Rate and Temporal Coding Mechanisms in the Anterior Cingulate Cortex for Pain Anticipation

Effects of Embodiment in Virtual Reality for Treatment of Chronic Pain: Pilot Open-Label Study

BACKGROUND

Chronic pain has long been a major health burden that has been addressed through numerous forms of pharmacological and nonpharmacological treatment. One of the tenets of modern medicine is to minimize risk while providing efficacy. Further, because of its noninvasive nature, virtual reality (VR) provides an attractive platform for potentially developing novel therapeutic modalities.

OBJECTIVE

The purpose of this study was to determine the feasibility of a novel VR-based digital therapy for the treatment of chronic pain.

METHODS

An open-label study assessed the feasibility of using virtual embodiment in VR to treat chronic pain. In total, 24 patients with chronic pain were recruited from local pain clinics and completed 8 sessions of a novel digital therapeutic that combines virtual embodiment with graded motor imagery to deliver functional rehabilitation exercises over the course of 4 weeks. Pain intensity as measured by a visual analog scale before and after each virtual embodiment training session was used as the primary outcome measure. Additionally, a battery of patient-reported pain questionnaires (Fear-Avoidance Beliefs Questionnaire, Oswestry Low Back Pain Disability Questionnaire, Pain Catastrophizing Scale, and Patient Health Questionnaire) were administered before and after 8 sessions of virtual embodiment training as exploratory outcome measures to assess if the measures are appropriate and warrant a larger randomized controlled trial.

RESULTS

A 2-way ANOVA on session × pre- versus postvirtual embodiment training revealed that individual virtual embodiment training sessions significantly reduced the intensity of pain as measured by the visual analog scale (P<.001). Perceived disability due to lower back pain as measured by the Oswestry Low Back Pain Disability Questionnaire significantly improved (P=.003) over the 4-week course of virtual embodiment regimen. Improvement was also observed on the helplessness subscale of the Pain Catastrophizing Scale (P=.02).

CONCLUSIONS

This study provides evidence that functional rehabilitation exercises delivered in VR are safe and may have positive effects on alleviating the symptoms of chronic pain. Additionally, the virtual embodiment intervention may improve perceived disability and helplessness of patients with chronic pain after 8 sessions. The results support the justification for a larger randomized controlled trial to assess the extent to which virtual embodiment training can exert an effect on symptoms associated with chronic pain.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04060875; https://clinicaltrials.gov/ct2/show/NCT04060875.

Oxytocin Modulation in Mindfulness-Based Pain Management for Chronic Pain

In the context of chronic pain management, opioid-based treatments have been heavily relied upon, raising concerns related to addiction and misuse. Non-pharmacological approaches, such as Mindfulness-Based Pain Management, offer alternative strategies. We conducted a mechanistic clinical study to investigate the impact of an 8-week Mindfulness-Based Pain Management intervention on chronic pain, the modulation of inflammatory markers, stress physiology, and oxytocin, and their interplay with clinical pain symptoms and perception, in comparison to a patient wait-list active control. A total of 65 participants, including 50 chronic pain patients and 15 healthy controls, underwent salivary assays to assess endocrine markers, oxytocin, interleukin (IL)-1b, IL-6, IL-8, tumor necrosis factor (TNF)-a, and dehydroepiandrosterone sulphate (DHEA-S). Psychological assessments were also conducted to evaluate aspects of pain perception, mindfulness, mood, and well-being. Findings revealed significant differences between chronic pain patients and healthy controls in various clinical metrics, highlighting the psychological distress experienced by patients. Following Mindfulness-Based Pain Management, oxytocin levels significantly increased in chronic pain patients, that was not observed in the patient wait-list control group. In contrast, cytokine and DHEA-S levels decreased (not to statistically significant margins) supporting anti-inflammatory effects of Mindfulness-Based Pain Management. The fact DHEA-S levels, a marker of stress, did attenuate but not to statistically meaningful levels, suggests that pain reduction was not solely related to stress reduction, and that oxytocin pathways may be more salient than previously considered. Psychological assessments demonstrated substantial improvements in pain perception and mood in the intervention group. These results contribute to the growing body of evidence regarding the effectiveness of mindfulness-based interventions in chronic pain management and underscore oxytocin's potential role as a therapeutic target.

An amygdala-to-cingulate cortex circuit for conflicting choices in chronic pain

Chronic pain is a complex experience with multifaceted behavioral manifestations, often leading to pain avoidance at the expense of reward approach. How pain facilitates avoidance in situations with mixed outcomes is unknown. The anterior cingulate cortex (ACC) plays a key role in pain processing and in value-based decision-making. Distinct ACC inputs inform about the sensory and emotional quality of pain. However, whether specific ACC circuits underlie pathological conflict assessment in pain remains underexplored. Here, we demonstrate that mice with chronic pain favor cold avoidance rather than reward approach in a conflicting task. This occurs along with selective strengthening of basolateral amygdala inputs onto ACC layer 2/3 pyramidal neurons. The amygdala-cingulate projection is necessary and sufficient for the conflicting cold avoidance. Further, low-frequency stimulation of this pathway restores AMPA receptor function and reduces avoidance in pain mice. Our findings provide insights into the circuits and mechanisms underlying cognitive aspects of pain and offer potential targets for treatment.

Interhemispheric neural characteristics of noxious mechano-nociceptive stimulation in the anterior cingulate cortex

Background

Pain is an unpleasant sensory and emotional experience. One of the most critical regions of the brain for pain processing is the anterior cingulate cortex (ACC). Several studies have examined the role of this region in thermal nociceptive pain. However, studies on mechanical nociceptive pain have been very limited to date. Although several studies have investigated pain, the interactions between the two hemispheres are still not clear. This study aimed to investigate nociceptive mechanical pain in the ACC bilaterally.

Methods

Local field potential (LFP) signals were recorded from seven male Wistar rats' ACC regions of both hemispheres. Mechanical stimulations with two intensities, high-intensity noxious (HN) and non-noxious (NN) were applied to the left hind paw. At the same time, the LFP signals were recorded bilaterally from awake and freely moving rats. The recorded signals were analyzed from different perspectives, including spectral analysis, intensity classification, evoked potential (EP) analysis, and synchrony and similarity of two hemispheres.

Results

By using spectro-temporal features and support vector machine (SVM) classifier, HN vs. no-stimulation (NS), NN vs. NS, and HN vs. NN were classified with accuracies of 89.6, 71.1, and 84.7%, respectively. Analyses of the signals from the two hemispheres showed that the EPs in the two hemispheres were very similar and occurred simultaneously; however, the correlation and phase locking value (PLV) between the two hemispheres changed significantly after HN stimulation. These variations persisted for up to 4 s after the stimulation. In contrast, variations in the PLV and correlation for NN stimulation were not significant.

Conclusions

This study showed that the ACC area was able to distinguish the intensity of mechanical stimulation based on the power activities of neural responses. In addition, our results suggest that the ACC region is activated bilaterally due to nociceptive mechanical pain. Additionally, stimulations above the pain threshold (HN) significantly affect the synchronicity and correlation between the two hemispheres compared to non-noxious stimuli.

Pain, from perception to action: A computational perspective

Pain is driven by sensation and emotion, and in turn, it motivates decisions and actions. To fully appreciate the multidimensional nature of pain, we formulate the study of pain within a closed-loop framework of sensory-motor prediction. In this closed-loop cycle, prediction plays an important role, as the interaction between prediction and actual sensory experience shapes pain perception and subsequently, action. In this Perspective, we describe the roles of two prominent computational theories-Bayesian inference and reinforcement learning-in modeling adaptive pain behaviors. We show that prediction serves as a common theme between these two theories, and that each of these theories can explain unique aspects of the pain perception-action cycle. We discuss how these computational theories and models can improve our mechanistic understandings of pain-centered processes such as anticipation, attention, placebo hypoalgesia, and pain chronification.

Temporal pain processing in the primary somatosensory cortex and anterior cingulate cortex

Pain is known to have sensory and affective components. The sensory pain component is encoded by neurons in the primary somatosensory cortex (S1), whereas the emotional or affective pain experience is in large part processed by neural activities in the anterior cingulate cortex (ACC). The timing of how a mechanical or thermal noxious stimulus triggers activation of peripheral pain fibers is well-known. However, the temporal processing of nociceptive inputs in the cortex remains little studied. Here, we took two approaches to examine how nociceptive inputs are processed by the S1 and ACC. We simultaneously recorded local field potentials in both regions, during the application of a brain-computer interface (BCI). First, we compared event related potentials in the S1 and ACC. Next, we used an algorithmic pain decoder enabled by machine-learning to detect the onset of pain which was used during the implementation of the BCI to automatically treat pain. We found that whereas mechanical pain triggered neural activity changes first in the S1, the S1 and ACC processed thermal pain with a reasonably similar time course. These results indicate that the temporal processing of nociceptive information in different regions of the cortex is likely important for the overall pain experience.

The Energy Homeostasis Principle: A Naturalistic Approach to Explain the Emergence of Behavior

It is still elusive to explain the emergence of behavior and understanding based on its neural mechanisms. One renowned proposal is the Free Energy Principle (FEP), which uses an information-theoretic framework derived from thermodynamic considerations to describe how behavior and understanding emerge. FEP starts from a whole-organism approach, based on mental states and phenomena, mapping them into the neuronal substrate. An alternative approach, the Energy Homeostasis Principle (EHP), initiates a similar explanatory effort but starts from single-neuron phenomena and builds up to whole-organism behavior and understanding. In this work, we further develop the EHP as a distinct but complementary vision to FEP and try to explain how behavior and understanding would emerge from the local requirements of the neurons. Based on EHP and a strict naturalist approach that sees living beings as physical and deterministic systems, we explain scenarios where learning would emerge without the need for volition or goals. Given these starting points, we state several considerations of how we see the nervous system, particularly the role of the function, purpose, and conception of goal-oriented behavior. We problematize these conceptions, giving an alternative teleology-free framework in which behavior and, ultimately, understanding would still emerge. We reinterpret neural processing by explaining basic learning scenarios up to simple anticipatory behavior. Finally, we end the article with an evolutionary perspective of how this non-goal-oriented behavior appeared. We acknowledge that our proposal, in its current form, is still far from explaining the emergence of understanding. Nonetheless, we set the ground for an alternative neuron-based framework to ultimately explain understanding.

Predictive coding models for pain perception

Pain is a complex, multidimensional experience that involves dynamic interactions between sensory-discriminative and affective-emotional processes. Pain experiences have a high degree of variability depending on their context and prior anticipation. Viewing pain perception as a perceptual inference problem, we propose a predictive coding paradigm to characterize evoked and non-evoked pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and the anterior cingulate cortex (ACC) of freely behaving rats-two regions known to encode the sensory-discriminative and affective-emotional aspects of pain, respectively. We further use predictive coding to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Specifically, we develop a phenomenological predictive coding model to describe the macroscopic dynamics of bottom-up and top-down activity. Supported by recent experimental data, we also develop a biophysical neural mass model to describe the mesoscopic neural dynamics in the S1 and ACC populations, in both naive and chronic pain-treated animals. Our proposed predictive coding models not only replicate important experimental findings, but also provide new prediction about the impact of the model parameters on the physiological or behavioral read-out-thereby yielding mechanistic insight into the uncertainty of expectation, placebo or nocebo effect, and chronic pain.

Detecting acute pain signals from human EEG

BACKGROUND

Advances in human neuroimaging has enabled us to study functional connections among various brain regions in pain states. Despite a wealth of studies at high anatomic resolution, the exact neural signals for the timing of pain remain little known. Identifying the onset of pain signals from distributed cortical circuits may reveal the temporal dynamics of pain responses and subsequently provide important feedback for closed-loop neuromodulation for pain.

NEW METHOD

Here we developed an unsupervised learning method for sequential detection of acute pain signals based on multichannel human EEG recordings. Following EEG source localization, we used a state-space model (SSM) to detect the onset of acute pain signals based on the localized regions of interest (ROIs).

RESULTS

We validated the SSM-based detection strategy using two human EEG datasets, including one public EEG recordings of 50 subjects. We found that the detection accuracy varied across tested subjects and detection methods. We also demonstrated the feasibility for cross-subject and cross-modality prediction of detecting the acute pain signals.

COMPARISON WITH EXISTING METHODS

In contrast to the batch supervised learning analysis based on a support vector machine (SVM) classifier, the unsupervised learning method requires fewer number of training trials in the online experiment, and shows comparable or improved performance than the supervised method.

CONCLUSIONS

Our unsupervised SSM-based method combined with EEG source localization showed robust performance in detecting the onset of acute pain signals.

Embodiment in Virtual Reality for the Treatment of Chronic Low Back Pain: A Case Series

Purpose

We describe two case studies that use embodiment in virtual reality as a treatment for chronic low back pain. The purpose of this case series was to determine the feasibility of a novel virtual reality-based digital therapeutic for the treatment of chronic pain.

Patients and Methods

Two patients with chronic low back pain received seven sessions, two sessions per week, of a novel digital therapeutic that combines virtual embodiment with graded motor imagery to deliver functional rehabilitation exercises using an off-the-shelf virtual reality system. Pain intensity was measured using a visual analog scale before and after each session to get an indication whether individual sessions of virtual embodiment training decrease pain intensity. Pain catastrophizing scale was assessed before the first session and after the seventh session to determine the extent to which virtual embodiment training can improve psychological symptoms of chronic low back pain.

Results

In both patients, pain intensity was improved after individual sessions of virtual embodiment training as measured by a paired t-test: (Patient A: t = 2.890, P < 0.05) and (Patient B: t = 5.346, P < 0.005). This indicates that individual sessions of virtual embodiment training decrease pain intensity. In both patients, improvements were observed in three subscales of the pain catastrophizing scale (rumination, magnification, and helplessness). This indicates that virtual embodiment training may have benefits for chronic pain symptoms such as pain intensity, pain-related mobility impairment, and disability.

Conclusion

This case series provides evidence that embodiment in virtual reality improves symptoms of persistent chronic low back pain. We propose a mechanism by which virtual embodiment may improve chronic pain symptoms by recontextualizing sensory feedback from the body as patients engage in functional rehabilitation exercises while in virtual reality.

Association between Chronic Pain and Alterations in the Mesolimbic Dopaminergic System

Chronic pain (pain lasting for >3 months) decreases patient quality of life and even occupational abilities. It can be controlled by treatment, but often persists even after management. To properly control pain, its underlying mechanisms must be determined. This review outlines the role of the mesolimbic dopaminergic system in chronic pain. The mesolimbic system, a neural circuit, delivers dopamine from the ventral tegmental area to neural structures such as the nucleus accumbens, prefrontal cortex, anterior cingulate cortex, and amygdala. It controls executive, affective, and motivational functions. Chronic pain patients suffer from low dopamine production and delivery in this system. The volumes of structures constituting the mesolimbic system are known to be decreased in such patients. Studies on administration of dopaminergic drugs to control chronic pain, with a focus on increasing low dopamine levels in the mesolimbic system, show that it is effective in patients with Parkinson’s disease, restless legs syndrome, fibromyalgia, dry mouth syndrome, lumbar radicular pain, and chronic back pain. However, very few studies have confirmed these effects, and dopaminergic drugs are not commonly used to treat the various diseases causing chronic pain. Thus, further studies are required to determine the effectiveness of such treatment for chronic pain.

A Predictive Coding Model for Evoked and Spontaneous Pain Perception

Pain is a complex multidimensional experience, and pain perception is still incompletely understood. Here we combine animal behavior, electrophysiology, and computer modeling to dissect mechanisms of evoked and spontaneous pain. We record the local field potentials (LFPs) from the primary somatosensory cortex (S1) and anterior cingulate cortex (ACC) of freely behaving rats during pain episodes, and develop a predictive coding model to investigate the temporal coordination of oscillatory activity between the S1 and ACC. Our preliminary results from computational simulations support the experimental findings and provide new predictions.

Different exercise modalities relieve pain syndrome in patients with knee osteoarthritis and modulate the dorsolateral prefrontal cortex: A multiple mode MRI study

OBJECTIVES

Knee osteoarthritis (KOA) is a common degenerative joint disease with no satisfactory intervention. Recently, both physical and mindfulness exercises have received considerable attention for their implications in KOA pain management, and the dorsolateral prefrontal cortex (DLPFC) has displayed a critical role in pain modulation. This study aimed to comparatively investigate the modulation effects of different exercises using multidisciplinary measurements.

METHODS

140 KOA patients were randomized into Tai Chi, Baduanjin, stationary cycling, or health education control groups for 12 weeks. Knee Injury and Osteoarthritis Outcome Score (KOOS), resting state functional magnetic resonance imaging (fMRI), structural MRI, and serum biomarkers were measured at baseline and at the end of the study.

RESULTS

We found: 1) increased KOOS pain subscores (pain reduction) and serum programmed cell death protein 1 (PD-1) levels in the three exercise groups compared to the control group; 2) decreased resting state functional connectivity (rsFC) of the DLPFC-supplementary motor area (SMA) and increased rsFC between the DLPFC and anterior cingulate cortex in all exercise groups compared to the control group; 3) significant associations between DLPFC-SMA rsFC with KOOS pain subscores and serum PD-1 levels at baseline; 4) significantly increased grey matter volume in the SMA in the Tai Chi and stationary cycling groups, and a trend toward significant increase in the Baduanjin group compared to the control group; 5) significant DLPFC rsFC differences among different exercise groups; and 6) that baseline DLPFC-SMA rsFC can predict the effect of mind-body exercise on pain improvement in KOA.

CONCLUSION

Our results suggest that different exercises can modulate both common and unique DLPFC (cognitive control) pathways, and altered DLPFC-SMA rsFC is associated with serum biomarker levels. Our findings also highlight the potentials of neuroimaging biomarkers in predicting the therapeutic effect of mind-body exercises on KOA pain.

Central network changes in patients with advanced monocular blindness: A voxel-based morphometric study

OBJECTIVE

To study the changes in gray matter volume (GMV) in patients with advanced monocular blindness (MB) using voxel-based morphometry (VBM).

METHODS

Thirty-one patients with advanced MB (25 males and six females) and 31 normal controls (25 males and six females) were enrolled. The t test was applied to determine the differences in GMV, white matter volume (WMV), and volume of cerebrospinal fluid in different regions of the brain. The local characteristics of spontaneous concentrations of brain tissue were evaluated by the VBM method. The effects of blindness duration on differences in the GMV were evaluated by correlation and regression analyses.

RESULTS

Compared with the control group, the GMV was decreased in the upper right margin, bilateral insular cortex, right cingulate gyrus, left occipital gyrus, and right suboccipital lobe, and negatively correlated with blindness duration in the upper right posterior margin, bilateral insular cortex, and right cingulate cortex.

CONCLUSIONS

We found that patients with MB showed abnormal WMV and GMV, as evidenced by local changes in the brain. In addition, reduced GMV in specific parts of the brain was associated with the duration of blindness, which may indicate neuropathological mechanisms of visual loss in patients with MB.

The role of hedonics in the Human Affectome

Experiencing pleasure and displeasure is a fundamental part of life. Hedonics guide behavior, affect decision-making, induce learning, and much more. As the positive and negative valence of feelings, hedonics are core processes that accompany emotion, motivation, and bodily states. Here, the affective neuroscience of pleasure and displeasure that has largely focused on the investigation of reward and pain processing, is reviewed. We describe the neurobiological systems of hedonics and factors that modulate hedonic experiences (e.g., cognition, learning, sensory input). Further, we review maladaptive and adaptive pleasure and displeasure functions in mental disorders and well-being, as well as the experience of aesthetics. As a centerpiece of the Human Affectome Project, language used to express pleasure and displeasure was also analyzed, and showed that most of these analyzed words overlap with expressions of emotions, actions, and bodily states. Our review shows that hedonics are typically investigated as processes that accompany other functions, but the mechanisms of hedonics (as core processes) have not been fully elucidated.

Cortical Pain Processing in the Rat Anterior Cingulate Cortex and Primary Somatosensory Cortex

Pain is a complex multidimensional experience encompassing sensory-discriminative, affective-motivational and cognitive-emotional components mediated by different neural mechanisms. Investigations of neurophysiological signals from simultaneous recordings of two or more cortical circuits may reveal important circuit mechanisms on cortical pain processing. The anterior cingulate cortex (ACC) and primary somatosensory cortex (S1) represent two most important cortical circuits related to sensory and affective processing of pain. Here, we recorded in vivo extracellular activity of the ACC and S1 simultaneously from male adult Sprague-Dale rats (n = 5), while repetitive noxious laser stimulations were delivered to animalÕs hindpaw during pain experiments. We identified spontaneous pain-like events based on stereotyped pain behaviors in rats. We further conducted systematic analyses of spike and local field potential (LFP) recordings from both ACC and S1 during evoked and spontaneous pain episodes. From LFP recordings, we found stronger phase-amplitude coupling (theta phase vs. gamma amplitude) in the S1 than the ACC (n = 10 sessions), in both evoked (p = 0.058) and spontaneous pain-like behaviors (p = 0.017, paired signed rank test). In addition, pain-modulated ACC and S1 neuronal firing correlated with the amplitude of stimulus-induced event-related potentials (ERPs) during evoked pain episodes. We further designed statistical and machine learning methods to detect pain signals by integrating ACC and S1 ensemble spikes and LFPs. Together, these results reveal differential coding roles between the ACC and S1 in cortical pain processing, as well as point to distinct neural mechanisms between evoked and putative spontaneous pain at both LFP and cellular levels.

Ensembles of change-point detectors: implications for real-time BMI applications

Brain-machine interfaces (BMIs) have been widely used to study basic and translational neuroscience questions. In real-time closed-loop neuroscience experiments, many practical issues arise, such as trial-by-trial variability, and spike sorting noise or multi-unit activity. In this paper, we propose a new framework for change-point detection based on ensembles of independent detectors in the context of BMI application for detecting acute pain signals. Motivated from ensemble learning, our proposed "ensembles of change-point detectors" (ECPDs) integrate multiple decisions from independent detectors, which may be derived based on data recorded from different trials, data recorded from different brain regions, data of different modalities, or models derived from different learning methods. By integrating multiple sources of information, the ECPDs aim to improve detection accuracy (in terms of true positive and true negative rates) and achieve an optimal trade-off of sensitivity and specificity. We validate our method using computer simulations and experimental recordings from freely behaving rats. Our results have shown superior and robust performance of ECPDS in detecting the onset of acute pain signals based on neuronal population spike activity (or combined with local field potentials) recorded from single or multiple brain regions.