Painful Issues in Pain Prediction

Establishing the relationship between subjective perception and neural responses: Insights from correlation analysis and representational similarity analysis

Exploring the relationship between sensory perception and brain responses holds important theoretical and clinical implications. However, commonly used methodologies like correlation analysis performed either intra- or inter- individually often yield inconsistent results across studies, limiting their generalizability. Representational similarity analysis (RSA), a method that assesses the perception-response relationship by calculating the correlation between behavioral and neural patterns, may offer a fresh perspective to reveal novel findings. Here, we delivered a series of graded sensory stimuli of four modalities (i.e., nociceptive somatosensory, non-nociceptive somatosensory, visual, and auditory) to/near the left or right hand of 107 healthy subjects and collected their single-trial perceptual ratings and electroencephalographic (EEG) responses. We examined the relationship between sensory perception and brain responses using within- and between-subject correlation analysis and RSA, and assessed their stability across different numbers of subjects and trials. We found that within-subject and between-subject correlations yielded distinct results: within-subject correlation revealed strong and reliable correlations between perceptual ratings and most brain responses, while between-subject correlation showed weak correlations that were vulnerable to the change of subject number. In addition to verifying the correlation results, RSA revealed some novel findings, i.e., correlations between behavioral and neural patterns were observed in some additional neural responses, such as "γ-ERS" in the visual modality. RSA results were sensitive to the trial number, but not to the subject number, suggesting that consistent results could be obtained for studies with relatively small sample sizes. In conclusion, our study provides a novel perspective on establishing the relationship between behavior and brain activity, emphasizing that RSA holds promise as a method for exploring this pattern relationship in future research.

Mental state decoders: game-changers or wishful thinking?

Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.

Methodological advice for the young at heart investigator: Triangulation to build better foundations

In medicine and science, one is typically taught the main theories in a discipline or field along with standard models before receiving more instructions on how to apply certain methods. The aim of this work is not to address one method, but rather methodology, the study and evaluation of methods, by taking a philosophy of science detour. In this, a critique of biomedicine will be used as a starting point to address some positions regarding reductionism, specifying notions such as systems and mechanisms, as well as regarding the mind-body problem discussing psychosomatic medicine and psychoneuroimmunology. Some recommendations to make science more pluralistic, robust and translationally-relevant will then be made as a way to foster constructive debates on reductionism and the mind-body problem and, in turn, favor more interdisciplinary research.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

Classification of primary dysmenorrhea by brain effective connectivity of the amygdala: a machine learning study

BACKGROUND

The amygdala plays a crucial role in the central pathogenesis mechanism of primary dysmenorrhea (PDM). However, the detailed pain modulation principles of the amygdala in PDM remain unclear. Here, we applied the Granger causality analysis (GCA) to investigate the directional effective connectivity (EC) alterations in the amygdala network of PDM patients.

METHODS

Thirty-seven patients with PDM and 38 healthy controls were enrolled in this study and underwent resting-state functional magnetic resonance imaging scans during the pain-free stage. GCA was employed to explore the amygdala-based EC network alteration in PDM. A multivariate pattern analysis (MVPA)-based machine learning approach was used to explore whether the altered amygdala EC could serve as an fMRI-based marker for classifying PDM and HC participants.

RESULTS

Compared to the healthy control group, patients with PDM showed significantly decreased EC from the amygdala to the right superior frontal gyrus (SFG), right superior parietal lobe/middle occipital gyrus, and left middle cingulate cortex, whereas increased EC was found from the amygdala to the bilateral medial orbitofrontal cortex. In addition, increased EC was found from the bilateral SFG to the amygdala, and decreased EC was found from the medial orbitofrontal cortex, caudate nucleus to the amygdala. The increased EC from the right SFG to the amygdala was associated with a plasma prostaglandin E2 level in PDM. The MVPA based on an altered amygdala EC pattern yielded a total accuracy of 86.84% for classifying the patients with PDM and HC.

CONCLUSION

Our study is the first to combine MVPA and EC to explore brain function alteration in PDM. The results could advance understanding of the neural theory of PDM in specifying the pain-free period.

Limits of decoding mental states with fMRI

A growing number of studies claim to decode mental states using multi-voxel decoders of brain activity. It has been proposed that the fixed, fine-grained, multi-voxel patterns in these decoders are necessary for discriminating between and identifying mental states. Here, we present evidence that the efficacy of these decoders might be overstated. Across various tasks, decoder patterns were spatially imprecise, as decoder performance was unaffected by spatial smoothing; 90% redundant, as selecting a random 10% of a decoder's constituent voxels recovered full decoder performance; and performed similarly to brain activity maps used as decoders. We distinguish decoder performance in discriminating between mental states from performance in identifying a given mental state, and show that even when discrimination performance is adequate, identification can be poor. Finally, we demonstrate that simple and intuitive similarity metrics explain 91% and 62% of discrimination performance within- and across-subjects, respectively. These findings indicate that currently used across-subject decoders of mental states are superfluous and inappropriate for decision-making.

Gender discrimination facilitates fMRI responses and connectivity to thermal pain

Gender discrimination is a serious social issue that has been shown to increase negative consequences, especially in females when accompanied by acute or chronic pain. Experiencing social pain through discrimination can increase an individual's evaluation of evoked physical pain. However, few studies have explored the mechanism underlying how gender discrimination modulates brain responses when individuals experience physical pain evoked by noxious stimuli. In this study, we addressed this issue using a gender discrimination fMRI paradigm with thermal pain stimulation. We found that discrimination indeed affected participants' own behavioral self-evaluation of noxious stimuli. Discrimination-encoded brain activations were identified in the temporopolar cortex, while brain activations to thermal stimuli after viewing pictures of discrimination were found in the dorsal anterior cingulate cortex (dACC). Brain activations in the temporopolar cortex and the dACC were correlated. Furthermore, pain perception-specific functional connectivity of the dACC-SII in the cue stage and the dACC-frontal in the pain stage were identified, suggesting a facilitative effect of gender discrimination on females' experience of physical pain. Our results indicate that the dACC may play a central role in mediating the affective aspect of physical pain after experiencing discrimination. These findings provide novel insights into the underlying mechanism of how gender discrimination facilitates females' experience of physical pain.

Brain Gene Expression Pattern Correlated with the Differential Brain Activation by Pain and Touch in Humans

Genes involved in pain and touch sensations have been studied extensively, but very few studies have tried to link them with neural activities in the brain. Here, we aimed to identify genes preferentially correlated to painful activation patterns by linking the spatial patterns of gene expression of Allen Human Brain Atlas with the pain-elicited neural responses in the human brain, with a parallel, control analysis for identification of genes preferentially correlated to tactile activation patterns. We identified 1828 genes whose expression patterns preferentially correlated to painful activation patterns and 411 genes whose expression patterns preferentially correlated to tactile activation pattern at the cortical level. In contrast to the enrichment for astrocyte and inhibitory synaptic transmission of genes preferentially correlated to tactile activation, the genes preferentially correlated to painful activation were mainly enriched for neuron and opioid- and addiction-related pathways and showed significant overlap with pain-related genes identified in previous studies. These findings not only provide important evidence for the differential genetic architectures of specific brain activation patterns elicited by painful and tactile stimuli but also validate a new approach to studying pain- and touch-related genes more directly from the perspective of neural responses in the human brain.

Feedforward and feedback pathways of nociceptive and tactile processing in human somatosensory system: A study of dynamic causal modeling of fMRI data

Nociceptive and tactile information is processed in the somatosensory system via reciprocal (i.e., feedforward and feedback) projections between the thalamus, the primary (S1) and secondary (S2) somatosensory cortices. The exact hierarchy of nociceptive and tactile information processing within this 'thalamus-S1-S2' network and whether the processing hierarchy differs between the two somatosensory submodalities remains unclear. In particular, two questions related to the ascending and descending pathways have not been addressed. For the ascending pathways, whether tactile or nociceptive information is processed in parallel (i.e., 'thalamus-S1' and 'thalamus-S2') or in serial (i.e., 'thalamus-S1-S2') remains controversial. For the descending pathways, how corticothalamic feedback regulates nociceptive and tactile processing also remains elusive. Here, we aimed to investigate the hierarchical organization for the processing of nociceptive and tactile information in the 'thalamus-S1-S2' network using dynamic causal modeling (DCM) combined with high-temporal-resolution fMRI. We found that, for both nociceptive and tactile information processing, both S1 and S2 received inputs from thalamus, indicating a parallel structure of ascending pathways for nociceptive and tactile information processing. Furthermore, we observed distinct corticothalamic feedback regulations from S1 and S2, showing that S1 generally exerts inhibitory feedback regulation independent of external stimulation whereas S2 provides additional inhibition to the thalamic activity during nociceptive and tactile information processing in humans. These findings revealed that nociceptive and tactile information processing have similar hierarchical organization within the somatosensory system in the human brain.

A New Apparatus for Recording Evoked Responses to Painful and Non-painful Sensory Stimulation in Freely Moving Mice

Experiments on pain processing in animals face several methodological challenges including the reproducible application of painful stimuli. Ideally, behavioral and physiological correlates of pain should be assessed in freely behaving mice, avoiding stress, fear or behavioral restriction as confounding factors. Moreover, the time of pain-evoked brain activity should be precisely related to the time of stimulation, such that pain-specific neuronal activity can be unambiguously identified. This can be achieved with laser-evoked heat stimuli which are also well established for human pain research. However, laser-evoked neuronal potentials are rarely investigated in awake unrestrained rodents, partially due to the practical difficulties in precisely and reliably targeting and triggering stimulation. In order to facilitate such studies we have developed a versatile stimulation and recording system for freely moving mice. The custom-made apparatus can provide both laser- and mechanical stimuli with simultaneous recording of evoked potentials and behavioral responses. Evoked potentials can be recorded from superficial and deep brain areas showing graded pain responses which correlate with pain-specific behavioral reactions. Non-painful mechanical stimuli can be applied as a control, yielding clearly different electrophysiological and behavioral responses. The apparatus is suited for simultaneous acquisition of precisely timed electrophysiological and behavioral evoked responses in freely moving mice. Besides its application in pain research it may be also useful in other fields of sensory physiology.

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

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

Waves of Change: Brain Sensitivity to Differential, not Absolute, Stimulus Intensity is Conserved Across Humans and Rats

Living in rapidly changing environments has shaped the mammalian brain toward high sensitivity to abrupt and intense sensory events-often signaling threats or affordances requiring swift reactions. Unsurprisingly, such events elicit a widespread electrocortical response (the vertex potential, VP), likely related to the preparation of appropriate behavioral reactions. Although the VP magnitude is largely determined by stimulus intensity, the relative contribution of the differential and absolute components of intensity remains unknown. Here, we dissociated the effects of these two components. We systematically varied the size of abrupt intensity increases embedded within continuous stimulation at different absolute intensities, while recording brain activity in humans (with scalp electroencephalography) and rats (with epidural electrocorticography). We obtained three main results. 1) VP magnitude largely depends on differential, and not absolute, stimulus intensity. This result held true, 2) for both auditory and somatosensory stimuli, indicating that sensitivity to differential intensity is supramodal, and 3) in both humans and rats, suggesting that sensitivity to abrupt intensity differentials is phylogenetically well-conserved. Altogether, the current results show that these large electrocortical responses are most sensitive to the detection of sensory changes that more likely signal the sudden appearance of novel objects or events in the environment.

Magnetic resonance imaging for chronic pain: diagnosis, manipulation, and biomarkers

Pain is a multidimensional subjective experience with biological, psychological, and social factors. Whereas acute pain can be a warning signal for the body to avoid excessive injury, long-term and ongoing pain may be developed as chronic pain. There are more than 100 million people in China living with chronic pain, which has raised a huge socioeconomic burden. Studying the mechanisms of pain and developing effective analgesia approaches are important for basic and clinical research. Recently, with the development of brain imaging and data analytical approaches, the neural mechanisms of chronic pain have been widely studied. In the first part of this review, we briefly introduced the magnetic resonance imaging and conventional analytical approaches for brain imaging data. Then, we reviewed brain alterations caused by several chronic pain disorders, including localized and widespread primary pain, primary headaches and orofacial pain, musculoskeletal pain, and neuropathic pain, and present meta-analytical results to show brain regions associated with the pathophysiology of chronic pain. Next, we reviewed brain changes induced by pain interventions, such as pharmacotherapy, neuromodulation, and acupuncture. Lastly, we reviewed emerging studies that combined advanced machine learning and neuroimaging techniques to identify diagnostic, prognostic, and predictive biomarkers in chronic pain patients.

Designing individual-specific and trial-specific models to accurately predict the intensity of nociceptive pain from single-trial fMRI responses

Using machine learning to predict the intensity of pain from fMRI has attracted rapidly increasing interests. However, due to remarkable inter- and intra-individual variabilities in pain responses, the performance of existing fMRI-based pain prediction models is far from satisfactory. The present study proposed a new approach which can design a prediction model specific to each individual or each experimental trial so that the specific model can achieve more accurate prediction of the intensity of nociceptive pain from single-trial fMRI responses. More precisely, the new approach uses a supervised k-means method on nociceptive-evoked fMRI responses to cluster individuals or trials into a set of subgroups, each of which has similar and consistent fMRI activation patterns. Then, for a new test individual/trial, the proposed approach chooses one subgroup of individuals/trials, which has the closest fMRI patterns to the test individual/trial, as training samples to train an individual-specific or a trial-specific pain prediction model. The new approach was tested on a nociceptive-evoked fMRI dataset and achieved significantly higher prediction accuracy than conventional non-specific models, which used all available training samples to train a model. The generalizability of the proposed approach is further validated by training specific models on one dataset and testing these models on an independent new dataset. This proposed individual-specific and trial-specific pain prediction approach has the potential to be used for the development of individualized and precise pain assessment tools in clinical practice.

Inferring pain experience in infants using quantitative whole-brain functional MRI signatures: a cross-sectional, observational study

Background

In the absence of verbal communication, it is challenging to infer an individual's sensory and emotional experience. In communicative adults, functional MRI (fMRI) has been used to develop multivariate brain activity signatures, which reliably capture elements of human pain experience. We aimed to translate whole-brain fMRI signatures that encode pain perception in adults to the newborn infant brain, to advance understanding of functional brain development and pain perception in early life.

Methods

In this cross-sectional, observational study, we recruited adults at the University of Oxford (Oxford, UK) and infants on the postnatal wards of John Radcliffe Hospital (Oxford, UK). Healthy full-term infants were eligible for inclusion if they were clinically stable, self-ventilating in air, and had no neurological abnormalities. Infants were consecutively recruited in two cohorts (A and B) due to the installation of a new fMRI scanner using the same recruitment criteria. Adults (aged ≥18 years) were eligible if they were postgraduate students or staff at the University of Oxford. Participants were stimulated with low intensity nociceptive stimuli (64, 128, 256, and 512 mN in adults; 64 and 128 mN in infants) during acquisition of fMRI data. fMRI pain signatures (neurologic pain signature [NPS] and stimulus intensity independent pain signature-1 [SIIPS1]), and four control signatures (the vicarious pain signature, the picture-induced negative emotion signature [PINES], the social rejection signature, and a global signal signature) were applied directly to the adult data and translated to the infant brain. We assessed the concordance of the signatures with the brain responses of adults and infants using cosine similarity scores, and we assessed stimulus intensity encoding of the signature responses using a Spearman rank correlation test. We also assessed brain activity in pro-pain and anti-pain components of the signatures.

Findings

Between May 22, 2013, and Jan 29, 2018, we recruited ten healthy participants to the adult cohort (five women and five men; mean age 28·3 years [range 23-36]), 15 infants to infant cohort A (six girls and nine boys; mean postnatal age 4 days [range 1-11]), and 22 infants to infant cohort B (11 girls and 11 boys; mean postnatal age 3 days [range 1-10]). The NPS was activated in both the adults and infants, and reliably encoded stimulus intensity. The NPS was activated in the adult cohort (p<0·0001) and both infant cohorts (p=0·048 for infant cohort A; p=0·001 for infant cohort B). The SIIPS1 was only expressed in adults. Pro-pain brain regions showed similar activation patterns in adults and infants, whereas responses in anti-pain brain regions were divergent.

Interpretation

Basic intensity encoding of nociceptive information is similar in adults and infants. However, translation of adult brain signatures to infants indicated substantial differences in infant cerebral processing of nociceptive information, which might reflect their absence of expectation, motivation, and contextualisation associated with pain. This study expands the use of brain activity pain signatures to non-verbal patients and provides a potential research approach to assess the impact of analgesic interventions on brain function in infants.

Funding

Wellcome Trust, Supporting the Sick Newborn and their Parents Medical Research Fund.

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.

A review on the ongoing quest for a pain signature in the human brain

Developing an objective biomarker for pain assessment is crucial for understanding neural coding mechanisms of pain in the human brain as well as for effective treatment of pain disorders. Neuroimaging techniques have been proven to be powerful tools in the ongoing quest for a pain signature in the human brain. Although there is still a long way to go before achieving a truly successful pain signature based on neuroimaging techniques, important progresses have been made through great efforts in the last two decades by the Pain Society. Here, we focus on neural responses to transient painful stimuli in healthy people, and review the relevant studies on the identification of a neuroimaging signature for pain.

The Neural Origin of Nociceptive-Induced Gamma-Band Oscillations

Gamma-band oscillations (GBOs) elicited by transient nociceptive stimuli are one of the most promising biomarkers of pain across species. Still, whether these GBOs reflect stimulus encoding in the primary somatosensory cortex (S1) or nocifensive behavior in the primary motor cortex (M1) is debated. Here we recorded neural activity simultaneously from the brain surface as well as at different depths of the bilateral S1/M1 in freely-moving male rats receiving nociceptive stimulation. GBOs measured from superficial layers of S1 contralateral to the stimulated paw not only had the largest magnitude, but also showed the strongest temporal and phase coupling with epidural GBOs. Also, spiking of superficial S1 interneurons had the strongest phase coherence with epidural GBOs. These results provide the first direct demonstration that scalp GBOs, one of the most promising pain biomarkers, reflect neural activity strongly coupled with the fast spiking of interneurons in the superficial layers of the S1 contralateral to the stimulated side.SIGNIFICANCE STATEMENT Nociceptive-induced gamma-band oscillations (GBOs) measured at population level are one of the most promising biomarkers of pain perception. Our results provide the direct demonstration that these GBOs reflect neural activity coupled with the spike firing of interneurons in the superficial layers of the primary somatosensory cortex (S1) contralateral to the side of nociceptive stimulation. These results address the ongoing debate about whether nociceptive-induced GBOs recorded with scalp EEG or epidurally reflect stimulus encoding in the S1 or nocifensive behavior in the primary motor cortex (M1), and will therefore influence how experiments in pain neuroscience will be designed and interpreted.

Ultralow-frequency neural entrainment to pain

Nervous systems exploit regularities in the sensory environment to predict sensory input, adjust behavior, and thereby maximize fitness. Entrainment of neural oscillations allows retaining temporal regularities of sensory information, a prerequisite for prediction. Entrainment has been extensively described at the frequencies of periodic inputs most commonly present in visual and auditory landscapes (e.g., >0.5 Hz). An open question is whether neural entrainment also occurs for regularities at much longer timescales. Here, we exploited the fact that the temporal dynamics of thermal stimuli in natural environment can unfold very slowly. We show that ultralow-frequency neural oscillations preserved a long-lasting trace of sensory information through neural entrainment to periodic thermo-nociceptive input as low as 0.1 Hz. Importantly, revealing the functional significance of this phenomenon, both power and phase of the entrainment predicted individual pain sensitivity. In contrast, periodic auditory input at the same ultralow frequency did not entrain ultralow-frequency oscillations. These results demonstrate that a functionally significant neural entrainment can occur at temporal scales far longer than those commonly explored. The non-supramodal nature of our results suggests that ultralow-frequency entrainment might be tuned to the temporal scale of the statistical regularities characteristic of different sensory modalities.

Primer on machine learning: utilization of large data set analyses to individualize pain management

PURPOSE OF REVIEW

Pain researchers and clinicians increasingly encounter machine learning algorithms in both research methods and clinical practice. This review provides a summary of key machine learning principles, as well as applications to both structured and unstructured datasets.

RECENT FINDINGS

Aside from increasing use in the analysis of electronic health record data, machine and deep learning algorithms are now key tools in the analyses of neuroimaging and facial expression recognition data used in pain research.

SUMMARY

In the coming years, machine learning is likely to become a key component of evidence-based medicine, yet will require additional skills and perspectives for its successful and ethical use in research and clinical settings.

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

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

[Generating knowledge from complex data sets in human experimental pain research]

Pain has a complex pathophysiology that is expressed in multifaceted and heterogeneous clinical phenotypes. This makes research on pain and its treatment a potentially data-rich field as large amounts of complex data are generated. Typical sources of such data are investigations with functional magnetic resonance imaging, complex quantitative sensory testing, next-generation DNA sequencing and functional genomic research approaches, such as those aimed at analgesic drug discovery or repositioning of drugs known from other indications as new analgesics. Extracting information from these big data requires complex data scientific-based methods belonging more to computer science than to statistics. A particular interest is currently focused on machine learning, the methods of which are used for the detection of interesting and biologically meaningful structures in high-dimensional data. Subsequently, classifiers can be created that predict clinical phenotypes from, e.g. clinical or genetic features acquired from subjects. In addition, knowledge discovery in big data accessible in electronic knowledge bases, can be used to generate hypotheses and to exploit the accumulated knowledge about pain for the discovery of new analgesic drugs. This enables so-called data-information-knowledge-wisdom (DIKW) approaches to be followed in pain research. This article highlights current examples from pain research to provide an overview about contemporary data scientific methods used in this field of research.

The parietal operculum preferentially encodes heat pain and not salience

Substantial controversy exists as to which part of brain activity is genuinely attributable to pain-related percepts and which activity is due to general aspects of sensory stimulation, such as its salience, or the accompanying arousal. The challenge posed by this question rests largely in the fact that pain per se exhibits highly intense but unspecific characteristics. These therefore should be matched by potential control conditions. Here, we used a unique combination of functional magnetic resonance imaging (fMRI) and behavioral and autonomic measures to address this longstanding debate in pain research. Subjects rated perceived intensity in a sequence alternating between heat and sound stimuli. Neuronal activity was monitored using fMRI. Either modality was presented in 6 different intensities, 3 of which lay above the pain threshold (for heat) or the unpleasantness threshold (for sound). We performed our analysis on 26 volunteers in which psychophysiological responses (as per skin conductance responses [SCRs]) did not differ between the 2 stimulus modalities. Having thus ascertained a comparable amount of stimulation-related but unspecific activation, we analyzed stimulus-response functions (SRFs) after painful stimulation and contrasted them with those of the matched acoustic control condition. Furthermore, analysis of fMRI data was performed on the brain surface to circumvent blurring issues stemming from the close proximity of several regions of interest located in heavily folded cortical areas. We focused our analyses on insular and peri-insular regions that are strongly involved in processing of painful stimuli. We employed an axiomatic approach to determine areas showing higher activation in painful compared to nonpainful heat and, at the same time, showing a steeper SRF for painful heat compared to unpleasant sound. Intriguingly, an area in the posterior parietal operculum emerged, whose response showed a pain preference after satisfying all axiomatic constraints. This result has important implications for the interpretation of functional imaging findings in pain research, because it clearly demonstrates that there are areas where activity following painful stimulation is not due to general attributes or results of sensory stimulation, such as salience or arousal. Conversely, several areas did not conform to the formulated axioms to rule out general factors as explanations.

The brain activity detected during pain could be due merely to the fact that pain is arousing and attention-grabbing, rather than being directly attributable to the pain itself. This study identifies an area of the brain — the parietal operculum — whose activity can only be explained by the painfulness of pain.

Identifying inter-individual differences in pain threshold using brain connectome: a test-retest reproducible study

Individuals are unique in terms of brain and behavior. Some are very sensitive to pain, while others have a high tolerance. However, how inter-individual intrinsic differences in the brain are related to pain is unknown. Here, we performed longitudinal test-retest analyses to investigate pain threshold variability among individuals using a resting-state fMRI brain connectome. Twenty-four healthy subjects who received four MRI sessions separated by at least 7 days were included in the data analysis. Subjects' pain thresholds were measured using two modalities of experimental pain (heat and pressure) on two different locations (heat pain: leg and arm; pressure pain: leg and thumbnail). Behavioral results showed strong inter-individual variability and strong within-individual stability in pain threshold. Resting state fMRI data analyses showed that functional connectivity profiles can accurately identify subjects across four sessions, indicating that an individual's connectivity profile may be intrinsic and unique. By using multivariate pattern analyses, we found that connectivity profiles could be used to predict an individual's pain threshold at both within-session and between-session levels, with the most predictive contribution from medial-frontal and frontal-parietal networks. These results demonstrate the potential of using a resting-state fMRI brain connectome to build a 'neural trait' for characterizing an individual's pain-related behavior, and such a 'neural trait' may eventually be used to personalize clinical assessments.

Multivariate resting-state functional connectivity predicts responses to real and sham acupuncture treatment in chronic low back pain

Despite the high prevalence and socioeconomic impact of chronic low back pain (cLBP), treatments for cLBP are often unsatisfactory, and effectiveness varies widely across patients. Recent neuroimaging studies have demonstrated abnormal resting-state functional connectivity (rsFC) of the default mode, salience, central executive, and sensorimotor networks in chronic pain patients, but their role as predictors of treatment responsiveness has not yet been explored. In this study, we used machine learning approaches to test if pre-treatment rsFC can predict responses to both real and sham acupuncture treatments in cLBP patients. Fifty cLBP patients participated in 4 weeks of either real (N = 24, age = 39.0 ± 12.6, 16 females) or sham acupuncture (N = 26, age = 40.0 ± 13.7, 15 females) treatment in a single-blinded trial, and a resting-state fMRI scan prior to treatment was used in data analysis. Both real and sham acupuncture can produce significant pain reduction, with those receiving real treatment experiencing greater pain relief than those receiving sham treatment. We found that pre-treatment rsFC could predict symptom changes with up to 34% and 29% variances for real and sham treatment, respectively, and the rsFC characteristics that were significantly predictive for real and sham treatment differed. These results suggest a potential way to predict treatment responses and may facilitate the development of treatment plans that optimize time, cost, and available resources.

Spatial Patterns of Brain Activity Preferentially Reflecting Transient Pain and Stimulus Intensity

How pain emerges in the human brain remains an unresolved question. Neuroimaging studies have suggested that several brain areas subserve pain perception because their activation correlates with perceived pain intensity. However, painful stimuli are often intense and highly salient; therefore, using both intensity- and saliency-matched control stimuli is crucial to isolate pain-selective brain responses. Here, we used these intensity/saliency-matched painful and non-painful stimuli to test whether pain-selective information can be isolated in the functional magnetic resonance imaging responses elicited by painful stimuli. Using two independent datasets, multivariate pattern analysis was able to isolate features distinguishing the responses triggered by (1) intensity/saliency-matched painful versus non-painful stimuli, and (2) high versus low-intensity/saliency stimuli regardless of whether they elicit pain. This indicates that neural activity in the so-called "pain matrix" is functionally heterogeneous, and part of it carries information related to both painfulness and intensity/saliency. The response features distinguishing these aspects are spatially distributed and cannot be ascribed to specific brain structures.

Brain oscillations reflecting pain-related behavior in freely moving rats

Supplemental Digital Content is Available in the Text.

We comprehensively characterized the physiological properties of pain-related brain oscillations in freely moving rats and provided a foundation for the animal-to-human translation of experimental findings.

Recording oscillatory brain activity holds great promise in pain research. However, experimental results are variable and often difficult to reconcile. Some of these inconsistencies arise from the use of hypothesis-driven analysis approaches that (1) do not assess the consistency of the observed responses within and across individuals, and (2) do not fully exploit information sampled across the entire cortex. Here, we address these issues by recording the electrocorticogram directly from the brain surface of 12 freely moving rats. Using a hypothesis-free approach, we isolated brain oscillations induced by graded nociceptive stimuli and characterized their relation to pain-related behavior. We isolated 4 responses, one phase-locked event-related potential, 2 non–phase-locked event-related synchronizations, and one non–phase-locked event-related desynchronization (ERD), in different frequency bands (δ/θ-ERD, θ/α–event-related synchronization, and gamma-band event-related synchronization). All responses except the δ/θ-ERD correlated with pain-related behavior at within-subject level. Notably, the gamma-band event-related synchronization was the only response that reliably correlated with pain-related behavior between subjects. These results comprehensively characterize the physiological properties of the brain oscillations elicited by nociceptive stimuli in freely moving rodents and provide a foundational work to improve the translation of experimental animal findings to human physiology and pathophysiology.

Inflammation and Depression: A Nervous Plea for Psychiatry to Not Become Immune to Interpretation

The possibility that inflammation plays a causal role in major depression is an important claim in the emerging field of immunopsychiatry and has generated hope for new treatments. The aims of the present review are first to provide some historical background and to consider the evidence in favor of the claim that inflammation is causally involved in major depression. The second part discusses some of the possibilities allowed for by the use of broad ‘umbrella’ concepts, such as inflammation and stress, in terms of proposing new working hypotheses and potential mechanisms. The third part reviews proposed biomarkers of inflammation and depression and the final part addresses how elements discussed in the preceding sections are used in immunopsychiatry. The ‘umbrella’ concepts of inflammation and stress, as well as insufficiently-met criteria based inferences and reverse inferences are being used to some extent in immunopsychiatry. The field is therefore encouraged to specify concepts and constructs, as well as to consider potential alternative interpretations and explanations for findings obtained. The hope is that pointing out some of the potential problems will allow for a clearer picture of immunopsychiatry’s current strengths and limitations and help the field mature.

Brain regions preferentially responding to transient and iso-intense painful or tactile stimuli

How pain emerges from cortical activities remains an unresolved question in pain neuroscience. A first step toward addressing this question consists in identifying brain activities that occur preferentially in response to painful stimuli in comparison to non-painful stimuli. A key confound that has affected this important comparison in many previous studies is the intensity of the stimuli generating painful and non-painful sensations. Here, we compared the brain activity during iso-intense painful and tactile sensations sampled by functional MRI in 51 healthy participants. Specifically, the perceived intensity was recorded for every stimulus and only the stimuli with rigorously matched perceived intensity were selected and compared between painful and tactile conditions. We found that all brain areas activated by painful stimuli were also activated by tactile stimuli, and vice versa. Neural responses in these areas were correlated with the perceived stimulus intensity, regardless of stimulus modality. More importantly, among these activated areas, we further identified a number of brain regions showing stronger responses to painful stimuli than to tactile stimuli when perceived intensity was carefully matched, including the bilateral opercular cortex, the left supplementary motor area and the right frontal middle and inferior areas. Among these areas, the right frontal middle area still responded more strongly to painful stimuli even when painful stimuli were perceived less intense than tactile stimuli, whereas in this condition other regions showed stronger responses to tactile stimuli. In contrast, the left postcentral gyrus, the visual cortex, the right parietal inferior gyrus, the left parietal superior gyrus and the right cerebellum had stronger responses to tactile stimuli than to painful stimuli when perceived intensity was matched. When tactile stimuli were perceived less intense than painful stimuli, the left postcentral gyrus and the right parietal inferior gyrus still responded more strongly to tactile stimuli while other regions now showed similar responses to painful and tactile stimuli. These results suggest that different brain areas may be engaged differentially when processing painful and tactile information, although their neural activities are not exclusively dedicated to encoding information of only one modality but are strongly determined by perceived stimulus intensity regardless of stimulus modality.

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Transient painful and tactile stimuli activate the same brain areas.

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Neural activity in these areas encode stimulus intensity.

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Among these areas, a few may be engaged differentially in pain and touch processing.

Neural indicators of perceptual variability of pain across species

While several features of brain activity can be used to predict the variability of painful percepts within a given individual, it is much more difficult to predict pain variability across individuals. Here, we used electrophysiology to sample brain activity of humans and rodents, and demonstrated that laser-induced gamma oscillations sampled by central electrodes predict pain sensitivity across individuals both reliably and selectively: reliably, because they consistently predict between-subject pain intensity in both humans and rodents; selectively, because they do not track the between-subject reported intensity of nonpainful but equally salient auditory, visual, and nonnociceptive somatosensory stimuli. This discovery indicates that variability in an individual’s pain sensitivity is, at least partly, explained by variability in the amplitude of gamma oscillations of that individual.

Individuals exhibit considerable and unpredictable variability in painful percepts in response to the same nociceptive stimulus. Previous work has found neural responses that, while not necessarily responsible for the painful percepts themselves, can still correlate well with intensity of pain perception within a given individual. However, there is no reliable neural response reflecting the variability in pain perception across individuals. Here, we use an electrophysiological approach in humans and rodents to demonstrate that brain oscillations in the gamma band [gamma-band event-related synchronization (γ-ERS)] sampled by central electrodes reliably predict pain sensitivity across individuals. We observed a clear dissociation between the large number of neural measures that reflected subjective pain ratings at within-subject level but not across individuals, and γ-ERS, which reliably distinguished subjective ratings within the same individual but also coded pain sensitivity across different individuals. Importantly, the ability of γ-ERS to track pain sensitivity across individuals was selective because it did not track the between-subject reported intensity of nonpainful but equally salient auditory, visual, and nonnociceptive somatosensory stimuli. These results also demonstrate that graded neural activity related to within-subject variability should be minimized to accurately investigate the relationship between nociceptive-evoked neural activities and pain sensitivity across individuals.

Nonpharmacological Management of Persistent Pain in Elite Athletes: Rationale and Recommendations

Persistent pain is common in elite athletes. The current review arose from a consensus initiative by the International Olympic Committee to advance the development of a standardized, scientific, and evidence-informed approach to management. We suggest that optimal management of persistent pain in elite athletes requires an understanding of contemporary pain science, including the rationale behind and implementation of a biopsychosocial approach to care. We argue that athletes and clinicians need to understand the biopsychosocial model because it applies to both pain and the impact of pain with special reference to the sport setting. Management relies on thorough and precise assessment that considers contributing factors across nociceptive, inflammatory, neuropathic, and centrally acting domains; these can include contextual and psychosocial factors. Pain management seeks to remove contributing factors wherever possible through targeted education; adjustment of mechanical loading, training, and performance schedules; psychological therapies; and management of inflammation.

Influence of Individual Differences in fMRI-Based Pain Prediction Models on Between-Individual Prediction Performance

Decoding subjective pain perception from functional magnetic resonance imaging (fMRI) data using machine learning technique is gaining a growing interest. Despite the well-documented individual differences in pain experience and brain responses, it still remains unclear how and to what extent these individual differences affect the performance of between-individual fMRI-based pain prediction. The present study is aimed to examine the relationship between individual differences in pain prediction models and between-individual prediction error, and, further, to identify brain regions that contribute to between-individual prediction error. To this end, we collected and analyzed fMRI data and pain ratings in a laser-evoked pain experiment. By correlating different types of individual difference metrics with between-individual prediction error, we are able to quantify the influence of these individual differences on prediction performance and reveal a set of brain regions whose activities are related to prediction error. Interestingly, we found that the precuneus, which does not have predictive capability to pain, could also affect the prediction error. This study elucidates the influence of interindividual variability in pain on the between-individual prediction performance, and the results will be useful for the design of more accurate and robust fMRI-based pain prediction models.

Magnitude and Temporal Variability of Inter-stimulus EEG Modulate the Linear Relationship Between Laser-Evoked Potentials and Fast-Pain Perception

The level of pain perception is correlated with the magnitude of pain-evoked brain responses, such as laser-evoked potentials (LEP), across trials. The positive LEP-pain relationship lays the foundation for pain prediction based on single-trial LEP, but cross-individual pain prediction does not have a good performance because the LEP-pain relationship exhibits substantial cross-individual difference. In this study, we aim to explain the cross-individual difference in the LEP-pain relationship using inter-stimulus EEG (isEEG) features. The isEEG features (root mean square as magnitude and mean square successive difference as temporal variability) were estimated from isEEG data (at full band and five frequency bands) recorded between painful stimuli. A linear model was fitted to investigate the relationship between pain ratings and LEP response for fast-pain trials on a trial-by-trial basis. Then the correlation between isEEG features and the parameters of LEP-pain model (slope and intercept) was evaluated. We found that the magnitude and temporal variability of isEEG could modulate the parameters of an individual's linear LEP-pain model for fast-pain trials. Based on this, we further developed a new individualized fast-pain prediction scheme, which only used training individuals with similar isEEG features as the test individual to train the fast-pain prediction model, and obtained improved accuracy in cross-individual fast-pain prediction. The findings could help elucidate the neural mechanism of cross-individual difference in pain experience and the proposed fast-pain prediction scheme could be potentially used as a practical and feasible pain prediction method in clinical practice.

Brain imaging tests for chronic pain: medical, legal and ethical issues and recommendations

Chronic pain is the greatest source of disability globally and claims related to chronic pain feature in many insurance and medico-legal cases. Brain imaging (for example, functional MRI, PET, EEG and magnetoencephalography) is widely considered to have potential for diagnosis, prognostication, and prediction of treatment outcome in patients with chronic pain. In this Consensus Statement, a presidential task force of the International Association for the Study of Pain examines the capabilities of brain imaging in the diagnosis of chronic pain, and the ethical and legal implications of its use in this way. The task force emphasizes that the use of brain imaging in this context is in a discovery phase, but has the potential to increase our understanding of the neural underpinnings of chronic pain, inform the development of therapeutic agents, and predict treatment outcomes for use in personalized pain management. The task force proposes standards of evidence that must be satisfied before any brain imaging measure can be considered suitable for clinical or legal purposes. The admissibility of such evidence in legal cases also strongly depends on laws that vary between jurisdictions. For these reasons, the task force concludes that the use of brain imaging findings to support or dispute a claim of chronic pain - effectively as a pain lie detector - is not warranted, but that imaging should be used to further our understanding of the mechanisms underlying pain.

Imaging empathy and prosocial emotions

Empathy is a multi-faceted construct with important implications for social behavior. Based on a selective review of the neuroscientific evidence collected in humans, the present paper discusses the neural representations underlying affect sharing, its relation to mentalizing, the importance of self-other distinction, the distinction between empathy, sympathy and compassion, and how these phenomena are linked to prosocial behavior. Apart from reviewing the literature, we also highlight open questions and how they might be addressed by a research approach that tries to integrate across these diverse constructs.

Altered white matter microarchitecture in the cingulum bundle in women with primary dysmenorrhea: A tract-based analysis study

Primary dysmenorrhea (PD), as characterized by painful menstrual cramps without organic causes, is associated with central sensitization and brain function changes. Previous studies showed the integrated role of the default mode network (DMN) in the pain connectome and its key contribution on how an individual perceives and copes with pain disorders. Here, we aimed to investigate whether the cingulum bundle connecting hub regions of the DMN was disrupted in young women with PD. Diffusion tensor imaging was obtained in 41 PD patients and 41 matched healthy controls (HC) during their periovulatory phase. The production of prostaglandins (PGs) was obtained in PD patients during their pain-free and pain phases. As compared with HC, PD patients had similar scores of pain intensity, anxiety, and depression in their pain-free phase. However, altered white matter properties mainly located in the posterior section of the cingulum bundle were observed in PD. Besides PGs being related to menstrual pain, a close relationship was found between the white matter properties of the cingulum bundle during the pain-free phase and the severity of the menstrual pain in PD patients. Our study suggested that PD had trait changes of white matter integrities in the cingulum bundle that persisted beyond the time of menstruation. We inferred that altered anatomical connections may lead to less-flexible communication within the DMN, and/or between the DMN and other pain-related brain networks, which may result in the central susceptibility to develop chronic pain conditions in PD's later life. Hum Brain Mapp 38:4430-4443, 2017. © 2017 Wiley Periodicals, Inc.

Evidence for Behaviorally Segregated, Spatiotemporally Overlapping Subnetworks in Phantom Sound Perception

One of the most intriguing questions in neuroscience is to understand the mechanism of information transfer between different brain areas. Recently, network theory has gained traction and is at the forefront of providing a possible explanation to not only the mechanism of information transfer but also in the identification of different neuropathologies. The perception of a phantom ringing in the ear called tinnitus, similar to other neuropathologies, has been shown to be accompanied by aberrant functional connectivity between different brain areas. Although, there have been independent studies showing that specific groups of areas encode individual symptoms of tinnitus, there has not been one study to show that tinnitus is the unified percept of distinguishable subnetworks encoding different behavioral aspects. This study combines resting-state functional connectivity obtained from the source-localized electroencephalography of 311 tinnitus patients and 264 controls, and a k-fold cross-validation machine learning algorithm to develop a predictive model that verifies the presence of behaviorally specific, spatiotemporally overlapping subnetworks in tinnitus. This reorganization is found to be exclusive to tinnitus, even when compared to physiologically similar disorders such as chronic pain, with each behavioral symptom having a unique oscillatory signature. This frequency-specific transmission of information, called multiplexing, enables different types of information to be carried between two brain regions through the same anatomical connection. In addition to understanding the efficient compensation mechanism of the brain in the presence of multisymptom disorders, the exclusivity of the prediction model presents an encouraging possibility for an objective neural marker for tinnitus.

A neurobiologist's attempt to understand persistent pain

This topical review starts with a warning that despite an impressive wealth of neuroscientific data, a reductionist approach can never fully explain persistent pain. One reason is the complexity of clinical pain (in contrast to experimentally induced pain). Another reason is that the "pain system" shows degeneracy, which means that an outcome can have several causes. Problems also arise from lack of conceptual clarity regarding words like nociceptors, pain, and perception. It is, for example, argued that "homeoceptor" would be a more meaningful term than nociceptor. Pain experience most likely depends on synchronized, oscillatory activity in a distributed neural network regardless of whether the pain is caused by tissue injury, deafferentation, or hypnosis. In experimental pain, the insula, the second somatosensory area, and the anterior cingulate gyrus are consistently activated. These regions are not pain-specific, however, and are now regarded by most authors as parts of the so-called salience network, which detects all kinds of salient events (pain being highly salient). The networks related to persistent pain seem to differ from the those identified experimentally, and show a more individually varied pattern of activations. One crucial difference seems to be activation of regions implicated in emotional and body-information processing in persistent pain. Basic properties of the "pain system" may help to explain why it so often goes awry, leading to persistent pain. Thus, the system must be highly sensitive not to miss important homeostatic threats, it cannot be very specific, and it must be highly plastic to quickly learn important associations. Indeed, learning and memory processes play an important role in persistent pain. Thus, behaviour with the goal of avoiding pain provocation is quickly learned and may persist despite healing of the original insult. Experimental and clinical evidence suggest that the hippocampal formation and neurogenesis (formation of new neurons) in the dentate gyrus are involved in the development and maintenance of persistent pain. There is evidence that persistent pain in many instances may be understood as the result of an interpretation of the organism's state of health. Any abnormal pattern of sensory information as well as lack of expected correspondence between motor commands and sensory feedback may be interpreted as bodily threats and evoke pain. This may, for example, be an important mechanism in many cases of neuropathic pain. Accordingly, many patients with persistent pain show evidence of a distorted body image. Another approach to understanding why the "pain system" so often goes awry comes from knowledge of the dynamic and nonlinear behaviour of neuronal networks. In real life the emergence of persistent pain probably depends on the simultaneous occurrence of numerous challenges, and just one extra (however small) might put the network into a an inflexible state with heightened sensitivity to normally innocuous inputs. Finally, the importance of seeking the meaning the patient attributes to his/her pain is emphasized. Only then can we understand why a particular person suffers so much more than another with very similar pathology, and subsequently be able to help the person to alter the meaning of the situation.

Pharmacologic attenuation of cross-modal sensory augmentation within the chronic pain insula

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Sensitivity to visual stimuli is increased in patients with fibromyalgia and improved with administration of pregabalin.

Pain can be elicited through all mammalian sensory pathways yet cross-modal sensory integration, and its relationship to clinical pain, is largely unexplored. Centralized chronic pain conditions such as fibromyalgia are often associated with symptoms of multisensory hypersensitivity. In this study, female patients with fibromyalgia demonstrated cross-modal hypersensitivity to visual and pressure stimuli compared with age- and sex-matched healthy controls. Functional magnetic resonance imaging revealed that insular activity evoked by an aversive level of visual stimulation was associated with the intensity of fibromyalgia pain. Moreover, attenuation of this insular activity by the analgesic pregabalin was accompanied by concomitant reductions in clinical pain. A multivariate classification method using support vector machines (SVM) applied to visual-evoked brain activity distinguished patients with fibromyalgia from healthy controls with 82% accuracy. A separate SVM classification of treatment effects on visual-evoked activity reliably identified when patients were administered pregabalin as compared with placebo. Both SVM analyses identified significant weights within the insular cortex during aversive visual stimulation. These data suggest that abnormal integration of multisensory and pain pathways within the insula may represent a pathophysiological mechanism in some chronic pain conditions and that insular response to aversive visual stimulation may have utility as a marker for analgesic drug development.