Resting-state electroencephalography reveals increased synchrony at theta and gamma frequencies in frontal brain areas and global network reorganization at gamma frequencies in chronic pain patients. Chronic pain is a common and severely disabling disease whose treatment is often unsatisfactory. Insights into the brain mechanisms of chronic pain promise to advance the understanding of the underlying pathophysiology and might help to develop disease markers and novel treatments. Here, we systematically exploited the potential of electroencephalography to determine abnormalities of brain function during the resting state in chronic pain. To this end, we performed state-of-the-art analyses of oscillatory brain activity, brain connectivity, and brain networks in 101 patients of either sex suffering from chronic pain. The results show that global and local measures of brain activity did not differ between chronic pain patients and a healthy control group. However, we observed significantly increased connectivity at theta (4-8 Hz) and gamma (>60 Hz) frequencies in frontal brain areas as well as global network reorganization at gamma frequencies in chronic pain patients. Furthermore, a machine learning algorithm could differentiate between patients and healthy controls with an above-chance accuracy of 57%, mostly based on frontal connectivity. These results suggest that increased theta and gamma synchrony in frontal brain areas are involved in the pathophysiology of chronic pain. Although substantial challenges concerning the reproducibility of the findings and the accuracy, specificity, and validity of potential electroencephalography-based disease markers remain to be overcome, our study indicates that abnormal frontal synchrony at theta and gamma frequencies might be promising targets for noninvasive brain stimulation and/or neurofeedback approaches. Affecting between 20% and 30% of the adult population,35 chronic pain is a leading cause of disability worldwide.27 Thus, advances in the understanding and treatment of chronic pain are urgently needed.Chronic pain is a disease characterized by ongoing pain and significant sensory, cognitive, and affective abnormalities55 In particular, the prefrontal cortex and subcortical brain areas including amygdala, hippocampus, and striatal areas have been implicated in chronic pain.72 Further insights into the brain mechanisms of chronic pain not only promise to advance the understanding of the underlying pathophysiology but could also be clinically highly useful. In particular, a brain-based marker of chronic pain could be immensely helpful for the diagnosis, prognosis, classification, prevention, and treatment of pain.71 Accordingly, the feasibility, limitations, and perspectives of brain-based markers of pain are currently intensively discussed in the pain research community64 and beyond.84 Recent functional magnetic resonance imaging (fMRI) studies have made important first steps towards such a brain-based marker of experimental80 and chronic pain.45Studies in animals and humans have revealed that chronic pain is associated with significant structural and functional changes of the brain.63 or noninvasive brain stimulation techniques.54 As a potential first step in that direction, some EEG studies have shown a slowing of neural oscillations together with an increase of oscillatory brain activity at theta frequencies (4-8 Hz) in chronic pain patients.76 These observations have been embedded in the thalamocortical dysrhythmia (TCD) model of neuropsychiatric disorders.41 In this model, abnormal thalamocortical theta activity yields abnormal oscillations at gamma (>30 Hz) frequencies, which eventually result in different neuropsychiatric symptoms including ongoing pain. This model is highly appealing, but evidence is sparse, contradictory, and mostly confined to small groups of patients suffering from neuropathic subtypes of chronic pain.53 Thus, a general EEG-based marker of chronic pain remains to be demonstrated.Using electroencephalography (EEG) to assess abnormalities of brain function and to establish a brain-based marker of chronic pain is particularly appealing because it is safe, cost-effective, broadly available, and potentially mobile. Moreover, an EEG-based marker of chronic pain might not only be helpful for the diagnosis and classification of chronic pain but might itself represent a target for novel therapeutic strategies such as neurofeedbackIn this study, we aimed to systematically and extensively exploit the potential of EEG to determine abnormalities of resting-state brain activity as a potential brain-based marker of chronic pain. In a large cohort of chronic pain patients and age- and sex-matched healthy controls, we analyzed global and local measures of oscillatory brain activity. Moreover, we performed connectivity analyses using phase-based and amplitude-based measures in source space as well as graph theory–based network analyses. In a univariate approach, we statistically compared these measures between patients and healthy controls. Moreover, in a multivariate machine learning approach, we tested whether patterns of these measures allow to distinguish between chronic pain patients and healthy controls.t-tests using G*Power22 showed that the number of participants allowed for detecting differences between groups of at least medium effect size (Cohen's d = 0.5) with a statistical power of 0.9.One hundred one patients  suffering from chronic pain and 84 age- and sex-matched healthy control participants  participated in the study. Inclusion criteria for patients were a clinical diagnosis of chronic pain, with pain lasting at least 6 months and a minimum reported average pain intensity ≥4/10 during the past 4 weeks . Exclusion criteria were acute changes of the pain condition during the past 3 months, for example, due to recent injuries or surgeries. Further exclusion criteria were major neurological diseases such as stroke, epilepsy, or dementia, major psychiatric diseases aside from depression, and severe general diseases. Finally, patients on medication with benzodiazepines were excluded, other medication was not restricted, and patients' medication was maintained. Demographic and clinical details of participants are shown in Tables Brain activity was recorded using EEG. Recordings were performed during the resting state, ie, participants were asked to stay in a relaxed and wakeful state, without any particular task. Electroencephalography data were recorded with eyes closed and eyes open for 5 minutes each. Because the eyes-closed condition showed better data quality and less muscle artifacts, analyses were focused on this condition.14 and a BrainAmp ExG MR amplifier (Brain Products). The EMG ground electrode was placed at vertebra C2. All data were sampled at 1000 Hz (0.1 μV resolution) and band-pass filtered between 0.016 and 250 Hz. Impedances were kept below 20 kΩ.Electroencephalography data were recorded using 64 electrodes consisting of all 10-20 system electrodes and the additional electrodes Fpz, CPz, POz, Oz, Iz, AF3/4, F5/6, FC1/2/3/4/5/6, FT7/8/9/10, C1/2/5/6, CP1/2/3/4/5/6, TP7/8/9/10, P5/6, and PO1/2/9/10 plus 2 electrodes below the outer canthus of each eye  and BrainAmp MR plus amplifiers . All EEG electrodes were referenced to FCz and grounded at AFz. Simultaneously, muscle activity was recorded with 2 bipolar surface electromyography (EMG) electrode montages placed on the right masseter and neck  muscles48 Pain Disability Index,18 painDETECT (PDQ),24 Beck Depression Inventory II (BDI-II),6 State-Trait-Anxiety Inventory,65 and the Veteran's RAND 12-Item Health Survey (VR-12).61Before the EEG recordings, patients completed the following questionnaires to assess pain characteristics and comorbidities: short-form McGill pain Questionnaire,34 and components representing eye movements and muscle artifacts were identified based on time courses and topographies. Furthermore, time intervals of 400 ms around data points with signal jumps exceeding ±100 µV were marked for rejection. Finally, all data were visually inspected and remaining bad segments marked. Subsequently, independent components representing artifacts were subtracted from the raw, unfiltered EEG data83 and EEG data were re-referenced to the average reference. The reference electrode FCz was added to the signal array.Preprocessing was performed using the BrainVision Analyzer software (Brain Products). Data were downsampled to 250 Hz. For artifact identification, a high-pass filter of 1 Hz and a 50-Hz notch filter for line noise removal were applied to the EEG data. Independent component analysis was performed,51 and Brain Connectivity Toolbox,57 along with custom-written code. Data were segmented into 2-second epochs with 1-second overlap. A 2-second epoch length was chosen to balance the stationarity of the signals and the number of samples for lower frequencies (down to 4 Hz).73 All analyses are based on these epochs and the following 4 frequency bands: theta (4-8 Hz), alpha (8-13 Hz), beta (14-30 Hz), and gamma (60-100 Hz). We observed strong nonstationary line noise around 45-55 Hz and therefore excluded the “low gamma” band from frequency band–specific analyses.Next, data were exported from the BrainVision Analyzer and further analyzed in Matlab  with the FieldTripFigure 68 and a frequency smoothing of 1 Hz and then averaged across epochs and electrodes. Power was first computed for the complete power spectrum, ie, 1 to 100 Hz, with a frequency resolution of 0.5 Hz. To remove line noise, a band-stop filter of 45 to 55 Hz was applied before computing power.Oscillatory brain activity (power) was analyzed in electrode space. Frequency-specific global power was computed for all epochs using a Fast Fourier Transformation with Slepian multitapers5 We also pursued alternative approaches to determine the peak frequency by (1) computing the center of gravity of the power spectrum,36 (2) computing the dominant peak frequency using longer time windows of 5 seconds, and by (3) computing the peak frequency on each single epoch and then averaging the peaks.26The individual dominant peak frequency was determined on the average of the epochs as the highest local maximum (larger than its 2 neighboring samples) of the amplitude in the frequency range of 6 to 14 Hz.To compare the spatial distribution of local brain activity between patients and healthy controls, power was averaged within each frequency band before comparing frequency-specific power topographies between groups using cluster-based permutation tests.Relative power was calculated by normalizing every power value  by the respective participant's total power. Total power was calculated by summing all power values across frequencies from 1 to 100 Hz and across all electrodes.74 to project the band-pass filtered data for each frequency band and participant from electrode space into source space. This was done with a combination of the FieldTrip toolbox51 and custom-written code. Spatial filters for every single person were computed based on the covariance matrices of the band-pass filtered data for each frequency band and a lead field matrix. A 3-dimensional grid with a 1-cm resolution covering the brain was defined, resulting in a total of 2020 voxels in the brain. The lead field was constructed for each voxel using a realistically shaped 3-shell boundary-element volume conduction model based on the template Montreal Neurological Institute (MNI) brain. We used a regularization parameter of 5% of the covariance matrix and chose the dipole orientation of most variance using singular value decomposition. Finally, the preprocessed and band-pass filtered EEG data of each subject were projected through the individual spatial filter to extract the amplitude time series of neuronal activity of each frequency band at each voxel.Connectivity analyses were performed in source space. For source analysis, we used linearly constrained minimum variance beamforming20 By contrast, phase-based connectivity seems more detached from structure and more strongly affected by contextual factors.20 Here, functional connectivity was investigated using the phase locking value (PLV),40 the debiased weighted phase lag index (dwPLI),78 and the orthogonalized amplitude envelope correlation (AEC).32 The PLV and dwPLI are based on the phase of the signals, whereas the AEC is based on the amplitude. The PLV is well established, highly sensitive, and captures both zero phase lag and nonzero phase lag connectivity. The PLV is sensitive to volume conduction effects because volume conduction can yield spurious synchrony at zero phase lag. Source space analysis can significantly reduce these volume conduction effects on PLV-based connectivity.29 Moreover, group contrasts of connectivity further reduce the likelihood of volume conduction effects because these effects are unlikely to differ between groups. The dwPLI captures nonzero phase lag connectivity only. The dwPLI is therefore not susceptible to volume conduction at the cost of reduced sensitivity because real synchrony at zero phase lag is also discarded. This similarly applies to connectivity analyses in electrode space and source space. Because of the explorative character of our study, we mainly report the more sensitive measure, the PLV.Connectivity analyses of EEG data were performed using phase-based and amplitude-based approaches that likely capture different and complementary neural communication processes  strongest connections and binarized. A binary connectivity matrix was computed to reduce the computational load and to facilitate interpretation23 and comparison with previous connectivity analyses.By applying graph–theoretical methods to the data, we reduced the high-dimensional EEG data to a few network measures, characterizing the organization of the whole brain network. Graph theory defines networks as collections of 57 We investigated 2 local network measures: the degree and the local CC. The degree is the number of a node's edges, ie, the number of connections to other nodes. The local CC is the fraction of the node's neighbors that are also neighbors of each other. Thus, both local measures depict how well connected a node is. In particular, the degree indicates how well connected a node is to all other nodes of the network, and the local CC indicates how well connected a node is to neighboring nodes. Four global network measures were included in the analysis: the global CC (gCC), global efficiency (gEff), small-worldness (S), and hub disruption index (kd).1 The gCC is the average of the local CC of all nodes indicating the prevalence of clustered connectivity around individual nodes.57 In functional brain networks, it is commonly regarded as a measure of functional segregation.57 The global efficiency is the inverse of the average shortest path length. In functional brain networks, it represents a measure of functional integration.57Small-worldness describes the ratio of CC and global efficiency and compares it with random networks. In functional brain networks, it can quantify the balance of functional segregation and integration57 and has been associated with the overall efficiency of communication in a network.82 Finally, the hub disruption index compares the degree of all nodes with those of a control group. Positive values indicate that strongly connected nodes increase and weakly connected nodes decrease their number of connections (“the rich get richer and the poor get poorer”). Conversely, negative values indicate that strongly connected nodes decrease and weakly connected nodes increase their number of connections (“the rich get poorer and the poor get richer”), which means a shift of the network towards a random network with less internal structure.We used common graph measures that characterize either a single node  or the complete network .r was computed between clinical parameters and brain measures, which were found to show significant relationships either in previous studies75 or our own. The global peak frequency, mean global power in the 4 frequency bands, hub disruption index and the mean theta and gamma PLV connectivity (averaged across voxels of clusters with significant differences between patients and controls), the PLV global efficiency in the gamma band, and the dwPLI hub disruption index in the gamma band were correlated with the following clinical parameters: current pain intensity, average pain intensity in the past 4 weeks quantified by a visual analogue scale, pain duration, pain disability quantified by the pain disability index,18 mental and physical quality of life quantified by the VR-12,61 depression quantified by the BDI-II,6 and medication as quantified by the medication quantification scale.30Pearson's 13 with a linear kernel, which was trained on all available features. The features were the peak frequency (one feature per participant), global power spectrum , local power , local strength of connectivity , local degree , local CC , and the global graph measures . This resulted in an SVM containing 73,228 features per participant.The machine learning analysis was conducted using the Statistics and Machine Learning Toolbox in Matlab as well as custom-written scripts. We implemented a support vector machine (SVM)To avoid overfitting, we implemented a so-called sequential forward feature selection. This approach first tests the predictive value of all single features separately. It next takes the most predictive feature and adds a second feature and tests whether the classification improves. This procedure is repeated until no further improvements of the classification can be achieved. In our data, the procedure resulted in a classification that was based on average on 5.5 features rather than more than 70.000 features.12 Thus, we conducted 2000 analyses: 1000 with 84 randomly picked patients and the 84 healthy controls, and 1000 with the same data but randomly shuffled labels between patients and controls. The 2 resulting distributions of 1000 cross-validation accuracies each were then statistically compared using a nonparametric permutation test.46 The sensitivity is defined as the rate of true positives, ie, correctly classified patients, divided by the number of total patient classifications. The specificity is defined as the rate of true negatives, ie, correctly classified healthy controls, divided by the number of total healthy classifications.The performance of the SVM was evaluated using a 10-fold cross-validation. First, the data set was randomly split into 10 folds. 9/10 folds were used to train the classifier, with a nested feature selection loop that used another 10-fold cross-validation within the training data to find the most predictive features. Then, the remaining 1/10, which were not included in the training and therefore also not in the feature selection, were classified, resulting in a certain classification accuracy. This procedure was then repeated, cycling through all folds, yielding a mean accuracy over the 10 folds. Because our groups were unbalanced regarding participant numbers, we randomly picked 84 data sets from the cohort of chronic pain patients for the classification procedure, repeating this 1000 times. To conclude whether this result truly exceeds chance level, we repeated the whole procedure with the same data, but the labels of chronic pain patients and healthy controls were randomly shuffled.Apart from the overall performance of the SVM, we also investigated which features contained the highest predictive value, ie, which features were most consistently picked by the SVMs by looking at the number of times a certain feature was included in the SVM by automatic feature selection.51 and custom-written Matlab scripts. The significance level for all statistical tests was set to 0.05 two-tailed. The underlying statistical test for all permutation tests was an unpaired T-test. We used cluster-based permutation tests with a cluster threshold of 0.05 to compare patients with healthy controls for all local analyses and the global power spectrum analysis.46 We report maximal and minimal T-values (t_max/min) for all cluster-based permutation tests that did not find any clusters. The other global measures were compared using nonparametric permutation tests, permuting between the patient group and healthy control group.46 To account for multiple comparisons within a specific measure, we corrected all P-values of tests that were performed on all 4 frequency bands using the Holm–Bonferroni method.33 To test whether the accuracy of the SVMs was above random chance level, we computed a nonparametric permutation test on the accuracy distribution of the SVM trained on the real data against the accuracy distribution of the SVM trained on the randomly labeled data. Here, a nonparametric permutation test was used to decrease the variety of different statistical tests. Pearson's r was calculated to find correlations between brain measures and clinical parameters and tested for statistical significance against the null hypothesis of no correlation. Resulting P-values were again corrected for multiple comparisons across the 4 frequency bands using the Holm–Bonferroni method, if applicable.Statistical analysis was performed using FieldTripAll scripts and data are available on request.P = 0.20, Fig. P ≥ 0.14).We first investigated whether chronic pain was associated with global changes of oscillatory brain activity. We determined the peak frequency of EEG activity in chronic pain patients and healthy controls by averaging the power spectra across all epochs and electrodes and determining the maximal power in the frequency range of 6 to 14 Hz. Peak frequency was 9.8 ± 1.2 Hz (mean ± SD) in chronic pain patients and 10.0 ± 1.4 Hz in healthy controls and did not differ significantly between groups , alpha (8-13 Hz), beta (14-30 Hz), and gamma (60-100 Hz) frequency bands. Group comparisons of the topographical maps did not show significant differences between patients and controls in any frequency band  in comparison with the control group. The strongest contrast was found in the supplementary motor area . Moreover, we also found that patients showed a significantly increased connectivity strength in the gamma band , which was maximal in the anterior prefrontal cortex . Because in both frequency bands only a single extended cluster of increased connectivity was found, the increase likely reflects frontal connectivity within the clusters rather than connectivity to targets outside of the clusters. No significant clusters were found in the alpha and beta bands . Analysis of amplitude-based connectivity did not show any significant differences in connectivity strength between chronic pain patients and healthy controls in any brain region or any frequency band. . In addition, we computed the weighted degree, ie, a thresholded but not binarized connectivity matrix. Cluster-based permutation tests of the weighted degree showed no significant differences between patients and controls in any frequency band either . This lack of a difference in (weighted) degree indicates that the difference in connectivity strength is not confined to the strongest connections but instead applies to connections of all strengths.To further assess connectivity patterns of chronic pain patients, we performed graph theory–based network analysis. We first examined the local properties of brain networks in chronic pain patients. A basic property of a node is the number of its connections to other nodes, which is termed the CC. This measure assesses the number of connections of neighboring nodes, ie, it measures the local integration of a node served by short-range connectivity. Comparing the CCs of all nodes between patients and controls did not show any significant differences at any frequency band, neither for phase-based nor amplitude-based connectivity .Another well-established measure that characterizes nodes is the d = 0.40), whereas the increase in the gamma band was of medium effect size (Cohen's d = 0.59).Taken together, the analysis of local measures of functional connectivity showed increases of phase-based connectivity in frontal and prefrontal cortices at theta and gamma frequencies in chronic pain patients. The increase in the theta band was of small effect size . Second, we assessed the global efficiency, which provides an account of the ease of long-distance communication and is commonly interpreted as a measure of functional integration in a network. After accounting for multiple comparisons, we found evidence for a decrease of global efficiency in patients in the gamma frequency band when investigating phase-based connectivity (Table P [corrected/uncorrected] = 0.013/0.0032). The effect size of this difference was small (Cohen's d = 0.44). Third, we computed the small-worldness, which is associated with communication efficiency within a network. We detected no changes in small-worldness between the 2 groups (Table P_min [corrected/uncorrected] = 0.26/0.064). Fourth, we analyzed the hub disruption index, which has been shown to differ between chronic pain patients and controls in previous functional magnetic resonance imaging studies.45 It shows potential shifts of connections that manifest on a global scale. Our results did not show a difference of the hub disruption index in any frequency band when comparing chronic pain patients with healthy controls (Table P_min [corrected/uncorrected] = 1/0.32).We next investigated whether chronic pain was associated with changes of global measures of functional connectivity and therefore computed graph measures that characterize different and complementary global properties of brain networks. Figure global efficiency at gamma frequencies in chronic pain patients. This decrease was of small effect size (Cohen's d = 0.44).In summary, global graph measures of functional connectivity showed a decrease of P-values of 0.3 (neck muscles) and 0.08 (masseter). Group comparisons of EMG-EEG connectivity band-pass filtered in the gamma band (60-100 Hz) yielded P-values of 0.6 (neck muscles) and 0.7 (masseter). These findings argue against a contamination of the observed connectivity differences between chronic pain patients and healthy controls by muscle artifacts.First, as muscle artifacts may cause spurious synchrony, we have analyzed connectivity between EMG signals and EEG signals. Electromyography signals were recorded from the right neck and masseter muscles and band-pass filtered at theta (4-8 Hz) and gamma (60-100 Hz) frequencies. Electroencephalography signals were band-pass filtered source signals from peak voxels of clusters where statistically significant group differences in EEG connectivity were found at theta (MNI coordinates = [−10 10 70]) and gamma (MNI coordinates = [−40 40 30]) frequencies. Statistical comparisons of PLV-based EMG-EEG connectivity did not show any significant differences between chronic pain patients and healthy controls. In particular, nonparametric permutation tests comparing EMG-EEG connectivity band-pass filtered in the theta band (4-8 Hz) between groups yielded 78 The dwPLI differs from the PLV by capturing nonzero phase lag connectivity only. The dwPLI is therefore not susceptible to volume conduction, which can yield artificial connectivity effects at the cost of reduced sensitivity because real synchrony at zero phase lag is also discarded. The results of the cluster-based permutation tests did not reveal any local difference in functional connectivity between patients and controls . This indicates that zero phase lag connectivity plays an important role in the increased frontal connectivity of patients. Concerning global graph measures . This difference together with a lack of a difference in the corresponding PLV-based analysis indicates that the difference mainly applies to nonzero phase lag connectivity.Second, we tested whether changes of functional connectivity in chronic pain patients can be detected using another common phase-based connectivity measure (dwPLI).global efficiency in the gamma band calculated with the PLV  and the hub disruption index in the gamma band calculated with the dwPLI  were both significantly lower in chronic pain patients irrespective of edge density and therefore showed a consistent and robust effect.Third, we performed control network analyses by calculating all graph measures with different edge densities using all 3 connectivity measures. This was done to examine the robustness of our results, which were based on a threshold of 10% strongest connections. No significant group differences in local graph measures were found for any of the connectivity measures. Regarding global measures, the P [corrected] = 0.0080, Cohen's d = 0.65) and gamma  bands. Similarly, PLV-based global efficiency  and the dwPLI-based hub disruption index  were decreased at gamma frequencies in chronic pain patients without depression.Finally, we tested whether depression plays a critical role in explaining differences between patients and controls. We therefore aimed to replicate the significant findings after excluding patients with a clinically relevant depression . This reanalysis did not qualitatively change any of the previously significant results. Frontal connectivity was increased for chronic pain patients without depression in the theta . Finally, we were interested to know which features of brain activity and/or connectivity were most relevant for the classification. The automatic feature selection on average picked 5.5 features for the SVMs. We therefore show the 5 most frequently picked features in Figure Finally, we performed a multivariate machine learning approach. This approach extends the previous univariate approaches by taking patterns of brain activity and connectivity into account rather than single pieces of information in isolation. Moreover, it complements the previous descriptive group analyses by adding a predictive, single-subject analysis. We used an SVM classifier to test whether patterns of brain activity and/or connectivity can distinguish between chronic pain patients and healthy controls. We trained a linear SVM on all aforementioned measures of brain activity and functional connectivity, using an automated sequential feature selection algorithm. The performance of the SVM was evaluated using a 10-fold cross-validation. The resulting mean accuracy was 57% ± 4% with a sensitivity of 60% ± 5% and a specificity of 57% ± 5%. To test whether this result exceeds chance level, we repeated the whole procedure with the same data but randomly shuffled labels of chronic pain patients and healthy controls. This resulted in a permutation distribution with 50% ± 5% accuracy. A nonparametric permutation test of the 2 accuracy distributions (Fig. Thus, a multivariate machine learning approach could statistically significantly distinguish between chronic pain patients and healthy controls based on EEG measures of brain activity and connectivity. In particular, frontal phase-based connectivity at theta and gamma frequencies provided important information for the classification.In this study, we systematically exploited the potential of EEG to determine abnormalities of brain function in chronic pain. Defining such abnormalities promises to advance the understanding of the neural basis of chronic pain. Moreover, they might serve as a brain-based marker and novel treatment target of chronic pain. To this end, we analyzed resting-state EEG recordings of a large cohort of patients suffering from chronic pain and compared them with those of age- and sex-matched healthy controls. The analyses ranged from simple global measures of brain activity to sophisticated connectivity and network analyses in source space. All analyses were data-driven and each analysis was rigorously corrected for multiple comparisons. To the very best of our knowledge, this approach represents the most extensive analysis of EEG data from one of the largest cohorts of chronic pain patients so far. The results show that global measures of brain activity and brain connectivity as measured by EEG did not differ between chronic pain patients and a healthy control group. However, our approach revealed a stronger phase-based connectivity at theta and gamma frequencies in the prefrontal cortex of chronic pain patients. Furthermore, we observed a global reorganization of brain networks in the gamma frequency band. Based on patterns of brain activity and connectivity, a multivariate machine learning approach could classify chronic pain patients and healthy controls with an accuracy of 57%.79 These findings have been related to the TCD model of chronic pain.42 In this model, abnormal nociceptive input causes abnormal thalamic bursts at theta frequencies. These theta oscillations are transmitted to the cerebral cortex where they result in disinhibition of neighboring areas, abnormal oscillations at gamma frequencies, and eventually in ongoing pain. This model is highly appealing, but evidence is still sparse. The present completely data-driven approach in a large cohort of chronic pain patients neither shows increased theta power nor a shift of global peak frequency and therefore does not directly support the TCD model of chronic pain.Previous resting-state EEG studies investigating alterations in chronic pain patients mainly reported an increase in theta power together with a slowing of the global peak frequency compared with healthy controls.72 and EEG47 studies as well as with recent reviews and theories,62 which have shown that structural and functional alterations in the prefrontal cortex play an important role in chronic pain. A more precise localization of the connectivity increases in the prefrontal cortex is beyond the spatial resolution of EEG. Hence, it remains unclear how the present observations relate to the multitude of functions represented in the prefrontal cortex, which include motor, cognitive control, emotional, evaluative, and modulatory functions.37 However, a role of the prefrontal cortex in chronic pain points to an important function of emotional-evaluative, motivational, and decision-making circuits rather than sensory circuits in chronic pain.55The univariate comparisons of brain activity/connectivity between groups and the multivariate machine learning approach congruently indicated increased functional connectivity of the prefrontal cortex in chronic pain patients. These findings are in accordance with fMRI9 In an animal model of chronic pain, these interneurons have been implicated in the modulation of pyramidal cell firing in the prefrontal cortex and pain behavior.85 This link between GABAergic inhibition, gamma oscillations, prefrontal cortex activity, and pain behavior is in accordance with the present observations. Functionally, gamma oscillations likely represent a basic feature of neuronal signaling and communication,81 which seems to be particularly related to the local processing and feedforward communication of currently important stimuli.53 These concepts would be in line with an association of chronic pain with prefrontal gamma oscillations possibly signaling the emotional, motivational, and evaluative aspects of pain.Our findings revealed that chronic pain is associated with frontal connectivity increases and a global disturbance of brain network organization at gamma frequencies. Mechanistically, gamma oscillations have been related to the activity of inhibitory parvalbumin-positive GABAergic interneurons.45 The present findings complement these studies by showing that global changes of functional connectivity also occur in the gamma band at much higher frequencies between 60 and 100 Hz.In addition, decreases of global efficiency and hub disruption index in the gamma band indicate that chronic pain is not only associated with local changes of functional connectivity but also a global reorganization of brain networks. This is in accordance with recent fMRI studies that have shown global changes of functional connectivity at infraslow frequencies below 0.1 Hz.76 which were not achieved in this study. The reasons for this disparity remain unclear because the available information on the previous approach does not allow for precise replication. Although the accuracy of the present study is far from being sufficient for practical purposes, this result has important implications. First, it indicates that prefrontal synchrony at theta and gamma frequencies might play a role in the pathophysiology of chronic pain. Second, the present approach might represent a step further towards a much sought-after brain-based marker of pain.71 fMRI recordings have already shown that, in principle, it is possible to establish such a marker.80 The present approach complements these fMRI approaches by using EEG recordings. Third, abnormal patterns of EEG activity in chronic pain might represent potential targets for novel therapeutic strategies such as non-invasive brain stimulation techniques54 and/or neurofeedback approaches.63 In particular, the emerging transcranial alternating current stimulation technique54 allows for the frequency-specific modulation of neuronal oscillations and synchrony and might, thus, represent a promising approach to modulate pain.The present machine learning approach shows that applying an SVM classifier to resting-state EEG data allows to distinguish between chronic pain patients and healthy controls with 57% accuracy. A recent study that pursued a closely related EEG approach showed accuracies of >90% for the classification of chronic pain patients vs healthy controls,70 and the specificity of the present results for chronic pain remains unclear. However, changes of prefrontal theta and gamma synchrony were similarly found when patients with depression were excluded. Moreover, the causal relationship between the observed changes of brain function and chronic pain remains unclear. However, a potential lack of specificity and/or causality does not necessarily limit the clinical usefulness and validity of a brain-based marker of chronic pain.15 Second, field spread and/or muscle artifacts can cause spurious synchrony of EEG signals. A rigorous artifact correction procedure and analysis in source space are best practice to reduce these effects. Moreover, the present analyses are based on comparisons between groups, and systematic differences of volume conduction between groups are unlikely. Furthermore, a lack of group differences in amplitude of oscillatory of brain activity argues against volume conduction effects on the observed group differences in connectivity. In addition, a lack of connectivity between EMG and EEG signals argues against a contamination of the observed differences in EEG connectivity by EMG signals. Nevertheless, muscle artifacts and volume conduction remain an inherent and delicate confound of EEG signals. Third, drug effects cannot be ultimately ruled out. We excluded patients taking benzodiazepines, which have known effects on EEG signals.4 However, in our representative cohort of chronic pain patients, most patients took nonopioid analgesics, opioids, and/or antidepressants. To control for drug effects, we quantified medication and found no significant correlations between medication and the observed EEG effects. Therefore, it is unlikely but not impossible that our effects are solely driven by drug effects. Fourth, for each measure of brain activity and connectivity, we have corrected for multiple comparisons across frequency and/or space. However, we have not corrected for multiple comparisons across the different measures of brain activity and brain connectivity. This has been done to decrease the risk of false-negative findings. However, it increases the risk of false-positive findings so that the current findings need replication and reproduction.Several limitations of this study need to be pointed out. First, abnormal oscillations and synchrony are observed in many neurological diseases including depressionIn conclusion, our data-driven, systematic, and extensive analysis of EEG data from a large cohort of chronic pain patients shows that local and global measures of brain activity did not differ between chronic pain patients and a healthy control group. These negative findings might help to clarify inconsistencies in previous studies and guide future research. Moreover, our study reveals increased prefrontal synchrony together with global network reorganization at gamma frequencies in chronic pain, which allows for differentiating chronic pain patients from healthy controls. These findings advance the understanding of the brain mechanisms of chronic pain. Beyond, the present observations might represent a step further towards a safe, low-cost, broadly available, and potentially mobile brain-based marker of pain. However, substantial challenges concerning the possibility of false-positive findings and the accuracy, specificity, and validity of such a marker remain to be overcome. Finally, the findings might open new therapeutic perspectives by revealing a potential target for novel noninvasive brain stimulation and neurofeedback strategies.The authors have no conflicts of interest to declare.


Pedal fat grafting has been shown to improve pain and functional impairment from forefoot fat pad atrophy.The authors aimed to determine if patient demographics and foot characteristics play a role in the level of impact that is achieved following surgery.The authors performed a retrospective review of patients who received forefoot autologous fat injections for the treatment of pedal fat pad atrophy. Patient improvement of pain and functional impairment were evaluated for correlation with patient characteristics, including gender, age, BMI, unilateral vs bilateral injections, flexible vs rigid arch, previous foot deformity or surgery, and presence of callus.2; 75% had injections performed bilaterally; 41% had a flexible arch, 73% had a past history of pedal deformity or surgery, and 43% had callus. Only female gender was found to correlate with an improvement in pain from the time of surgery to 12 months later (P = 0.02).Forty-four patients received fat injections into the ball of their foot; 73% of them were women; their mean age was 61 years, and mean BMI was 26.6 kg/mBilateral rigid, high arched foot type is a risk factor for foot pain and disproportionately represented among these patients. The only patient characteristic found to be correlated with improvement in pain at 12 months post-surgery was female gender. BMI and laterality of injections impacted the course of improvement after surgery. Given current data, all patients with suspected pedal fat pad atrophy should be considered for soft tissue augmentation. Fat pads in the foot provide protection and shock absorption during the gait cycle. Pedal fat pad atrophy, therefore, causes foot pain and disability as well as an impairment in the ability to perform activities of daily living. Forefoot pedal fat pad atrophy is a diagnosis of exclusion where patients experience foot pain under the metatarsal head without specific parameters for fat pad thickness. Advancing age, obesity, abnormal foot mechanics, corticosteroid injections, previous surgery, and overuse from athletic training or prolonged standing increase risk for developing this condition.2Frequent foot pain is a common problem experienced by approximately 1 in 5 middle-aged and older adults.3Previous research has demonstrated that autologous pedal fat grafting significantly improves the quality of life for patients experiencing foot pain as a result of pedal fat pad atrophy. Patients express an increase in functional ability, a decrease in pain, and further ability to perform activities of daily living compared with patients receiving standard of care therapy. Furthermore, patients not receiving pedal fat grafting show a worsening of their foot pain and disability over time. Objectively, following forefoot fat injections, fat is retained at long-term follow-up. Increased fat is seen redistributed around the metatarsal head with no increased tissue thickness of the fat pad under the metatarsal head of the forefoot.4 For pedal fat grafting, there has been no previous analysis of which patients see the most improvement and no current report guiding patient selection.Patient selection is an important consideration for all elective procedures. Patients not expected to benefit should, whenever possible, not undergo surgery. In other areas of plastic surgery, such as body contouring, identification of patient characteristics likely to impact success is an integral part of preoperative patient care.5A retrospective review was conducted on previously collected data to determine the impact of patient characteristics and demographics on foot pain and disability following forefoot pedal fat grafting. Typical characteristics seen in patients presenting for foot fat grafting were assessed, including type of arch, differentiation between rigid and collapsible arch, history of previous foot operations , or presence of callous. Data came from a previous prospective, randomized clinical trial and a case series approved by the University of Pittsburgh Institutional Review Board from January 2014 to September 2017. In both studies, patients provided informed consent and were included if they had foot pain under the head of the metatarsals, were diagnosed with fat pad atrophy by a foot and ankle specialist, and were at least 6 months out from any surgical intervention or injection into the foot. Exclusion criteria included patients with open ulcerations or osteomyelitis, diabetes, active infection anywhere in the body, diagnosis of cancer within the last 12 months and/or presently receiving chemotherapy or radiation treatment, known coagulopathy, systemic disease that would render the fat harvest and injection procedure unsafe to the patient, pregnancy, and tobacco use within the past year. Randomization was conducted by an independent research coordinator not involved in the trial using GraphSoft random number generator software . Patients underwent a surgical procedure as previously described.3 There were no differences between the intervention and control groups in our previous studies.Fifty-one patients with forefoot fad pad atrophy received injections into their forefoot fat pads. Seven patients withdrew from studies before sufficient data being collected for analysis and were excluded. Forty-four patients were included in the final analysis. Demographics and foot characteristics are presented in 6 It was administered on paper at each visit by a member of the research team and answers were subsequently deidentified before analysis. Composite scores assessing pain and function were analyzed with other data not included.Foot pain and function were measured using a patient-reported outcome tool entitled the Manchester Foot Pain and Disability Index. This validated assessment tool includes items regarding function, personal appearance, pain, and work/leisure activities.P < 0.05.One-way ANOVA tests were used to examine the relationship between patient characteristics and demographics with change in patient foot pain and function composite survey scores. Patient foot pain and function were examined separately. Changes in composite survey scores were compared across gender, age above or below 65, BMI above or below 25, bilateral or unilateral injections, flexible or rigid foot arch, previous history or no previous history of pedal deformity or surgery, and presence or absence of callus. Changes in survey scores included were from the time of surgery to 6 months post-surgery, time of surgery to 12 months post-surgery, and from 6 months post-surgery to 12 months post-surgery. Change was calculated such that a negative change in survey score indicates an improvement in patient status . The Manchester Foot Pain and Disability Index includes 5 items asking about pain and 10 items asking about function. Each item is scored from 0 to 2 based on the answer given. Therefore, the range for possible composite survey scores was from 0 to 10 for pain scores and from 0 to 20 for function scores. All statistical analyses were performed using Stata/SE version 15.1  with statistical significance set to the level of Patients included in the analysis had a mean age of 61.2 years at the time of screening ; 32 patients (73%) identified as female, and 12 patients (27%) identified as male. The mean follow-up time for patients after surgery was 15.4 months . Insufficient data were available to compare outcomes by patient characteristics beyond 12 months. Following the procedure, patients experienced bruising at the donor site, soreness, and pain. No serious adverse events or unanticipated events occurred .P = 0.005). There was no significant difference in change in scores from the time of surgery to 6 months or from time of surgery to 12 months among those with BMI above 25 compared to those with BMI below 25.The average total change in function scores from the time of surgery to 12 months was −4.3 with a standard deviation of 5.3 . The majP = 0.02). Bilateral injections showed a significant difference in pain scores from the time of surgery to 6 months with a mean difference in scores of −2.1 (P = 0.03). Bilateral vs unilateral injections did not significantly impact a change in pain scores from 6 months to 12 months or from time of surgery to 12 months.The average total change in pain scores from the time of surgery to 12 months was 2.4 with a standard deviation of 2.9 . The maj5 The thickness of the fat pad is initially increased and then returns to baseline with long-term increases in the volume of the fat pad distributed around the metatarsal head.3 Other benefits of pedal autologous fat grafting to various areas of the foot have been shown, including improvement of skin quality, treating adult flatfoot deformity, and decreasing the frequency of diabetic foot wounds.7-9Previous publications have shown the effectiveness of autologous pedal fat grafting at achieving long-lasting improvements in pain and function for patients with pedal fat atrophy.There is wide variability in the amount of improvement seen and the course of improvement following surgery. We found a significant difference in the effect of the procedure by patient gender and a difference in the course of improvement by patient BMI. Additionally, the course following surgery was different for patients who received bilateral vs unilateral injections. This suggests that patient characteristics and foot mechanics may be useful in identifying who is most likely to benefit from this procedure and predicting the course and type of improvement they are likely to experience.1 Additionally, women on average have thinner pedal fat pads than men, a correlation related to their lower height and BMI.10 In our analysis, female gender was the only factor found to decrease pain at long-term follow-up. One possible explanation for this effect is an initial thinner fat pad in female patients, providing a higher potential for improvement.Our sample was disproportionately female, appropriately correlating with an increased prevalence of foot pain in women as compared with men.There are 2 hypotheses about the mechanism for the alleviation of pain. The increased volume may offer support despite the absence of increased thickness under the metatarsal head or fat grafting may induce a qualitative rather than a quantitative change in the fat pad, which improves symptoms. Patients insoles are padded after surgery to provide off-loading of the forefoot for 6-8 weeks and they are instructed to avoid strenuous activity. However, we did not totally off-load the grafted region and future studies investigating complete off-loading vs early ambulation are warranted.The major limitation of this study was the limited sample size. As the evidence grows for the effectivity of autologous pedal fat grafting as a treatment for pedal fat grafting, so does the importance of selecting those patients most likely to benefit and avoiding subjecting to surgery patients unlikely to benefit. However, the limited available data meant that we were unable to fully elucidate which patient characteristics were most significant in effecting the course following surgery. Furthermore, given the retrospective nature of this analysis, we had limited data available on patient demographics and foot characteristics. It is likely that there are influencing factors that were not available to be included in this report. Another major limitation of this study was the subjective nature of the outcome examined. Patient-reported outcomes are an important and popular measure of the study. However, a patient’s perception of their recent functional ability and pain is inherently subjective and vulnerable to bias. We are currently working to establish a reliable foot pain questionnaire specifically for fat pad atrophy. Pedal fat atrophy is a diagnosis of exclusion without specific criteria for tissue thickness; therefore, patients not benefiting may have other biomechanical reasons for their pain.Patient characteristics correlate with the impact of pedal fat grafting surgery and the time course of improvement following surgery. Female gender correlates with improvement in pain at long-term follow-up, while the laterality of injections and BMI impacts the course of improvement following surgery. However, given current data, we advocate for all patients with suspected fat pad atrophy to be considered for soft tissue augmentation. Large-scale studies are called for to further elucidate the impact of various patient characteristics on the probability of success and the course of improvement following pedal fat grafting.


Acupuncture is widely used for pain diseases while evidence of its efficacy for sciatica is insufficient. We aim to explore the feasibility and efficacy of acupuncture with different acupoint selecting strategies for sciatica induced by lumbar disc herniation.This is a multicenter, three-arm, patient-assessor-blinded randomized controlled pilot trial. Ninety patients will be assigned randomly into 3 groups including disease-affected meridians (DAM) group, non-affected meridians (NAM) group, and sham acupuncture (SA) group in a 1:1:1 ratio. The trial involves a 4-week treatment along with follow-up for 22 weeks. The primary outcome is the change of leg pain intensity measured by the visual analogue scale (VAS) from baseline to week 4 after randomization. Secondary outcomes include functional status, back pain intensity, and quality of life. Adverse events will also be recorded.The results will inspire the optimal acupuncture strategy for sciatica and help establish a better design as well as power calculation for a full-scale study.ChiCTR2000030680 .The online version contains supplementary material available at 10.1186/s13063-020-04961-4. Sciatica is characterized by radiating leg pain along the course of the sciatic nerve sometimes accompanied by back pain and neurological deficits . The preConservative treatments are the first-line options for sciatica , 6. MediAcupuncture has been widely used for pain diseases. Meta-analysis indicated that acupuncture had a persistent effect on 4 chronic pains which decreased only 15% after 1 year . Howeverhttp://www.chictr.org.cn) on 9th March 2020 with the registration number of ChiCTR2000030680. The protocol is reported in accordance with the Standard Protocol Items (SPIRIT)  aged between 18 and 70 years; (2) having symptoms of sciatica for 1 month or longer from the onset, diagnosed as unilateral radiating leg pain below the knee with positive straight-leg raise test or at least one neurological deficit symptoms as paraesthesia, muscle weakness, or reflex abnormalities. The imaging evidence of lumbar disc herniation is requested on either magnetic resonance imaging (MRI) or computed tomography (CT); (3) leg pain intensity for 40 mm or more measured by the visual analogue scale (VAS) [Patients will be excluded if they meet any of the following criteria: (1) patients who have or are suspected to have severe spinal diseases  or progressive neurological symptoms ; (2) patients with sciatica caused by other conditions than lumbar disc herniation; (3) patients who have undergone surgery for lumbar disc herniation within the past 6 months; (4) patients who plan to have spinal surgery or other interventional therapies during the first 4 weeks of the trial; (5) patients continually taking antiepileptic medication, antidepressant medication, opioids or corticosteroids; (6) patients who have cardiovascular, liver, kidney, or hematopoietic system diseases, mental health disorders, or other severe coexisting diseases ; (7) patients who are pregnant, breastfeeding, or planning to conceive during the trial; (8) patients who received acupuncture therapy within the past 6 months.Patients will be removed if they are unwilling to continue participation. The reasons for dropouts will be recorded.An independent statistician will generate the random sequence by the central block randomization method using IBM SPSS Statistics, version 21.0 . Eligible patients will be assigned randomly into the disease-affected meridians (DAM) group, non-affected meridians (NAM) group, and sham acupuncture (SA) in a 1:1:1 ratio. An independent researcher in charge of informing assignment will implement the allocation schedule through a centralized telephone randomization procedure. To ensure the allocation concealment, acupuncturists will make a call to ask for the information of assignment before the first treatment. The independent researcher will send messages containing allocation details immediately. Before randomization, trial information will be provided to each eligible patient. Researchers (doctors) will get written informed consent from eligible patients who are willing to participate.Patients, outcome assessors, and statistician will be blinded to the assignment. Differences between groups present mainly at point selections and acupuncture performance. Blunt-tipped needles for blinding will be similar to conventional needles. Adhesive foam pads will be put on treating points to cover the difference in acupuncture performance Fig. .Fig. 2SAll patients will receive advice for managing sciatica in daily life such as remaining active, using a hard bed, or losing weight. Based on the assignment, they will receive 12 sessions of 30-min treatments over 4 weeks . Acupoints are predefined based on experienced acupuncturists’ consensus and literature review . LocatioSemi-standardized treatment will be provided with acupoints on affected meridians in this group. There will be four obligatory acupoints and five adjunct acupoints. Obligatory acupoints are bilateral Dachangshu (BL25) and Guanyuanshu (BL26). Depending on the distribution of the pain, sciatica localized at the side of the affected leg will be treated with adjunct acupoints on gallbladder meridian including Huangtiao (GB30), Fengshi (GB31), Xiyangguan (GB33), Yanglingquan (GB34), and Xuanzhong (GB39). Sciatica localized at the back of the affected leg will be treated with adjunct acupoints on bladder meridian including Zhibian (BL54), Chengfu (BL36), Weizhong (BL40), Chengshan (BL57), and Kunlun (BL60). For patients with pain in both side and back, adjunct acupoints will be selected from the ten adjunct acupoints above based on the personal experiences of acupuncturists.After strict disinfection of the skin around acupoints and acupuncturists’ hands using 75% alcohol, adhesive foam pads (10-mm diameter and 5-mm height) will be placed on all acupoints with patients in a prone position. Disposable stainless steel acupuncture needles  will be inserted in BL25 and BL26 on the affected side deeply to 40–70 mm. After performing twirling, lifting, and thrusting manipulations for about 10 s, patients are expected to have “de qi” sensation  radiating down to the affected leg. Other acupoints will be treated with needles (0.30 mm × 40/50/75 mm) inserted to normal depths to reach “de qi” sensation locally. All needles will be retained for 30 min.Nine acupoints on non-affected meridians will be used as follows: bilateral Yaoyan (EX-B7) and Pigen (EX-B4) and unilateral Yinbao (LR9), Ququan (LR8), Ligou (LR5), Fuliu (KI7), and Gongsun (SP4) on the affected leg. All acupoints will be treated by needles (0.30 × 40/50 mm) inserted into the skin with manipulations later to reach the “de qi” sensation. Other treatment settings are the same as those for the DAM group.We select nine non-acupoints in this group with locations shown in Table The primary outcome is defined as the change of leg pain intensity on VAS from baseline to week 4 measuring pain over the prior 24 h. The VAS presents as a 0–100 mm ruler with 0 representing no pain and 100 representing unbearable pain. The specific score will be determined by the distance from 0 to the patient’s mark.Secondary outcomes include VAS for leg pain and back pain at other time points, Oswestry Disability Index (ODI) , the SciAdverse events will be recorded, valued, and treated timely. Continuous investigation and reexaminations will be conducted until the end of the study. Independent clinical specialists who are blinded to the assignment will judge the severity and relationship between adverse events and acupuncture. Sever adverse events will be reported to the principal investigator and the data and safety monitor board (DSMB) within 24 h after occurrence to determine subsequent participation. The occurrence ratio of adverse events will be calculated at the end of the study.Data will be collected clearly and completely on case report forms (CRFs). All original data sources will be preserved including CRFs, informed consents, and inspection results. An independent data manager is in charge of the completed CRFs, he will be asked to input data doubly for proofreading. All data related to the trial will be saved for at least 5 years after publication. Readers will be permitted to access the original data by contacting the corresponding author. Information on patients will remain anonymous including name, age, and telephone number. An independent DSMB is established to ensure the integrity of the research data. The DSMB will review the progress and decide whether a premature closure is needed or not.The study protocol has been reviewed and revised by experts of acupuncture, orthopedics, statistics, and methodology. All researchers who conduct the study will attend a series of training to promote the implementation. Data will be monitored on 3 levels by the head of sub-centers, researchers from the Beijing University of Chinese Medicine, and the DSMB pre-set for this study. The Research Ethics Committee of Beijing University of Chinese Medicine will audit the study on a regular basis independently.As a pilot study, we determine to use a convenience sample instead of a sample size calculation. Ninety patients will be enrolled in this trial, 30 in each group. The data of this pilot trial will be applied to facilitate the calculation of the sample size for a further RCT.Continuous variables will be described by means and standard deviations or 95% confidence intervals when following a normal distribution. If not, the data will be shown by medians and interquartile ranges. Categorical variables will be described by frequencies and percentages.P < 0.05 as exploratory.All data will be analyzed following the intention-to-treat principle primarily. A per-protocol analysis will be used for primary outcome as sensitivity analysis covering patients who complete at least 10 treatment sessions without obvious violation. Analysis of variance (ANOVA), Kruskal-Wallis H test, or chi-square test will be used to compare the equilibrium between three groups at baseline. As for outcomes, one-way repeated measures ANOVA or Kruskal-Wallis H test will be applied for continuous variables and chi-square tests or Fisher’s exact test will be used for categorical variables as appropriate. Data analyses will be performed using IBM SPSS21.0 with the statistical significance of two-tailed Acupuncture might be a potential therapy for sciatica while methodological issues  appear frequently in previous studies. Two recent trials provided restricted evidence in favor of acupuncture but both were conducted in a single center with small sample sizes , 30. GenThe mechanism of sciatica may relate to the distortion of the nerve roots and effect of local inflammatory cytokines . AcupuncAccording to the Traditional Chinese Medicine theory, sciatica belongs to disorders of gallbladder meridian and bladder meridian. Acupuncturists tend to choose acupoints on disease-affected meridians in clinical practice which is expected to produce better effect. In our study, acupoints in the DAM group are all attached to the two affected meridians while those in the NAM group are on other meridians less related to sciatica. The comparative results will inspire the optimal acupuncture strategy for sciatica.The feasibility and efficacy of acupuncture for sciatica will be evaluated in this study. Other strengths involve that it is a multicenter study designed following the Good Clinical Practice guideline . AcupoinRecruitment began on June 9, 2020, and was completed on September 27, 2020. This pilot study  is expected to be finished by the end of March 2021.Affected by the COVID-19, the Guang’anmen Hospital failed to get administrative support from government and was not permitted to recruit patients. To ensure recruitment and reach the target sample size, we had to add 3 other centers: the Union Hospital, Tongji Medical College, Huazhong University of Science and Technology; the Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology; and the Shenzhen Hospital, Beijing University of Chinese Medicine. Finally, 6 hospitals are conducting this pilot study. The final center was confirmed on 26 June, 2020.We planned to enroll patients with acute, subcute, and chronic sciatica initially. However, as many patients chose to stay at home rather than visit doctors under the impact of the COVID-19 pandemic, we realized it could be very hard to enroll patients with acute sciatica. After discussions with experts, we finally decided to enroll patients with sciatica for 1 month or longer. This change was made on May 28, 2020.The length of time for patients receiving acupuncture treatment previously was adjusted from 1 year to 6 months in exclusion criteria. This change was made based on advice from methodological experts who suggested that it would be enough to exclude the lasting effects of acupuncture in 6 months. This change was made on May 28, 2020.Based on suggestions from experts, we have added “the Sciatica Frequency and Bothersomeness Index” as an additional outcome to measure the frequency and bothersomeness for sciatica. It is commonly used in previous studies with good reliability . This chAdditional file 1. SPIRIT 2013 Checklist: Recommended items to address in a clinical trial protocol and related documents.


The possibility of local infiltration analgesia (LIA) replacing interscalene blockade (ISB) as an integral component of a multimodal clinical pathway for total shoulder arthroplasty (TSA) needs to be further investigated. We thus further designed a randomized controlled study to compare LIA with ISB in the treatment of TSA.P value < .05 was considered statistically significant.This blinded and randomised study was performed after approval of the institutional review board in the first affiliated hospital of Jinan University. The included patients were all aged over 18 years and underwent shoulder arthroplasty because of osteoarthritis of the shoulder. Subjects were randomized into 2 groups as follows: LIA or ISB. The primary outcome of this noninferiority study is opioid consumption within the first 24 hours following surgery. Secondary outcomes included pain scores, length of hospital stay, complication, and satisfaction score. For the present trial, we hypothesized that there would be no difference in pain score levels and opioid medication use throughout admission.This study protocol was registered in Research Registry (researchregistry5640). The growth rates of TSA are higher than the rates for total hip and knee procedures in the United States and were predicted to further increase by between 192% and 322% by 2015 based on 2008 numbers. However, due to the soft tissue injury and large amount of bone destruction involved, undesirable postoperative pain remains a challenge for both patients and surgeons after TSA.Currently, total shoulder arthroplasty (TSA) has been widely used in treatment for patients with degenerative arthritis and rotator-cuff-deficient conditions of the glenohumeral joint. In shoulder literature, various techniques can be used to relieve postoperative pain, including anti-inflammatory medications, opioids, epidural anesthesia, interscalene blockade (ISB), and local infiltration analgesia (LIA). The ISB allows delivery of local anesthetic in a controlled manner to the trunks of the brachial plexus between the anterior and middle scalene muscles. Compared with patients receiving only general anesthesia, patients who undergo preoperative ISB have shorter hospital stays and a reduced need for analgesics. However, ISB is also associated with risks, such as failure of nerve blockade, residual neurapraxia, displacement of the catheter postoperatively, systemic toxicity, and respiratory and neurologic complications. LIA has recently gained popularity for its potential to provide extended postoperative pain relief. It is a surgeon-controlled analgesic technique that used to reduce the pain in the early postoperative period with no influence on muscle strength. However, the possibility of LIA replacing ISB as an integral component of a multimodal clinical pathway for TSA needs to be further investigated.Pain management after TSA is an important variable in the perioperative period that can influence participation in physical therapy, discharge from the hospital or outpatient surgery center, and patient satisfaction. In addition, inadequate pain management has been shown to contribute to an increased incidence of postoperative complications.,6,8 We thus further designed a randomised controlled study to compare LIA with ISB in the treatment of TSA. For the present trial, we hypothesized that there would be no difference in pain score levels and opioid medication use throughout admission.Despite the evidence above, concerns exist regarding the incomplete analgesia of LIA. Previous studies have been published to explore the efficacy between LIA vs ISB in reducing pain after TSA, but with different conclusions.2This blinded and randomised study was performed after approval of the institutional review board in the first affiliated hospital of Jinan University GNJN011258). It was carried out in accordance with the principles of the Helsinki Declaration. Data are presented according to the CONSORT statement. This trial was also registered in Research Registry (researchregistry5640). The flowchart of this trial is shown in Figure 1258. It 2.1The included patients were all aged over 18 years and underwent shoulder arthroplasty because of osteoarthritis of the shoulder. The exclusion criteria were as follows: contraindication to ISB , history of allergy to one of the treatments in the study, chronic opioid treatment for more than 3 months, history of shoulder surgery on the same side, weight of less than 50 kg, American Society of Anesthesiologists score greater than 3, and/or incapacity to provide informed consent and pain self-assessment.2.2www.randomization.com) by a research assistant. A unique randomization code was used with no restrictions to either of the 2 study groups: LIA or ISB. The results of the randomization were maintained in opaque envelopes and stored with the research coordinator. The patient, anesthesiologist, surgeon, physiotherapists, acute pain nurses, and outcome assessors were unaware of study group allocation.After written informed consent was taken, patients were randomly allocated to one of the 2 groups in a 1:1 ratio using a computer-generated list of random numbers with a block randomization technique  mixed in 20 ml of sterile saline. This mixture was infiltrated locally using a standardized protocol at the completion of component implantation and before closure of the wound. The injection protocol was as follows. A 60-ml syringe with a 1-inch, 18-gauge needle was used to administer the injection; 5 ml was injected into the periosteum; 10 ml was injected into the deltoid in 2-ml increments spread over the deltoid muscle anteriorly; 10 ml was injected into the pectoralis muscle, again in 2-ml increments; and the final 15 ml was injected evenly along the incision before wound closure. After surgery, patients in both groups were admitted to the orthopedic floor. They were prescribed a standardized postoperative pain regimen consisting of 650 mg acetaminophen every 8 hours, oxycodone 5 mg every 4 hours as needed for pain levels <5, oxycodone 10 mg every 4 hours as needed for pain levels >5, and morphine 2 mg intravenously every 4 hours as needed for severe breakthrough pain.2.3.2A perineural interscalene catheter was inserted before surgery under ultrasound control by a senior anesthesiologist. The anesthesiologist injected 20 ml of 0.2% ropivacaine during the TSA procedure after a negative aspiration test to confirm the absence of blood reflux in the catheter. In the recovery room, a continuous perfusion of 0.2% ropivacaine was started at 5 ml/hour for 48 hours; then, the catheter was removed.2.4The primary outcome of this noninferiority study is opioid consumption within the first 24 hours following surgery. Subjects receive standardized postoperative multimodal analgesics. Supplemental oxycodone 5 to 15 mg every 3 hours for pain is available to each subject. Nurses administer a 5-mg oxycodone tablet for numerical rating scale (NRS) scores of 1 to 3, 10 mg for NRS scores of 4 to 6, and 15 mg for NRS scores of 7 to 10. Oral hydromorphone is substituted for oxycodone in the instances of patient allergy or intolerance. Rescue analgesia is available with IV hydromorphone 0.5 mg every 1 hour as needed for pain of greater than NRS of 7 when refractory to oral opioids. Opioid consumption during the first 48 hours postoperatively is retrieved from the electronic medical record and convert to IV morphine equivalents for analysis. The average and worst NRS pain scores at rest and with activity are determined by blinded investigators at 24 and 48 hours postoperatively.Secondary outcomes included pain scores, length of hospital stay, complication, and satisfaction score. Pain scores were recorded at 1, 6, 12, 24, and 48 hours post-operatively, using a NRS (0–10) at rest and during 45-degree passive flexion of shoulder. Length of hospital stay was calculated by measuring the time from the completion of surgery through discharge for each patient.2.5t test. If the numerical variable has a non-normal distribution or unequal variance, the Wilcoxon Mann–Whitney U test was used (ASA grade); Pearson Chi-Squared test or Fisher exact test was used to analyze the qualitative variable (complication). The nature of the hypothesis testing was 2-tailed, and a P value <.05 was considered statistically significant.All the data were analyzed using SPSS v. 24 . We used the Kolmogorov–Smirnov test to assess whether variable distributions violated the assumption of normality. Data are presented as mean and standard deviation or with medians and 25th to 75th percentiles as appropriate. The normal distributed numerical variable  was analyzed by Student 3–12 Weber and Jain evaluated the efficacy of ISB in a review of 218 patients. They found that 13% of ISBs in their study failed, and 5% of their patients had an abnormal neurologic response the day after surgery. Misamore et al demonstrated that 16% of patients undergoing ISB experience immediate postoperative block side effects, with 4.4% of patients experiencing persistent neurologic complications. This study displayed similar results, with 3% of patients experiencing a persistent neurologic complication. A study by Fredrickson and Price suggested that an increase in postoperative motor blockade can be experienced with ISB and is associated with a reduction in patient satisfaction. These findings along with the desire for early mobilization of the operative extremity suggest the utility of an alternative method for pain control in shoulder arthroplasty.Regional anesthesia in the form of ISB has proved to be an effective mode of postoperative analgesic control for patients undergoing TSA. The safety and complication rates of INB have been evaluated in various studies.The purpose of this study was to compare the efficacy of LIA and ISB in immediate postoperative pain control as measured by patients subjective pain scores and in-hospital opioid medication use. We hypothesized that there would be no difference in pain score levels and opioid medication use throughout admission. The main limitation is that the clinical effects and complications of ISB were compared with that of LIA without the control group in the present study. According to the beneficence principle for patients, no control group with placebo was designed in the protocol of the present study. We do not think this limitation would affect the results tremendously.Conceptualization: Jianbin He.Data curation: Jianbin He.Formal analysis: Jianbin He.Funding acquisition: Yalan Li.Investigation: Jianbin He, Yalan Li.Methodology: Jianbin He, Yalan Li.Resources: Yalan Li.Software: Yalan Li.Supervision: Yalan Li.Validation: Jianbin He.Visualization: Jianbin He.Writing – original draft: Jianbin He, Yalan LiWriting – review & editing: Yalan Li.


Pain management is the pillar of caring for patients with traumatic rib fractures. Intravenous lidocaine (IVL) is a well-established non-opioid analgesic for post-operative pain, yet its efficacy has yet to be investigated in trauma patients. We hypothesized that IVL is associated with decreased inpatient opioid requirements among patients with rib fractures.We retrospectively evaluated adult patients presenting to our Level 1 trauma center with isolated chest wall injuries. After 1:1 propensity score matching patients who received vs did not receive IVL, we compared the two groups’ average daily opioid use, opioid use in the last 24 hours of admission, and pain scores during admissions hours 24–48. We performed multivariable linear regression for these outcomes , adjusting for age as a moderating factor and controlling for hospital length of stay and injury severity.We identified 534 patients, among whom 226 received IVL. Those who received IVL were older and had more serious injury. Compared to propensity-score matched patients who did not receive IVL, patients who received IVL had similar average daily opioid use and pain scores, but 40% lower opioid use during the last 24 hours of admission (p = 0.002). Multivariable regression–with and without sensitivity analysis–did not show an effect of IVL on any outcomes.IVL was crudely associated with decreased opioid requirements in the last 24 hours of admission, the time period associated with opioid use at 90 days post-discharge. However, we did not observe beneficial effects of IVL on multivariable adjusted analyses; we are conducting a randomized control trial to further evaluate IVL’s opioid-sparing effects for patients with rib fractures. Traumatic rib fractures confer significant morbidity and mortality. Poor respiratory effort limited by pain can result in pneumonia, respiratory failure, and ultimately, death –3. This The efficacy of intravenous lidocaine (IVL) as an adjunct analgesic in rib fractures has not been investigated. Lidocaine is a widely used analgesic that is short-acting, easy to titrate, and has anti-inflammatory properties along with one of the best anesthetic safety profiles –17. EvenStarting in 2012, our institution implemented an option for physicians to order systemic IVL as a non-opioid adjunct analgesic for patients with rib fractures. We aimed to assess whether this new pain management approach was associated with decreased opioid usage. We hypothesized that patients who received IVL would have lower hospital opioid utilization and similar pain scores as matched patients who did not receive IVL.2+B2+C2, where A, B, and C, are AIS of the three most severely injured body regions. AIS for the six body regions range from 1 (minor) to 6 (unsurvivable). Stanford’s Institutional Review Board approved this study and waived informed consent requirement for this retrospective EMR study.Using our institution’s trauma registry and electronic medical records (EMR) from 2012–2017, we retrospectively evaluated adult patients (age ≥18) admitted to our Level I trauma center with isolated chest wall injuries . Of note, injury severity of trauma patients is quantified using the Injury Severity Score (ISS): ISS = APatients were excluded if they had 1) chronic opioid dependence (defined by diagnosis on the patient problem list), 2) rib fractures due to cardiopulmonary resuscitation, 3) presentation to the hospital greater than 24 hours after injury, or 4) discharge from the emergency department without inpatient admission as determined by EMR review.IVL infusion was started at 1mg/kg/hr. Systemic lidocaine levels were measured every 8 hours (therapeutic range 1.5–5 mcg/mL). While bolus dose of 1–1.5 mg/kg is standard practice for perioperative IVL, bolus dose was removed from rib fracture IVL protocol in favor of longer time to therapeutic level  and mitigating side effects. IVL infusion rate was titrated either by the surgical intensive care unit (SICU) or pain management service (for patients not in the SICU) for adequate analgesia (NRS ≤4) within therapeutic plasma levels. Trained nursing staff and SICU or pain management service monitored potential side effects, which include but are not limited to bradycardia, arrhythmia, numbness, metallic taste, dizziness, or headaches. All patients on IVL had visible bedside sign indicating they were on IVL infusion.IVL infusion (yes/no) was the main explanatory variable. Patients were assigned to the IVL group if they received any lidocaine over the course of their stay.The outcomes of interests were: mean daily opioids administered during the hospitalization, total opioids administered in the last 24 hours of hospitalization, and mean 0–10 numeric rating scale (NRS) pain score measured from 24–48 hours after admission. Opioid administration data , or patch) was aggregated from EMR and converted to total oral morphine equivalents (OME) using established conversion factors . We exclAdditional information captured from the EMR and trauma registry included patient age, gender, smoking status, ISS, mechanism of injury, number of rib fractures, presence of pulmonary contusion, and presence of scapular, clavicular or sternal fractures. We assessed hospitalization characteristics including hospital length of stay (LOS), SICU admission, incidence of pneumonia, discharge disposition, 30-day readmission rates, and mortality during hospitalization.Descriptive statistics were calculated within study groups, including frequencies, proportions, medians and interquartile ranges. We assessed the trend of IVL use within the study population over time in one-year intervals using the Mann-Kendall test  .To account for the potential treatment selection bias in administration of IVL, we performed propensity score (PS) matching between patients who received IVL and those who did not. We applied a 1:1 optimal matching model without replacement, matching for age, number of rib fractures, initial pain score (mean NRS pain score during the first 6 hours of admission), presence of pulmonary contusion, and presence of scapular, clavicular or sternal fractures. Matching was performed using the “Matchit” package in R .Data analysis was conducted using the PS-matched data. Categorical variables were analyzed using chi-square tests (if expected cell sizes ≥ 5) or Fisher’s exact tests (if any expected cell size < 5), and continuous variables were analyzed using Mann-Whitney U tests due to nonparametric distributions of OME measures and pain scores. Simple and multivariable linear regression models were calculated using the “lm” function from the “stats” R package  . PrimaryDuring the 6-year study period, we identified 534 adult patients with isolated chest wall injuries in our trauma registry. Among those, 42.3% (n = 226) received IVL. Demographic, injury, and hospitalization characteristics are presented in After propensity score matching, all 226 patients in the IVL group were matched with 226 of the non-IVL group . Overall, the matching improved balance between the two groups by 48.1%. However, some imbalance remained with significant differences between IVL and non-IVL groups on certain matching variables . CompareUnadjusted comparisons demonstrated no significant differences in daily opioid use between IVL and non-IVL groups; however opioid use in the last 24 hours of admission was 40% lower in the IVL group  as a moderating factor, and controlling for HLOS and ISS are presented in Similar to the primary analyses, we observed significant main effects of age on OME outcomes in sensitivity analyses. There were no significant differences in OME/day by IVL group in univariate or multivariable analyses. Unlike the primary analysis, the transformed OME in the last 24 hrs was significantly lower in IVL patients vs. non-IVL (p = 0.02) in univariate analysis. However, the difference was no longer significant in multivariable analysis (p = 0.08) when adjusted for covariates .In propensity-matched analyses of adult rib fracture patients with isolated chest wall injury, pain management with IVL was associated with a 40% lower opioid utilization in the last 24 hours of hospitalization in unadjusted comparisons. To the best of our knowledge, our study is the first to evaluate an analgesic associated with reduced opioid use for rib fracture patients during the last 24 hours of admission. The crude association between IVL and decreased opioid use in the last 24 hours of admission is particularly interesting given this time period’s association with opioid use at 90 days post-discharge, and possibly, post-discharge opioid prescribing patterns. Clinicians may be more likely to prescribe post-discharge opioids based on requirements during the last days, rather than early days, of admission.However, the association between IVL and decreased opioid use in the last 24 hours of admission was not seen when stratified by age group (<65 vs ≥65 years). In both adjusted and unadjusted analyses, IVL was not significantly associated with opioid use during hospitalization or pain scores during admission hours 24–48. Our findings show conflicting evidence and only partially corroborate previous studies that showed an association between IVL use and decreased opioid utilization after various operations –24. NotaThe retrospective nature of our study has inherent limitations. The decision to prescribe IVL was based on clinician judgement. Although we performed propensity score matching based on clinical factors that may influence whether a patient receives IVL, there remained some differences between matched IVL and non-IVL groups that may be better controlled in a randomized trial. The IVL group patients tended to be older, with more severe injuries, longer hospitalizations, were more likely to be admitted to the SICU, and more likely to be discharged to a skilled nursing facility. This partially reflects our institutional practice, wherein any patient 65 or older with 2 or more rib fractures is initially monitored in the SICU. Additionally, IVL may have been pre-emptively administered for patients expected to have worse pain or higher opioid utilization, leading to a selection of older and sicker patients in the IVL group. We also limited our study cohort to adults with isolated chest wall injuries and did not investigate the impact of IVL for poly-trauma patients with rib fractures. Lastly, our clinical team ensures adequate analgesia among rib fracture patients with deep inspiration and coughing, but we could not delineate NRS pain scores recorded for specific activities. Studying NRS pain scores during standardized activities (i.e. minimum incentive spirometry inspiration volume) may unveil associations between IVL and pain scores among patients with rib fractures.Future investigations evaluating the efficacy of IVL among patients with rib fractures will need adequate statistical power to detect differences within stratified age groups (<65 and ≥65 years). Having shown opioid-sparing effect in other surgical populations, the efficacy of IVL for other subgroups within the trauma population, such poly-trauma patients with rib fractures, may be considered. Further investigations are also needed to uncover the underlying mechanisms of IVL’s long-lasting analgesic effects.Our findings suggest IVL requires further investigation for patients with traumatic rib fractures; IVL was crudely associated decreased opioid requirements in the last 24 hours of admission, the time period associated with opioid use at 90 days post-discharge. However, given the lack of observed IVL effects on opioids when stratified by age group and on multivariable adjusted analyses, we are currently conducting a randomized controlled trial to further evaluate the opioid-sparing effects of IVL. IVL is easy to deliver, easy to titrate, and has relatively few contraindications; its analgesic effect is known to last long-term beyond the duration of infusion. In addition to potentially minimizing opioid utilization during admission through multimodality pain management, we must continue investigating analgesic strategies to decrease opioid utilization after hospitalization.