Global disruption of degree rank order: a hallmark of chronic pain

Immersive virtual reality-based rehabilitation for subacute stroke: a randomized controlled trial

OBJECTIVE

Few effective treatments improve upper extremity (UE) function after stroke. Immersive virtual reality (imVR) is a novel and promising strategy for stroke UE recovery. We assessed the extent to which imVR-based UE rehabilitation can augment conventional treatment and explored changes in brain functional connectivity (FC) that were related to the rehabilitation.

METHODS

An assessor-blinded, parallel-group randomized controlled trial was performed with 40 subjects randomly assigned to either imVR or Control group (1:1 allocation), each receiving rehabilitation 5 times per week for 3 weeks. Subjects in the imVR received both imVR and conventional rehabilitation, while those in the Control received conventional rehabilitation only. Our primary and secondary outcomes were the Fugl-Meyer assessment's upper extremity subscale (FMA-UE) and the Barthel Index (BI), respectively. Both intention-to-treat (ITT) and per-protocol (PP) analyses were performed to assess the effectiveness of the trial. For both the FMA-UE/BI, a one-way analysis of covariance (ANCOVA) model was used, with the FMA-UE/BI at post-intervention or at follow-up, respectively, as the dependent variable, the two groups as the independent variable, baseline FMA-UE/BI, age, sex, site, time since onset, hypertension and diabetes as covariates.

RESULTS

Both ITT and PP analyses demonstrated the effectiveness of imVR-based rehabilitation. The FMA-UE score was greater in the imVR compared with the Control at the post-intervention (mean difference: 9.1 (95% CI 1.6, 16.6); P = 0.019) and follow-up (mean difference:11.5 (95% CI 1.9, 21.0); P = 0.020). The results were consistent for BI scores. Moreover, brain FC analysis found that the motor function improvements were associated with a change in degree in ipsilesional premotor cortex and ipsilesional dorsolateral prefrontal cortex immediately following the intervention and in ipsilesional visual region and ipsilesional middle frontal gyrus after the 12-week follow-up.

CONCLUSIONS

ImVR-based rehabilitation is an effective tool that can improve the recovery of UE functional capabilities of subacute stroke patients when added to standard care. These improvements were associated with distinctive brain changes at two post-stroke timepoints. The study results will benefit future patients with stroke and provide evidence for a promising new method of stroke rehabilitation.

TRIAL REGISTRATION

ClinicalTrials.gov identifier: NCT03086889.

Adolescents' pain-related ontogeny shares a neural basis with adults' chronic pain in basothalamo-cortical organization

During late adolescence, the brain undergoes ontogenic organization altering subcortical-cortical circuitry. This includes regions implicated in pain chronicity, and thus alterations in the adolescent ontogenic organization could predispose to pain chronicity in adulthood - however, evidence is lacking. Using resting-state functional magnetic resonance imaging from a large European longitudinal adolescent cohort and an adult cohort with and without chronic pain, we examined links between painful symptoms and brain connectivity. During late adolescence, thalamo-, caudate-, and red nucleus-cortical connectivity were positively and subthalamo-cortical connectivity negatively associated with painful symptoms. Thalamo-cortical connectivity, but also subthalamo-cortical connectivity, was increased in adults with chronic pain compared to healthy controls. Our results indicate a shared basis in basothalamo-cortical circuitries between adolescent painful symptomatology and adult pain chronicity, with the subthalamic pathway being differentially involved, potentially due to a hyperconnected thalamo-cortical pathway in chronic pain and ontogeny-driven organization. This can inform neuromodulation-based prevention and early intervention.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we employ graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Within this network, 63% of brain areas share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes that have similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain that processes nociceptive information. Furthermore, the vulnerability of this network to attack presents the possibility of alleviating pain by targeting the most connected brain areas in the network.

Subcortical brain anatomy as a potential biomarker of persistent pain after total knee replacement in osteoarthritis

ABSTRACT

The neural mechanisms for the persistence of pain after a technically successful arthroplasty in osteoarthritis (OA) remain minimally studied, and direct evidence of the brain as a predisposing factor for pain chronicity in this setting has not been investigated. We undertook this study as a first effort to identify presurgical brain and clinical markers of postarthroplasty pain in knee OA. Patients with knee OA (n = 81) awaiting total arthroplasty underwent clinical and psychological assessment and brain magnetic resonance imagining. Postoperative pain scores were measured at 6 months after surgery. Brain subcortical anatomic properties (volume and shape) and clinical indices were studied as determinants of postoperative pain. We show that presurgical subcortical volumes (bilateral amygdala, thalamus, and left hippocampus), together with shape deformations of the right anterior hippocampus and right amygdala, associate with pain persistence 6 months after surgery in OA. Longer pain duration, higher levels of presurgical anxiety, and the neuropathic character of pain were also prognostic of postsurgical pain outcome. Brain and clinical indices accounted for unique influences on postoperative pain. Our study demonstrates the presence of presurgical subcortical brain factors that relate to postsurgical persistence of OA pain. These preliminary results challenge the current dominant view that mechanisms of OA pain predominantly underlie local joint mechanisms, implying novel clinical management and treatment strategies.

Network targets for therapeutic brain stimulation: towards personalized therapy for pain

Precision neuromodulation of central brain circuits is a promising emerging therapeutic modality for a variety of neuropsychiatric disorders. Reliably identifying in whom, where, and in what context to provide brain stimulation for optimal pain relief are fundamental challenges limiting the widespread implementation of central neuromodulation treatments for chronic pain. Current approaches to brain stimulation target empirically derived regions of interest to the disorder or targets with strong connections to these regions. However, complex, multidimensional experiences like chronic pain are more closely linked to patterns of coordinated activity across distributed large-scale functional networks. Recent advances in precision network neuroscience indicate that these networks are highly variable in their neuroanatomical organization across individuals. Here we review accumulating evidence that variable central representations of pain will likely pose a major barrier to implementation of population-derived analgesic brain stimulation targets. We propose network-level estimates as a more valid, robust, and reliable way to stratify personalized candidate regions. Finally, we review key background, methods, and implications for developing network topology-informed brain stimulation targets for chronic pain.

Local brain oscillations and interregional connectivity differentially serve sensory and expectation effects on pain

Pain emerges from the integration of sensory information about threats and contextual information such as an individual's expectations. However, how sensory and contextual effects on pain are served by the brain is not fully understood so far. To address this question, we applied brief painful stimuli to 40 healthy human participants and independently varied stimulus intensity and expectations. Concurrently, we recorded electroencephalography. We assessed local oscillatory brain activity and interregional functional connectivity in a network of six brain regions playing key roles in the processing of pain. We found that sensory information predominantly influenced local brain oscillations. In contrast, expectations exclusively influenced interregional connectivity. Specifically, expectations altered connectivity at alpha (8 to 12 hertz) frequencies from prefrontal to somatosensory cortex. Moreover, discrepancies between sensory information and expectations, i.e., prediction errors, influenced connectivity at gamma (60 to 100 hertz) frequencies. These findings reveal how fundamentally different brain mechanisms serve sensory and contextual effects on pain.

Graph theory analysis reveals an assortative pain network vulnerable to attacks

The neural substrate of pain experience has been described as a dense network of connected brain regions. However, the connectivity pattern of these brain regions remains elusive, precluding a deeper understanding of how pain emerges from the structural connectivity. Here, we use graph theory to systematically characterize the architecture of a comprehensive pain network, including both cortical and subcortical brain areas. This structural brain network consists of 49 nodes denoting pain-related brain areas, linked by edges representing their relative incoming and outgoing axonal projection strengths. Sixty-three percent of brain areas in this structural pain network share reciprocal connections, reflecting a dense network. The clustering coefficient, a measurement of the probability that adjacent nodes are connected, indicates that brain areas in the pain network tend to cluster together. Community detection, the process of discovering cohesive groups in complex networks, successfully reveals two known subnetworks that specifically mediate the sensory and affective components of pain, respectively. Assortativity analysis, which evaluates the tendency of nodes to connect with other nodes with similar features, indicates that the pain network is assortative. Finally, robustness, the resistance of a complex network to failures and perturbations, indicates that the pain network displays a high degree of error tolerance (local failure rarely affects the global information carried by the network) but is vulnerable to attacks (selective removal of hub nodes critically changes network connectivity). Taken together, graph theory analysis unveils an assortative structural pain network in the brain processing nociceptive information, and the vulnerability of this network to attack opens up the possibility of alleviating pain by targeting the most connected brain areas in the network.

Influence of exercise on pain is associated with resting-state functional connections: A cross-sectional functional brain imaging study

Exercise is associated with lower prevalence and severity of pain, and is widely recommended for pain management. However, the mechanisms the exercise effect on pain remain unclear. In this study, we examined the association of exercise with pain and aimed to identify its neurobiological mediators. We utilized a baseline data of a clinical trial for people with low back pain. Participants reported pain intensity and exercise habit, as well as pain-related psychological and emotional assessments. We also obtained brain imaging data using a resting-state functional MRI and performed mediation analyses to identify brain regions mediating the exercise effect on pain. Forty-five people with low back pain (mean pain intensity = 59.6 and mean duration = 9.9 weeks) were included in this study. Participants with an exercise habit (n = 29) showed significant less pain compared to those without an exercise habit (n = 16). Mediation analysis using resting-state functional connectivity identified the left thalamus, right amygdala, and medial prefrontal cortex as statistical mediators of the exercise effect on pain (indirect effect = -0.460, 95% confidence interval = -0.767 to -0.153). In conclusion, our findings suggest that brain function of the specific regions is probably a neuro-mechanism of exercise alleviating pain.

Brain Connectivity Predicts Chronic Pain in Acute Mild Traumatic Brain Injury

OBJECTIVES

Previous studies have established the role of the cortico-mesolimbic and descending pain modulation systems in chronic pain prediction. Mild traumatic brain injury (mTBI) is an acute pain model where chronic pain is prevalent and complicated for prediction. In this study, we set out to study whether functional connectivity (FC) of the nucleus accumbens (NAc) and the periaqueductal gray matter (PAG) is predictive of pain chronification in early-acute mTBI.

METHODS

To estimate FC, resting-state functional magnetic resonance imaging (fMRI) of 105 participants with mTBI following a motor vehicle collision was acquired within 72 hours post-accident. Participants were classified according to pain ratings provided at 12-months post-collision into chronic pain (head/neck pain ≥30/100, n = 44) and recovery (n = 61) groups, and their FC maps were compared.

RESULTS

The chronic pain group exhibited reduced negative FC between NAc and a region within the primary motor cortex corresponding with the expected representation of the area of injury. A complementary pattern was also demonstrated between PAG and the primary somatosensory cortex. PAG and NAc also shared increased FC to the rostral anterior cingulate cortex (rACC) within the recovery group. Brain connectivity further shows high classification accuracy (area under the curve [AUC] = .86) for future chronic pain, when combined with an acute pain intensity report.

INTERPRETATION

FC features obtained shortly after mTBI predict its transition to long-term chronic pain, and may reflect an underlying interaction of injury-related primary sensorimotor cortical areas with the mesolimbic and pain modulation systems. Our findings indicate a potential predictive biomarker and highlight targets for future early preventive interventions. ANN NEUROL 2022;92:819-833.

Reversal of neuropathic pain is associated with corticostriatal functional reorganization after nerve repair in the spared nerve injury model

ABSTRACT

Following surgical repair after peripheral nerve injury, neuropathic pain diminishes in most patients but can persist in a small proportion of cases, the mechanism of which remains poorly understood. Based on the spared nerve injury (SNI), we developed a rat nerve repair (NR) model, where a delayed reconstruction of the SNI-injured nerves resulted in alleviating chronic pain-like behavior only in a subpopulation of rats. Multiple behavioral measures were assayed over 11-week presurgery and postsurgery periods (tactile allodynia, pain prick responses, sucrose preference, motor coordination, and cold allodynia) in SNI (n = 10), sham (n = 8), and NR (n = 12) rats. All rats also underwent resting-state functional magnetic resonance imaging under anesthesia at multiple time points postsurgery, and at 10 weeks, histology and retrograde labeling were used to calculate peripheral reinnervation. Behavioral measures indicated that at approximately 5 weeks postsurgery, the NR group separated to pain persisting (NR persisting, n = 5) and recovering (NR recovering, n = 7) groups. Counts of afferent nerves and dorsal root ganglion cells were not different between NR groups. Therefore, NR group differences could not be explained by peripheral reorganization. By contrast, large brain functional connectivity differences were observed between NR groups, where corticolimbic reorganization paralleled with pain recovery (repeated-measures analysis of variance, false discovery rate, P < 0.05), and functional connectivity between accumbens and medial frontal cortex was related both to tactile allodynia (nociception) and to sucrose preference (anhedonia) in the NR group. Our study highlights the importance of brain circuitry in the reversal of neuropathic pain as a natural pain-relieving mechanism. Further studies regarding the therapeutic potentials of these processes are warranted.

Longitudinal resting-state electroencephalography in patients with chronic pain undergoing interdisciplinary multimodal pain therapy

ABSTRACT

Chronic pain is a major healthcare issue posing a large burden on individuals and society. Converging lines of evidence indicate that chronic pain is associated with substantial changes of brain structure and function. However, it remains unclear which neuronal measures relate to changes of clinical parameters over time and could thus monitor chronic pain and treatment responses. We therefore performed a longitudinal study in which we assessed clinical characteristics and resting-state electroencephalography data of 41 patients with chronic pain before and 6 months after interdisciplinary multimodal pain therapy. We specifically assessed electroencephalography measures that have previously been shown to differ between patients with chronic pain and healthy people. These included the dominant peak frequency; the amplitudes of neuronal oscillations at theta, alpha, beta, and gamma frequencies; as well as graph theory-based measures of brain network organization. The results show that pain intensity, pain-related disability, and depression were significantly improved after interdisciplinary multimodal pain therapy. Bayesian hypothesis testing indicated that these clinical changes were not related to changes of the dominant peak frequency or amplitudes of oscillations at any frequency band. Clinical changes were, however, associated with an increase in global network efficiency at theta frequencies. Thus, changes in chronic pain might be reflected by global network changes in the theta band. These longitudinal insights further the understanding of the brain mechanisms of chronic pain. Beyond, they might help to identify biomarkers for the monitoring of chronic pain.

Functional MRI in Radiology—A Personal Review

We, here, provide a personal review article on the development of a functional MRI in the radiology departments of two German university medicine units. Although the international community for human brain mapping has met since 1995, the researchers fascinated by human brain function are still young and innovative. However, the impact of functional magnetic resonance imaging (fMRI) on prognosis and treatment decisions is restricted, even though standardized methods have been developed. The tradeoff between the groundbreaking studies on brain function and the attempt to provide reliable biomarkers for clinical decisions is large. By describing some historical developments in the field of fMRI, from a personal view, the rise of this method in clinical neuroscience during the last 25 years might be understandable. We aim to provide some background for (a) the historical developments of fMRI, (b) the establishment of two research units for fMRI in the departments of radiology in Germany, and (c) a description of some contributions within the selected fields of systems neuroscience, clinical neurology, and behavioral psychology.

Long-term neuropathic pain behaviors correlate with synaptic plasticity and limbic circuit alteration: a comparative observational study in mice

ABSTRACT

Neuropathic pain has long-term consequences in affective and cognitive disturbances, suggesting the involvement of supraspinal mechanisms. In this study, we used the spared nerve injury (SNI) model to characterize the development of sensory and aversive components of neuropathic pain and to determine their electrophysiological impact across prefrontal cortex and limbic regions. Moreover, we evaluated the regulation of several genes involved in immune response and inflammation triggered by SNI. We showed that SNI led to sensorial hypersensitivity (cold and mechanical stimuli) and depressive-like behavior lasting 12 months after nerve injury. Of interest, changes in nonemotional cognitive tasks (novel object recognition and Y maze) showed in 1-month SNI mice were not evident normal in the 12-month SNI animals. In vivo electrophysiology revealed an impaired long-term potentiation at prefrontal cortex-nucleus accumbens core pathway in both the 1-month and 12-month SNI mice. On the other hand, a reduced neural activity was recorded in the lateral entorhinal cortex-dentate gyrus pathway in the 1-month SNI mice, but not in the 12-month SNI mice. Finally, we observed the upregulation of specific genes involved in immune response in the hippocampus of 1-month SNI mice, but not in the 12-month SNI mice, suggesting a neuroinflammatory response that may contribute to the SNI phenotype. These data suggest that distinct brain circuits may drive the psychiatric components of neuropathic pain and pave the way for better investigation of the long-term consequences of peripheral nerve injury for which most of the available drugs are to date unsatisfactory.

Functional brain mapping in patients with chronic back pain shows age-related differences

ABSTRACT

Low back pain is the most common pain condition and cause for disability in older adults. Older adults suffering from low back pain are more disabled than their healthy peers, are more predisposed to frailty, and tend to be undertreated. The cause of increased prevalence and severity of this chronic pain condition in older adults is unknown. Here, we draw on accumulating data demonstrating a critical role for brain limbic and sensory circuitries in the emergence and experience of chronic low back pain (CLBP) and the availability of resting-state brain activity data collected at different sites to study how brain activity patterns predictive of CLBP differ between age groups. We apply a data-driven multivariate searchlight analysis to amplitude of low-frequency fluctuation brain maps to classify patients with CLBP with >70% accuracy. We observe that the brain activity pattern including the paracingulate gyrus, insula/secondary somatosensory area, inferior frontal, temporal, and fusiform gyrus predicted CLBP. When separated by age groups, brain patterns predictive of older patients with CLBP showed extensive involvement of limbic brain areas including the ventromedial prefrontal cortex, the nucleus accumbens, and hippocampus, whereas only anterior insula paracingulate and fusiform gyrus predicted CLBP in the younger patients. In addition, we validated the relationships between back pain intensity ratings and CLBP brain activity patterns in an independent data set not included in our initial patterns' identification. Our results are the first to directly address how aging affects the neural signature of CLBP and point to an increased role of limbic brain areas in older patients with CLBP.

Altered resting-state functional connectivity within corticostriatal and subcortical-striatal circuits in chronic pain

Brain corticostriatal circuits are important for understanding chronic pain and highly relevant to motivation and cognitive processes. It has been demonstrated that in patients with chronic back pain, altered nucleus accumbens (NAcc)-medial prefrontal cortex (MPFC) circuit fMRI-based activity is predictive of patient outcome. We evaluated the NAcc-MPFC circuit in patients with another chronic pain condition, fibromyalgia, to extend these important findings. First, we compared fMRI-based NAcc-MPFC resting-state functional connectivity in patients with fibromyalgia (N = 32) vs. healthy controls (N = 37). Compared to controls, the NAcc-MPFC circuit's connectivity was significantly reduced in fibromyalgia. In addition, within the fibromyalgia group, NAcc-MPFC connectivity was significantly correlated with trait anxiety. Our expanded connectivity analysis of the NAcc to subcortical brain regions showed reduced connectivity of the right NAcc with mesolimbic circuit regions (putamen, thalamus, and ventral pallidum) in fibromyalgia. Lastly, in an exploratory analysis comparing our fibromyalgia and healthy control cohorts to a separate publicly available dataset from patients with chronic back pain, we identified reduced NAcc-MPFC connectivity across both the patient groups with unique alterations in NAcc-mesolimbic connectivity. Together, expanding upon prior observed alterations in brain corticostriatal circuits, our results provide novel evidence of altered corticostriatal and mesolimbic circuits in chronic pain.

Temporal Grading Index of Functional Network Topology Predicts Pain Perception of Patients With Chronic Back Pain

Chronic back pain (CBP) is a maladaptive health problem affecting the brain function and behavior of the patient. Accumulating evidence has shown that CBP may alter the organization of functional brain networks; however, whether the severity of CBP is associated with changes in dynamics of functional network topology remains unclear. Here, we generated dynamic functional networks based on resting-state functional magnetic resonance imaging (rs-fMRI) of 34 patients with CBP and 34 age-matched healthy controls (HC) in the OpenPain database via a sliding window approach, and extracted nodal degree, clustering coefficient (CC), and participation coefficient (PC) of all windows as features to characterize changes of network topology at temporal scale. A novel feature, named temporal grading index (TGI), was proposed to quantify the temporal deviation of each network property of a patient with CBP to the normal oscillation of the HCs. The TGI of the three features achieved outstanding performance in predicting pain intensity on three commonly used regression models (i.e., SVR, Lasso, and elastic net) through a 5-fold cross-validation strategy, with the minimum mean square error of 0.25 ± 0.05; and the TGI was not related to depression symptoms of the patients. Furthermore, compared to the HCs, brain regions that contributed most to prediction showed significantly higher CC and lower PC across time windows in the CBP cohort. These results highlighted spatiotemporal changes in functional network topology in patients with CBP, which might serve as a valuable biomarker for assessing the sensation of pain in the brain and may facilitate the development of CBP management/therapy approaches.

Assessing Pain Research: A Narrative Review of Emerging Pain Methods, Their Technosocial Implications, and Opportunities for Multidisciplinary Approaches

Pain research traverses many disciplines and methodologies. Yet, despite our understanding and field-wide acceptance of the multifactorial essence of pain as a sensory perception, emotional experience, and biopsychosocial condition, pain scientists and practitioners often remain siloed within their domain expertise and associated techniques. The context in which the field finds itself today-with increasing reliance on digital technologies, an on-going pandemic, and continued disparities in pain care-requires new collaborations and different approaches to measuring pain. Here, we review the state-of-the-art in human pain research, summarizing emerging practices and cutting-edge techniques across multiple methods and technologies. For each, we outline foreseeable technosocial considerations, reflecting on implications for standards of care, pain management, research, and societal impact. Through overviewing alternative data sources and varied ways of measuring pain and by reflecting on the concerns, limitations, and challenges facing the field, we hope to create critical dialogues, inspire more collaborations, and foster new ideas for future pain research methods.

A multistudy analysis reveals that evoked pain intensity representation is distributed across brain systems

Information is coded in the brain at multiple anatomical scales: locally, distributed across regions and networks, and globally. For pain, the scale of representation has not been formally tested, and quantitative comparisons of pain representations across regions and networks are lacking. In this multistudy analysis of 376 participants across 11 studies, we compared multivariate predictive models to investigate the spatial scale and location of evoked heat pain intensity representation. We compared models based on (a) a single most pain-predictive region or resting-state network; (b) pain-associated cortical-subcortical systems developed from prior literature ("multisystem models"); and (c) a model spanning the full brain. We estimated model accuracy using leave-one-study-out cross-validation (CV; 7 studies) and subsequently validated in 4 independent holdout studies. All spatial scales conveyed information about pain intensity, but distributed, multisystem models predicted pain 20% more accurately than any individual region or network and were more generalizable to multimodal pain (thermal, visceral, and mechanical) and specific to pain. Full brain models showed no predictive advantage over multisystem models. These findings show that multiple cortical and subcortical systems are needed to decode pain intensity, especially heat pain, and that representation of pain experience may not be circumscribed by any elementary region or canonical network. Finally, the learner generalization methods we employ provide a blueprint for evaluating the spatial scale of information in other domains.

Brain Functional Topology Alteration in Right Lateral Occipital Cortex Is Associated With Upper Extremity Motor Recovery

Stroke is a chief cause of sudden brain damage that severely disrupts the whole-brain network. However, the potential mechanisms of motor recovery after stroke are uncertain and the prognosis of poststroke upper extremity recovery is still a challenge. This study investigated the global and local topological properties of the brain functional connectome in patients with subacute ischemic stroke and their associations with the clinical measurements. A total of 57 patients, consisting of 29 left-sided and 28 right-sided stroke patients, and 32 age- and gender-matched healthy controls (HCs) were recruited to undergo a resting-state functional magnetic resonance imaging (rs-fMRI) study; patients were also clinically evaluated with the Upper Extremity Fugl-Meyer Assessment (FMA_UE). The assessment was repeated at 15 weeks to assess upper extremity functional recovery for the patient remaining in the study (12 left- 20 right-sided stroke patients). Global graph topological disruption indices of stroke patients were significantly decreased compared with HCs but these indices were not significantly associated with FMA_UE. In addition, local brain network structure of stroke patients was altered, and the altered regions were dependent on the stroke site. Significant associations between local degree and motor performance and its recovery were observed in the right lateral occipital cortex (R LOC) in the right-sided stroke patients. Our findings suggested that brain functional topologies alterations in R LOC are promising as prognostic biomarkers for right-sided subacute stroke. This cortical area might be a potential target to be further validated for non-invasive brain stimulation treatment to improve poststroke upper extremity recovery.

Scalp EEG-based Pain Detection using Convolutional Neural Network

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

Brain Imaging Biomarkers for Chronic Pain

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

Brain mechanisms of chronic pain: critical role of translational approach

Chronic pain is a leading cause of disability worldwide and its prevalence is likely to increase over the next decades. Treatment for chronic pain remains insufficient and therapeutical advances have not made comparable progress with that for many chronic disorders, thus amplifying the concern on the future burden of the disease. At the same time, and even after decades of intense research, the underlying pathophysiology of chronic pain remains minimally understood. We believe advancing our current understanding of chronic pain requires mechanistically explicit, hypothesis-driven, and clinically focused models. In this review we highlight some of the main findings over the last decades that have contributed to the present knowledge of brain mechanisms of chronic pain, and how such advances were possible due to a reverse translational research approach. We argue that this approach is essential in the chronic pain field, in order to generate new scientific hypotheses, probe physiological mechanisms, develop therapeutic strategies and translate findings back into promising human clinical trials.

Altered Dynamic Brain Connectivity in Individuals With Sickle Cell Disease and Chronic Pain Secondary to Hip Osteonecrosis

Individuals with sickle cell disease (SCD) exhibit changes in static brain connectivity in rest. However, little known as chronic pain associated with hip osteonecrosis affects dynamic brain connectivity during rest and the motor imagery task. The aim of this study was to investigate the characteristics of the dynamic functional brain connectivity of individuals with SCD and chronic pain secondary to hip osteonecrosis. This is a cross-sectional study comparing the dynamic brain connectivity of healthy individuals (n = 18) with the dynamic brain connectivity of individuals with SCD and chronic pain (n = 22). Individuals with SCD and chronic pain were stratified into high- or low-intensity pain groups based on pain intensity at the time of assessment. Dynamic brain connectivity was assessed through electroencephalography in 3 stages, resting state with eyes closed, and during hip (painful for the SCD individuals) and hand (control, nonpainful) motor imagery. Average weight of the edges and full synchronization time (FST)-time required for 95% of the possible edges to appear over time during a given task-were evaluated. Regarding the average weight of the edges, individuals with SCD and high-intensity pain presented higher edge weight during hip motor imagery. The average weight of the edges correlated positively with pain intensity and depression symptoms. Individuals with SCD and chronic pain complete the cerebral network at rest more quickly (lower FST). Individuals with SCD and chronic pain/hip osteonecrosis have impaired dynamic brain network with shorter FST in rest network and more pronounced diffuse connectivity in individuals with high-intensity pain. The dynamic brain network evaluated by time-varying graphs and motif synchronization was able to identify differences between groups.

Accelerated functional brain aging in pre-clinical familial Alzheimer's disease

Resting state functional connectivity (rs-fMRI) is impaired early in persons who subsequently develop Alzheimer's disease (AD) dementia. This impairment may be leveraged to aid investigation of the pre-clinical phase of AD. We developed a model that predicts brain age from resting state (rs)-fMRI data, and assessed whether genetic determinants of AD, as well as beta-amyloid (Aβ) pathology, can accelerate brain aging. Using data from 1340 cognitively unimpaired participants between 18-94 years of age from multiple sites, we showed that topological properties of graphs constructed from rs-fMRI can predict chronological age across the lifespan. Application of our predictive model to the context of pre-clinical AD revealed that the pre-symptomatic phase of autosomal dominant AD includes acceleration of functional brain aging. This association was stronger in individuals having significant Aβ pathology.

Recommendations for the Development of Socioeconomically-Situated and Clinically-Relevant Neuroimaging Models of Pain

Pain is a complex, multidimensional experience that emerges from interactions among sensory, affective, and cognitive processes in the brain. Neuroimaging allows us to identify these component processes and model how they combine to instantiate the pain experience. However, the clinical impact of pain neuroimaging models has been limited by inadequate population sampling - young healthy college students are not representative of chronic pain patients. The biopsychosocial approach to pain management situates a person's pain within the diverse socioeconomic environments they live in. To increase the clinical relevance of pain neuroimaging models, a three-fold biopsychosocial approach to neuroimaging biomarker development is recommended. The first level calls for the development of diagnostic biomarkers via the standard population-based (nomothetic) approach with an emphasis on diverse sampling. The second level calls for the development of treatment-relevant models via a constrained person-based (idiographic) approach tailored to unique individuals. The third level calls for the development of prevention-relevant models via a novel society-based (social epidemiologic) approach that combines survey and neuroimaging data to predict chronic pain risk based on one's socioeconomic conditions. The recommendations in this article address how we can leverage pain's complexity in service of the patient and society by modeling not just individuals and populations, but also the socioeconomic structures that shape any individual's expectations of threat, safety, and resource availability.

Pancreatic Pain-Knowledge Gaps and Research Opportunities in Children and Adults: Summary of a National Institute of Diabetes and Digestive and Kidney Diseases Workshop

ABSTRACT

A workshop was sponsored by the National Institute of Diabetes and Digestive and Kidney Diseases to focus on research gaps and opportunities in pancreatic pain. The event was held on July 21, 2021, and structured into 4 sessions: (1) pathophysiology; (2) biomarkers, mediators, and pharmacology of pain; (3) pain assessment; and (4) pain treatment challenges and opportunities. The current state of knowledge was reviewed; many knowledge gaps and research needs were identified that require further investigation. Common themes included the need to better understand the underlying mechanisms of pain in pancreatic diseases, the relationship of visceral neural pathways and central pain centers, the role of behavioral factors and disorders on the perception of pain, and differences in pain perception and processes in children when compared with adults. In addition, the role of genetic risk factors for pain and the mechanisms and role of placebos in pain treatment were discussed. Methods of pain assessment including quantitative sensory testing were examined, as well as the process of central sensitization of pain. Finally, newer approaches to pain management including cognitive behavioral therapy, nerve stimulation, experimental (nonopioid) drugs, and cannabinoid compounds were covered.

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

Sex differences in brain modular organization in chronic pain

ABSTRACT

Men and women can exhibit different pain sensitivities, and many chronic pain conditions are more prevalent in one sex. Although there is evidence of sex differences in the brain, it is not known whether there are sex differences in the organization of large-scale functional brain networks in chronic pain. Here, we used graph theory with modular analysis and machine-learning of resting-state-functional magnetic resonance imaging data from 220 participants: 155 healthy controls and 65 individuals with chronic low back pain due to ankylosing spondylitis, a form of arthritis. We found an extensive overlap in the graph partitions with the major brain intrinsic systems (ie, default mode, central, visual, and sensorimotor modules), but also sex-specific network topological characteristics in healthy people and those with chronic pain. People with chronic pain exhibited higher cross-network connectivity, and sex-specific nodal graph properties changes (ie, hub disruption), some of which were associated with the severity of the chronic pain condition. Females exhibited atypically higher functional segregation in the mid cingulate cortex and subgenual anterior cingulate cortex and lower connectivity in the network with the default mode and frontoparietal modules, whereas males exhibited stronger connectivity with the sensorimotor module. Classification models on nodal graph metrics could classify an individual's sex and whether they have chronic pain with high accuracies (77%-92%). These findings highlight the organizational abnormalities of resting-state-brain networks in people with chronic pain and provide a framework to consider sex-specific pain therapeutics.

Brain gray matter abnormalities in osteoarthritis pain: a cross-sectional evaluation

ABSTRACT

The interaction between osteoarthritis (OA) pain and brain properties remains minimally understood, although anatomical and functional neuroimaging studies suggest that OA, similar to other chronic pain conditions, may impact as well as partly be determined by brain properties. Here, we studied brain gray matter (GM) properties in OA patients scheduled to undergo total joint replacement surgery. We tested the hypothesis that brain regional GM volume is distinct between hip OA (HOA) and knee OA (KOA) patients, relative to healthy controls and moreover, that these properties are related to OA pain. Voxel-based morphometry group contrasts showed lower anterior cingulate GM volume only in HOA. When we reoriented the brains (flipped) to examine the hemisphere contralateral to OA pain, precentral GM volume was lower in KOA and HOA, and 5 additional brain regions showed distortions between groups. These GM changes, however, did not reflect clinical parameters. Next, we subdivided the brain into larger regions, approximating Brodmann areas, and performed univariable and machine learning-based multivariable contrasts. The univariable analyses approximated voxel-based morphometry results. Our multivariable model distinguished between KOA and controls, was validated in a KOA hold-out sample, and generalized to HOA. The multivariable model in KOA, but not HOA, was related to neuropathic OA pain. These results were mapped into term space (using Neurosynth), providing a meta-analytic summary of brain anatomical distortions in OA. Our results indicate more subtle cortical anatomical differences in OA than previously reported and also emphasize the interaction between OA pain, namely its neuropathic component, and OA brain anatomy.

Optimizing Chronic Pain Treatment with Enhanced Neuroplastic Responsiveness: A Pilot Randomized Controlled Trial

Chronic pain affects mental and physical health and alters brain structure and function. Interventions that reduce chronic pain are also associated with changes in the brain. A number of non-invasive strategies can promote improved learning and memory and increase neuroplasticity in older adults. Intermittent fasting and glucose administration represent two such strategies with the potential to optimize the neurobiological environment to increase responsiveness to recognized pain treatments. The purpose of the pilot study was to test the feasibility and acceptability of intermittent fasting and glucose administration paired with a recognized pain treatment activity, relaxation and guided imagery. A total of 32 adults (44% W, 56% M), 50 to 85 years of age, with chronic knee pain for three months or greater participated in the study. Four sessions were completed over an approximate two-week period. Findings indicate the ability to recruit, randomize, and retain participants in the protocol. The procedures and measures were reasonable and completed without incident. Participant adherence was high and exit interview feedback positive. In summary, the pilot study was feasible and acceptable, providing the evidence necessary to move forward with a larger clinical trial.

Hippocampus shape deformation: a potential diagnostic biomarker for chronic back pain in women

ABSTRACT

Sex differences in the quality and prevalence of chronic pain are manifold, with women generally presenting higher incidence and severity. Uncovering chronic pain-related sex differences inform neural mechanisms and may lead to novel treatment routes. In a multicenter morphological study (total n = 374), we investigated whether the shape of subcortical regions would reflect sex differences in back pain. Given the hormone-dependent functions of the hippocampus, and its role in the transition to chronic pain, this region constituted our primary candidate. We found that the anterior part of the left hippocampus (alHP) presented outer deformation in women with chronic back pain (CBP), identified in CBP in the United States (n = 77 women vs n = 78 men) and validated in a Chinese data set (n = 29 women vs n = 58 men with CBP, in contrast to n = 53 female and n = 43 male healthy controls). Next, we examined this region in subacute back pain who persisted with back pain a year later (SBPp; n = 18 women vs n = 18 men) and in a subgroup with persistent back pain for 3 years. Weeks after onset of back pain, there was no deformation within alHP, but at 1 and 3 years women exhibited a trend for outer deformation. The alHP partly overlapped with the subiculum and entorhinal cortex, whose functional connectivity, in healthy subjects, was associated with emotional and episodic memory related terms (Neurosynth, reverse inference). These findings suggest that in women the alHP undergoes anatomical changes with pain persistence, highlighting sexually dimorphic involvement of emotional and episodic memory-related circuitry with chronic pain.

Multi-modal biomarkers of low back pain: A machine learning approach

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Widespread differences in cortical thickness (CT) were observed in patients with low back pain.

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Changes in CT correlated with self-reported clinical scores of pain and emotion.

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Changes in resting state fMRI metrics of functional networks.

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Support vector machines separated low back pain patients from controls with a high performance.

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Multi-modal biomarkers can be useful when identifying personalized treatments for low back pain.

Chronic low back pain (LBP) is a very common health problem worldwide and a major cause of disability. Yet, the lack of quantifiable metrics on which to base clinical decisions leads to imprecise treatments, unnecessary surgery and reduced patient outcomes. Although, the focus of LBP has largely focused on the spine, the literature demonstrates a robust reorganization of the human brain in the setting of LBP. Brain neuroimaging holds promise for the discovery of biomarkers that will improve the treatment of chronic LBP. In this study, we report on morphological changes in cerebral cortical thickness (CT) and resting-state functional connectivity (rsFC) measures as potential brain biomarkers for LBP. Structural MRI scans, resting state functional MRI scans and self-reported clinical scores were collected from 24 LBP patients and 27 age-matched healthy controls (HC). The results suggest widespread differences in CT in LBP patients relative to HC. These differences in CT are correlated with self-reported clinical summary scores, the Physical Component Summary and Mental Component Summary scores. The primary visual, secondary visual and default mode networks showed significant age-corrected increases in connectivity with multiple networks in LBP patients. Cortical regions classified as hubs based on their eigenvector centrality (EC) showed differences in their topology within motor and visual processing regions. Finally, a support vector machine trained using CT to classify LBP subjects from HC achieved an average classification accuracy of 74.51%, AUC = 0.787 (95% CI: 0.66–0.91). The findings from this study suggest widespread changes in CT and rsFC in patients with LBP while a machine learning algorithm trained using CT can predict patient group. Taken together, these findings suggest that CT and rsFC may act as potential biomarkers for LBP to guide therapy.

The Modular Organization of Pain Brain Networks: An fMRI Graph Analysis Informed by Intracranial EEG

Intracranial EEG (iEEG) studies have suggested that the conscious perception of pain builds up from successive contributions of brain networks in less than 1 s. However, the functional organization of cortico-subcortical connections at the multisecond time scale, and its accordance with iEEG models, remains unknown. Here, we used graph theory with modular analysis of fMRI data from 60 healthy participants experiencing noxious heat stimuli, of whom 36 also received audio stimulation. Brain connectivity during pain was organized in four modules matching those identified through iEEG, namely: 1) sensorimotor (SM), 2) medial fronto-cingulo-parietal (default mode-like), 3) posterior parietal-latero-frontal (central executive-like), and 4) amygdalo-hippocampal (limbic). Intrinsic overlaps existed between the pain and audio conditions in high-order areas, but also pain-specific higher small-worldness and connectivity within the sensorimotor module. Neocortical modules were interrelated via “connector hubs” in dorsolateral frontal, posterior parietal, and anterior insular cortices, the antero-insular connector being most predominant during pain. These findings provide a mechanistic picture of the brain networks architecture and support fractal-like similarities between the micro-and macrotemporal dynamics associated with pain. The anterior insula appears to play an essential role in information integration, possibly by determining priorities for the processing of information and subsequent entrance into other points of the brain connectome.

Reorganization of functional brain network architecture in chronic osteoarthritis pain

Osteoarthritis (OA) manifests with chronic pain, motor impairment, and proprioceptive changes. However, the role of the brain in the disease is largely unknown. Here, we studied brain networks using the mathematical properties of graphs in a large sample of knee and hip OA (KOA, n = 91; HOA, n = 23) patients. We used a robust validation strategy by subdividing the KOA data into discovery and testing groups and tested the generalizability of our findings in HOA. Despite brain global topological properties being conserved in OA, we show there is a network wide pattern of reorganization that can be captured at the subject‐level by a single measure, the hub disruption index. We localized reorganization patterns and uncovered a shift in the hierarchy of network hubs in OA: primary sensory and motor regions and parahippocampal gyrus behave as hubs and insular cortex loses its central placement. At an intermediate level of network structure, frontoparietal and cingulo‐opercular modules showed preferential reorganization. We examined the association between network properties and clinical correlates: global disruption indices and isolated degree properties did not reflect clinical parameters; however, by modeling whole brain nodal degree properties, we identified a distributed set of regions that reliably predicted pain intensity in KOA and generalized to hip OA. Together, our findings reveal that while conserving global topological properties, brain network architecture reorganizes in OA, at both global and local scale. Network connectivity related to OA pain intensity is dissociated from the major hub disruptions, challenging the extent of dependence of OA pain on nociceptive signaling.

Altered network architecture of functional brain communities in chronic nociplastic pain

Neuroimaging has enhanced our understanding of the neural correlates of pain. Yet, how neural circuits interact and contribute to persistent pain remain largely unknown. Here, we investigate the mesoscale organization of the brain through intrinsic functional communities generated from resting state functional MRI data from two independent datasets, a discovery cohort of 43 Fibromyalgia (FM) patients and 20 healthy controls (HC) as well as a replication sample of 34 FM patients and 21 HC. Using normalized mutual information, we found that the global network architecture in chronic pain patients is less stable (more variable). Subsequent analyses of node community assignment revealed the composition of the communities differed between FM and HC. Furthermore, differences in network organization were associated with the changes in the composition of communities between patients with varying levels of clinical pain. Together, this work demonstrates that intrinsic network communities differ substantially between patients with FM and controls. These differences may represent a novel aspect of the pathophysiology of chronic nociplastic pain.

A neuroimaging marker for predicting longitudinal changes in pain intensity of subacute back pain based on large-scale brain network interactions

Identification of predictive neuroimaging markers of pain intensity changes is a crucial issue to better understand macroscopic neural mechanisms of pain. Although a single connection between the medial prefrontal cortex and nucleus accumbens has been suggested as a powerful marker, how the complex interactions on a large-scale brain network can serve as the markers is underexplored. Here, we aimed to identify a set of functional connections predictive of longitudinal changes in pain intensity using large-scale brain networks. We re-analyzed previously published resting-state functional magnetic resonance imaging data of 49 subacute back pain (SBP) patients. We built a network-level model that predicts changes in pain intensity over one year by combining independent component analysis and a penalized regression framework. Connections involving top-down pain modulation, multisensory integration, and mesocorticolimbic circuits were identified as predictive markers for pain intensity changes. Pearson’s correlations between actual and predicted pain scores were r = 0.33–0.72, and group classification results between SBP patients with persisting pain and recovering patients, in terms of area under the curve (AUC), were 0.89/0.75/0.75 for visits four/three/two, thus outperforming the previous work (AUC 0.83/0.73/0.67). This study identified functional connections important for longitudinal changes in pain intensity in SBP patients, providing provisional markers to predict future pain using large-scale brain networks.

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.

Whole-brain functional network disruption in chronic pain with disk herniation

Brain functional network properties are globally disrupted in multiple musculoskeletal chronic pain conditions. Back pain with lumbar disk herniation (LDH) is highly prevalent and a major route for progression to chronic back pain. However, brain functional network properties remain unknown in such patients. Here, we examined resting-state functional magnetic resonance imaging-based functional connectivity networks in chronic back pain patients with clear evidence for LDH (LDH-chronic pain n = 146), in comparison to healthy controls (HCs, n = 165). The study was conducted in China, thus providing the opportunity to also examine the influence of culture on brain functional reorganization with chronic pain. The data were equally subdivided into discovery and validation subgroups (n = 68 LDH-chronic pain and n = 68 HC, for each subgroup), and contrasted to an off-site data set (n = 272, NITRC 1000). Graph disruption indices derived from 3 network topological measurements, degree, clustering coefficient, and efficiency, which respectively represent network hubness, segregation, and integration, were significantly decreased compared with HC, across all predefined link densities, in both discovery and validation groups. However, global mean clustering coefficient and betweenness centrality were decreased in the discovery group and showed trend in the validation group. The relationship between pain and graph disruption indices was limited to males with high education. These results deviate somewhat from recent similar analysis for other musculoskeletal chronic pain conditions, yet we cannot determine whether the differences are due to types of pain or also to cultural differences between patients studied in China and the United States.

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

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

Scale-free amplitude modulation of low-frequency fluctuations in episodic migraine

Arrhythmic fluctuations in neural activity occur at many levels of the nervous system. Such activity does not have a characteristic temporal periodicity but can exhibit statistical similarities, most commonly power-law scaling behavior, which is indicative of scale-free dynamics. The recurrence of scaling laws across many different systems and its manifestation in behavior has prompted a search for unifying principles in human brain function. With this in mind, a focused search for abnormities in scale-free dynamics is of considerable clinical relevance to migraine and other clinical pain disorders. Here, we examined the scale-free properties of the resting-state functional magnetic resonance imaging (fMRI) signal in the broadband frequency range known to be related to spontaneous neural activity (0.01-0.1 Hz). In a large cohort of episodic migraine patients (N = 40), we observed that the strength of long-range temporal correlations in the fMRI signal (captured by the scaling exponent α) was significantly higher in the sensorimotor network compared with healthy controls. Increases in the scaling exponent were positively correlated with fMRI signal variance and negatively correlated with the patient's self-reported headache intensity. These changes in the fMRI signal suggest that the temporal structure of amplitude fluctuations carries valuable information about the dynamic state of the underlying neuronal networks and ensuing sensory impairments in migraine. The demonstrated scaling laws pose a novel quantitative approach for examining clinically relevant interindividual variability in migraine and other pain disorders.

Distinct thalamocortical network dynamics are associated with the pathophysiology of chronic low back pain

Thalamocortical dysrhythmia is a key pathology of chronic neuropathic pain, but few studies have investigated thalamocortical networks in chronic low back pain (cLBP) given its non-specific etiology and complexity. Using fMRI, we propose an analytical pipeline to identify abnormal thalamocortical network dynamics in cLBP patients and validate the findings in two independent cohorts. We first identify two reoccurring dynamic connectivity states and their associations with chronic and temporary pain. Further analyses show that cLBP patients have abnormal connectivity between the ventral lateral/posterolateral nucleus (VL/VPL) and postcentral gyrus (PoCG) and between the dorsal/ventral medial nucleus and insula in the less frequent connectivity state, and temporary pain exacerbation alters connectivity between the VL/VPL and PoCG and the default mode network in the more frequent connectivity state. These results extend current findings on thalamocortical dysfunction and dysrhythmia in chronic pain and demonstrate that cLBP pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

Thalamocortical dysrhythmia is a key pathology of chronic pain. Here, the authors propose an analytical pipeline to study dynamic fMRI brain networks and demonstrate that chronic low back pain pathophysiology and clinical pain intensity are associated with distinct thalamocortical network dynamics.

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.

Composite Score Is a Better Reflection of Patient Response to Chronic Pain Therapy Compared With Pain Intensity Alone

OBJECTIVE

The pain Numeric Rating Scale (NRS) score became standard when pain was introduced as the fifth vital sign in the 1990s. Although plagued with issues, it remains the basis for primary outcome measures in clinical trials for chronic pain therapies. Multidimensional composite scoring that considers all aspects of the chronic pain experience may provide a more meaningful response measure. Herein we propose a multidimensional responder index.

MATERIALS AND METHODS

Data were extracted from an ongoing prospective, multicenter study on DeRidder Burst spinal cord stimulation (B-SCS) for chronic back and/or leg pain (NCT03082261). The analysis cohort consisted of subjects who completed the NRS, Pain Catastrophizing Scale (PCS), EuroQol-5D (EQ-5D), and eight-item Patient-Reported Outcomes Measurement Information System Physical Function preoperatively and at 12 months after implant.

RESULTS

A principal component analysis showed that each of the four measures contributed equally to the variance in the data set, confirming that pain score should not be used alone. Subjects who failed to respond on NRS responded on both PCS and EQ-5D. Eighty-one percent of subjects responded on at least two measures. The responder algorithm yielded an 84% success rate at both 6- and 12-month time points.

CONCLUSIONS

Our study suggests that therapeutic response, similar to the chronic pain experience, is multidimensional. Careful consideration should be made to incorporate composite endpoints in future SCS clinical trials.

Investigation of robust visual reaction and functional connectivity in the rat brain induced by rocuronium bromide with functional MRI

Functional magnetic resonance imaging (fMRI) has been used extensively to understand the brain function of a wide range of neurological and psychiatric disorders. When applied to animal studies, anesthesia is always used to reduce the movement of the animal and also reduce the impacts on the results of fMRI. Several awake models have been proposed by applying physical animal movement restrictions. However, restraining devices were designed for individual subject which limits the promotion of fMRI in awake animals. Here, a clinical muscle relaxant rocuronium bromide (RB) was introduced to restrain the animal in fMRI scanning time. The fMRI reactions of the animal induced with RB and the other two commonly used anesthesia protocols were investigated. The results of the fMRI showed that there were increased functional connectivity and well-round visual responses in the RB induced state. Furthermore, significant BOLD signal changes were found in the cortex and thalamus regions when the animal revived from isoflurane, which should be essential to further understand the effects of anesthesia on the brain.

Cellular Circuits in the Brain and Their Modulation in Acute and Chronic Pain

Chronic, pathological pain remains a global health problem and a challenge to basic and clinical sciences. A major obstacle to preventing, treating, or reverting chronic pain has been that the nature of neural circuits underlying the diverse components of the complex, multidimensional experience of pain is not well understood. Moreover, chronic pain involves diverse maladaptive plasticity processes, which have not been decoded mechanistically in terms of involvement of specific circuits and cause-effect relationships. This review aims to discuss recent advances in our understanding of circuit connectivity in the mammalian brain at the level of regional contributions and specific cell types in acute and chronic pain. A major focus is placed on functional dissection of sub-neocortical brain circuits using optogenetics, chemogenetics, and imaging technological tools in rodent models with a view towards decoding sensory, affective, and motivational-cognitive dimensions of pain. The review summarizes recent breakthroughs and insights on structure-function properties in nociceptive circuits and higher order sub-neocortical modulatory circuits involved in aversion, learning, reward, and mood and their modulation by endogenous GABAergic inhibition, noradrenergic, cholinergic, dopaminergic, serotonergic, and peptidergic pathways. The knowledge of neural circuits and their dynamic regulation via functional and structural plasticity will be beneficial towards designing and improving targeted therapies.

Pain- and Fatigue-Related Functional and Structural Changes in Ankylosing Spondylitis: An fRMI Study

Background: Chronic pain and fatigue are two cardinal features of ankylosing spondylitis (AS) and how to effectively treat these conditions continues to be a challenge. The underlying mechanisms and the relationship between AS-related pain and fatigue remain poorly understood. The present study was conducted, therefore, to explore the brain functional and structural changes associated with pain and fatigue in AS.

Methods: A total of 65 AS patients (48 men and 17 women; 32.33 ± 8.6 years) and 53 age- and sex-matched controls were enrolled in the study. The patients underwent clinical assessment based on Total Back Pain scores, Fatigue Severity Scale, Bath Ankylosing Spondylitis Disease Activity Index, (BASDAI), high-sensitivity C-reactive Protein (hsCRP), erythrocyte sedimentation rate (ESR), and Beck Depression Inventory (BDI). Using 3T magnetic resonance imaging (3T-MRI), we analyzed the brain functional (connectivity and nodal properties) and structural (covariance and gray matter volumes) differences between AS patients and controls. Furthermore, we extracted the values of the significantly changed regions in the AS cohort and explored their association with pain and fatigue.

Results: In AS patients, there were functional and structural abnormalities distributed in the default mode network (DMN), salience network (SN), sensory/somatomotor network (SMN), dorsal attention network (DAN), task control network (TCN), and visual network, and some regions showed both types of changes. Among these, the functional connectivity (FC) between the left insula and medial prefrontal cortex, the betweenness centrality of the left medial prefrontal cortex and the gray matter volume of the right putamen tracked both pain and fatigue. In addition, pain was related to within-DMN FC disruption and nodal function / gray matter volumes changes in DMN, SN, and the visual network, while fatigue mainly involved the SMN, DAN, and TCN. Moreover, certain changes were also related to BASDAI and inflammation level.

Conclusion: This study offers new insights into understanding the neural mechanism of AS-related pain and fatigue, and could help to stratify patients based on the correlation features and ultimately move towards a personalized therapy.

The medial temporal lobe in nociception: a meta-analytic and functional connectivity study

Recent neuroimaging studies implicate the medial temporal lobe (MTL) in nociception and pain modulation. Here, we aim to identify which subregions of the MTL are involved in human pain and to test its connectivity in a cohort of chronic low-back pain patients (CBP). We conducted 2 coordinate-based meta-analyses to determine which regions within the MTL showed consistent spatial patterns of functional activation (1) in response to experimental pain in healthy participants and (2) in chronic pain compared with healthy participants. We followed PRISMA guidelines and performed activation likelihood estimate (ALE) meta-analyses. The first meta-analysis revealed consistent activation in the right anterior hippocampus (right antHC), parahippocampal gyrus, and amygdala. The second meta-analysis revealed consistently less activation in patients' right antHC, compared with healthy participants. We then conducted a seed-to-voxel resting state functional connectivity of the right antHC seed with the rest of the brain in 77 CBP and 79 age-matched healthy participants. We found that CBP had significantly weaker antHC functional connectivity to the medial prefrontal cortex compared with healthy participants. Taken together, these data indicate that the antHC has abnormally lower activity in chronic pain and reduced connectivity to the medial prefrontal cortex in CBP. Future studies should investigate the specific role of the antHC in the development and management of chronic pain.

Hub disruption in patients with chronic neck pain: a graph analytical approach

Chronic pain is known to alter the brain's network dynamics. These dynamics are often demonstrated by identifying alterations in the brain network topology. A common approach used for this purpose is graph theory. To date, little is known on how these potentially altered networks in chronic pain relate to the symptoms reported by these patients. Here, we applied a graph theoretical approach to identify network changes in patients suffering from chronic neck pain, a group that is often neglected in chronic pain research. Participants with chronic traumatic and nontraumatic neck pain were compared to healthy pain-free controls. They showed higher levels of self-reported symptoms of sensitization, higher levels of disability, and impaired sensorimotor control. Furthermore, the brain suffering from chronic neck pain showed altered network properties in the posterior cingulate cortex, amygdala, and pallidum compared with the healthy pain-free brain. These regions have been identified as brain hubs (ie, regions that are responsible for orchestrating communication between other brain regions) and are therefore known to be more vulnerable in brain disorders including chronic pain. We were furthermore able to uncover associations between these altered brain network properties and the symptoms reported by patients. Our findings indicate that chronic neck pain patients reflect brain network alterations and that targeting the brain in patients might be of utmost importance.

Imaging vs quantitative sensory testing to predict chronic pain treatment outcomes

In this article, I review the concept of personalized pain management and consider how brain imaging and quantitative sensory testing can be used to derive biomarkers of chronic pain treatment outcome. I review how different modalities of brain imaging can be used to acquire information about brain structure and function and how this information can be linked to individual measures of pain.

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

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