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BACKGROUND Traditional pain interventions limit fluctuations in pain sensation, which may paradoxically impair endogenous pain modulatory systems (EPMS). However, controlled exposures to clinically relevant pain (e.g. delayed onset muscle soreness [DOMS]) may build capacity in the EPMS. Emerging evidence suggests that regional signal variability (RSV) may be an important indicator of efficiency and modulatory capacity within brain regions. This study sought to determine the role of RSV in both susceptibility to and trainability of pain response following repeated DOMS inductions. METHODS Baseline and follow-up resting-state fMRI was performed on 12 healthy volunteers ~40 days apart. Between scanning visits, participants received four weekly DOMS inductions in alternating elbow flexors and were supplied seven days of post-induction pain ratings. Voxel-wise standard deviation of signal intensity was calculated to measure RSV. Associations among DOMS-related pain and RSV were assessed with regression. Relationships among baseline and change measurements were probed (i.e. susceptibility to DOMS; trainability following multiple inductions). RESULTS Significant association between baseline RSV in left middle frontal gyrus (MFG) and right cerebellum and reductions in DOMS-related pain unpleasantness were detected. Furthermore, increases in RSV were associated with reduced DOMS pain intensity (left lingual gyrus, right MTG, left MTG, left precuneus) and unpleasantness (left MTG, right SFG). DISCUSSION Findings suggest that RSV may be an indicator of EPMS resilience and responsivity to training, as well as an indicator that is responsive to training. Involved regions underlie cognitive, affective and representation processes. Results further clarify the potential role of RSV as an indicator of pain modulation and resilience. SIGNIFICANCE Regional signal variability may be an important indicator of endogenous pain modulatory system responsivity to training following repeated bouts of clinically relevant pain and may in fact be responsive to training itself.

The current investigation sought to clarify mechanisms of treatment effects in mindfulness-based stress reduction (MBSR). Self-compassion and mindful awareness were assessed first as dispositional influences and then as mediators of outcome in unique models. One hundred thirty individuals participating in the 8-week MBSR intervention were recruited (73.08% female, mean age = 46.97, SD = 14.07). Measures of psychosocial well-being (Brief Stress Inventory [BSI], Perceived Stress Scale-10 [PSS]), mindful awareness (Mindful Awareness and Attention Scale [MAAS]), and self-compassion (Self-Compassion Scale [SCS]) were collected at preintervention and postintervention. Regression was conducted to examine the influence of baseline MAAS and SCS on change in PSS and BSI scores. Serial multiple mediator models were conducted separately with pre/postintervention BSI and PSS values as criterion, and preintervention/postintervention MAAS and SCS values as mediators. Higher levels of baseline self-compassion were predictive of greater reductions in PSS scores (β = 0.16). Reductions in BSI scores were serially mediated by change in self-compassion both directly (MBSR → ΔSCS → ΔBSI β = 0.06) and indirectly through mindful awareness (MBSR → ΔMAAS → ΔSCS → ΔBSI β = 0.09). Results provide support for the role of self-compassion as both a predictor of treatment effect and a process through which MBSR operates. Mechanisms underlying MBSR effects appear to be unique to the outcome of interest.

Analgesic treatments that aim to eliminate pain display marginal success in relieving chronic pain and may increase pain vulnerability. Repeated exposure to pain may result in increased pain modulation via engagement of anti-nociceptive brain regions. It was hypothesized that repeated exposure to delayed onset muscle soreness (DOMS) would result in increased pain modulatory capacity (PMC) via functional neural adaptation. 23 healthy participants completed Baseline and Follow Up resting-state fMRI and quantitative sensory testing (QST) visits 40 days apart. Participants were randomized to two groups: A Repeated DOMS Group (RD Group) that received four, weekly DOMS inductions and a Control Group that received one baseline induction. Daily pain ratings were collected for seven days post-induction, as were quantitative sensory testing (QST) metrics at baseline and Follow Up. Regional functional connectivity (FC) was estimated among areas involved in pain modulation. Seed and network FC was estimated among areas involved in pain modulation and sensory processing. Changes in FC were compared between groups. The RD Group displayed significant reductions in post-DOMS pain ratings and significant changes in thermal QST measures. RD Group participants displayed greater adaptation in nucleus accumbens-medial prefrontal cortex (NAc-mPFC) FC and in sensorimotor network (SMN) connectivity with the dorsomedial, ventromedial, and rostromedial prefrontal cortices. Changes in SMN-PFC connectivity correlated with reductions in post-DOMS affective distress. Results suggest that repeated exposure to clinically-relevant pain results in adaptations among brain regions involved in pain modulation. Repeated exposure to clinically-relevant pain may serve as a mechanism to increase PMC via inhibition of emotional valuation of painful stimuli.

Musculoskeletal pain is an aversive experience that exists within a variety of conditions and can result in significant impairment for individuals. Gaining greater understanding of the factors related to pain vulnerability and resilience to musculoskeletal pain may help target at-risk individuals for early intervention. This analysis builds on our previous work identifying regions where greater gray matter density was associated with lower pain following standardized, exercise induced musculoskeletal injury. Here we sought to examine the relationship between baseline resting state functional connectivity in a priori regions and networks, and delayed onset muscle soreness (DOMS) pain intensity following a single session of eccentric exercise in healthy adults. Participants completed a baseline functional MRI scan and a high intensity trunk exercise protocol in the erector spinae. Pain intensity ratings were collected 48-hours later. Resting state functional connectivity from four seed regions and 3 networks were separately regressed on pain intensity scores. Results revealed that connectivity between left middle frontal gyrus, the left occipital gyrus and cerebellar network seeds and clusters associated with discriminative, emotional, and cognitive aspects of pain were associated with lower post-DOMS pain. Results suggest resilience to clinically relevant pain is associated with aspects of regional and network neural coherence. Investigations of pain modulatory capacity that integrate multimodal neuroimaging metrics are called for. Perspective: Our results provide key support for the role of structural and functional coherence in regional and network connectivity in adaptive pain response and represent an important step in clarifying neural mechanisms of resilience to clinically relevant pain.

Depressive symptoms are common among individuals with chronic pain. Previous work suggests that chronic pain patients have difficulty regulating emotional responses, which is a risk factor for the development of major depressive disorder (MDD). Function of the mesocorticolimbic system, a neural network associated with reward processing, contributes to emotion regulation. This network’s dysfunction has been described in chronic pain and MDD research and potentially underlies the relationship among emotion dysregulation, chronic pain, and MDD development. Given that mood induction paradigms have been used to measure emotion regulation, the present study examined intrinsic mesocorticolimbic functional connectivity (FC) after induced sad mood in individuals with and without chronic low back pain (cLBP). Thirty-three MDD-free individuals (17 cLBP) underwent resting-state scanning before and after sad memory-evoked mood induction. A Group [cLBP, healthy control (HC)] x Mood (Neutral, Sadness) repeated measures ANCOVA was conducted on seed-based FC data using a mesolimbic a priori region of interest. Interaction effects were identified in the orbital frontal cortex and inferior frontal gyrus [F((2, 29))=21.07, p(FDR) < .05. h(p)(2)=.5]. Whereas cLBP showed significantly greater FC between these two regions and the mesolimbic seed under neutral mood, FC among these regions increased in HC and decreased in cLBP under sad mood. Exploratory graph theory analyses further describe between-group differences in mesocorticolimbic network properties. Findings support previous literature describing mesocorticolimbic dysfunction in cLBP and demonstrate aberrant function in emotion regulation. Mesocorticolimbic dysfunction during emotion regulation might contribute to the development of certain depressive phenotypes in chronic pain patients.

Background Acute alcohol intoxication has wide-ranging neurobehavioral effects on psychomotor, attentional, inhibitory, and memory-related cognitive processes. These effects are mirrored in disruption of neural metabolism, functional activation, and functional network coherence. Metrics of intraregional neural dynamics such as regional signal variability (RSV) and brain entropy (BEN) may capture unique aspects of neural functional capacity in healthy and clinical populations; however, alcohol's influence on these metrics is unclear. The present study aimed to elucidate the influence of acute alcohol intoxication on RSV and to clarify these effects with subsequent BEN analyses. Methods 26 healthy adults between 25 and 45 years of age (65.4% women) participated in 2 counterbalanced sessions. In one, participants consumed a beverage containing alcohol sufficient to produce a breath alcohol concentration of 0.08 g/dl. In the other, they consumed a placebo beverage. Approximately 35 minutes after beverage consumption, participants completed a 9-minute resting-state fMRI scan. Whole-brain, voxel-wise standard deviation was used to assess RSV, which was compared between sessions. Within clusters displaying alterations in RSV, sample entropy was calculated to assess BEN. Results Compared to the placebo, alcohol intake resulted in widespread reductions in RSV in the bilateral middle frontal, right inferior frontal, right superior frontal, bilateral posterior cingulate, bilateral middle temporal, right supramarginal gyri, and bilateral inferior parietal lobule. Within these clusters, significant reductions in BEN were found in the bilateral middle frontal and right superior frontal gyri. No effects were noted in subcortical or cerebellar areas. Conclusions Findings indicate that alcohol intake produces diffuse reductions in RSV among structures associated with attentional processes. Within these structures, signal complexity was also reduced in a subset of frontal regions. Neurobehavioral effects of acute alcohol consumption may be partially driven by disruption of intraregional neural dynamics among regions involved in higher-order cognitive and attentional processes.

Musculoskeletal pain is an aversive experience that exists within a variety of conditions and can result in significant impairment for individuals. Gaining greater understanding of the factors related to pain vulnerability and resilience to musculoskeletal pain may help target at-risk individuals for early intervention. This analysis builds on our previous work identifying regions where lower gray matter density (GMD) was associated with individuals who report pain following standardized, exercise induced musculoskeletal injury. Here we sought to examine the relationship between baseline resting state functional connectivity (rsFC) in a priori regions and networks, and delayed onset muscle soreness (DOMS) pain intensity following a single session of eccentric exercise. The present study is a secondary analysis from a portion of a larger, blinded, randomized experimental trial. All participants were healthy adults between the ages of 18 and 40 years recruited from the university and surrounding community. The primary outcome measure was functional connectivity assessed by using T2 rest image. Participants completed a baseline functional MRI scan and a high intensity trunk exercise protocol to induce DOMS in the erector spinae. Pain intensity ratings were collected 48-hours later. rsFC from four seed regions and three networks were separately regressed on pain intensity scores. Our results revealed that connectivity between left middle frontal gyrus, the left occipital gyrus and cerebellar network seeds and clusters associated with discriminative, emotional, and cognitive aspects of pain were associated with lower post-DOMS pain. Our results provide key support for the role of structural and functional coherence in regional and network connectivity in adaptive pain response and represent an important step in clarifying neural mechanisms of resilience to clinically relevant pain. Resilience to clinically relevant pain is associated with aspects of regional and network neural coherence. Investigations of pain modulatory capacity that integrate multimodal neuroimaging metrics are called for. R01AT006334, PI Dr. Mark Bishop.

Chronic fatigue syndrome (CFS) is a disorder associated with fatigue, pain, and structural/functional abnormalities seen during magnetic resonance brain imaging (MRI). Therefore, we evaluated the performance of structural MRI (sMRI) abnormalities in the classification of CFS patients versus healthy controls and compared it to machine learning (ML) classification based upon self-report (SR). Participants included 18 CFS patients and 15 healthy controls (HC). All subjects underwent T1-weighted sMRI and provided visual analogue-scale ratings of fatigue, pain intensity, anxiety, depression, anger, and sleep quality. sMRI data were segmented using FreeSurfer and 61 regions based on functional and structural abnormalities previously reported in patients with CFS. Classification was performed in RapidMiner using a linear support vector machine and bootstrap optimism correction. We compared ML classifiers based on (1) 61 a priori sMRI regional estimates and (2) SR ratings. The sMRI model achieved 79.58% classification accuracy. The SR (accuracy = 95.95%) outperformed both sMRI models. Estimates from multiple brain areas related to cognition, emotion, and memory contributed strongly to group classification. This is the first ML-based group classification of CFS. Our findings suggest that sMRI abnormalities are useful for discriminating CFS patients from HC, but SR ratings remain most effective in classification tasks.

A better understanding of the neural mechanisms underlying pain processing and analgesia may aid in the development and personalization of effective treatments for chronic pain. Clarification of the neural predictors of individual variability in placebo analgesia (PA) could aid in this process. The present study examined whether the strength of effective connectivity (EC) among pain-related brain regions could predict future placebo analgesic response in healthy individuals. In Visit 1, fMRI data were collected from 24 healthy subjects (13 female, mean age=22.56, SD=2.94) while experiencing painful thermal stimuli. During Visit 2, subjects were conditioned to expect less pain via a surreptitiously lowered temperature applied at two of the four sites on their feet. They were subsequently scanned again using the Visit 1 (painful) temperature. Subjects used an electronic VAS to rate their pain following each stimulus. Differences in ratings at conditioned and unconditioned sites were used to measure placebo response (PA scores). Dynamic causal modeling was used to estimate the EC among a set of brain regions related to pain processing at Visit 1 (periaqueductal gray, thalamus, rostral anterior cingulate cortex, dorsolateral prefrontal cortex). Individual PA scores from Visit 2 were regressed on salient EC parameters estimates from Visit 1. Results indicate that both greater left hemisphere modulatory DLPFC→PAG connectivity and right hemisphere, endogenous thalamus→DLPFC connectivity were significantly predictive of future placebo response (R2 = 0.82). To our knowledge, this is the first study to identify the value of EC in understanding individual differences in PA, and may suggest the potential modifiability of endogenous pain modulation.

INTRODUCTION: Chronic fatiguing illnesses like cancer, multiple sclerosis, chronic fatigue syndrome, or depression are frequently associated with comorbidities including depression, pain, and insomnia, making the study of their neural correlates challenging. To study fatigue without such comorbidities, functional connectivity (FC) analyses were used in healthy individuals to study brain activity during recall of a fatiguing event inside the MRI scanner. A positive mood induction served as control condition. METHOD: Using SPM8 and the CONN toolbox, FC was tested using seed- and independent component- based (ICA) analyses. Differences in the FC correlations between seed-to-voxel and ICA clusters between conditions were assessed with permutation testing. RESULTS: 17 participants (59% women) achieved mean (SD) in-scanner fatigue VAS ratings of 31.85 (20.61). Positive mood induction resulted in happiness ratings of 46.07 (18.99) VAS. Brain regions where alterations in FC correlated with fatigue included the globus pallidum, left lateral occipital cortex, and cuneus. FC of happiness involved the parahippocampal gyrus, both supplemental motor areas, as well as right superior frontal gyrus. Using data-driven ICA, we identified an intra-cerebellar network where several regions were significantly associated with fatigue, but not happiness ratings. Results of permutation testing provided evidence that the detected clusters correlated differentially with self-reported fatigue and happiness. CONCLUSIONS: Our study suggests that functional interactions between globus pallidum and occipital structures contribute to experimental fatigue in healthy individuals. They also highlight the important role of cortico-cerebellar interactions in producing feelings of fatigue. FC of occipital structures contributed to both experimental fatigue and happiness ratings.

Test-retest reliability, or reproducibility of results over time, is poorly established for functional brain connectivity (fcMRI) during painful stimulation. As reliability informs the validity of research findings, it is imperative to examine, especially given recent emphasis on using functional neuroimaging as a tool for biomarker development. Although proposed pain neural signatures have been derived using complex, multivariate algorithms, even the reliability of less complex fcMRI findings has yet to be reported. This study examined the test-retest reliability for fcMRI of pain-related brain regions, and self-reported pain (through visual analogue scales [VASs]). Thirty-two healthy individuals completed 3 consecutive fMRI runs of a thermal pain task. Functional connectivity analyses were completed on pain-related brain regions. Intraclass correlations were conducted on fcMRI values and VAS scores across the fMRI runs. Intraclass correlations coefficients for fcMRI values varied widely (range = -.174-.766), with fcMRI between right nucleus accumbens and medial prefrontal cortex showing the highest reliability (range = .649-.766). Intraclass correlations coefficients for VAS scores ranged from .906 to .947. Overall, self-reported pain was more reliable than fcMRI data. These results highlight that fMRI findings might be less reliable than inherently assumed and have implications for future studies proposing pain markers.

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

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

The dorsolateral prefrontal cortex (DLPFC) is implicated in pain modulation through multiple psychological processes. Recent noninvasive brain stimulation studies suggest that interhemispheric DLPFC connectivity influences pain tolerance and discomfort by altering interhemispheric inhibition. The structure and role of interhemispheric DLPFC connectivity in pain processing have not been investigated. The present study used dynamic causal modeling (DCM) for fMRI to investigate transcallosal DLPFC connectivity during painful stimulation in healthy volunteers. DCM parameters were used to predict individual differences in sensitivity to noxious heat stimuli. Bayesian model selection results indicated that influences among the right DLPFC (rDLPFC) and left DLPFC (lDLPFC) are modulated during painful stimuli. Regression analyses revealed that greater rDLPFC→lDLPFC couplings were associated with higher suprathreshold pain temperatures. These results highlight the role of interhemispheric connectivity in pain modulation and support the preferential role of the right hemisphere in pain processing. Knowledge of these mechanisms may improve understanding of abnormal pain modulation in chronic pain populations.

The use of placebo to reduce pain is well documented; however, knowledge of the neural mechanisms underlying placebo analgesia remains incomplete. This study used functional magnetic resonance imaging data from 30 healthy individuals and dynamic causal modeling to investigate changes in effective connectivity associated with the placebo analgesic response. Before scanning, participants were conditioned to expect less thermal pain at 2 of 4 sites on their feet. Visual analog scale pain ratings revealed a significant but small difference between the baseline and placebo sites (mean difference = 6.63, t(29) = 3.91, P ≤ .001, d = .97), confirming an analgesic effect. However, no significant differences in the magnitude of brain activation between conditions were observed via traditional random effects general linear modeling. Dynamic causal modeling was then used to investigate changes in effective connectivity during placebo analgesia. The results indicate that during placebo analgesia but not baseline condition, couplings between brain regions, including those involved in cognitive processes (eg, attention, expectation, evaluation), were significantly enhanced. Specifically, a significantly consistent decrease in the dorsolateral prefrontal cortex → periaqueductal gray coupling was found. These findings highlight the differences between pain processing and modulation at the network level. Moreover, our results suggest that small placebo effects may be better characterized via changes in the temporal dynamics among pain modulatory regions than only via changes in the magnitude of blood oxygenation level dependent activation. Further application of nuanced analytical approaches that are sensitive to temporal dynamics of pain-related processes such as dynamic causal modeling are necessary to better understand the neural mechanisms underlying pain processing in patient populations. Perspective Changes in effective connectivity among pain-related brain regions may be more sensitive detectors of the neural representation of small placebo effects than are changes in the magnitude of brain activation. Knowledge of these mechanisms highlights the importance of integrated neural networks in the understanding of pain modulation.