Baseline Brain Gray Matter Volume as a Predictor of Acupuncture Outcome in Treating Migraine

Exploring potential neuroimaging biomarkers for the response to non-steroidal anti-inflammatory drugs in episodic migraine

BACKGROUND

Non-steroidal anti-inflammatory drugs (NSAIDs) are considered first-line medications for acute migraine attacks. However, the response exhibits considerable variability among individuals. Thus, this study aimed to explore a machine learning model based on the percentage of amplitude oscillations (PerAF) and gray matter volume (GMV) to predict the response to NSAIDs in migraine treatment.

METHODS

Propensity score matching was adopted to match patients having migraine with response and nonresponse to NSAIDs, ensuring consistency in clinical characteristics and migraine-related features. Multimodal magnetic resonance imaging was employed to extract PerAF and GMV, followed by feature selection using the least absolute shrinkage and selection operator regression and recursive feature elimination algorithms. Multiple predictive models were constructed and the final model with the smallest predictive residuals was chosen. The model performance was evaluated using the area under the receiver operating characteristic (ROCAUC) curve, area under the precision-recall curve (PRAUC), balance accuracy (BACC), sensitivity, F1 score, positive predictive value (PPV), and negative predictive value (NPV). External validation was performed using a public database. Then, correlation analysis was performed between the neuroimaging predictors and clinical features in migraine.

RESULTS

One hundred eighteen patients with migraine (59 responders and 59 non-responders) were enrolled. Six features (PerAF of left insula and left transverse temporal gyrus; and GMV of right superior frontal gyrus, left postcentral gyrus, right postcentral gyrus, and left precuneus) were observed. The random forest model with the lowest predictive residuals was selected and model metrics (ROCAUC, PRAUC, BACC, sensitivity, F1 score, PPV, and NPV) in the training and testing groups were 0.982, 0.983, 0.927, 0.976, 0.930, 0.889, and 0.973; and 0.711, 0.648, 0.639, 0.667,0.649, 0.632, and 0.647, respectively. The model metrics of external validation were 0.631, 0.651, 0.611, 0.808, 0.656, 0.553, and 0.706. Additionally, a significant positive correlation was found between the GMV of the left precuneus and attack time in non-responders.

CONCLUSIONS

Our findings suggest the potential of multimodal neuroimaging features in predicting the efficacy of NSAIDs in migraine treatment and provide novel insights into the neural mechanisms underlying migraine and its optimized treatment strategy.

A predictive study of the efficacy of transcutaneous auricular vagus nerve stimulation in the treatment of major depressive disorder: An fMRI-based machine learning analysis

BACKGROUND

In order to improve taVNS efficacy, the usage of fMRI to explore the predictive neuroimaging markers would be beneficial for screening the appropriate MDD population before treatment.

METHODS

A total of 86 MDD patients were recruited in this study, and all subjects were conducted with the clinical scales and resting-state functional magnetic resonance imaging (fMRI) scan before and after 8 weeks' taVNS treatment. A two-stage feature selection strategy combining Machine Learning and Statistical was used to screen out the critical brain functional connections (FC) that were significantly associated with efficacy prediction, then the efficacy prediction model was constructed for taVNS treating MDD. Finally, the model was validated by separated the responding and non-responding patients.

RESULTS

This study showed that taVNS produced promising clinical efficacy in the treatment of mild and moderate MDD. Eleven FCs were selected out and were found to be associated with the cortico-striatal-pallidum-thalamic loop, the hippocampus and cerebellum and the HAMD-17 scores. The prediction model was created based on these FCs for the efficacy prediction of taVNS treatment. The R-square of the conducted regression model for predicting HAMD-17 reduction rate is 0.44, and the AUC for classifying the responding and non-responding patients is 0.856.

CONCLUSION

The study demonstrates the validity and feasibility of combining neuroimaging and machine learning techniques to predict the efficacy of taVNS on MDD, and provides an effective solution for personalized and precise treatment for MDD.

Research hotspots and trends on acupuncture treatment for headache: a bibliometric analysis from 2003 to 2023

Background

While acupuncture treatment has gained extensive usage in addressing headaches, there remains a notable gap in the literature analysis for this field. Therefore, this study aims to conduct a literature review using Citespace, VOSviewer, and Bibliometrix, aiming to examine the current status, strengths, and potential future directions in the utilization of acupuncture for headache treatment.

Methods

Relevant literature on acupuncture treatment for headaches between 2003 and 2023 was retrieved from the Web of Science (WoS) core database. Utilizing CiteSpace 6.1.R6, VOSviewer 1.6.18, and Bibliometrix 4.1.4, we conducted bibliometric analyses across various categories, including countries/regions, institutions, authors, journals, references, and keywords.

Results

A total of 808 research reports were included. China and the United States have significantly contributed to this field. Chengdu University of Chinese Medicine holds the record for the highest number of published papers. Liu Lu has the highest publication output, while Linde K has the highest citation rate. MEDICINE leads in publication frequency, while CEPHALALGIA holds the highest citation rate. The Long-term Effect of Acupuncture for Migraine Prophylaxis a Randomized Clinical Trial is the most cited reference. Migraine was the most researched type. Filiform needle acupuncture was the most widely used stimulation method. The safety and efficacy of acupuncture have received significant attention. Modern mechanism research shows that depression, brain functional connectivity, and neuroimaging technology have become research hotspots in the acupuncture treatment of headaches.

Conclusion

Acupuncture treatment for headaches has established a stable trend with a promising developmental trajectory. Research in this field mainly focuses on different acupuncture prevention and treatment for various types of headaches, the safety and efficacy of acupuncture, etc. Research on the mechanism of action mainly focuses on interpreting bidirectional and holistic regulation between pain and emotion by acupuncture and the regulation of brain function connection and neuroimaging technology by acupuncture. Future research should expand on the advantages and indications of acupuncture treatment for different headaches and their modern mechanisms.

Predicting the efficacy of non-steroidal anti-inflammatory drugs in migraine using deep learning and three-dimensional T1-weighted images

Deep learning (DL) models based on individual images could contribute to tailored therapies and personalized treatment strategies. We aimed to construct a DL model using individual 3D structural images for predicting the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in migraine. A 3D convolutional neural network model was constructed, with ResNet18 as the classification backbone, to link structural images to predict the efficacy of NSAIDs. In total, 111 patients were included and allocated to the training and testing sets in a 4:1 ratio. The prediction accuracies of the ResNet34, ResNet50, ResNeXt50, DenseNet121, and 3D ResNet18 models were 0.65, 0.74, 0.65, 0.70, and 0.78, respectively. This model, based on individual 3D structural images, demonstrated better predictive performance in comparison to conventional models. Our study highlights the feasibility of the DL algorithm based on brain structural images and suggests that it can be applied to predict the efficacy of NSAIDs in migraine treatment.

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.

Imaging the brain and vascular reactions to headache treatments: a systematic review

BACKGROUND

Neuroimaging studies have made an important contribution to our understanding of headache pathophysiology. This systematic review aims to provide a comprehensive overview and critical appraisal of mechanisms of actions of headache treatments and potential biomarkers of treatment response disclosed by imaging studies.

MAIN BODY

We performed a systematic literature search on PubMed and Embase databases for imaging studies investigating central and vascular effects of pharmacological and non-pharmacological treatments used to abort and prevent headache attacks. Sixty-three studies were included in the final qualitative analysis. Of these, 54 investigated migraine patients, 4 cluster headache patients and 5 patients with medication overuse headache. Most studies used functional magnetic resonance imaging (MRI) (n = 33) or molecular imaging (n = 14). Eleven studies employed structural MRI and a few used arterial spin labeling (n = 3), magnetic resonance spectroscopy (n = 3) or magnetic resonance angiography (n = 2). Different imaging modalities were combined in eight studies. Despite of the variety of imaging approaches and results, some findings were consistent. This systematic review suggests that triptans may cross the blood-brain barrier to some extent, though perhaps not sufficiently to alter the intracranial cerebral blood flow. Acupuncture in migraine, neuromodulation in migraine and cluster headache patients, and medication withdrawal in patients with medication overuse headache could promote headache improvement by reverting headache-affected pain processing brain areas. Yet, there is currently no clear evidence for where each treatment acts, and no firm imaging predictors of efficacy. This is mainly due to a scarcity of studies and heterogeneous treatment schemes, study designs, subjects, and imaging techniques. In addition, most studies used small sample sizes and inadequate statistical approaches, which precludes generalizable conclusions.

CONCLUSION

Several aspects of headache treatments remain to be elucidated using imaging approaches, such as how pharmacological preventive therapies work, whether treatment-related brain changes may influence therapy effectiveness, and imaging biomarkers of clinical response. In the future, well-designed studies with homogeneous study populations, adequate sample sizes and statistical approaches are needed.

Structural and functional improvement of amygdala sub-regions in postpartum depression after acupuncture

Objective

This study aimed to analyze the changes in structure and function in amygdala sub-regions in patients with postpartum depression (PPD) before and after acupuncture.

Methods

A total of 52 patients with PPD (All-PPD group) were included in this trial, 22 of which completed 8 weeks of acupuncture treatment (Acu-PPD group). An age-matched control group of 24 healthy postpartum women (HPW) from the hospital and community were also included. Results from the 17-Hamilton Depression Scale (17-HAMD) and the Edinburgh Postnatal Depression Scale (EPDS) were evaluated, and resting-state functional magnetic resonance imaging (rs-fMRI) scans were performed at baseline and after the acupuncture treatment. Sub-regions of the amygdala were used as seed regions to measure gray matter volume (GMV) and analyzed for resting-state functional connectivity (RSFC) values separately. Finally, correlation analyses were performed on all patients with PPD to evaluate association values between the clinical scale scores, GMV, and RSFC values, while controlling for age and education. Pearson's correlation analyses were conducted to investigate the relevance between GMV and RSFC values of brain regions that differed before and after acupuncture treatment and clinical scale scores in Acu-PPD patients.

Results

The HAMD scores for Acu-PPD were reduced after acupuncture treatment (P < 0.05), suggesting the positive effects of acupuncture on depression symptoms. Structurally, the All-PPD group showed significantly decreased GMV in the left lateral part of the amygdala (lAMG.L) and the right lateral part of the amygdala (lAMG.R) compared to the HPW group (P < 0.05). In addition, the GMV of lAMG.R was marginally increased in the Acu-PPD group after acupuncture (P < 0.05). Functionally, the Acu-PPD group showed a significantly enhanced RSFC between the left medial part of the amygdala (mAMG.L) and the left vermis_6, an increased RSFC between the right medial part of the amygdala (mAMG.R) and left vermis_6, and an increased RSFC between the lAMG.R and left cerebelum_crus1 (P < 0.05). Moreover, correlation studies revealed that the GMV in the lAMG.R was significantly related to the EPDS scores in the All-PPD group (P < 0.05).

Conclusion

Our findings demonstrated that the structure of amygdala sub-regions is impaired in patients with PPD. Acupuncture may improve depressive symptoms in patients with PPD, and the mechanism may be attributed to changes in the amygdala sub-region structure and the functional connections of brain areas linked to the processing of negative emotions. The fMRI-based technique can provide comprehensive neuroimaging evidence to visualize the central mechanism of action of acupuncture in PPD.

Effect of acupuncture on the modulation of functional brain regions in migraine: A meta-analysis of fMRI studies

Background

Acupuncture, a traditional Chinese medicine therapy, is an effective migraine treatment, especially in improving pain. In recent years, many acupuncture brain imaging studies have found significant changes in brain function following acupuncture treatment of migraine, providing a new perspective to elucidate the mechanism of action of acupuncture.

Objective

To analyse and summarize the effects of acupuncture on the modulation of specific patterns of brain region activity changes in migraine patients, thus providing a mechanism for treating migraine by acupuncture.

Methods

Chinese and English articles published up to May 2022 were searched in three English databases (PubMed, Embase and Cochrane) and four Chinese databases (China national knowledge infrastructure, CNKI; Chinese Biomedical Literature database, CBM; the Chongqing VIP database, VIP; and the Wanfang database, WF). A neuroimaging meta-analysis on ALFF, ReHo was performed on the included studies using Seed-based d Mapping with Permutation of Subject Images (SDM-PSI) software. Subgroup analyses were used to compare differences in brain regions between acupuncture and other groups. Meta-regression was used to explore the effect of demographic information and migraine alterations on brain imaging outcomes. Linear models were drawn using MATLAB 2018a, and visual graphs for quality evaluation were produced using R and RStudio software.

Results

A total of 7 studies comprising 236 patients in the treatment group and 173 in the control group were included in the meta-analysis. The results suggest that acupuncture treatment helps to improve pain symptoms in patients with migraine. The left angular gyrus is hyperactivation, and the left superior frontal gyrus and the right superior frontal gyrus are hypoactivated. The migraine group showed hyperactivation in the corpus callosum compared to healthy controls.

Conclusion

Acupuncture can significantly regulate changes in brain regions in migraine patients. However, due to the experimental design of neuroimaging standards are not uniform, the results also have some bias. Therefore, to better understand the potential mechanism of acupuncture on migraine, a large sample, multicenter controlled trial is needed for further study. In addition, the application of machine learning methods in neuroimaging studies could help predict the efficacy of acupuncture and screen migraine patients suitable for acupuncture treatment.

Recent trends in acupuncture for chronic pain: A bibliometric analysis and review of the literature

BACKGROUND

Acupuncture has been increasingly used in patients with chronic pain, yet no bibliometric analysis of acupuncture studies for chronic pain exists.

OBJECTIVES

To investigate the characteristics, hotspots and frontiers of global scientific output in acupuncture research for chronic pain over the past decade.

METHODS

We retrieved publications on acupuncture for chronic pain published from 2011 to 2022 from the Science Citation Index Expanded (SCI-expanded) of the Web of Science Core Collection (WoSCC). The co-occurrence relationships of journals/countries/institutions/authors/keywords were performed using VOSviewer V6.1.2, and CiteSpace V1.6.18 analyzed the clustering and burst analysis of keywords and co-cited references.

RESULTS

A total of 1616 articles were retrieved. The results showed that the number of annual publications on acupuncture for chronic pain has increased over time, with the main types of literature being original articles (1091 articles, 67.5 %) and review articles (351 articles, 21.7 %). China had the most publications (598 articles, 37 %), with Beijing University of Traditional Chinese Medicine (93 articles, 5.8 %) and Evidence-based Complementary and Alternative Medicine ranked first (169 articles, 10.45 %) as the most prolific affiliate and journal, respectively. Liang FR was the most productive author (43 articles), and the article published by Vickers Andrew J in 2012 had the highest number of citations (625 citations). Recently, "acupuncture" and "pain" appeared most frequently. The hot topics in acupuncture for chronic pain based on keywords clustering analysis were experimental design, hot diseases, interventions, and mechanism studies. According to burst analysis, the main research frontiers were functional connectivity (FC), depression, and risk.

CONCLUSION

This study provides an in-depth perspective on acupuncture for chronic pain studies, revealing pivotal points, research hotspots, and research trends. Valuable ideas are provided for future research activities.

Circulating exosomal microRNA profiles in migraine patients receiving acupuncture treatment: A placebo-controlled clinical trial

Background

Acupuncture has a long history of being used in Chinese medicine for the treatment of migraine. However, molecular biomarkers for diagnosis and prognosis of migraine and its treatment are lacking. This study aimed to explore whether acupuncture could regulate differentially expressed exosomal miRNAs between patients with migraine without aura (MWoA) and healthy controls (HCs) and to identify diagnostic biomarkers that helped differentiate MWoA patients from HCs and identify prognostic biomarkers that helped to predict the effect of acupuncture.

Methods

Here, we isolated serum exosomes from patients with MWoA and HCs before and after true and sham acupuncture treatment. Then, small RNA sequencing and bioinformatics analysis were performed to screen out key miRNAs specifically responding to acupuncture treatment. Pearson's correlation analysis was used to evaluate the correlation between miRNAs and clinical phenotypes. Finally, we applied a machine learning method to identify diagnostic biomarkers of MWoA patients and identify prognostic biomarkers that helped to predict the effect of acupuncture.

Results

Small RNA sequencing identified 68 upregulated and 104 downregulated miRNAs in MWoA patients compared to those in HCs. Further, we identified eight upregulated and four downregulated miRNAs in migraine patients after true acupuncture treatment (trAMWoA), but not in the sham acupuncture treatment (shAMWoA) or HC group. Among them, has-miR-378a-5p was positively correlated with time unable to work, study, or do housework due to migraine (p < 0.05), whereas has-miR-605-3p was negatively correlated with the restrictive subscale of the migraine-specific quality of life questionnaire (MSQ) (p < 0.05). We then evaluated the diagnostic and prognostic potential of these 12 miRNAs in patients with MWoA. The combination of serum levels of exosomal has-miR-369-5p, has-miR-145-5p, and has-miR-5,010-3p could serve as diagnostic and prognostic biomarkers for MWoA patients following acupuncture treatment.

Conclusion

This is the first study on the serum exosomal miRNA profiles of migraineurs before and after acupuncture treatment. Our results improve our understanding of the molecular functions of miRNAs in MWoA. More importantly, they expand our view of evaluating the clinical outcomes of migraine patients treated with acupuncture, using exosomal RNA markers.

Clinical Trial Registration

Chinese Clinical Trial Registry, ChiCTR2000034417, July 2020.

Prediction and associated factors of non-steroidal anti-inflammatory drugs efficacy in migraine treatment

Background: The selection strategy of non-steroidal anti-inflammatory drugs (NSAIDs) for migraine is hard to judge whether it is effective, leading to unnecessary exposure to insufficient or lengthy treatment trials. The goal of the study was to investigate potential predictors of NSAIDs efficacy in migraine therapy and to explore their influence on efficacy. Methods: 610 migraine patients were recruited and assigned into responders and non-responders. Potential predictors among demographic and clinical characteristics for NSAIDs efficacy were extracted using multivariable logistic regression (LR) analysis, and were applied to construct prediction models via machine learning (ML) algorithms. Finally, Cochran-Mantel-Haenszel tests were used to examine the impact of each predictor on drug efficacy. Results: Multivariate LR analysis revealed migraine-related (disease duration, headache intensity and frequency) and psychiatric (anxiety, depression and sleep disorder) characteristics were predictive of NSAIDs efficacy. The accuracies of ML models using support vector machine, decision tree and multilayer perceptron were 0.712, 0.741, and 0.715, respectively. Cochran-Mantel-Haenszel test showed that, for variables with homogeneity of odds ratio, disease duration, frequency, anxiety, and depression and sleep disorder were associated with decreased likelihood of response to all NSAIDs. However, the variabilities in the efficacy of acetaminophen and celecoxib between patients with mild and severe headache intensity were not confirmed. Conclusion: Migraine-related and psychiatric parameters play a critical role in predicting the outcomes of acute migraine treatment. These models based on predictors could optimize drug selection and improve benefits from the start of treatment.

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.

Neurological mechanism and treatment effects prediction of acupuncture on migraine without aura: Study protocol for a randomized controlled trial

Introduction

Acupuncture is an effective treatment in migraine without aura (MWoA), but the neurological mechanism has not been investigated using multimodal magnetic resonance imaging (MRI). This trial will combine functional MRI, structural MRI, and diffusion tensor imaging to explore the potential neural mechanism of acupuncture on MWoA, and will use machine learning approach to predict acupuncture treatment effects.

Methods

In this multimodal neuroimaging randomized controlled trial, a total of 60 MWoA participants will be randomly allocated to two groups: the real acupuncture treatment group and the sham acupuncture control group. This trial will include a 4-week baseline phase, a 4-week treatment phase, and a 12-week follow-up phase. Participants will undergo 12 acupuncture or sham acupuncture sessions during the treatment phase. The Headache Diary, Migraine-Specific Quality of Life Questionnaire, Headache Impact Test, Beck Depression Inventory-II, and Beck Anxiety Inventory will be utilized to evaluate the clinical efficacy. Multimodal MRI scans will be employed to investigate the mechanism of acupuncture at baseline, at the end of treatment, and after follow-up. Multimodal MRI data will be used to predict acupuncture treatment effects using machine learning technology.

Discussion

This study hypothesized that acupuncture therapy may treat MWoA by restoring the neuropathological alterations in brain activity. Our finding should provide valuable scientific proof for the effects of acupuncture and demonstrate the usefulness of acupuncture in the treatment of MWoA. Moreover, acupuncture response prediction might decrease healthcare expenses and time lags for patients.

Trial registration number

[ChiCTR2100044251].

Changes in brain connectivity linked to multisensory processing of pain modulation in migraine with acupuncture treatment

Migraine without aura (MWoA) is a major neurological disorder with unsatisfactory adherence to current medications. Acupuncture has emerged as a promising method for treating MWoA. However, the brain mechanism underlying acupuncture is yet unclear. The present study aimed to examine the effects of acupuncture in regulating brain connectivity of the key regions in pain modulation. In this study, MWoA patients were recruited and randomly assigned to 4 weeks of real or sham acupuncture. Resting-state functional magnetic resonance imaging (fMRI) data were collected before and after the treatment. A modern neuroimaging literature meta-analysis of 515 fMRI studies was conducted to identify pain modulation-related key regions as regions of interest (ROIs). Seed-to-voxel resting state-functional connectivity (rsFC) method and repeated-measures two-way analysis of variance were conducted to determine the interaction effects between the two groups and time (baseline and post-treatment). The changes in rsFC were evaluated between baseline and post-treatment in real and sham acupuncture groups, respectively. Clinical data at baseline and post-treatment were also recorded in order to determine between-group differences in clinical outcomes as well as correlations between rsFC changes and clinical effects. 40 subjects were involved in the final analysis. The current study demonstrated significant improvement in real acupuncture vs sham acupuncture on headache severity (monthly migraine days), headache impact (6-item Headache Impact Test), and health-related quality of life (Migraine-Specific Quality of Life Questionnaire). Five pain modulation-related key regions, including the right amygdala (AMYG), left insula (INS), left medial orbital superior frontal gyrus (PFCventmed), left middle occipital gyrus (MOG), and right middle cingulate cortex (MCC), were selected based on the meta-analysis on brain imaging studies. This study found that 1) after acupuncture treatment, migraine patients of the real acupuncture group showed significantly enhanced connectivity in the right AMYG/MCC-left MTG and the right MCC-right superior temporal gyrus (STG) compared to that of the sham acupuncture group; 2) negative correlations were established between clinical effects and increased rsFC in the right AMYG/MCC-left MTG; 3) baseline right AMYG-left MTG rsFC predicts monthly migraine days reduction after treatment. The current results suggested that acupuncture may concurrently regulate the rsFC of two pain modulation regions in the AMYG and MCC. MTG and STG may be the key nodes linked to multisensory processing of pain modulation in migraine with acupuncture treatment. These findings highlighted the potential of acupuncture for migraine management and the mechanisms underlying the modulation effects.

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.

Percutaneous Tibial Nerve Stimulation vs Sham Stimulation for Fecal Incontinence in Women: NeurOmodulaTion for Accidental Bowel Leakage Randomized Clinical Trial

INTRODUCTION

To determine whether percutaneous tibial nerve stimulation (PTNS) is superior to sham stimulation for the treatment of fecal incontinence (FI) in women refractory to first-line treatments.

METHODS

Women aged 18 years or older with ≥3 months of moderate-to-severe FI that persisted after a 4-week run-in phase were randomized 2:1 (PTNS:sham stimulation) to 12 weekly 30-minute sessions in this multicenter, single-masked, controlled superiority trial. The primary outcome was change from baseline FI severity measured by St. Mark score after 12 weeks of treatment (range 0-24; minimal important difference, 3-5 points). The secondary outcomes included electronic bowel diary events and quality of life. The groups were compared using an adjusted general linear mixed model.

RESULTS

Of 199 women who entered the run-in period, 166 (of 170 eligible) were randomized, (111 in PTNS group and 55 in sham group); the mean (SD) age was 63.6 (11.6) years; baseline St. Mark score was 17.4 (2.7); and recording was 6.6 (5.5) FI episodes per week. There was no difference in improvement from baseline in St. Mark scores in the PTNS group when compared with the sham group (-5.3 vs -3.9 points, adjusted difference [95% confidence interval] -1.3 [-2.8 to 0.2]). The groups did not differ in reduction in weekly FI episodes (-2.1 vs -1.9 episodes, adjusted difference [95% confidence interval] -0.26 [-1.85 to 1.33]). Condition-specific quality of life measures did not indicate a benefit of PTNS over sham stimulation. Serious adverse events occurred in 4% of each group.

DISCUSSION

Although symptom reduction after 12 weeks of PTNS met a threshold of clinical importance, it did not differ from sham stimulation. These data do not support the use of PTNS as conducted for the treatment of FI in women.

Machine prescription for chronic migraine

Responsive to treatment individually, chronic migraine remains strikingly resistant collectively, incurring the second-highest population burden of disability worldwide. A heterogeneity of responsiveness, requiring prolonged-currently heuristic-individual evaluation of available treatments, may reflect a diversity of causal mechanisms, or the failure to identify the most important, single causal factor. Distinguishing between these possibilities, now possible through the application of complex modelling to large-scale data, is critical to determine the optimal approach to identify new interventions in migraine and making the best use of existing ones. Examining a richly phenotyped cohort of 1446 consecutive unselected patients with chronic migraine, here we use causal multitask Gaussian process models to estimate individual treatment effects across 10 classes of preventatives. Such modelling enables us to quantify the accessibility of heterogeneous responsiveness to high-dimensional modelling, to infer the likely scale of the underlying causal diversity. We calculate the treatment effects in the overall population, and the conditional treatment effects among those modelled to respond and compare the true response rates between these two groups. Identifying a difference in response rates between the groups supports a diversity of causal mechanisms. Moreover, we propose a data-driven machine prescription policy, estimating the time-to-response when sequentially trialling preventatives by individualized treatment effects and comparing it to expert guideline sequences. All model performances are quantified out-of-sample. We identify significantly higher true response rates among individuals modelled to respond, compared with the overall population (mean difference of 0.034; 95% confidence interval 0.003-0.065; P = 0.033), supporting significant heterogeneity of responsiveness and diverse causal mechanisms. The machine prescription policy yields an estimated 35% reduction in time-to-response (3.750 months; 95% confidence interval 3.507-3.993; P < 0.0001) compared with expert guidelines, with no substantive increase in expense per patient. We conclude that the highly distributed mode of causation in chronic migraine necessitates high-dimensional modelling for optimal management. Machine prescription should be considered an essential clinical decision-support tool in the future management of chronic migraine.

Revealing the Neural Mechanism Underlying the Effects of Acupuncture on Migraine: A Systematic Review

Background: Migraine is a chronic neurological disorder characterized by attacks of moderate or severe headache and various neurological symptoms. Migraine is typically treated by pharmacological or non-pharmacological therapies to relieve pain or prevent migraine attacks. Pharmacological therapies show limited efficacy in relieving headache and are often accompanied by adverse effects, while the benefits of acupuncture, a non-pharmacological therapy, have been well-documented in both the treatment and prevention of acute migraine attacks. However, the underlying mechanism of the effect of acupuncture on relieving migraine remains unclear. Recent advances in neuroimaging technology have offered new opportunities to explore the underlying neural mechanism of acupuncture in treating migraine. To pave the way for future research, this review provides an overview neuroimaging studies on the use of acupuncture for migraine in the last 10 years.

Methods: Using search terms about acupuncture, neuroimaging and migraine, we searched PubMed, Embase, Cochrane Central Register of Controlled Trials, and China National Knowledge Infrastructure from January 2009 to June 2020 for neuroimaging studies that examined the effect of acupuncture in migraine. All published randomized and non-randomized controlled neuroimaging studies were included. We summarized the proposed neural mechanism underlying acupuncture analgesia in acute migraine, and the proposed neural mechanism underlying the sustained effect of acupuncture in migraine prophylaxis.

Results: A total of 619 articles were retrieved. After removing reviews, meta-analyses, animal studies and etc., 15 articles were eligible and included in this review. The methods used were positron emission computed tomography (PET-CT; n = 2 studies), magnetic resonance spectroscopy (n = 1), and functional magnetic resonance imaging (fMRI; n = 12). The analyses used included the regional homogeneity (ReHo) method (n = 3), amplitude of low frequency (ALFF) method (n = 2), independent component analysis (ICA; n = 3), seed-based analysis (SBA; n = 1), both ICA and SBA (n = 1), Pearson's correlation to calculate functional connectivity (FC) between brain regions (n = 1), and a machine learning method (n = 1). Five studies focused on the instant effect of acupuncture, and the research objects were those with acute migraine (n = 2) and migraine in the interictal phase (n = 3). Ten studies focused on the lasting effect of acupuncture, and all the studies selected migraine patients in the interictal phase. This review included five task-based studies and 10 resting-state studies. None of the studies conducted a correlation analysis between functional brain changes and instant clinical efficacy. For studies that performed a correlation analysis between functional brain changes and sustained clinical efficacy, the prophylactic effect of acupuncture on migraine might be through regulation of the visual network, default mode network (DMN), sensory motor network, frontoparietal network (FPN), limbic system, and/or descending pain modulatory system (DPMS).

Conclusion: The neural mechanism underlying the immediate effect of acupuncture analgesia remains unclear, and the neural mechanism of sustained acupuncture treatment for migraine might be related to the regulation of pain-related brain networks. The experimental design of neuroimaging studies that examined the effect of acupuncture in migraine also have some shortcomings, and it is necessary to standardize and optimize the experimental design. Multi-center neuroimaging studies are needed to provide a better insight into the neural mechanism underlying the effect of acupuncture on migraine. Multi-modality neuroimaging studies that integrate multiple data analysis methods are required for cross-validation of the neuroimaging results. In addition, applying machine learning methods in neuroimaging studies can help to predict acupuncture efficacy and screen for migraineurs for whom acupuncture treatment would be suitable.

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.

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.