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

Cracking the Chronic Pain code: A scoping review of Artificial Intelligence in Chronic Pain research

OBJECTIVE

The aim of this review is to identify gaps and provide a direction for future research in the utilization of Artificial Intelligence (AI) in chronic pain (CP) management.

METHODS

A comprehensive literature search was conducted using various databases, including Ovid MEDLINE, Web of Science Core Collection, IEEE Xplore, and ACM Digital Library. The search was limited to studies on AI in CP research, focusing on diagnosis, prognosis, clinical decision support, self-management, and rehabilitation. The studies were evaluated based on predefined inclusion criteria, including the reporting quality of AI algorithms used.

RESULTS

After the screening process, 60 studies were reviewed, highlighting AI's effectiveness in diagnosing and classifying CP while revealing gaps in the attention given to treatment and rehabilitation. It was found that the most commonly used algorithms in CP research were support vector machines, logistic regression and random forest classifiers. The review also pointed out that attention to CP mechanisms is negligible despite being the most effective way to treat CP.

CONCLUSION

The review concludes that to achieve more effective outcomes in CP management, future research should prioritize identifying CP mechanisms, CP management, and rehabilitation while leveraging a wider range of algorithms and architectures.

SIGNIFICANCE

This review highlights the potential of AI in improving the management of CP, which is a significant personal and economic burden affecting more than 30% of the world's population. The identified gaps and future research directions provide valuable insights to researchers and practitioners in the field, with the potential to improve healthcare utilization.

Applications of Artificial Intelligence in Pain Medicine

PURPOSE OF REVIEW

This review explores the current applications of artificial intelligence (AI) in the field of pain medicine with a focus on machine learning.

RECENT FINDINGS

Utilizing a literature search conducted through the PubMed database, several current trends were identified, including the use of AI as a tool for diagnostics, predicting pain progression, predicting treatment response, and performance of therapy and pain management. Results of these studies show promise for the improvement of patient outcomes. Current gaps in the research and subsequent directions for future study involve AI in optimizing and improving nerve stimulation and more thoroughly predicting patients' responses to treatment.

Functional connectivity patterns are altered by low back pain and cause different responses to sham and real dry needling therapies: a systematic review of fMRI studies

BACKGROUND

There is a relationship between low back pain (LBP) and central nervous system dysfunction. Needling therapies (e.g. acupuncture, dry needling) are proposed to impact the nervous system, however their specific influence is unclear.

PURPOSE

Determine how needling therapies alter functional connectivity and LBP as measured by functional magnetic resonance imaging (fMRI).

METHODS

Databases were searched following PRISMA guidelines. Studies using fMRI on individuals with LBP receiving dry needling or acupuncture compared to control or sham treatments were included.

RESULTS

Eight studies were included, all of which used acupuncture. The quality of studies ranged from good (n = 6) to excellent (n = 2). After acupuncture, individuals with LBP demonstrated significant functional connectivity changes across several networks, notably the salience, somatomotor, default mode network (DMN) and limbic networks. A meta-analysis demonstrated evidence of no effect to potential small effect of acupuncture in reducing LBP (SMD -0.28; 95% CI: -0.70, 0.13).

CONCLUSION

Needling therapies, like acupuncture, may have a central effect on patients beyond the local tissue effects, reducing patients' pain and disability due to alterations in neural processing, including the DMN, and potentially other central nervous system effects. The meta-analysis should be interpreted with caution due to the narrow focus and confined sample used.

Gray Matter Abnormalities in Patients with Chronic Low Back Pain: A Systematic Review and Meta-Analysis of Voxel-Based Morphometry Studies

BACKGROUND

Numerous studies utilizing voxel-based morphometry (VBM) have documented gray matter (GM) alterations in patients with chronic low back pain (CLBP) compared to healthy controls. However, the inconsistency in GM abnormalities observed across different studies has hindered their potential application as objective neuroimaging biomarkers or therapeutic targets. To address this issue, we conducted a comprehensive meta-analysis of VBM studies to identify robust GM differences between CLBP patients and healthy controls.

METHODS

The databases including PubMed, Embase, and Web of Science were systematically searched from January 2000 to September 2022 to identify eligible neuroimaging studies. In this coordinate-based meta-analysis of VBM studies, the Seed-based d Mapping with Permutation of Subject Images method was used to quantitatively assess regional differences in GM between CLBP patients and healthy controls.

RESULTS

Thirteen VBM studies, involving a total of 574 CLBP patients and 1239 healthy controls, were included in the meta-analysis. The findings revealed that CLBP patients exhibited increased GM in the left striatum and left postcentral gyrus and decreased GM in the left superior frontal gyrus, left cerebellum, right striatum, left insula, and right middle occipital gyrus compared to healthy controls. The jackknife sensitivity analysis confirmed the robustness of these neuroimaging findings.

CONCLUSIONS

This study provides new insights into potential treatment strategies for CLBP and identifies neuroimaging biomarkers for pain chronification. These findings highlight the importance of considering regional GM abnormalities in the development of clinical interventions for CLBP.

The Alterations in the Brain Corresponding to Low Back Pain: Recent Insights and Advances

Low back pain (LBP) is a leading cause of global disabilities. Numerous molecular, cellular, and anatomical factors are implicated in LBP. Current issues regarding neurologic alterations in LBP have focused on the reorganization of peripheral nerve and spinal cord, but neural mechanisms of exactly what LBP impacts on the brain required further researches. Based on existing clinical studies that chronic pain problems were accompanying alterations in brain structures and functions, researchers proposed logical conjectures that similar alterations occur in LBP patients as well. With recent extensive studies carried out using noninvasive neuroimaging technique, increasing number of abnormalities and alterations has been identified. Here, we reviewed brain alterations including white matters, grey matters, and neural circuits between brain areas, which are involved in chronic LBP. Moreover, brain structural and functional connectivity abnormalities are correlated to the happening and transition of LBP. The negative emotions related to back pain indicate possible alterations in emotional brain regions. Thus, the aim of this review is to summarize current findings on the alterations corresponding to LBP in the brain. It will not only further our understanding of etiology of LBP and understanding of negative emotions accompanying with back pain but also provide ideas and basis for new accesses to the diagnosis, treatment, and rehabilitation afterward based on integral medicine.

Current and Emerging Technologies to Address the Placebo Response Challenge in CNS Clinical Trials: Promise, Pitfalls, and Pathways Forward

Excessive placebo response rates have long been a major challenge for central nervous system (CNS) drug discovery. As CNS trials progressively shift toward digitalization, decentralization, and novel remote assessment approaches, questions are emerging about whether innovative technologies can help mitigate the placebo response. This article begins with a conceptual framework for understanding placebo response. We then critically evaluate the potential of a range of innovative technologies and associated research designs that might help mitigate the placebo response and enhance detection of treatment signals. These include technologies developed to directly address placebo response; technology-based approaches focused on recruitment, retention, and data collection with potential relevance to placebo response; and novel remote digital phenotyping technologies. Finally, we describe key scientific and regulatory considerations when evaluating and selecting innovative strategies to mitigate placebo response. While a range of technological innovations shows potential for helping to address the placebo response in CNS trials, much work remains to carefully evaluate their risks and benefits.

Measures of sleep disturbance are not routinely captured in trials for chronic low back pain: a systematic scoping review of 282 trials

STUDY OBJECTIVES

To investigate the extent to which sleep measures are reported in intervention trials for chronic low back pain.

METHODS

A systematic scoping review was conducted. Ovid MEDLINE, Cochrane CENTRAL, and CINAHL were queried for trials published between January 2010 and December 2022 using keywords related to chronic low back pain. Two reviewers screened and reviewed abstracts and full texts for eligibility criteria and extracted data. Randomized intervention trials with the aim to treat pain or disability related to chronic low back pain in adults were included. Data were pooled and synthesized from trials that included a measure of sleep.

RESULTS

Two hundred eighty-two trials conducted in 40 different countries were included in the final review. Twenty-six trials (9.2%) assessed any sleep measure, and 13 (4.6%) collected a formal sleep disturbance measure at multiple time points. Three trials analyzed the mediating effects of sleep disturbance on pain. Reporting of sleep measures was no better in more recently published trials; trials published in 2010 (22%; n = 2/9) and 2022 (23%; n = 3/13) had the highest reporting rates.

CONCLUSIONS

The poor adherence to guideline recommendations for capturing measures of sleep quality or disturbance limits clinicians' and researchers' understanding of how sleep may influence treatment effects for chronic low back pain. There is an opportunity to improve the understanding of the relationship between sleep and pain with improved collection and reporting of sleep disturbance measures.

CITATION

Neilson BD, Dickerson C, Young JL, Shepherd MH, Rhon DI. Measures of sleep disturbance are not routinely captured in trials for chronic low back pain: a systematic scoping review of 282 trials. J Clin Sleep Med. 2023;19(11):1961-1970.

Clinical-functional brain connectivity signature predicts longitudinal symptom improvement after acupuncture treatment in patients with functional dyspepsia

Whilst acupuncture has been shown to be an effective treatment for functional dyspepsia (FD), its efficacy varies significantly among patients. Knowing beforehand how each patient responds to acupuncture treatment will facilitate the ability to produce personalized prescriptions, therefore, improving acupuncture efficacy. The objective of this study was to construct the prediction model, based on the clinical-neuroimaging signature, to forecast the individual symptom improvement of FD patients following a 4-week acupuncture treatment and to identify the critical predictive features that could potentially serve as biomarkers for predicting the efficacy of acupuncture for FD. Clinical-functional brain connectivity signatures were extracted from samples in the training-test set (100 FD patients) and independent validation set (60 FD patients). Based on these signatures and support vector machine algorithms, prediction models were developed in the training test set, followed by model performance evaluation and predictive features extraction. Subsequently, the external robustness of the extracted predictive features in predicting acupuncture efficacy was evaluated by the independent validation set. The developed prediction models possessed an accuracy of 88% in predicting acupuncture responders, as well as an R2 of 0.453 in forecasting symptom relief. Factors that contributed significantly to stronger responsiveness of patients to acupuncture therapy included higher resting-state functional connectivity associated with the orbitofrontal gyrus, caudate, hippocampus, and anterior insula, as well as higher baseline scores of the Symptom Index of Dyspepsia and shorter durations of the condition. Furthermore, the robustness of these features in predicting the efficacy of acupuncture for FD was verified through various machine learning algorithms and independent samples and remained stable in univariate and multivariate analyses. These findings suggest that it is both feasible and reliable to predict the efficacy of acupuncture for FD based on the pre-treatment clinical-neuroimaging signature. The established prediction framework will promote the identification of suitable candidates for acupuncture treatment, thereby improving the efficacy and reducing the cost of acupuncture for FD.

Placebo Response among Different Types of Sham Acupuncture for Low Back Pain: A Systematic Review and Meta-Analysis of Randomized Controlled Trials

BACKGROUND

Low back pain (LBP) is a prevalent and debilitating condition that poses a significant burden on healthcare systems. Acupuncture has been proposed as a promising intervention for LBP, but the evidence supporting its specific effect is insufficient, and the use of sham acupuncture as a control in clinical trials presents challenges due to variations in sham acupuncture techniques and the magnitude of the placebo effect.

OBJECTIVE

To investigate the magnitude of the placebo response of sham acupuncture in trials of acupuncture for nonspecific LBP, and to assess whether different types of sham acupuncture are associated with different responses.

METHODS

Four databases including PubMed, EMBASE, MEDLINE, and the Cochrane Library were searched through April 15, 2023, and randomized controlled trials (RCTs) were included if they randomized patients with LBP to receive acupuncture or sham acupuncture intervention. The main outcomes included the placebo response in pain intensity, back-specific function and quality of life. Placebo response was defined as the change in these outcome measures from baseline to the end of treatment. Random-effects models were used to synthesize the results, standardized mean differences (SMDs, Hedges'g) were applied to estimate the effect size.

RESULTS

A total of 18 RCTs with 3,321 patients were included. Sham acupuncture showed a noteworthy pooled placebo response in pain intensity in patients with LBP [SMD -1.43, 95% confidence interval (CI) -1.95 to -0.91, I2=89%]. A significant placebo response was also shown in back-specific functional status (SMD -0.49, 95% CI -0.70 to -0.29, I2=73%), but not in quality of life (SMD 0.34, 95% CI -0.20 to 0.88, I2=84%). Trials in which the sham acupuncture penetrated the skin or performed with regular needles had a significantly higher placebo response in pain intensity reduction, but other factors such as the location of sham acupuncture did not have a significant impact on the placebo response.

CONCLUSIONS

Sham acupuncture is associated with a large placebo response in pain intensity among patients with LBP. Researchers should also be aware that the types of sham acupuncture applied may potentially impact the evaluation of the efficacy of acupuncture. Nonetheless, considering the nature of placebo response, the effect of other contextual factors cannot be ruled out in this study. (PROSPERO registration No. CRD42022304416).

Psychological and neurological predictors of acupuncture effect in patients with chronic pain: a randomized controlled neuroimaging trial

ABSTRACT

Chronic pain has been one of the leading causes of disability. Acupuncture is globally used in chronic pain management. However, the efficacy of acupuncture treatment varies across patients. Identifying individual factors and developing approaches that predict medical benefits may promise important scientific and clinical applications. Here, we investigated the psychological and neurological factors collected before treatment that would determine acupuncture efficacy in knee osteoarthritis. In this neuroimaging-based randomized controlled trial, 52 patients completed a baseline assessment, 4-week acupuncture or sham-acupuncture treatment, and an assessment after treatment. The patients, magnetic resonance imaging operators, and outcome evaluators were blinded to treatment group assignment. First, we found that patients receiving acupuncture treatment showed larger pain intensity improvements compared with patients in the sham-acupuncture arm. Second, positive expectation, extraversion, and emotional attention were correlated with the magnitude of clinical improvements in the acupuncture group. Third, the identified neurological metrics encompassed striatal volumes, posterior cingulate cortex (PCC) cortical thickness, PCC/precuneus fractional amplitude of low-frequency fluctuation (fALFF), striatal fALFF, and graph-based small-worldness of the default mode network and striatum. Specifically, functional metrics predisposing patients to acupuncture improvement changed as a consequence of acupuncture treatment, whereas structural metrics remained stable. Furthermore, support vector machine models applied to the questionnaire and brain features could jointly predict acupuncture improvement with an accuracy of 81.48%. Besides, the correlations and models were not significant in the sham-acupuncture group. These results demonstrate the specific psychological, brain functional, and structural predictors of acupuncture improvement and may offer opportunities to aid clinical practices.

Acupuncture-Neuroimaging Research Trends over Past Two Decades: A Bibliometric Analysis

OBJECTIVE

To identify topics attracting growing research attention as well as frontier trends of acupuncture-neuroimaging research over the past two decades.

METHODS

This paper reviewed data in the published literature on acupuncture neuroimaging from 2000 to 2020, which was retrieved from the Web of Science database. CiteSpace was used to analyze the publication years, countries, institutions, authors, keywords, co-citation of authors, journals, and references.

RESULTS

A total of 981 publications were included in the final review. The number of publications has increased in the recent 20 years accompanied by some fluctuations. Notably, the most productive country was China, while Harvard University ranked first among institutions in this field. The most productive author was Tian J with the highest number of articles (50), whereas the most co-cited author was Hui KKS (325). Evidence-Based Complementary and Alternative Medicine (92) was the most prolific journal, while Neuroimage was the most co-cited journal (538). An article written by Hui KKS (2005) exhibited the highest co-citation number (112). The keywords "acupuncture" (475) and "electroacupuncture" (0.10) had the highest frequency and centrality, respectively. Functional magnetic resonance imaging (fMRI) ranked first with the highest citation burst (6.76).

CONCLUSION

The most active research topics in the field of acupuncture-neuroimaging over the past two decades included research type, acupoint specificity, neuroimaging methods, brain regions, acupuncture modality, acupoint specificity, diseases and symptoms treated, and research type. Whilst research frontier topics were "nerve regeneration", "functional connectivity", "neural regeneration", "brain network", "fMRI" and "manual acupuncture".

Optimizing and Accelerating the Development of Precision Pain Treatments for Chronic Pain: IMMPACT Review and Recommendations

Large variability in the individual response to even the most-efficacious pain treatments is observed clinically, which has led to calls for a more personalized, tailored approach to treating patients with pain (ie, "precision pain medicine"). Precision pain medicine, currently an aspirational goal, would consist of empirically based algorithms that determine the optimal treatments, or treatment combinations, for specific patients (ie, targeting the right treatment, in the right dose, to the right patient, at the right time). Answering this question of "what works for whom" will certainly improve the clinical care of patients with pain. It may also support the success of novel drug development in pain, making it easier to identify novel treatments that work for certain patients and more accurately identify the magnitude of the treatment effect for those subgroups. Significant preliminary work has been done in this area, and analgesic trials are beginning to utilize precision pain medicine approaches such as stratified allocation on the basis of prespecified patient phenotypes using assessment methodologies such as quantitative sensory testing. Current major challenges within the field include: 1) identifying optimal measurement approaches to assessing patient characteristics that are most robustly and consistently predictive of inter-patient variation in specific analgesic treatment outcomes, 2) designing clinical trials that can identify treatment-by-phenotype interactions, and 3) selecting the most promising therapeutics to be tested in this way. This review surveys the current state of precision pain medicine, with a focus on drug treatments (which have been most-studied in a precision pain medicine context). It further presents a set of evidence-based recommendations for accelerating the application of precision pain methods in chronic pain research. PERSPECTIVE: Given the considerable variability in treatment outcomes for chronic pain, progress in precision pain treatment is critical for the field. An array of phenotypes and mechanisms contribute to chronic pain; this review summarizes current knowledge regarding which treatments are most effective for patients with specific biopsychosocial characteristics.

Placebo and nocebo effects: from observation to harnessing and clinical application

Placebo and nocebo effects are salubrious benefits and negative outcomes attributable to non-specific symbolic components. Leveraging advanced experimental and analytical approaches, recent studies have elucidated complicated neural mechanisms that may serve as a solid basis for harnessing the powerful self-healing and self-harming capacities and applying these findings to improve medical practice and minimize the unintended exacerbation of symptoms in medical practice. We review advances in employing psychosocial, pharmacological, and neuromodulation approaches to modulate/harness placebo and nocebo effects. While these approaches show promising potential, translating these research findings into clinical settings still requires careful methodological, technical, and ethical considerations.

Iliopsoas hematoma secondary to small needle scalpel for the treatment of nonspecific low back pain: A case report

INTRODUCTION

Nonspecific low back pain (NSLBP) is one of the most common symptoms which can happen all ages and it accounts for the majority of low back pain (LBP). Current clinical studies have shown that rest, physical therapy, acupuncture (including small needle scalpel) and non-steroidal antiinflammatory drugs are effective treatments for NSLBP. However, the safety of small needle scalpel for treatment of NSLBP was reported rarely.

PATIENT CONCERNS

A 63-year-old female patient was referred to the emergency department for right lower back pain, right lower quadrant ache, weakness of flexion right hip joints and worsening pain with walking after the treatment of small needle scalpel, which was performed by a rural doctor; the symptoms had been lasting for 9 hours.

DIAGNOSIS

She was diagnosed with traumatic iliopsoas hematoma because she experienced increased back pain after accepting small needle scalpel. Clopidogrel was stopped and the patient did not received a blood transfusion and just monitored Blood routine examination, liver and function, coagulation function after admission.

INTERVENTIONS

She had rest in bed absolutely for 3 days after admission. On the fourth day, she restarted taking Clopidogrel 75 mg every day and has gradually increased time for ambulation. She was discharged home and was ambulating with the help of a walking frame on day 7 and her follow-up abdominal CT scan on day 11 revealed reduced slightly hematoma. She was treated with rest, and showed an gradual recovery in approximately 3 weeks.

OUTCOMES

At day 85, the patient's LBP symptoms had completely disappeared and the result of liver function, renal function, coagulation function, blood routine was normal.

CONCLUSION

Small needle scalpel is a form of acupuncture. In China, small needle scalpel therapy has been used to treat various kinds of chronic pain. Anticoagulation therapy is a risk for bleeding, and patients who used Clopidogrel prepare to adopting small needle scalpel needs to be very cautious.

Resting-state functional connectivity predicts motor cortex stimulation-dependent pain relief in fibromyalgia syndrome patients

MRI-based resting-state functional connectivity (rsFC) has been shown to predict response to pharmacological and non-pharmacological treatments for chronic pain, but not yet for motor cortex transcranial magnetic stimulation (M1-rTMS). Twenty-seven fibromyalgia syndrome (FMS) patients participated in this double-blind, crossover, and sham-controlled study. Ten daily treatments of 10 Hz M1-rTMS were given over 2 weeks. Before treatment series, patients underwent resting-state fMRI and clinical pain evaluation. Significant pain reduction occurred following active, but not sham, M1-rTMS. The following rsFC patterns predicted reductions in clinical pain intensity after the active treatment: weaker rsFC of the default-mode network with the middle frontal gyrus (r = 0.76, p < 0.001), the executive control network with the rostro-medial prefrontal cortex (r = 0.80, p < 0.001), the thalamus with the middle frontal gyrus (r = 0.82, p < 0.001), and the pregenual anterior cingulate cortex with the inferior parietal lobule (r = 0.79, p < 0.001); and stronger rsFC of the anterior insula with the angular gyrus (r =  - 0.81, p < 0.001). The above regions process the attentional and emotional aspects of pain intensity; serve as components of the resting-state networks; are modulated by rTMS; and are altered in FMS. Therefore, we suggest that in FMS, the weaker pre-existing interplay between pain-related brain regions and networks, the larger the pain relief resulting from M1-rTMS.

Mindfulness-Based Stress Reduction, Cognitive Behavioral Therapy, and Acupuncture in Chronic Low Back Pain: Protocol for Two Linked Randomized Controlled Trials

BACKGROUND

Nonpharmacologic mind-body therapies have demonstrated efficacy in low back pain. However, the mechanisms underlying these therapies remain to be fully elucidated.

OBJECTIVE

In response to these knowledge gaps, the Stanford Center for Low Back Pain-a collaborative, National Institutes of Health P01-funded, multidisciplinary research center-was established to investigate the common and distinct biobehavioral mechanisms of three mind-body therapies for chronic low back pain: cognitive behavioral therapy (CBT) that is used to treat pain, mindfulness-based stress reduction (MBSR), and electroacupuncture. Here, we describe the design and implementation of the center structure and the associated randomized controlled trials for characterizing the mechanisms of chronic low back pain treatments.

METHODS

The multidisciplinary center is running two randomized controlled trials that share common resources for recruitment, enrollment, study execution, and data acquisition. We expect to recruit over 300 chronic low back pain participants across two projects and across different treatment arms within each project. The first project will examine pain-CBT compared with MBSR and a wait-list control group. The second project will examine real versus sham electroacupuncture. We will use behavioral, psychophysical, physical measure, and neuroimaging techniques to characterize the central pain modulatory and emotion regulatory systems in chronic low back pain at baseline and longitudinally. We will characterize how these interventions impact these systems, characterize the longitudinal treatment effects, and identify predictors of treatment efficacy.

RESULTS

Participant recruitment began on March 17, 2015, and will end in March 2023. Recruitment was halted in March 2020 due to COVID-19 and resumed in December 2021.

CONCLUSIONS

This center uses a comprehensive approach to study chronic low back pain. Findings are expected to significantly advance our understanding in (1) the baseline and longitudinal mechanisms of chronic low back pain, (2) the common and distinctive mechanisms of three mind-body therapies, and (3) predictors of treatment response, thereby informing future delivery of nonpharmacologic chronic low back pain treatments.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02503475; https://clinicaltrials.gov/ct2/show/NCT02503475.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

PRR1-10.2196/37823.

Deqi Sensation to Predict Acupuncture Effect on Functional Dyspepsia: A Machine Learning Study

Objectives

The aim of the study was to predict the effect of acupuncture for treating functional dyspepsia (FD) using the support vector machine (SVM) techniques based on initial deqi sensations of patients.

Methods

This retrospective study involved 90 FD patients who had received four weeks of acupuncture treatment. The support vector classification model was used to distinguish higher responders (patients with Symptom Index of Dyspepsia improvement score ≥ 2) from lower responders (patients with Symptom Index of Dyspepsia improvement score < 2). A support vector regression model was used to predict the change in the Symptom Index of Dyspepsia at the end of acupuncture treatment. Deqi sensations of patients in the first acupuncture treatment of a 20-session acupuncture intervention were defined as features and used to train models. Models were validated by 10-fold cross-validation and evaluated by accuracy, specificity, sensitivity, the area under the receive-operating curve, the coefficient of determination (R²), and the mean squared error.

Results

The two models could predict the efficacy of acupuncture successfully. These models had an accuracy of 0.84 in predicting acupuncture response, and an R² of 0.16 in the prediction of symptom improvements, respectively. The presence or absence of deqi sensation, the duration of deqi sensation, distention, and pain were finally selected as significant predicting features.

Conclusion

Based on the SVM algorithms and deqi sensation, the current study successfully predicted the acupuncture response as well as clinical symptom improvement in FD patients at the end of treatment. Our prediction models are expected to promote the clinical efficacy of acupuncture treatment for FD, reduce medical expenditures, and optimize the allocation of medical resources.

Predicting acupuncture efficacy for functional dyspepsia based on functional brain network features: a machine learning study

Acupuncture is effective in treating functional dyspepsia (FD), while its efficacy varies significantly from different patients. Predicting the responsiveness of different patients to acupuncture treatment based on the objective biomarkers would assist physicians to identify the candidates for acupuncture therapy. One hundred FD patients were enrolled, and their clinical characteristics and functional brain MRI data were collected before and after treatment. Taking the pre-treatment functional brain network as features, we constructed the support vector machine models to predict the responsiveness of FD patients to acupuncture treatment. These features contributing critically to the accurate prediction were identified, and the longitudinal analyses of these features were performed on acupuncture responders and non-responders. Results demonstrated that prediction models achieved an accuracy of 0.76 ± 0.03 in predicting acupuncture responders and non-responders, and a R2 of 0.24 ± 0.02 in predicting dyspeptic symptoms relief. Thirty-eight functional brain network features associated with the orbitofrontal cortex, caudate, hippocampus, and anterior insula were identified as the critical predictive features. Changes in these predictive features were more pronounced in responders than in non-responders. In conclusion, this study provided a promising approach to predicting acupuncture efficacy for FD patients and is expected to facilitate the optimization of personalized acupuncture treatment plans for FD.

Artificial intelligence-directed acupuncture: a review

Acupuncture is widely used around the whole world nowadays and exhibits significant efficacy against many chronic diseases, especially in pain-related diseases. With the rapid development of artificial intelligence (AI), its implementation into acupuncture has achieved a series of significant breakthroughs in many areas of acupuncture practice, such as acupoints selection and prescription, acupuncture manipulation identification, acupuncture efficacy prediction, and so on. The paper will discuss the significant theoretical and technical achievements in AI-directed acupuncture. AI-based data mining methods uncovered crucial acupoint combinations for treating various diseases, which provide a scientific basis for acupoints prescription in clinical practice. Furthermore, the rapid development of modern TCM instruments facilitates the integration of modern medical instruments, AI techniques, and acupuncture. This integration significantly improves the quantification, objectification, and standardization of acupuncture as well as the delivery of clinical personalized acupuncture therapy. Machine learning-based clinical efficacy prediction of acupuncture can help doctors screen patients who may benefit from acupuncture treatment. However, the existing challenges require additional work for developing AI-directed acupuncture. Some include a better understanding of ancient Chinese philosophy for AI researchers, TCM acupuncture theory-based explanation of the knowledge discoveries, construction of acupuncture databases, and clinical trials for novel knowledge validation. This review aims to summarize the major contribution of AI techniques to the discovery of novel acupuncture knowledge, the improvement for acupuncture safety and efficacy, the development and inheritance of acupuncture, and the major challenges for the further development of AI-directed acupuncture. The development of acupuncture can progress with the help of AI.

Acupuncture mechanism studies employing task-based fMRI: a scoping review protocol

BACKGROUND

Acupuncture is a widely used alternative and complementary therapy. Functional magnetic resonance imaging (fMRI) is an important technique to explore the underlying mechanism of acupuncture, and the task-based fMRI can reflect the instant effects or sustained effects of acupuncture in the brain. This scoping review aims to summarize the characteristics of acupuncture mechanism studies employing task-based fMRI and conclude a reference for future studies.

METHODS/DESIGN

This review will follow the Guidance for Conducting Scoping Reviews. Eligible articles will be collected from 7 databases (PubMed, Embase, Cochrane, CNKI, WanFang, VIP, and CBM) with the related keywords such as "Acupuncture" and "fMRI"; those articles should be published from January 1, 2000, to December 31, 2021; and the language should be restricted in English or Chinese. Each research step will involve at least two reviewers. The PRISMA-ScR (Preferred Reporting Items for Systemic Reviews and Meta-Analysis Extension for Scoping Reviews) will be used to organize the review. Data will be extracted from the illegible articles, and findings will be presented in tables and narrative form. A descriptive qualitative approach to analysis will be conducted to form the scoping review.

DISCUSSION

This review aims to clarify the extent of acupuncture mechanism studies employing task-based fMRI. It is supposed to make a critical evaluation or propose quality requirements for future studies by summarizing the objectives and designs of eligible studies. What is more, directional suggestions will be provided for further studies.

SCOPING REVIEW REGISTRATION

Open Science Framework https://osf.io/zjrdc/ .

Brain Mechanism of Acupuncture Treatment of Chronic Pain: An Individual-Level Positron Emission Tomography Study

Objective

Acupuncture has been shown to be effective in the treatment of chronic pain. However, their neural mechanism underlying the effective acupuncture response to chronic pain is still unclear. We investigated whether metabolic patterns in the pain matrix network might predict acupuncture therapy responses in patients with primary dysmenorrhea (PDM) using a machine-learning-based multivariate pattern analysis (MVPA) on positron emission tomography data (PET).

Methods

Forty-two patients with PDM were selected and randomized into two groups: real acupuncture and sham acupuncture (three menstrual cycles). Brain metabolic data from the three special brain networks (the sensorimotor network (SMN), default mode network (DMN), and salience network (SN)) were extracted at the individual level by using PETSurfer in fluorine-18 fluorodeoxyglucose positron emission tomography (18F-FDG-PET) data. MVPA analysis based on metabolic network features was employed to predict the pain relief after treatment in the pooled group and real acupuncture treatment, separately.

Results

Paired t-tests revealed significant alterations in pain intensity after real but not sham acupuncture treatment. Traditional mass-univariate correlations between brain metabolic and alterations in pain intensity were not significant. The MVPA results showed that the brain metabolic pattern in the DMN and SMN did predict the pain relief in the pooled group of patients with PDM (R2 = 0.25, p = 0.005). In addition, the metabolic pattern in the DMN could predict the pain relief after treatment in the real acupuncture treatment group (R2 = 0.40, p = 0.01).

Conclusion

This study indicates that the individual-level metabolic patterns in DMN is associated with real acupuncture treatment response in chronic pain. The present findings advanced the knowledge of the brain mechanism of the acupuncture treatment in chronic pain.

Memory-directed acupuncture as a neuromodulatory treatment for PTSD: Theory, clinical model and case studies

Posttraumatic stress disorder (PTSD) poses an ongoing challenge to society, to health systems, and to the trauma victims themselves. Today PTSD is often considered an incurable chronic problem that lacks effective treatment. While PTSD is closely related to memory, it also affects many physiological systems. PTSD is usually treated with medications and psychotherapy with moderate success, leaving a substantial proportion of patients with enduring distress and disability. Therefore, a search for better treatment options is vital. In this paper, we propose a model in which a conversation-based technique is integrated with bodily manipulation through acupuncture. This approach first emerged in clinical experience showing intriguing results from treating PTSD patients using acupuncture as a main strategy. Its theoretical foundations derive from the clinic and rely on contemporary neuroscience's understanding of memory consolidation and reconsolidation processes. Research shows that acupuncture can have potentially positive effects at three levels: (a) achieving a balance between sympathetic and parasympathetic neural activity; (b) reducing activation in the limbic system, hence inducing a calming effect; (c) reshaping the functional connectivity map within important and relevant cortical regions that encompass the default-mode network. We suggest that coupling traumatic memory retrieval leading to reconsolidation, combined with acupuncture, offers considerable potential for positive clinical improvement in patients with PTSD. This may explain the positive results of the described case studies and can pave the path for future advances in research and treatment in this field.

Early Fractional Amplitude of Low Frequency Fluctuation Can Predict the Efficacy of Transcutaneous Auricular Vagus Nerve Stimulation Treatment for Migraine Without Aura

Migraine is a common primary headache disorder. Transcutaneous auricular vagus nerve stimulation (taVNS) has been verified to be effective in patients with migraine without aura (MWoA). However, there are large interindividual differences in patients’ responses to taVNS. This study aimed to explore whether pretreatment fractional amplitude of low frequency fluctuation (fALFF) features could predict clinical outcomes in MWoA patients after 4-week taVNS. Sixty MWoA patients and sixty well-matched healthy controls (HCs) were recruited, and migraineurs received 4-week taVNS treatment. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected, and the significant differences of fALFF were detected between MWoA patients and HCs using two-sample t-test. A mask of these significant regions was generated and used for subsequent analysis. The abnormal fALFF in the mask was used to predict taVNS efficacy for MWoA using a support vector regression (SVR) model combining with feature select of weight based on the LIBSVM toolbox. We found that (1) compared with HCs, MWoA patients exhibited increased fALFF in the left thalamus, left inferior parietal gyrus (IPG), bilateral precentral gyrus (PreCG), right postcentral gyrus (PoCG), and bilateral supplementary motor areas (SMAs), but decreased in the bilateral precuneus and left superior frontal gyrus (SFG)/medial prefrontal cortex (mPFC); (2) after 4-week taVNS treatment, the fALFF values significantly decreased in these brain regions based on the pretreatment comparison. Importantly, the decreased fALFF in the bilateral precuneus was positively associated with the reduction in the attack times (r = 0.357, p = 0.005, Bonferroni correction, 0.05/5), whereas the reduced fALFF in the right PoCG was negatively associated with reduced visual analog scale (VAS) scores (r = −0.267, p = 0.039, uncorrected); (3) the SVR model exhibited a good performance for prediction (r = 0.411, p < 0.001),which suggests that these extracted fALFF features could be used as reliable biomarkers to predict the treatment response of taVNS for MWoA patients. This study demonstrated that the baseline fALFF features have good potential for predicting individualized treatment response of taVNS in MWoA patients, and those weight brain areas are mainly involved in the thalamocortical (TC) circuits, default mode network (DMN), and descending pain modulation system (DPMS). This will contribute to well understanding the mechanism of taVNS in treating MWoA patients and may help to screen ideal patients who respond well to taVNS treatment.

The neurologic pain signature responds to nonsteroidal anti-inflammatory treatment vs placebo in knee osteoarthritis

Supplemental Digital Content is Available in the Text.

fMRI-based measures, validated for nociceptive pain, respond to acute osteoarthritis pain, are not sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Introduction:

Many drug trials for chronic pain fail because of high placebo response rates in primary endpoints. Neurophysiological measures can help identify pain-linked pathophysiology and treatment mechanisms. They can also help guide early stop/go decisions, particularly if they respond to verum treatment but not placebo. The neurologic pain signature (NPS), an fMRI-based measure that tracks evoked pain in 40 published samples and is insensitive to placebo in healthy adults, provides a potentially useful neurophysiological measure linked to nociceptive pain.

Objectives:

This study aims to validate the NPS in knee osteoarthritis (OA) patients and test the effects of naproxen on this signature.

Methods:

In 2 studies (50 patients, 64.6 years, 75% females), we (1) test the NPS and other control signatures related to negative emotion in knee OA pain patients; (2) test the effect of placebo treatments; and (3) test the effect of naproxen, a routinely prescribed nonsteroidal anti-inflammatory drug in OA.

Results:

The NPS was activated during knee pain in OA (d = 1.51, P < 0.001) and did not respond to placebo (d = 0.12, P = 0.23). A single dose of naproxen reduced NPS responses (vs placebo, NPS d = 0.34, P = 0.03 and pronociceptive NPS component d = 0.38, P = 0.02). Naproxen effects were specific for the NPS and did not appear in other control signatures.

Conclusion:

This study provides preliminary evidence that fMRI-based measures, validated for nociceptive pain, respond to acute OA pain, do not appear sensitive to placebo, and are mild-to-moderately sensitive to naproxen.

Predicting acupuncture efficacy for functional dyspepsia based on routine clinical features: a machine learning study in the framework of predictive, preventive, and personalized medicine

Background

Acupuncture is safe and effective for functional dyspepsia (FD), while its efficacy varies among individuals. Predicting the response of different FD patients to acupuncture treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). In the current study, the individual efficacy prediction models were developed based on the support vector machine (SVM) algorithm and routine clinical features, aiming to predict the efficacy of acupuncture in treating FD and identify the FD patients who were appropriate to acupuncture treatment.

Methods

A total of 745 FD patients were collected from two clinical trials. All the patients received a 4-week acupuncture treatment. Based on the demographic and baseline clinical features of 80% of patients in trial 1, the SVM models were established to predict the acupuncture response and improvements of symptoms and quality of life (QoL) at the end of treatment. Then, the left 20% of patients in trial 1 and 193 patients in trial 2 were respectively applied to evaluate the internal and external generalizations of these models.

Results

These models could predict the efficacy of acupuncture successfully. In the internal test set, models achieved an accuracy of 0.773 in predicting acupuncture response and an R ² of 0.446 and 0.413 in the prediction of QoL and symptoms improvements, respectively. Additionally, these models had well generalization in the independent validation set and could also predict, to a certain extent, the long-term efficacy of acupuncture at the 12-week follow-up. The gender, subtype of disease, and education level were finally identified as the critical predicting features.

Conclusion

Based on the SVM algorithm and routine clinical features, this study established the models to predict acupuncture efficacy for FD patients. The prediction models developed accordingly are promising to assist doctors in judging patients' responses to acupuncture in advance, so that they could tailor and adjust acupuncture treatment plans for different patients in a prospective rather than the reactive manner, which could greatly improve the clinical efficacy of acupuncture treatment for FD and save medical expenditures.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00271-8.

Using Artificial Intelligence-based Methods to Address the Placebo Response in Clinical Trials

The placebo response is a highly complex psychosocial-biological phenomenon that has challenged drug development for decades, particularly in neurological and psychiatric disease. While decades of research have aimed to understand clinical trial factors that contribute to the placebo response, a comprehensive solution to manage the placebo response in drug development has yet to emerge. Advanced data analytic techniques, such as artificial intelligence (AI), might be needed to take the next leap forward in mitigating the negative consequences of high placebo-response rates. The objective of this review was to explore the use of techniques such as AI and the sub-discipline of machine learning (ML) to address placebo response in practical ways that can positively impact drug development. This examination focused on the critical factors that should be considered in applying AI and ML to the placebo response issue, examples of how these techniques can be used, and the regulatory considerations for integrating these approaches into clinical trials.

Analgesia Effect of Verum and Sham Acupuncture Treatments in Primary Dysmenorrhea: A MRI Pilot Study

Acupuncture is an alternative treatment for primary dysmenorrhea (PDM). However, mechanisms by which acupuncture exerts its analgesic properties are still unclear. This study aimed to explore the cerebral blood flow (CBF) response to verum and sham acupuncture treatments, and further investigate whether pre-treatment CBF is capable of assessing symptom changes after interventions. A total of 11 PDM patients in the verum group and 12 patients in the sham group participated in this study. Pain rating index (PRI), CBF, and gonadal hormone levels were acquired before and after 8-week treatments. Both verum and sham acupuncture treatments exert its analgesic effect on PDM after intervention as PRI reduced (p < 0.05). Blood gonadal levels were not significantly different after acupuncture in both groups (all p > 0.05). In the verum group, intervention-related decreases in CBF were observed in the right dorsal anterior cingulate cortex. In the sham group, regions identified as showing reductions in CBF after acupuncture included the left ventromedial prefrontal cortex, left caudate, and left insula. Patients with higher baseline CBF in the left precuneus and right hippocampus were accompanied with worse treatment response to acupuncture intervention. Mechanisms of verum and sham acupuncture treatments are dissimilar as manifested by different brain responses.

Neuroimaging Studies of Acupuncture on Low Back Pain: A Systematic Review

Objectives: This study was conducted in order to investigate the study design and main outcomes of acupuncture neuroimaging studies on low back pain (LBP). Methods: Neuroimaging studies of acupuncture on LBP were collected from three English databases such as PubMed and four Chinese databases such as China National Knowledge Infrastructure (CNKI) from inception to December 31, 2020. Study selection, data extraction, and assessment of risk of bias were performed independently by two investigators. The quality of studies was appraised with the Cochrane's risk of bias tools. Information on basic information, methodology, and brain imaging data were extracted. Results: The literature search returned 310 potentially eligible studies and 19 articles met inclusion criteria; 78.9% of studies chose manual acupuncture as the intervention, 89.5% of studies evaluated functional changes elicited by acupuncture, and 68.4% of studies used resting-state fMRI as imaging condition. The most frequently reported acupuncture-induced brain alterations of LBP patients were in the prefrontal cortex, insula, cerebellum, primary somatosensory cortex, and anterior cingulate cortex. There was a significant correlation between improved clinical outcomes and changes in the brain. Conclusions: The results suggested that improving abnormal structure and functional activities in the brain of the LBP patient is an important mechanism of acupuncture treatment for LBP. The brain regions involved in acupuncture analgesia for LBP were mainly located in the pain matrix, default mode network (DMN), salience network (SN), and descending pain modulatory system (DPMS). However, it was difficult to draw a generalized conclusion due to the heterogeneity of study designs. Further well-designed multimodal neuroimaging studies investigating the mechanism of acupuncture on LBP are warranted.

Brain Circuits Involved in the Development of Chronic Musculoskeletal Pain: Evidence From Non-invasive Brain Stimulation

It has been well-documented that the brain changes in states of chronic pain. Less is known about changes in the brain that predict the transition from acute to chronic pain. Evidence from neuroimaging studies suggests a shift from brain regions involved in nociceptive processing to corticostriatal brain regions that are instrumental in the processing of reward and emotional learning in the transition to the chronic state. In addition, dysfunction in descending pain modulatory circuits encompassing the periaqueductal gray and the rostral anterior cingulate cortex may also be a key risk factor for pain chronicity. Although longitudinal imaging studies have revealed potential predictors of pain chronicity, their causal role has not yet been determined. Here we review evidence from studies that involve non-invasive brain stimulation to elucidate to what extent they may help to elucidate the brain circuits involved in pain chronicity. Especially, we focus on studies using non-invasive brain stimulation techniques [e.g., transcranial magnetic stimulation (TMS), particularly its repetitive form (rTMS), transcranial alternating current stimulation (tACS), and transcranial direct current stimulation (tDCS)] in the context of musculoskeletal pain chronicity. We focus on the role of the motor cortex because of its known contribution to sensory components of pain via thalamic inhibition, and the role of the dorsolateral prefrontal cortex because of its role on cognitive and affective processing of pain. We will also discuss findings from studies using experimentally induced prolonged pain and studies implicating the DLPFC, which may shed light on the earliest transition phase to chronicity. We propose that combined brain stimulation and imaging studies might further advance mechanistic models of the chronicity process and involved brain circuits. Implications and challenges for translating the research on mechanistic models of the development of chronic pain to clinical practice will also be addressed.

Effect of Acupuncture on Gut-Brain Axis Parameters in Patients with Atopic Dermatitis: A Study Protocol for a Randomized, Participant- and Assessor-Blind, Sham-Controlled Trial

Atopic dermatitis (AD) is a relapsing and remitting chronic inflammatory skin disease for which a variety of etiological factors are involved. Treatment strategies should be multifaceted and have few side effects. In this respect, acupuncture has become increasingly popular as a safe, consistently effective, and drug-free therapy that treats multiple AD symptoms. We aim to not only verify the effectiveness of acupuncture but also suggest patient-specific response determinants and a new underlying mechanism implicating the gut-brain axis. We have designed a randomized, participant-blinded, sham-controlled clinical trial for 60 mild to moderate AD patients. In a previous study, we observed that the clinical skin symptoms of AD were closely associated with gastrointestinal (GI) symptoms. From these findings, we developed an intervention with six acupuncture points: three for AD symptoms and three for GI symptoms. Also, since high responders and low responders to the acupuncture treatment could be identified in the previous study, we now aim to explore response-determining factors, with a particular focus on GI symptoms. Therefore, we will precisely evaluate not only AD symptoms using the SCORAD, EASI, and DLQI tools, but also GI symptoms using the GSRS, TDS, BSFS, and AR tools and abdominal examination. AD develops in association with complicated pathophysiological factors, such as skin barrier function, genetic susceptibility, and immunological factors. Moreover, the underlying mechanism by which acupuncture treatment works has not been clearly elucidated. We, therefore, will conduct a simultaneous cross-sectional study with a sample of 40 healthy individuals, wherein potential indicators, such as fMRI, gut microbiota, and serum TARC and ATX, will be investigated to determine the gut-brain axis-associated mechanism of acupuncture. We expect that the results of this study could provide important clinical evidence for the effects of acupuncture and help elucidate the therapeutic mechanisms that underlie acupuncture's efficacy in AD treatment. This trial is registered with https://clinicaltrials.gov/ct2/show/KCT0005422 (Trial registration: Korean Clinical Trial Registry (http://cris.nih.go.kr; registration number: KCT0005422); date of registration: September 23, 2020).

Progress of Acupuncture Therapy in Diseases Based on Magnetic Resonance Image Studies: A Literature Review

The neural mechanisms of acupuncture are not well-understood. Over the past decades, an increasing number of studies have used MRI to investigate the response of the brain to acupuncture. The current review aims to provide an update on acupuncture therapy in disease. The PubMed, Embase, Web of Science, and Cochrane Library databases were searched from inception to January 31, 2021. Article selection and data extraction were conducted by two review authors. A total of 107 publications about MRI in acupuncture were included, the collective findings of which were as follows: (1) stroke and GB34 (Yanglingquan) are the most studied disease and acupoint. Related studies suggested that the mechanism of acupuncture treatment for stroke may associate with structural and functional plasticity, left and right hemispheres balance, and activation of brain areas related to movement and cognition. GB34 is mainly used in stroke and Parkinson's disease, which mainly activates brain response in the premotor cortex, the supplementary motor area, and the supramarginal gyrus; (2) resting-state functional MRI (rs-fMRI) and functional connectivity (FC) analysis are the most frequently used approaches; (3) estimates of efficacy and brain response to acupuncture depend on the type of sham acupuncture (SA) used for comparison. Brain processing after acupuncture differs between patients and health controls (HC) and occurs mainly in disorder-related areas. Factors that influence the effect of acupuncture include depth of needling, number and locations of acupoints, and deqi and expectation effect, each contributing to the brain response. While studies using MRI have increased understanding of the mechanism underlying the effects of acupuncture, there is scope for development in this field. Due to the small sample sizes, heterogeneous study designs, and analytical methods, the results were inconsistent. Further studies with larger sample sizes, careful experimental design, multimodal neuroimaging techniques, and standardized methods should be conducted to better explain the efficacy and specificity of acupuncture, and to prepare for accurate efficacy prediction in the future.

Functional Disruptions of the Brain in Low Back Pain: A Potential Imaging Biomarker of Functional Disability

Chronic low back pain (LBP) is one of the leading causes of disability worldwide. While LBP research has largely focused on the spine, many studies have demonstrated a restructuring of human brain architecture accompanying LBP and other chronic pain states. Brain imaging presents a promising source for discovering noninvasive biomarkers that can improve diagnostic and prognostication outcomes for chronic LBP. This study evaluated graph theory measures derived from brain resting-state functional connectivity (rsFC) as prospective noninvasive biomarkers of LBP. We also proposed and tested a hybrid feature selection method (Enet-subset) that combines Elastic Net and an optimal subset selection method. We collected resting-state functional MRI scans from 24 LBP patients and 27 age-matched healthy controls (HC). We then derived graph-theoretical features and trained a support vector machine (SVM) to classify patient group. The degree centrality (DC), clustering coefficient (CC), and betweenness centrality (BC) were found to be significant predictors of patient group. We achieved an average classification accuracy of 83.1% (p < 0.004) and AUC of 0.937 (p < 0.002), respectively. Similarly, we achieved a sensitivity and specificity of 87.0 and 79.7%. The classification results from this study suggest that graph matrices derived from rsFC can be used as biomarkers of LBP. In addition, our findings suggest that the proposed feature selection method, Enet-subset, might act as a better technique to remove redundant variables and improve the performance of the machine learning classifier.

Evidence of Potential Mechanisms of Acupuncture from Functional MRI Data for Migraine Prophylaxis

PURPOSE OF REVIEW

To summarize the clinical neuroimaging evidence pertaining to the potential mechanisms of acupuncture for migraine prophylaxis.

RECENT FINDINGS

From a descriptive perspective, converging evidence from recent neuroimaging studies, mainly from functional MRI (fMRI) studies, has demonstrated that when compared with sham acupuncture, verum acupuncture could normalize the decrease of the functional connectivity of the rostral ventromedial medulla-trigeminocervical complex (RVM/TCC) network, frontal-parietal network, cingulo-opercular networks, and default mode network and could normalize sensorimotor network connectivity with sensory-, affective-, and cognitive-related brain areas. These areas overlap with those of the pain matrix. Verum acupuncture works in a more targeted and unique manner compared with sham acupuncture in patients with migraine. These findings from neuroimaging studies may provide new perspectives on the validation of acupoints specificity and confirm the central modulating effects of acupuncture as a migraine prevention treatment. However, the exact mechanism by which acupuncture works for migraine prophylaxis remains unclear and warrants investigation. Future studies with larger sample sizes are still needed to confirm the current results and to further evaluate the complex and specific effects of acupuncture by analyzing different stimulus conditions, such as verum vs. sham acupuncture, deqi vs. no deqi, different acupuncture points or meridians, and different manipulation methods. Moreover, instead of focusing on the changes in a single area of the brain, researchers should focus more on the relationships among the functional connectivity network of brain areas such as the RVM/TCC, thalamus, anterior cingulate cortex (ACC), superior temporal gyrus (STG), and supplementary motor area (SMA) to explore the underlying mechanism of the effects of acupuncture.

Manipulating placebo analgesia and nocebo hyperalgesia by changing brain excitability

Harnessing placebo and nocebo effects has significant implications for research and medical practice. Placebo analgesia and nocebo hyperalgesia, the most well-studied placebo and nocebo effects, are thought to initiate from the dorsal lateral prefrontal cortex (DLPFC) and then trigger the brain's descending pain modulatory system and other pain regulation pathways. Combining repeated transcranial direct current stimulation (tDCS), an expectancy manipulation model, and functional MRI, we investigated the modulatory effects of anodal and cathodal tDCS at the right DLPFC on placebo analgesia and nocebo hyperalgesia using a randomized, double-blind and sham-controlled design. We found that compared with sham tDCS, active tDCS could 1) boost placebo and blunt nocebo effects and 2) modulate brain activity and connectivity associated with placebo analgesia and nocebo hyperalgesia. These results provide a basis for mechanistic manipulation of placebo and nocebo effects and may lead to improved clinical outcomes in medical practice.

Resting-State fMRI in Studies of Acupuncture

Research exploring the mechanism of acupuncture has been a hot topic in medicine. Resting-state functional magnetic resonance imaging (rs-fMRI) research is a noninvasive and extensive method, which is aimed at the research of the mechanism of acupuncture. Researchers use fMRI technologies to inspect the acupuncture process. The authors reviewed the application of rs-fMRI in acupuncture research in recent 10 years from the aspects of studying acupoints, subjects, acupuncture methods, and intensities. The results found that the application of rs-fMRI in acupuncture research mainly includes research on the onset mechanism of acupuncture treatment; visual evidence of diagnosis and treatment of dominant diseases; efficacy assessments; physiological mechanism of acupoint stimulation; and specific visualization of acupoints.

Peripheral ERK modulates acupuncture-induced brain neural activity and its functional connectivity

Acupuncture has been widely used as a therapeutic intervention, and the brain network plays a crucial role in its neural mechanism. This study aimed to investigate the acupuncture mechanism from peripheral to central by identifying how the peripheral molecular signals induced by acupuncture affect the brain neural responses and its functional connectivity. We confirmed that peripheral ERK activation by acupuncture plays a role in initiating acupuncture-induced peripheral proteomic changes in mice. The brain neural activities in the neocortex, hippocampus, thalamus, hypothalamus, periaqueductal grey, and nucleus of the solitary tract (Sol) were significantly changed after acupuncture, and these were altered by peripheral MEK/MAPK inhibition. The arcuate nucleus and lateral hypothalamus were the most affected by acupuncture and peripheral MEK/MAPK inhibition. The hypothalamic area was the most contributing brain region in contrast task PLS analysis. Acupuncture provoked extensive changes in brain functional connectivity, and the posterior hypothalamus showed the highest betweenness centrality after acupuncture. After brain hub identification, the Sol and cingulate cortex were selected as hub regions that reflect both degree and betweenness centrality after acupuncture. These results suggest that acupuncture activates brain functional connectivity and that peripheral ERK induced by acupuncture plays a role in initiating brain neural activation and its functional connectivity.

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.

Applying the Power of the Mind in Acupuncture Treatment of Pain

Background: Healing is a complicated process that can have several components including the self-healing properties of the body, the nonspecific effects of treatment (e.g., the power of the mind), and the specific effects of an intervention. This article first discusses the brain imaging studies on placebo acupuncture analgesia and the modulation effects of expectancy on real acupuncture in healthy subjects. Then, it introduces some attempts to translate findings from healthy subjects to patient population using power of the mind as a way to enhance acupuncture's treatment effects on chronic pain. After that, a new alternative method which merges acupuncture and imagery, while also drawing on power of the mind, is presented. Finally, the specific effects of acupuncture are discussed. Conclusions: Elucidating the mechanism underlying power of the mind would provide new opportunities for boosting the therapeutic effect of acupuncture treatment and furthering the development of new alternative interventions.

Acupuncture for chronic nonspecific low back pain

BACKGROUND

Chronic nonspecific low back pain (LBP) is very common; it is defined as pain without a recognizable etiology that lasts for more than three months. Some clinical practice guidelines suggest that acupuncture can offer an effective alternative therapy. This review is a split from an earlier Cochrane review and it focuses on chronic LBP.

OBJECTIVES

To assess the effects of acupuncture compared to sham intervention, no treatment, or usual care for chronic nonspecific LBP.

SEARCH METHODS

We searched CENTRAL, MEDLINE, Embase, CINAHL, two Chinese databases, and two trial registers to 29 August 2019 without restrictions on language or publication status. We also screened reference lists and LBP guidelines to identify potentially relevant studies.

SELECTION CRITERIA

We included only randomized controlled trials (RCTs) of acupuncture for chronic nonspecific LBP in adults. We excluded RCTs that investigated LBP with a specific etiology. We included trials comparing acupuncture with sham intervention, no treatment, and usual care. The primary outcomes were pain, back-specific functional status, and quality of life; the secondary outcomes were pain-related disability, global assessment, or adverse events.

DATA COLLECTION AND ANALYSIS

Two review authors independently screened the studies, assessed the risk of bias and extracted the data. We meta-analyzed data that were clinically homogeneous using a random-effects model in Review Manager 5.3. Otherwise, we reported the data qualitatively. We used the GRADE approach to assess the certainty of the evidence.

MAIN RESULTS

We included 33 studies (37 articles) with 8270 participants. The majority of studies were carried out in Europe, Asia, North and South America. Seven studies (5572 participants) conducted in Germany accounted for 67% of the participants. Sixteen trials compared acupuncture with sham intervention, usual care, or no treatment. Most studies had high risk of performance bias due to lack of blinding of the acupuncturist. A few studies were found to have high risk of detection, attrition, reporting or selection bias. We found low-certainty evidence (seven trials, 1403 participants) that acupuncture may relieve pain in the immediate term (up to seven days) compared to sham intervention (mean difference (MD) -9.22, 95% confidence interval (CI) -13.82 to -4.61, visual analogue scale (VAS) 0-100). The difference did not meet the clinically important threshold of 15 points or 30% relative change. Very low-certainty evidence from five trials (1481 participants) showed that acupuncture was not more effective than sham in improving back-specific function in the immediate term (standardized mean difference (SMD) -0.16, 95% CI -0.38 to 0.06; corresponding to the Hannover Function Ability Questionnaire (HFAQ, 0 to 100, higher values better) change (MD 3.33 points; 95% CI -1.25 to 7.90)). Three trials (1068 participants) yielded low-certainty evidence that acupuncture seemed not to be more effective clinically in the short term for quality of life (SMD 0.24, 95% CI 0.03 to 0.45; corresponding to the physical 12-item Short Form Health Survey (SF-12, 0-100, higher values better) change (MD 2.33 points; 95% CI 0.29 to 4.37)). The reasons for downgrading the certainty of the evidence to either low to very low were risk of bias, inconsistency, and imprecision. We found moderate-certainty evidence that acupuncture produced greater and clinically important pain relief (MD -20.32, 95% CI -24.50 to -16.14; four trials, 366 participants; (VAS, 0 to 100), and improved back function (SMD -0.53, 95% CI -0.73 to -0.34; five trials, 2960 participants; corresponding to the HFAQ change (MD 11.50 points; 95% CI 7.38 to 15.84)) in the immediate term compared to no treatment. The evidence was downgraded to moderate certainty due to risk of bias. No studies reported on quality of life in the short term or adverse events. Low-certainty evidence (five trials, 1054 participants) suggested that acupuncture may reduce pain (MD -10.26, 95% CI -17.11 to -3.40; not clinically important on 0 to 100 VAS), and improve back-specific function immediately after treatment (SMD: -0.47; 95% CI: -0.77 to -0.17; five trials, 1381 participants; corresponding to the HFAQ change (MD 9.78 points, 95% CI 3.54 to 16.02)) compared to usual care. Moderate-certainty evidence from one trial (731 participants) found that acupuncture was more effective in improving physical quality of life (MD 4.20, 95% CI 2.82 to 5.58) but not mental quality of life in the short term (MD 1.90, 95% CI 0.25 to 3.55). The certainty of evidence was downgraded to moderate to low because of risk of bias, inconsistency, and imprecision. Low-certainty evidence suggested a similar incidence of adverse events immediately after treatment in the acupuncture and sham intervention groups (four trials, 465 participants) (RR 0.68 95% CI 0.46 to 1.01), and the acupuncture and usual care groups (one trial, 74 participants) (RR 3.34, 95% CI 0.36 to 30.68). The certainty of the evidence was downgraded due to risk of bias and imprecision. No trial reported adverse events for acupuncture when compared to no treatment. The most commonly reported adverse events in the acupuncture groups were insertion point pain, bruising, hematoma, bleeding, worsening of LBP, and pain other than LBP (pain in leg and shoulder).

AUTHORS' CONCLUSIONS

We found that acupuncture may not play a more clinically meaningful role than sham in relieving pain immediately after treatment or in improving quality of life in the short term, and acupuncture possibly did not improve back function compared to sham in the immediate term. However, acupuncture was more effective than no treatment in improving pain and function in the immediate term. Trials with usual care as the control showed acupuncture may not reduce pain clinically, but the therapy may improve function immediately after sessions as well as physical but not mental quality of life in the short term. The evidence was downgraded to moderate to very low-certainty considering most of studies had high risk of bias, inconsistency, and small sample size introducing imprecision. The decision to use acupuncture to treat chronic low back pain might depend on the availability, cost and patient's preferences.

Neurological and psychological mechanisms of the specific and nonspecific effects of acupuncture on knee osteoarthritis: study protocol for a randomized, controlled, crossover trial

Background

Acupuncture, as one of the promising non-pharmacological interventions, has been proved to be beneficial for patients. However, the magnitude of acupuncture’s specific and nonspecific effects, as well as their neurological and psychological determinants, remains unclear. Therefore, this study is designed to examine the acupuncture efficacy, investigate whether the brain mechanisms between the specific and nonspecific effects of acupuncture are different, and to evaluate how psychological factors affect the acupuncture effects.

Methods

This is a randomized, controlled, crossover clinical trial. A total of 60 patients with knee osteoarthritis will receive 4 weeks of acupuncture treatment and 4 weeks of sham acupuncture treatment in a random order separated by a washout period of 2 weeks. The changes in clinical characteristics based on pain-related scales will be assessed to investigate the clinical efficacy of acupuncture. Resting state functional magnetic resonance imaging (fMRI) scans will be used to identify the brain activity changes related to the specific and nonspecific effects of acupuncture. The questionnaires of psychological factors will be used to evaluate patients’ psychological properties. Correlation and mediation analyses will be conducted among psychological factors, brain activity changes, and symptoms improvement to explore the neurological and psychological correlates of the acupuncture effects.

Discussion

This study will concentrate on distinguishing and clarifying the specific and nonspecific effects of acupuncture. The results of this study may contribute to rationally optimize the acupuncture therapies by flexible application of the specific and nonspecific effects of acupuncture.

Trial registration

Chinese Clinical Trial Registry ChiCTR1900025807. Registered on 9 September 2019

Supplementary information

The online version contains supplementary material available at 10.1186/s13063-020-04908-9.

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

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

The Spontaneous Activity Pattern of the Middle Occipital Gyrus Predicts the Clinical Efficacy of Acupuncture Treatment for Migraine Without Aura

The purpose of the present study was to explore whether and to what extent the neuroimaging markers could predict the relief of the symptoms of patients with migraine without aura (MWoA) following a 4-week acupuncture treatment period. In study 1, the advanced multivariate pattern analysis was applied to perform a classification analysis between 40 patients with MWoA and 40 healthy subjects (HS) based on the z-transformed amplitude of low-frequency fluctuation (zALFF) maps. In study 2, the meaningful classifying features were selected as predicting features and the support vector regression models were constructed to predict the clinical efficacy of acupuncture in reducing the frequency of migraine attacks and headache intensity in 40 patients with MWoA. In study 3, a region of interest-based comparison between the pre- and post-treatment zALFF maps was conducted in 33 patients with MwoA to assess the changes in predicting features after acupuncture intervention. The zALFF value of the foci in the bilateral middle occipital gyrus, right fusiform gyrus, left insula, and left superior cerebellum could discriminate patients with MWoA from HS with higher than 70% accuracy. The zALFF value of the clusters in the right and left middle occipital gyrus could effectively predict the relief of headache intensity (R ² = 0.38 ± 0.059, mean squared error = 2.626 ± 0.325) and frequency of migraine attacks (R ² = 0.284 ± 0.072, mean squared error = 20.535 ± 2.701) after the 4-week acupuncture treatment period. Moreover, the zALFF values of these two clusters were both significantly reduced after treatment. The present study demonstrated the feasibility and validity of applying machine learning technologies and individual cerebral spontaneous activity patterns to predict acupuncture treatment outcomes in patients with MWoA. The data provided a quantitative benchmark for selecting acupuncture for MWoA.

Resting-State Functional Connectivity Patterns Predict Acupuncture Treatment Response in Primary Dysmenorrhea

Primary dysmenorrhea (PDM) is a common complaint in women throughout the menstrual years. Acupuncture has been shown to be effective in dysmenorrhea; however, there are large interindividual differences in patients’ responses to acupuncture treatment. Fifty-four patients with PDM were recruited and randomized into real or sham acupuncture treatment groups (over the course of three menstrual cycles). Pain-related functional connectivity (FC) matrices were constructed at baseline and post-treatment period. The different neural mechanisms altered by real and sham acupuncture were detected with multivariate analysis of variance. Multivariate pattern analysis (MVPA) based on a machine learning approach was used to explore whether the different FC patterns predicted the acupuncture treatment response in the PDM patients. The results showed that real but not sham acupuncture significantly relieved pain severity in PDM patients. Real and sham acupuncture displayed differences in FC alterations between the descending pain modulatory system (DPMS) and sensorimotor network (SMN), the salience network (SN) and SMN, and the SN and default mode network (DMN). Furthermore, MVPA found that these FC patterns at baseline could predict the acupuncture treatment response in PDM patients. The present study verified differentially altered brain mechanisms underlying real and sham acupuncture in PDM patients and supported the use of neuroimaging biomarkers for individual-based precise acupuncture treatment in patients with PDM.

Machine Learning in Neuroimaging: A New Approach to Understand Acupuncture for Neuroplasticity

The effects of acupuncture facilitating neural plasticity for treating diseases have been identified by clinical and experimental studies. In the last two decades, the application of neuroimaging techniques in acupuncture research provided visualized evidence for acupuncture promoting neuroplasticity. Recently, the integration of machine learning (ML) and neuroimaging techniques becomes a focus in neuroscience and brings a new and promising approach to understand the facilitation of acupuncture on neuroplasticity at the individual level. This review is aimed at providing an overview of this rapidly growing field by introducing the commonly used ML algorithms in neuroimaging studies briefly and analyzing the characteristics of the acupuncture studies based on ML and neuroimaging, so as to provide references for future research.

Imagined and Actual Acupuncture Effects on Chronic Low Back Pain: A Preliminary Study

Background

Research suggests that imagined experiences can produce brain responses similar to those produced by actual experiences. Shared brain responses that support both imagination and perception may underlie the functional nature of mental imagery. In a previous study, we combined acupuncture and imagery to develop a new treatment method, video-guided acupuncture imagery treatment (VGAIT). We found that VGAIT significantly increased pain thresholds in healthy subjects. The aim of this study is to extend our previous finding by investigating whether VGAIT can relieve symptoms in patients with chronic low back pain.

Methods

We first performed a single-arm study in which we administered video-guided acupuncture imagery treatment (VGAIT) on patients with chronic low back pain (cLBP) (Study 1, n = 18, 12 females). We then compared our findings to those from a recently published study in which real or sham acupuncture treatment was applied on patients with cLBP (Study 2, n = 50, 31 females) using a similar protocol. All patients in Studies 1 and 2 received 6 treatments over 4 weeks.

Results

All three treatments (VGAIT, real, and sham acupuncture) significantly reduced pain severity as measured by a low back pain bothersomeness score. VGAIT produced similar effects to real acupuncture (p = 0.97) and nonsignificantly greater pain bothersomeness relief compared to sham acupuncture (p = 0.14). Additional analysis showed that there was no significant difference on the sensations evoked by different treatment modalities.

Conclusion

These findings support VGAIT as a promising method for pain management.

Characterizing the analgesic effects of real and imagined acupuncture using functional and structure MRI

Acupuncture and imagery interventions for pain management have a long history. The present study comparatively investigated whether acupuncture and video-guided acupuncture imagery treatment (VGAIT, watching a video of acupuncture on the participant's own body while imagining it being applied) could modulate brain regional connectivity to produce analgesic effects. The study also examined whether pre-intervention brain functional and structural features could be used to predict the magnitude of analgesic effects. Twenty-four healthy participants were recruited and received four different interventions (real acupuncture, sham acupuncture, VGAIT, and VGAIT control) in random order using a cross-over design. Pain thresholds and magnetic resonance imaging (MRI) data were collected before and after each intervention. We first compared the modulatory effects of real acupuncture and VGAIT on intra- and inter-regional intrinsic brain connectivity and found that real acupuncture decreased regional homogeneity (ReHo) and functional connectivity (FC) in sensorimotor areas, whereas VGAIT increased ReHo in basal ganglia (BG) (i.e., putamen) and FC between the BG subcortical network and default mode network. The altered ReHo and FC were associated with changes in pain threshold after real acupuncture and VGAIT, respectively. A multimodality fusion approach with pre-intervention ReHo and gray matter volume (GMV) as features was used to explore the brain profiles underlying individual variability of pain threshold changes by real acupuncture and VGAIT. Variability in acupuncture responses was associated with ReHo and GMV in BG, whereas VGAIT responses were associated with ReHo and GMV in the anterior insula. These results suggest that, through different pathways, both real acupuncture and VGAIT can modulate brain systems to produce analgesic effects.

Acupuncture Treatment Modulates the Connectivity of Key Regions of the Descending Pain Modulation and Reward Systems in Patients with Chronic Low Back Pain

Chronic low back pain (cLBP) is a common disorder with unsatisfactory treatment options. Acupuncture has emerged as a promising method for treating cLBP. However, the mechanism underlying acupuncture remains unclear. In this study, we investigated the modulation effects of acupuncture on resting state functional connectivity (rsFC) of the periaqueductal gray (PAG) and ventral tegmental area (VTA) in patients with cLBP. Seventy-nine cLBP patients were recruited and assigned to four weeks of real or sham acupuncture. Resting state functional magnetic resonance imaging data were collected before the first and after the last treatment. Fifty patients completed the study. We found remission of pain bothersomeness in all treatment groups after four weeks, with greater pain relief after real acupuncture compared to sham acupuncture. We also found that real acupuncture can increase VTA/PAG rsFC with the amygdala, and the increased rsFC was associated with decreased pain bothersomeness scores. Baseline PAG-amygdala rsFC could predict four-week treatment response. Our results suggest that acupuncture may simultaneously modulate the rsFC of key regions in the descending pain modulation (PAG) and reward systems (VTA), and the amygdala may be a key node linking the two systems to produce antinociceptive effects. Our findings highlight the potential of acupuncture for chronic low back pain management.

An fMRI-based neural marker for migraine without aura

OBJECTIVE

To identify and validate an fMRI-based neural marker for migraine without aura (MwoA) and to examine its association with treatment response.

METHODS

We conducted cross-sectional studies with resting-state fMRI data from 230 participants and machine learning analyses. In studies 1 through 3, we identified, cross-validated, independently validated, and cross-sectionally validated an fMRI-based neural marker for MwoA. In study 4, we assessed the relationship between the neural marker and treatment responses in migraineurs who received a 4-week real or sham acupuncture treatment, or were waitlisted, in a registered clinical trial.

RESULTS

In study 1 (n = 116), we identified a neural marker with abnormal functional connectivity within the visual, default mode, sensorimotor, and frontal-parietal networks that could discriminate migraineurs from healthy controls (HCs) with 93% sensitivity and 89% specificity. In study 2 (n = 38), we investigated the generalizability of the marker by applying it to an independent cohort of migraineurs and HCs and achieved 84% sensitivity and specificity. In study 3 (n = 76), we verified the specificity of the marker with new datasets of migraineurs and patients with other chronic pain disorders (chronic low back pain and fibromyalgia) and demonstrated 78% sensitivity and 76% specificity for discriminating migraineurs from nonmigraineurs. In study 4 (n = 116), we found that the changes in the marker responses showed significant correlation with the changes in headache frequency in response to real acupuncture.

CONCLUSION

We identified an fMRI-based neural marker that captures distinct characteristics of MwoA and can link disease pattern changes to brain changes.

Evaluating Cortical Alterations in Patients With Chronic Back Pain Using Neuroimaging Techniques: Recent Advances and Perspectives

Chronic back pain (CBP) is a leading cause of disability and results in considerable socio-economic burdens worldwide. Although CBP patients are commonly diagnosed and treated with a focus on the “end organ dysfunction” (i.e., peripheral nerve injuries or diseases), the evaluation of CBP remains flawed and problematic with great challenges. Given that the peripheral nerve injuries or diseases are insufficient to define the etiology of CBP in some cases, the evaluation of alterations in the central nervous system becomes particularly necessary and important. With the development of advanced neuroimaging techniques, extensive studies have been carried out to identify the cortical abnormalities in CBP patients. Here, we provide a comprehensive overview on a series of novel findings from these neuroimaging studies to improve our understanding of the cortical abnormalities originated in the disease. First, CBP patients normally exhibit central sensitization to external painful stimuli, which is indexed by increased pain sensitivity and brain activations in pain-related brain regions. Second, long-term suffering from chronic pain leads to emotional disorders, cognitive impairments, and the abnormalities of the relevant brain networks among CBP patients. Third, CBP is associated with massive cortical reorganization, including structural, functional, and metabolic brain changes. Overall, a deep insight into the neural mechanisms underlying the development and outcome of CBP through more sophisticated neuroimaging investigations could not only improve our current understanding of the etiology of CBP but also facilitate the diagnosis and treatment of CBP based on precision medicine.