophizing with function limitations are investigated through fibromyalgia symptomatology (i.e., fatigue, pain severity, and depression) in 231 women with fibromyalgia. In the model, cognitive fusion and two catastrophizing components (magnification and helplessness) were associated with poorer functioning indirectly through fibromyalgia symptomatology. Only the rumination component of catastrophizing had a direct association with functional limitations. All fibromyalgia symptoms were linked to increased functional limitations. A parsimonious model with significant associations only obtained an excellent fit (S-B χ² = 774.191, df = 543, p < 0.001; CFI = 0.943; RMSEA = 0.043; CAIC = −2724.04) and accounted for 50% of the variance of functional limitations. These results suggest that the relationship between psychological cognitive processes, fibromyalgia symptomatology, and functional limitations is complex and support the need for comprehensive models such as the present. The findings are discussed in the context of personalized psychological treatments (i.e., the need to address certain cognitive processes according to the problematic symptomatology or outcome).

The Multimodal Assessment Model of Pain: A Novel Framework for Further Integrating the Subjective Pain Experience Within Research and Practice

OBJECTIVES

Pain assessment is enigmatic. Although clinicians and researchers must rely upon observations to evaluate pain, the personal experience of pain is fundamentally unobservable. This raises the question of how the inherent subjectivity of pain can and should be integrated within assessment. Current models fail to tackle key facets of this problem, such as what essential aspects of pain are overlooked when we only rely on numeric forms of assessment, and what types of assessment need to be prioritized to ensure alignment with our conceptualization of pain as a subjective experience. We present the multimodal assessment model of pain (MAP) as offering practical frameworks for navigating these challenges.

METHODS

This is a narrative review.

RESULTS

MAP delineates qualitative (words, behaviors) and quantitative (self-reported measures, non-self-reported measures) assessment and regards the qualitative pain narrative as the best available root proxy for inferring pain in others. MAP offers frameworks to better address pain subjectivity by: (1) delineating separate criteria for identifying versus assessing pain. Pain is identified through narrative reports, while comprehensive assessment is used to infer why pain is reported; (2) integrating compassion-based and mechanism-based management by both validating pain reports and assessing underlying processes; (3) conceptualizing comprehensive pain assessment as both multidimensional and multimodal (listening/observing and measuring); and (4) describing how qualitative data help validate and contextualize quantitative pain measures.

DISCUSSION

MAP is expected to help clinicians validate pain reports as important and legitimate, regardless of other findings, and help our field develop more comprehensive, valid, and compassionate approaches to assessing pain.

Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis

Characterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute nociceptive pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute nociceptive pain.

Distinguishing pain from nociception, salience, and arousal: How autonomic nervous system activity can improve neuroimaging tests of specificity

Pain is a subjective, multidimensional experience that is distinct from nociception. A large body of work has focused on whether pain processing is supported by specific, dedicated brain circuits. Despite advances in human neuroscience and neuroimaging analysis, dissociating acute pain from other sensations has been challenging since both pain and non-pain stimuli evoke salience and arousal responses throughout the body and in overlapping brain circuits. In this review, we discuss these challenges and propose that brain-body interactions in pain can be leveraged in order to improve tests for pain specificity. We review brain and bodily responses to pain and nociception and extant efforts toward identifying pain-specific brain networks. We propose that autonomic nervous system activity should be used as a surrogate measure of salience and arousal to improve these efforts and enable researchers to parse out pain-specific responses in the brain, and demonstrate the feasibility of this approach using example fMRI data from a thermal pain paradigm. This new approach will improve the accuracy and specificity of functional neuroimaging analyses and help to overcome current difficulties in assessing pain specific responses in the human brain.

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

One of the key ambitions of neuroimaging-based pain biomarker research is to augment patient and clinician reporting of clinically relevant phenomena with neural measures for prediction, prognosis, and detection of pain. Despite years of productive research on the neuroimaging of pain, such applications have seen little advancement. However, recent developments in identifying brain-based biomarkers of pain through advances in technology and multivariate pattern analysis provide some optimism. Here, we (1) define and review the different types of potential neuroimaging-based biomarkers, their clinical and research applications, and their limitations and (2) describe frameworks for evaluation of pain biomarkers used in other fields (eg, genetics, cancer, cardiovascular disease, immune system disorders, and rare diseases) to achieve broad clinical and research utility and minimize the risks of misapplication of this emerging technology. To conclude, we discuss future directions for neuroimaging-based biomarker research to achieve the goal of personalized pain medicine.

Delineating conditions and subtypes in chronic pain using neuroimaging

Differentiating subtypes of chronic pain still remains a challenge—both from a subjective and objective point of view. Personalized medicine is the current goal of modern medical care and is limited by the subjective nature of patient self-reporting of symptoms and behavioral evaluation. Physiology-focused techniques such as genome and epigenetic analyses inform the delineation of pain groups; however, except under rare circumstances, they have diluted effects that again, share a common reliance on behavioral evaluation. The application of structural neuroimaging towards distinguishing pain subtypes is a growing field and may inform pain-group classification through the analysis of brain regions showing hypertrophic and atrophic changes in the presence of pain. Analytical techniques such as machine-learning classifiers have the capacity to process large volumes of data and delineate diagnostically relevant information from neuroimaging analysis. The issue of defining a “brain type” is an emerging field aimed at interpreting observed brain changes and delineating their clinical identity/significance. In this review, 2 chronic pain conditions (migraine and irritable bowel syndrome) with similar clinical phenotypes are compared in terms of their structural neuroimaging findings. Independent investigations are compared with findings from application of machine-learning algorithms. Findings are discussed in terms of differentiating patient subgroups using neuroimaging data in patients with chronic pain and how they may be applied towards defining a personalized pain signature that helps segregate patient subgroups (eg, migraine with and without aura, with or without nausea; irritable bowel syndrome vs other functional gastrointestinal disorders).

The search for pain biomarkers in the human brain

Non-invasive functional brain imaging is used more than ever to investigate pain in health and disease, with the prospect of finding new means to alleviate pain and improve patient wellbeing. The observation that several brain areas are activated by transient painful stimuli, and that the magnitude of this activity is often graded with pain intensity, has prompted researchers to extract features of brain activity that could serve as biomarkers to measure pain objectively. However, most of the brain responses observed when pain is present can also be observed when pain is absent. For example, similar brain responses can be elicited by salient but non-painful auditory, tactile and visual stimuli, and such responses can even be recorded in patients with congenital analgesia. Thus, as argued in this review, there is still disagreement on the degree to which current measures of brain activity exactly relate to pain. Furthermore, whether more recent analysis techniques can be used to identify distributed patterns of brain activity specific for pain can be only warranted using carefully designed control conditions. On a more general level, the clinical utility of current pain biomarkers derived from human functional neuroimaging appears to be overstated, and evidence for their efficacy in real-life clinical conditions is scarce. Rather than searching for biomarkers of pain perception, several researchers are developing biomarkers to achieve mechanism-based stratification of pain conditions, predict response to medication and offer personalized treatments. Initial results with promising clinical perspectives need to be further tested for replicability and generalizability.

Neuroimaging the pain network - Implications for treatment

In this chapter, we provide an overview of neuroimaging studies in chronic pain. We start with an introduction about the phenomenology of pain. In the following section, the application of functional and structural imaging techniques is shown in selected chronic pain syndromes (chronic back pain, fibromyalgia syndrome (FMS), phantom limb pain, and complex regional pain syndrome (CRPS)), and commonalities and peculiarities of imaging correlates across different types of chronic pain are discussed. We conclude this chapter with implications for treatments, with focus on behavioral interventions, sensory and motor trainings, and mirror and motor imagery trainings.

Within- and Between-Session Prefrontal Cortex Response to Virtual Reality Exposure Therapy for Acrophobia

Exposure Therapy (ET) has demonstrated its efficacy in the treatment of phobias, anxiety and Post-traumatic Stress Disorder (PTSD), however, it suffers a high drop-out rate because of too low or too high patient engagement in treatment. Virtual Reality Exposure Therapy (VRET) is comparably effective regarding symptom reduction and offers an alternative tool to facilitate engagement for avoidant participants. Neuroimaging studies have demonstrated that both ET and VRET normalize brain activity within a fear circuit. However, previous studies have employed brain imaging technology which restricts people's movement and hides their body, surroundings and therapist from view. This is at odds with the way engagement is typically controlled. We used a novel combination of neural imaging and VR technology-Functional Near-Infrared Spectroscopy (fNIRS) and Immersive Projection Technology (IPT), to avoid these limitations. Although there are a few studies that have investigated the effect of VRET on a brain function after the treatment, the present study utilized technologies which promote ecological validity to measure brain changes after VRET treatment. Furthermore, there are no studies that have measured brain activity within VRET session. In this study brain activity within the prefrontal cortex (PFC) was measured during three consecutive exposure sessions. N = 13 acrophobic volunteers were asked to walk on a virtual plank with a 6 m drop below. Changes in oxygenated (HbO) hemoglobin concentrations in the PFC were measured in three blocks using fNIRS. Consistent with previous functional magnetic resonance imaging (fMRI) studies, the analysis showed decreased activity in the DLPFC and MPFC during first exposure. The activity increased toward normal across three sessions. The study demonstrates potential efficacy of a method for measuring within-session neural response to virtual stimuli that could be replicated within clinics and research institutes, with equipment better suited to an ET session and at fraction of the cost, when compared to fMRI. This has application in widening access to, and increasing ecological validity of, immersive neuroimaging across understanding, diagnosis, assessment and treatment of, a range of mental disorders such as phobia, anxiety and PTSD or addictions.

Discordant Relationship Between Evaluation of Facial Expression and Subjective Pain Rating Due to the Low Pain Magnitude

Introduction

Facial expression to pain is an important pain indicator; however, facial movements look unresponsive when perceiving mild pain. The present study investigates whether pain magnitude modulates the relationship between subjective pain rating and an observer's evaluation of facial expression.

Methods

Twelve healthy volunteers were recruited to obtain 108 samples for pain rating with Visual Analogue Scale (VAS). Subjects underwent three different mechanical painful stimuli (monofilament forces of 100 g, 300 g, and 600 g) over three sessions and their facial expressions were videotaped throughout all sessions. Three observers independently evaluated facial expression of the subjects with a four-point categorical scale (no pain, mild pain, moderate pain, and severe pain). The correlations between subjective pain ratings and the evaluation of facial expression were analyzed in dichotomous group which was low pain ratings (VAS<30), or high pain rating (VAS≥30).

Results

Subjective pain ratings was significantly correlated with the evaluation of facial expression in high pain ratings, however no correlation was found between them in mild pain ratings. In mild pain ratings, most of the subjects (78%) were rated as no pain by observers, despite the fact that subjects reported pain.

Conclusion

The results suggest that the evaluation of facial expression of pain was difficult for the observer to detect pain severity when the subjects feel mild pain.

Unraveling the Mechanisms of Manual Therapy: Modeling an Approach

Synopsis Manual therapy interventions are popular among individual health care providers and their patients; however, systematic reviews do not strongly support their effectiveness. Small treatment effect sizes of manual therapy interventions may result from a "one-size-fits-all" approach to treatment. Mechanistic-based treatment approaches to manual therapy offer an intriguing alternative for identifying patients likely to respond to manual therapy. However, the current lack of knowledge of the mechanisms through which manual therapy interventions inhibit pain limits such an approach. The nature of manual therapy interventions further confounds such an approach, as the related mechanisms are likely a complex interaction of factors related to the patient, the provider, and the environment in which the intervention occurs. Therefore, a model to guide both study design and the interpretation of findings is necessary. We have previously proposed a model suggesting that the mechanical force from a manual therapy intervention results in systemic neurophysiological responses leading to pain inhibition. In this clinical commentary, we provide a narrative appraisal of the model and recommendations to advance the study of manual therapy mechanisms. J Orthop Sports Phys Ther 2018;48(1):8-18. doi:10.2519/jospt.2018.7476.

Biomarkers for Musculoskeletal Pain Conditions: Use of Brain Imaging and Machine Learning

Chronic musculoskeletal pain condition often shows poor correlations between tissue abnormalities and clinical pain. Therefore, classification of pain conditions like chronic low back pain, osteoarthritis, and fibromyalgia depends mostly on self report and less on objective findings like X-ray or magnetic resonance imaging (MRI) changes. However, recent advances in structural and functional brain imaging have identified brain abnormalities in chronic pain conditions that can be used for illness classification. Because the analysis of complex and multivariate brain imaging data is challenging, machine learning techniques have been increasingly utilized for this purpose. The goal of machine learning is to train specific classifiers to best identify variables of interest on brain MRIs (i.e., biomarkers). This report describes classification techniques capable of separating MRI-based brain biomarkers of chronic pain patients from healthy controls with high accuracy (70-92%) using machine learning, as well as critical scientific, practical, and ethical considerations related to their potential clinical application. Although self-report remains the gold standard for pain assessment, machine learning may aid in the classification of chronic pain disorders like chronic back pain and fibromyalgia as well as provide mechanistic information regarding their neural correlates.

Brain activations during pain: a neuroimaging meta-analysis of patients with pain and healthy controls

In response to recent publications from pain neuroimaging experiments, there has been a debate about the existence of a primary pain region in the brain. Yet, there are few meta-analyses providing assessments of the minimum cerebral denominators of pain. Here, we used a statistical meta-analysis method, called activation likelihood estimation, to define (1) core brain regions activated by pain per se, irrelevant of pain modality, paradigm, or participants and (2) activation likelihood estimation commonalities and differences between patients with chronic pain and healthy individuals. A subtraction analysis of 138 independent data sets revealed that the minimum denominator for activation across pain modalities and paradigms included the right insula, secondary sensory cortex, and right anterior cingulate cortex (ACC). Common activations for healthy subjects and patients with pain alike included the thalamus, ACC, insula, and cerebellum. A comparative analysis revealed that healthy individuals were more likely to activate the cingulum, thalamus, and insula. Our results point toward the central role of the insular cortex and ACC in pain processing, irrelevant of modality, body part, or clinical experience; thus, furthering the importance of ACC and insular activation as key regions for the human experience of pain.

Measuring Pain for Patients Seeking Physical Therapy: Can Functional Magnetic Resonance Imaging (fMRI) Help?

In the multidisciplinary fields of pain medicine and rehabilitation, advancing techniques such as functional magnetic resonance imaging (fMRI) are used to enhance our understanding of the pain experience. Given that such measures, in some circles, are expected to help us understand the brain in pain, future research in pain measurement is undeniably rich with possibility. However, pain remains intensely personal and represents a multifaceted experience, unique to each individual; no single measure in isolation, fMRI included, can prove or quantify its magnitude beyond the patient self-report. Physical therapists should be aware of cutting-edge advances in measuring the patient's pain experience, and they should work closely with professionals in other disciplines (eg, magnetic resonance physicists, biomedical engineers, radiologists, psychologists) to guide the exploration and development of multimodal pain measurement and management on a patient-by-patient basis. The primary purpose of this perspective article is to provide a brief overview of fMRI and inform physical therapist clinicians of the pros and cons when utilized as a measure of the patient's perception of pain. A secondary purpose is to describe current known factors that influence the quality of fMRI data and its analyses, as well as the potential for future clinical applications relevant to physical therapist practice. Lastly, the interested reader is introduced and referred to existing guidelines and recommendations for reporting fMRI research.

Higher pain rating results in lower variability of somatosensory cortex activation by painful mechanical stimuli: An fMRI study

OBJECTIVE

The aim of this study was to find pain-related brain activity which corresponds to self-report pain ratings based on degree of response and repeatability.

METHODS

Three painful mechanical stimuli were applied to the right hands of 25 healthy volunteers using monofilaments (forces of 0.98N, 2.94N, and 5.88N). Simultaneously, brain activities were evaluated using functional MRI for a constant stimulus conducted three times in a session. In first assessment, the average percent signal change (PSC) of neuronal response was measured for each region of interest (ROI), secondary repeatability of PSC conducted three times over the session was evaluated for each ROI.

RESULTS

Although the average PSCs for trice stimuli conducted in one session increased in accordance with pain ratings in the somatosensory cortex (S1) and anterior cingulate cortex (ACC), there was a different response between S1 and ACC when subjects rated intense pain; a stable response in S1 against a variable response in ACC.

CONCLUSIONS

These results imply that there are different cognitive responses between sensory discrimination and affective component to constant painful stimulus each time.

SIGNIFICANCE

Consistency of brain activity based on PSC may be an important biomarker which, along with its neuronal activity, gauges self-report pain ratings.

Tracking local anesthetic effects using a novel perceptual reference approach

Drug effects of loco-regional anesthetics are commonly measured by unidimensional pain rating scales. These scales require subjects to transform their perceptual correlates of stimulus intensities onto a visual, verbal, or numerical construct that uses a unitless cognitive reference frame. The conceptual understanding and execution of this magnitude estimation task may vary among individuals and populations. To circumvent inherent shortcomings of conventional experimental pain scales, this study used a novel perceptual reference approach to track subjective sensory perceptions during onset of an analgesic nerve block. In 34 male subjects, nociceptive electric stimuli of 1-ms duration were repetitively applied to left (target) and right (reference) mandibular canines every 5 s for 600 s, with a side latency of 1 ms. Stimulus strength to the target canine was programmed to evoke a tolerable pain intensity perception and remained constant at this level throughout the experiment. A dose of 0.6 ml of articaine 4% was submucosally injected at the left mental foramen. Subjects then reported drug effects by adjusting the stimulus strength (in milliamperes) to the reference tooth, so that the perceived intensity in the reference tooth was equi-intense to the target tooth. Pain and stimulus perception offsets were indicated by subjects. Thus, the current approach for matching the sensory experience in one anatomic location after regional anesthesia allows detailed tracking of evolving perceptual changes in another location. This novel perceptual reference approach facilitates direct and accurate quantification of analgesic effects with high temporal resolution. We propose using this method for future experimental investigations of analgesic/anesthetic drug efficacy.

Local Pain Dynamics during Constant Exhaustive Exercise

The purpose of this study was to delineate the topological dynamics of pain and discomfort during constant exercise performed until volitional exhaustion. Eleven physical education students were tested while cycling and running at a “hard” intensity level (e.g., corresponding to Borg’s RPE (6–20) = 15). During the tests, participants reported their discomfort and pain on a body map every 15s. “Time on task” for each participant was divided into five equal non-overlapping temporal windows within which their ratings were considered for analysis. The analyses revealed that the number of body locations with perceived pain and discomfort increased throughout the five temporal windows until reaching the mean (± SE) values of 4.2 ± 0.7 and 4.1 ± 0.6 in cycling and running, respectively. The dominant locations included the quadriceps and hamstrings during cycling and quadriceps and chest during running. In conclusion, pain seemed to spread throughout the body during constant cycling and running performed up to volitional exhaustion with differences between cycling and running in the upper body but not in the lower body dynamics.

Comparison of machine classification algorithms for fibromyalgia: neuroimages versus self-report

Recent studies have posited that machine learning (ML) techniques accurately classify individuals with and without pain solely based on neuroimaging data. These studies claim that self-report is unreliable, making "objective" neuroimaging classification methods imperative. However, the relative performance of ML on neuroimaging and self-report data have not been compared. This study used commonly reported ML algorithms to measure differences between "objective" neuroimaging data and "subjective" self-report (ie, mood and pain intensity) in their ability to discriminate between individuals with and without chronic pain. Structural magnetic resonance imaging data from 26 individuals (14 individuals with fibromyalgia and 12 healthy controls) were processed to derive volumes from 56 brain regions per person. Self-report data included visual analog scale ratings for pain intensity and mood (ie, anger, anxiety, depression, frustration, and fear). Separate models representing brain volumes, mood ratings, and pain intensity ratings were estimated across several ML algorithms. Classification accuracy of brain volumes ranged from 53 to 76%, whereas mood and pain intensity ratings ranged from 79 to 96% and 83 to 96%, respectively. Overall, models derived from self-report data outperformed neuroimaging models by an average of 22%. Although neuroimaging clearly provides useful insights for understanding neural mechanisms underlying pain processing, self-report is reliable and accurate and continues to be clinically vital.

PERSPECTIVE

The present study compares neuroimaging, self-reported mood, and self-reported pain intensity data in their ability to classify individuals with and without fibromyalgia using ML algorithms. Overall, models derived from self-reported mood and pain intensity data outperformed structural neuroimaging models.

How do babies feel pain?

Functional MRI studies suggest that healthy full-term newborn babies experience some aspects of pain in a similar way to adults.

Pharmacologic modulation of hand pain in osteoarthritis: a double-blind placebo-controlled functional magnetic resonance imaging study using naproxen

OBJECTIVE

In an attempt to shed light on management of chronic pain conditions, there has long been a desire to complement behavioral measures of pain perception with measures of underlying brain mechanisms. Using functional magnetic resonance imaging (fMRI), we undertook this study to investigate changes in brain activity following the administration of naproxen or placebo in patients with pain related to osteoarthritis (OA) of the carpometacarpal (CMC) joint.

METHODS

A placebo-controlled, double-blind, 2-period crossover study was performed in 19 individuals with painful OA of the CMC joint of the right hand. Following placebo or naproxen treatment periods, a functionally relevant task was performed, and behavioral measures of the pain experience were collected in identical fMRI examinations. Voxelwise and a priori region of interest analyses were performed to detect between-period differences in brain activity.

RESULTS

Significant reductions in brain activity following treatment with naproxen, compared to placebo, were observed in brain regions commonly associated with pain perception, including the bilateral primary somatosensory cortex, thalamus, and amygdala. Significant relationships between changes in perceived pain intensity and changes in brain activity were also observed in brain regions previously associated with pain intensity.

CONCLUSION

This study demonstrates the sensitivity of fMRI to detect the mechanisms underlying treatments of known efficacy. The data illustrate the enticing potential of fMRI as an adjunct to self-report for detecting early signals of efficacy of novel therapies, both pharmacologic and nonpharmacologic, in small numbers of individuals with persistent pain.

Neurophysiology of the esophagus

The following, from the 12th OESO World Conference: Cancers of the Esophagus, includes commentaries on the methods and characteristics of esophageal afferents in humans; the pitfalls in characterization of mechanosensitive afferents; the sensitization of esophageal afferents in human studies; the brain source modeling in the understanding of the esophagus-brain axis; the use of evoked brain potentials in the esophagus; and measuring descending inhibition in animal and human studies.

The Psychological Evaluation of Patients with Chronic Pain: a Review of BHI 2 Clinical and Forensic Interpretive Considerations

Pain is the most common reason why patients see a physician. Within the USA, it has been estimated that at least 116 million US adults suffer from chronic pain, with an estimated annual national economic cost of $560-635 billion. While pain is in part a sensory process, like sight, touch, or smell, pain is also in part an emotional experience, like depression, anxiety, or anger. Thus, chronic pain is arguably the quintessential biopsychosocial condition. Due to the overwhelming evidence of the biopsychosocial nature of pain and the value of psychological assessments, the majority of chronic pain guidelines recommend a psychological evaluation as an integral part of the diagnostic workup. One biopsychosocial inventory designed for the assessment of patients with chronic pain is the Battery for Health Improvement 2 (BHI 2). The BHI 2 is a standardized psychometric measure, with three validity measures, 16 clinical scales, and a multidimensional assessment of pain. This article will review how the BHI 2 was developed, BHI 2 concepts, validation research, and an overview of the description and interpretation of its scales. Like all measures, the BHI 2 has strengths and weaknesses of which the forensic psychologist should be aware, and particular purposes for which it is best suited. Guided by that knowledge, the BHI 2 can play a useful role in the forensic psychologist's toolbox.

Multivariate decoding of cerebral blood flow measures in a clinical model of on-going postsurgical pain

Recent reports of multivariate machine learning (ML) techniques have highlighted their potential use to detect prognostic and diagnostic markers of pain. However, applications to date have focussed on acute experimental nociceptive stimuli rather than clinically relevant pain states. These reports have coincided with others describing the application of arterial spin labeling (ASL) to detect changes in regional cerebral blood flow (rCBF) in patients with on-going clinical pain. We combined these acquisition and analysis methodologies in a well-characterized postsurgical pain model. The principal aims were (1) to assess the classification accuracy of rCBF indices acquired prior to and following surgical intervention and (2) to optimise the amount of data required to maintain accurate classification. Twenty male volunteers, requiring bilateral, lower jaw third molar extraction (TME), underwent ASL examination prior to and following individual left and right TME, representing presurgical and postsurgical states, respectively. Six ASL time points were acquired at each exam. Each ASL image was preceded by visual analogue scale assessments of alertness and subjective pain experiences. Using all data from all sessions, an independent Gaussian Process binary classifier successfully discriminated postsurgical from presurgical states with 94.73% accuracy; over 80% accuracy could be achieved using half of the data (equivalent to 15 min scan time). This work demonstrates the concept and feasibility of time-efficient, probabilistic prediction of clinically relevant pain at the individual level. We discuss the potential of ML techniques to impact on the search for novel approaches to diagnosis, management, and treatment to complement conventional patient self-reporting.

Test-retest reliability of pain-related brain activity in healthy controls undergoing experimental thermal pain

Although functional magnetic resonance imaging (fMRI) has been proposed as a method to elucidate pain-related biomarkers, little information exists related to psychometric properties of fMRI findings. This knowledge is essential for potential translation of this technology to clinical settings. The purpose of this study was to assess the test-retest reliability of pain-related brain activity and how it compares to the reliability of self-report. Twenty-two healthy controls (mean age = 22.6 years, standard deviation = 2.9) underwent 3 runs of an fMRI paradigm that used thermal stimuli to elicit experimental pain. Functional MRI summary statistics related to brain activity during thermal stimulation periods were extracted from bilateral anterior cingulate cortices and anterior insula. Intraclass correlations (ICCs) were conducted on these summary statistics and generally showed "good" test-retest reliability in all regions of interest (ICC range = .32-.88; mean = .71); however, these results did not surpass ICC values from pain ratings, which fell within the "excellent" range (ICC range = .93-.96; mean = .94). Findings suggest that fMRI is a valuable tool for measuring pain mechanisms but did not show an adequate level of test-retest reliability for fMRI to potentially act as a surrogate for individuals' self-report of pain.

PERSPECTIVE

This study is one of the first reports to demonstrate the test-retest reliability of fMRI findings related to pain processing and provides a comparison to the reliability of subjective reports of pain. This information is essential for determining whether fMRI technology should be potentially translated for clinical use.

A tool for classifying individuals with chronic back pain: using multivariate pattern analysis with functional magnetic resonance imaging data

Chronic pain is one of the most prevalent health problems in the world today, yet neurological markers, critical to diagnosis of chronic pain, are still largely unknown. The ability to objectively identify individuals with chronic pain using functional magnetic resonance imaging (fMRI) data is important for the advancement of diagnosis, treatment, and theoretical knowledge of brain processes associated with chronic pain. The purpose of our research is to investigate specific neurological markers that could be used to diagnose individuals experiencing chronic pain by using multivariate pattern analysis with fMRI data. We hypothesize that individuals with chronic pain have different patterns of brain activity in response to induced pain. This pattern can be used to classify the presence or absence of chronic pain. The fMRI experiment consisted of alternating 14 seconds of painful electric stimulation (applied to the lower back) with 14 seconds of rest. We analyzed contrast fMRI images in stimulation versus rest in pain-related brain regions to distinguish between the groups of participants: 1) chronic pain and 2) normal controls. We employed supervised machine learning techniques, specifically sparse logistic regression, to train a classifier based on these contrast images using a leave-one-out cross-validation procedure. We correctly classified 92.3% of the chronic pain group (N = 13) and 92.3% of the normal control group (N = 13) by recognizing multivariate patterns of activity in the somatosensory and inferior parietal cortex. This technique demonstrates that differences in the pattern of brain activity to induced pain can be used as a neurological marker to distinguish between individuals with and without chronic pain. Medical, legal and business professionals have recognized the importance of this research topic and of developing objective measures of chronic pain. This method of data analysis was very successful in correctly classifying each of the two groups.