What reliability can and cannot tell us about pain report and pain neuroimaging

Reliability of task-based fMRI in the dorsal horn of the human spinal cord

The application of functional magnetic resonance imaging (fMRI) to the human spinal cord is still a relatively small field of research and faces many challenges. Here we aimed to probe the limitations of task-based spinal fMRI at 3T by investigating the reliability of spinal cord blood oxygen level dependent (BOLD) responses to repeated nociceptive stimulation across two consecutive days in 40 healthy volunteers. We assessed the test-retest reliability of subjective ratings, autonomic responses, and spinal cord BOLD responses to short heat pain stimuli (1s duration) using the intraclass correlation coefficient (ICC). At the group level, we observed robust autonomic responses as well as spatially specific spinal cord BOLD responses at the expected location, but no spatial overlap in BOLD response patterns across days. While autonomic indicators of pain processing showed good-to-excellent reliability, both β-estimates and z-scores of task-related BOLD responses showed poor reliability across days in the target region (gray matter of the ipsilateral dorsal horn). When taking into account the sensitivity of gradient-echo echo planar imaging (GE-EPI) to draining vein signals by including the venous plexus in the analysis, we observed BOLD responses with fair reliability across days. Taken together, these results demonstrate that heat pain stimuli as short as one second are able to evoke a robust and spatially specific BOLD response, which is however strongly variable within participants across time, resulting in low reliability in the dorsal horn gray matter. Further improvements in data acquisition and analysis techniques are thus necessary before event-related spinal cord fMRI as used here can be reliably employed in longitudinal designs or clinical settings.

To Calibrate or not to Calibrate? A Methodological Dilemma in Experimental Pain Research

To calibrate or not to calibrate? This question is raised by almost everyone designing an experimental pain study with supra-threshold stimulation. The dilemma is whether to individualize stimulus intensity to the pain threshold / supra-threshold pain level of each participant or whether to provide the noxious stimulus at a fixed intensity so that everyone receives the identical input. Each approach has unique pros and cons which need to be considered to i) accurately design an experiment, ii) enhance statistical inference in the given data and, iii) reduce bias and the influence of confounding factors in the individual study e.g., body composition, differences in energy absorption and previous experience. Individualization requires calibration, a procedure already irritating the nociceptive system but allowing to match the pain level across individuals. It leads to a higher variability of the stimulus intensity, thereby influencing the encoding of "noxiousness" by the central nervous system. Results might be less influenced by statistical phenomena such as ceiling/floor effects and the approach does not seem to rise ethical concerns. On the other hand, applying a fixed (standardized) intensity reduces the problem of intensity encoding leading to a large between-subjects variability in pain responses. Fixed stimulation intensities do not require pre-exposure. It can be proposed that one method is not preferable over another, however the choice depends on the study aim and the desired level of external validity. This paper discusses considerations for choosing the optimal approach for experimental pain studies and provides recommendations for different study designs. PERSPECTIVE: To calibrate pain or not? This dilemma is related to almost every experimental pain research. The decision is a trade-off between statistical power and greater control of stimulus encoding. The article decomposes both approaches and presents the pros and cons of either approach supported by data and simulation experiment.

Altered Intrinsic Brain Activity in Patients With Toothache Using the Percent Amplitude of a Fluctuation Method: A Resting-State fMRI Study

Objective

The percent amplitude of fluctuation (PerAF) technique was utilized to evaluate the neural functions of specific cerebrum areas in patients with toothache (TA).

Patients and Methods

An aggregation of 18 patients with TA (eight males and 10 females) were included in the study. We also recruited 18 healthy controls (HCs; eight men and 10 women) aligned for sex and age. Resting functional magnetic resonance imaging (rs-fMRI) scans were obtained. Then, we utilized the PerAF method and a support vector machine (SVM) to analyze the image data and measure neural abnormalities in related cerebrum areas. Receiver operating characteristic (ROC) curve analysis was utilized to appraise the two data sets.

Results

The PerAF signals in the right dorsolateral superior frontal gyrus (RDSFG) and the right posterior central gyrus (RPCG) of TA sufferers were lower than HC signals. These results may reveal neural dysfunctions in relevant cerebrum regions. The AUC values of PerAF in the two areas were 0.979 in the RDSFG and 0.979 in the RPCG. The SVM results suggested that PerAF could be utilized to distinguish the TA group from HCs with a sensitivity of 75.00%, a specificity of 66.67%, and an accuracy of 70.83%.

Conclusion

Patients with TA had marked differences in PerAF values in some regions of the cerebrum. Changes in PerAF values represented distinctions in blood oxygen level dependent semaphore intensity, which reflected the overactivity or inactivation of some cerebrum areas in those suffering from TA. At the same time, we analyzed the PerAF values of TAs with ROC curve, which can be helpful for the diagnosis of TA severity and subsequent treatment. Our results may help to elucidate the pathological mechanism of TA.

Effect sizes and test-retest reliability of the fMRI-based neurologic pain signature

Identifying biomarkers that predict mental states with large effect sizes and high test-retest reliability is a growing priority for fMRI research. We examined a well-established multivariate brain measure that tracks pain induced by nociceptive input, the Neurologic Pain Signature (NPS). In N = 295 participants across eight studies, NPS responses showed a very large effect size in predicting within-person single-trial pain reports (d = 1.45) and medium effect size in predicting individual differences in pain reports (d = 0.49). The NPS showed excellent short-term (within-day) test-retest reliability (ICC = 0.84, with average 69.5 trials/person). Reliability scaled with the number of trials within-person, with ≥60 trials required for excellent test-retest reliability. Reliability was tested in two additional studies across 5-day (N = 29, ICC = 0.74, 30 trials/person) and 1-month (N = 40, ICC = 0.46, 5 trials/person) test-retest intervals. The combination of strong within-person correlations and only modest between-person correlations between the NPS and pain reports indicate that the two measures have different sources of between-person variance. The NPS is not a surrogate for individual differences in pain reports but can serve as a reliable measure of pain-related physiology and mechanistic target for interventions.

Investigating the specificity of the neurologic pain signature against breathlessness and finger opposition

ABSTRACT

Brain biomarkers of pain, including pain-predictive "signatures" based on brain activity, can provide measures of neurophysiological processes and potential targets for interventions. A central issue relates to the specificity of such measures, and understanding their current limits will both advance their development and explore potentially generalizable properties of pain to other states. Here, we used 2 data sets to test the neurologic pain signature (NPS), an established pain neuromarker. In study 1, brain activity was measured using high-field functional magnetic resonance imaging (7T fMRI, N = 40) during 5 to 25 seconds of experimental breathlessness (induced by inspiratory resistive loading), conditioned breathlessness anticipation, and finger opposition. In study 2, we assessed anticipation and breathlessness perception (3T, N = 19) under blinded saline (placebo) and remifentanil administration. The NPS responded to breathlessness, anticipation, and finger opposition, although no direct comparisons with painful events were possible. Local NPS patterns in anterior or midinsula, S2, and dorsal anterior cingulate responded to breathlessness and finger opposition and were reduced by remifentanil. Local NPS responses in the dorsal posterior insula did not respond to any manipulations. Therefore, significant global NPS activity alone is not specific for pain, and we offer insight into the overlap between NPS responses, breathlessness, and somatomotor demand.

Affective brain patterns as multivariate neural correlates of cardiovascular disease risk

This study tested whether brain activity patterns evoked by affective stimuli relate to individual differences in an indicator of pre-clinical atherosclerosis: carotid artery intima-media thickness (CA-IMT). Adults (aged 30-54 years) completed functional magnetic resonance imaging (fMRI) tasks that involved viewing three sets of affective stimuli. Two sets included facial expressions of emotion, and one set included neutral and unpleasant images from the International Affective Picture System (IAPS). Cross-validated, multivariate and machine learning models showed that individual differences in CA-IMT were partially predicted by brain activity patterns evoked by unpleasant IAPS images, even after accounting for age, sex and known cardiovascular disease risk factors. CA-IMT was also predicted by brain activity patterns evoked by angry and fearful faces from one of the two stimulus sets of facial expressions, but this predictive association did not persist after accounting for known cardiovascular risk factors. The reliability (internal consistency) of brain activity patterns evoked by affective stimuli may have constrained their prediction of CA-IMT. Distributed brain activity patterns could comprise affective neural correlates of pre-clinical atherosclerosis; however, the interpretation of such correlates may depend on their psychometric properties, as well as the influence of other cardiovascular risk factors and specific affective cues.

Boost in Test-Retest Reliability in Resting State fMRI with Predictive Modeling

Recent studies found low test-retest reliability in functional magnetic resonance imaging (fMRI), raising serious concerns among researchers, but these studies mostly focused on the reliability of individual fMRI features (e.g., individual connections in resting state connectivity maps). Meanwhile, neuroimaging researchers increasingly employ multivariate predictive models that aggregate information across a large number of features to predict outcomes of interest, but the test-retest reliability of predicted outcomes of these models has not previously been systematically studied. Here we apply 10 predictive modeling methods to resting state connectivity maps from the Human Connectome Project dataset to predict 61 outcome variables. Compared with mean reliability of individual resting state connections, we find mean reliability of the predicted outcomes of predictive models is substantially higher for all 10 modeling methods assessed. Moreover, improvement was consistently observed across all scanning and processing choices (i.e., scan lengths, censoring thresholds, volume- vs. surface-based processing). For the most reliable methods, the reliability of predicted outcomes was mostly, though not exclusively, in the "good" range (above 0.60). Finally, we identified three mechanisms that help to explain why predicted outcomes of predictive models have higher reliability than individual imaging features. We conclude that researchers can potentially achieve higher test-retest reliability by making greater use of predictive models.

Great Expectations: A Critical Review of and Suggestions for the Study of Reward Processing as a Cause and Predictor of Depression

Both human and animal studies support the relationship between depression and reward processing abnormalities, giving rise to the expectation that neural signals of these processes may serve as biomarkers or mechanistic treatment targets. Given the great promise of this research line, we scrutinized those findings and the theoretical claims that underlie them. To achieve this, we applied the framework provided by classical work on causality as well as contemporary approaches to prediction. We identified a number of conceptual, practical, and analytical challenges to this line of research and used a preregistered meta-analysis to quantify the longitudinal associations between reward processing abnormalities and depression. We also investigated the impact of measurement error on reported data. We found that reward processing abnormalities do not reach levels that would be useful for clinical prediction, yet the available evidence does not preclude a possible causal role in depression.

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.

Toward a unified framework for interpreting machine-learning models in neuroimaging

Machine learning is a powerful tool for creating computational models relating brain function to behavior, and its use is becoming widespread in neuroscience. However, these models are complex and often hard to interpret, making it difficult to evaluate their neuroscientific validity and contribution to understanding the brain. For neuroimaging-based machine-learning models to be interpretable, they should (i) be comprehensible to humans, (ii) provide useful information about what mental or behavioral constructs are represented in particular brain pathways or regions, and (iii) demonstrate that they are based on relevant neurobiological signal, not artifacts or confounds. In this protocol, we introduce a unified framework that consists of model-, feature- and biology-level assessments to provide complementary results that support the understanding of how and why a model works. Although the framework can be applied to different types of models and data, this protocol provides practical tools and examples of selected analysis methods for a functional MRI dataset and multivariate pattern-based predictive models. A user of the protocol should be familiar with basic programming in MATLAB or Python. This protocol will help build more interpretable neuroimaging-based machine-learning models, contributing to the cumulative understanding of brain mechanisms and brain health. Although the analyses provided here constitute a limited set of tests and take a few hours to days to complete, depending on the size of data and available computational resources, we envision the process of annotating and interpreting models as an open-ended process, involving collaborative efforts across multiple studies and laboratories.

Convergent neural representations of experimentally-induced acute pain in healthy volunteers: A large-scale fMRI meta-analysis

Characterizing a reliable, pain-related neural signature is critical for translational applications. Many prior fMRI studies have examined acute nociceptive pain-related brain activation in healthy participants. However, synthesizing these data to identify convergent patterns of activation can be challenging due to the heterogeneity of experimental designs and samples. To address this challenge, we conducted a comprehensive meta-analysis of fMRI studies of stimulus-induced pain in healthy participants. Following pre-registration, two independent reviewers evaluated 4,927 abstracts returned from a search of 8 databases, with 222 fMRI experiments meeting inclusion criteria. We analyzed these experiments using Activation Likelihood Estimation with rigorous type I error control (voxel height p < 0.001, cluster p < 0.05 FWE-corrected) and found a convergent, largely bilateral pattern of pain-related activation in the secondary somatosensory cortex, insula, midcingulate cortex, and thalamus. Notably, these regions were consistently recruited regardless of stimulation technique, location of induction, and participant sex. These findings suggest a highly-conserved core set of pain-related brain areas, encouraging applications as a biomarker for novel therapeutics targeting acute nociceptive pain.

Placebo Effects on the Neurologic Pain Signature: A Meta-analysis of Individual Participant Functional Magnetic Resonance Imaging Data

Importance

Placebo effects reduce pain and contribute to clinical analgesia, but after decades of research, it remains unclear whether placebo treatments mainly affect nociceptive processes or other processes associated with pain evaluation.

Objective

We conducted a systematic, participant-level meta-analysis to test the effect of placebo treatments on pain-associated functional neuroimaging responses in the neurologic pain signature (NPS), a multivariate brain pattern tracking nociceptive pain.

Data Sources

Medline (PubMed) was searched from inception to May 2015; the search was augmented with results from previous meta-analyses and expert recommendations.

Study Selection

Eligible studies were original investigations that were published in English in peer-reviewed journals and that involved functional neuroimaging of the human brain with evoked pain delivered under stimulus intensity-matched placebo and control conditions. The authors of all eligible studies were contacted and asked to provide single-participant data.

Data Extraction and Synthesis

Data were collected between December 2015 and November 2017 following the Preferred Reporting Items for Systematic Review and Meta-Analyses of individual participant data guidelines. Results were summarized across participants and studies in a random-effects model.

Main Outcomes and Measures

The main, a priori outcome was NPS response; pain reports were assessed as a secondary outcome.

Results

We obtained data from 20 of 28 identified eligible studies, resulting in a total sample size of 603 healthy individuals. The NPS responses to painful stimulation compared with baseline conditions were positive in 575 participants (95.4%), with a very large effect size (g = 2.30 [95% CI, 1.92 to 2.69]), confirming its sensitivity to nociceptive pain in this sample. Placebo treatments showed significant behavioral outcomes on pain ratings in 17 of 20 studies (85%) and in the combined sample (g = -0.66 [95% CI, -0.80 to -0.53]). However, placebo effects on the NPS response were significant in only 3 of 20 studies (15%) and were very small in the combined sample (g = -0.08 [95% CI, -0.15 to -0.01]). Similarly, analyses restricted to studies with low risk of bias (g = -0.07 [95% CI, -0.15 to 0.00]) indicated very small effects, and analyses of just placebo responders (g = -0.22 [95% CI, -0.34 to -0.11]) indicated small effects, as well.

Conclusions and Relevance

Placebo treatments have moderate analgesic effects on pain reports. The very small effects on NPS, a validated measure that tracks levels of nociceptive pain, indicate that placebo treatments affect pain via brain mechanisms largely independent of effects on bottom-up nociceptive processing.

Neuroimaging-based biomarkers for pain: state of the field and current directions

Chronic pain is an endemic problem involving both peripheral and brain pathophysiology. Although biomarkers have revolutionized many areas of medicine, biomarkers for pain have remained controversial and relatively underdeveloped. With the realization that biomarkers can reveal pain-causing mechanisms of disease in brain circuits and in the periphery, this situation is poised to change. In particular, brain pathophysiology may be diagnosable with human brain imaging, particularly when imaging is combined with machine learning techniques designed to identify predictive measures embedded in complex data sets. In this review, we explicate the need for brain-based biomarkers for pain, some of their potential uses, and some of the most popular machine learning approaches that have been brought to bear. Then, we evaluate the current state of pain biomarkers developed with several commonly used methods, including structural magnetic resonance imaging, functional magnetic resonance imaging and electroencephalography. The field is in the early stages of biomarker development, but these complementary methodologies have already produced some encouraging predictive models that must be tested more extensively across laboratories and clinical populations.

Negative mood influences default mode network functional connectivity in patients with chronic low back pain: implications for functional neuroimaging biomarkers

The default mode network (DMN) has been proposed as a biomarker for several chronic pain conditions. Default mode network functional connectivity (FC) is typically examined during resting-state functional neuroimaging, in which participants are instructed to let thoughts wander. However, factors at the time of data collection (eg, negative mood) that might systematically impact pain perception and its brain activity, influencing the application of the DMN as a pain biomarker, are rarely reported. This study measured whether positive and negative moods altered DMN FC patterns in patients with chronic low back pain (CLBP), specifically focusing on negative mood because of its clinical relevance. Thirty-three participants (CLBP = 17) underwent resting-state functional magnetic resonance imaging scanning before and after sad and happy mood inductions, and rated levels of mood and pain intensity at the time of scanning. Two-way repeated-measures analysis of variances were conducted on resting-state functional connectivity data. Significant group (CLBP > healthy controls) × condition (sadness > baseline) interaction effects were identified in clusters spanning parietal operculum/postcentral gyrus, insular cortices, anterior cingulate cortex, frontal pole, and a portion of the cerebellum (PFDR < 0.05). However, only 1 significant cluster covering a portion of the cerebellum was identified examining a two-way repeated-measures analysis of variance for happiness > baseline (PFDR < 0.05). Overall, these findings suggest that DMN FC is affected by negative mood in individuals with and without CLBP. It is possible that DMN FC seen in patients with chronic pain is related to an affective dimension of pain, which is important to consider in future neuroimaging biomarker development and implementation.

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.

A Brain Phenotype for Stressor-Evoked Blood Pressure Reactivity

Background

Individuals who exhibit large‐magnitude blood pressure (BP) reactions to acute psychological stressors are at risk for hypertension and premature death by cardiovascular disease. This study tested whether a multivariate pattern of stressor‐evoked brain activity could reliably predict individual differences in BP reactivity, providing novel evidence for a candidate neurophysiological source of stress‐related cardiovascular risk.

Methods and Results

Community‐dwelling adults (N=310; 30–51 years; 153 women) underwent functional magnetic resonance imaging with concurrent BP monitoring while completing a standardized battery of stressor tasks. Across individuals, the battery evoked an increase systolic and diastolic BP relative to a nonstressor baseline period (M ∆systolic BP/∆diastolic BP=4.3/1.9 mm Hg [95% confidence interval=3.7–5.0/1.4–2.3 mm Hg]). Using cross‐validation and machine learning approaches, including dimensionality reduction and linear shrinkage models, a multivariate pattern of stressor‐evoked functional magnetic resonance imaging activity was identified in a training subsample (N=206). This multivariate pattern reliably predicted both systolic BP (r=0.32; P<0.005) and diastolic BP (r=0.25; P<0.01) reactivity in an independent subsample used for testing and replication (N=104). Brain areas encompassed by the pattern that were strongly predictive included those implicated in psychological stressor processing and cardiovascular responding through autonomic pathways, including the medial prefrontal cortex, anterior cingulate cortex, and insula.

Conclusions

A novel multivariate pattern of stressor‐evoked brain activity may comprise a phenotype that partly accounts for individual differences in BP reactivity, a stress‐related cardiovascular risk factor.