Brain Imaging Biomarkers for Chronic Pain

Morphological and functional brain changes in chronic cancer-related pain: A systematic review

The purpose of this study was to perform a systematic review of the available literature on morphological and functional brain changes measured by modern neuroimaging techniques in patients suffering from chronic cancer-related pain. A systematic search was conducted in PubMed, Embase, and Web of Science using different keyword combinations. In addition, a hand search was performed on the reference lists and several databases to retrieve supplementary primary studies. Eligible articles were assessed for methodological quality and risk of bias and reviewed by two independent researchers. The search yielded only four studies, three of which used MRI and one PET-CT. None of the studies measured longitudinal morphological (i.e., gray or white matter) changes. All studies investigated functional brain changes and found differences in specific brain regions and networks between patients with chronic cancer-related pain and pain-free cancer patients or healthy volunteers. Some of these alterations were found in brain networks that also show changes in non-cancer populations with chronic pain (e.g., the default mode network and salience network). However, specific findings were inconsistent, and there was substantial variation in imaging methodology, analysis, sample size, and study quality. There is a striking lack of research on morphological brain changes in patients with chronic cancer-related pain. Moreover, only a few studies investigated functional brain changes. In the retrieved studies, there is some evidence that alterations occur in brain networks also involved in other chronic non-cancer pain syndromes. However, the low sample sizes of the studies, finding inconsistencies, and methodological heterogeneity do not allow for robust conclusions.

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.

Differences in symptom experience among patients with systemic sclerosis: a cluster analytic approach to identifying subgroups

OBJECTIVES

Symptoms of people who have SSc are heterogeneous and difficult to address clinically. Because diverse symptoms often co-occur and may share common underlying mechanisms, identifying symptoms that cluster together may better target treatment approaches. We sought to identify and characterize patient subgroups based on symptom experience.

METHODS

An exploratory hierarchical agglomerative cluster analysis was conducted to identify subgroups from a large SSc cohort from a single US academic medical centre. Patient-reported symptoms of pain interference, fatigue, sleep disturbance, dyspnoea, depression and anxiety were used for clustering. A multivariate analysis of variance (MANOVA) was used to examine the relative contribution of each variable across subgroups. Analyses of variance were performed to determine participant characteristics based on subgroup assignment. Presence of symptom clusters were tallied within subgroup.

RESULTS

Participants (n = 587; 84% female, 41% diffuse cutaneous subtype, 59% early disease) divided into three subgroups via cluster analysis based on symptom severity: (i) no/minimal, (ii) mild, and (iii) moderate. Participants in mild and moderate symptoms subgroups had similar disease severity, but different symptom presentation. In the mild symptoms subgroup, pain, fatigue and sleep disturbance was the main symptom cluster. Participants in the moderate symptoms subgroup were characterized by co-occurring pain, fatigue, sleep disturbance, depression and anxiety.

CONCLUSION

Identification of distinct symptom clusters, particularly among SSc patients who experience mild and moderate symptoms, suggests potential differences in treatment approach and in mechanisms underlying symptom experience that require further study.

Heart rate variability is not suitable as surrogate marker for pain intensity in patients with chronic pain

ABSTRACT

The search towards more objective outcome measurements and consequently surrogate markers for pain started decades ago; however, no generally accepted biomarker for pain has qualified yet. The goal is to explore the value of heart rate variability (HRV) as surrogate marker for pain intensity chronic pain setting. Pain intensity scores and HRV were collected in 366 patients with chronic pain, through a cross-sectional multicenter study. Pain intensity was measured with both the Visual Analogue Scale and Numeric Rating Scale, while 16 statistical HRV parameters were derived. Canonical correlation analysis was performed to evaluate the correlation between the dependent pain variables and the HRV parameters. Surrogacy was determined for each HRV parameter with point estimates between 0 and 1 whereby values close to 1 indicate a strong association between the surrogate and the true endpoint at the patient level. Weak correlations were revealed between HRV parameters and pain intensity scores. The highest surrogacy point estimate was found for mean heart rate as marker for average pain intensity on the Numeric Rating Scale with point estimates of 0.0961 (95% CI from 0.0384 to 0.1537) and 0.0209 (95% CI from 0 to 0.05) for patients without medication use, and medication use respectively. This study indicated that HRV parameters as separate entities are no suitable surrogacy candidates for pain intensity, in a population of chronic pain patients. Further potential surrogate candidates and clinical robust true endpoints should be explored, in order to find a surrogate measure for the highly individual pain experience.

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.

The Roles of Imaging Biomarkers in the Management of Chronic Neuropathic Pain

Chronic neuropathic pain (CNP) affects around 10% of the general population and has a significant social, emotional, and economic impact. Current diagnosis techniques rely mainly on patient-reported outcomes and symptoms, which leads to significant diagnostic heterogeneity and subsequent challenges in management and assessment of outcomes. As such, it is necessary to review the approach to a pathology that occurs so frequently, with such burdensome and complex implications. Recent research has shown that imaging methods can detect subtle neuroplastic changes in the central and peripheral nervous system, which can be correlated with neuropathic symptoms and may serve as potential markers. The aim of this paper is to review available imaging methods used for diagnosing and assessing therapeutic efficacy in CNP for both the preclinical and clinical setting. Of course, further research is required to standardize and improve detection accuracy, but available data indicate that imaging is a valuable tool that can impact the management of CNP.

Topology of pain networks in patients with temporomandibular disorder and pain-free controls with and without concurrent experimental pain: A pilot study

Temporomandibular disorders (TMD) involve chronic pain in the masticatory muscles and jaw joints, but the mechanisms underlying the pain are heterogenous and vary across individuals. In some cases, structural, functional, and metabolic changes in the brain may underlie the condition. In the present study, we evaluated the functional connectivity between 86 regions of interest (ROIs), which were chosen based on previously reported neuroimaging studies of pain and differences in brain morphology identified in an initial surface-based morphometry analysis. Our main objectives were to investigate the topology of the network formed by these ROIs and how it differs between individuals with TMD and chronic pain (n = 16) and pain-free control participants (n = 12). In addition to a true resting state functional connectivity scan, we also measured functional connectivity during a 6-min application of a noxious cuff stimulus applied to the left leg. Our principal finding is individuals with TMD exhibit more suprathreshold correlations (higher nodal degree) among all ROIs but fewer "hub" nodes (i.e., decreased betweenness centrality) across conditions and across all pain pathways. These results suggest is this pain-related network of nodes may be "over-wired" in individuals with TMD and chronic pain compared to controls, both at rest and during experimental pain.

The missing mechanistic link: Improving behavioral treatment efficacy for pediatric chronic pain

Pediatric chronic pain is a significant global issue, with biopsychosocial factors contributing to the complexity of the condition. Studies have explored behavioral treatments for pediatric chronic pain, but these treatments have mixed efficacy for improving functional and psychological outcomes. Furthermore, the literature lacks an understanding of the biobehavioral mechanisms contributing to pediatric chronic pain treatment response. In this mini review, we focus on how neuroimaging has been used to identify biobehavioral mechanisms of different conditions and how this modality can be used in mechanistic clinical trials to identify markers of treatment response for pediatric chronic pain. We propose that mechanistic clinical trials, utilizing neuroimaging, are warranted to investigate how to optimize the efficacy of behavioral treatments for pediatric chronic pain patients across pain types and ages.