Whole-brain structural magnetic resonance imaging-based classification of primary dysmenorrhea in pain-free phase: a machine learning study

Brain Network Dynamics in Women With Primary Dysmenorrhea During the Pain-Free Periovulation Phase

The human brain is a dynamic system that shows frequency-specific features. Neuroimaging studies have shown that both healthy individuals and those with chronic pain disorders experience pain influenced by various processes that fluctuate over time. Primary dysmenorrhea (PDM) is a chronic visceral pain that disrupts the coordinated activity of brain's functional network. However, it remains unclear whether the dynamic interactions across the whole-brain network over time and their associations with neurobehavioral symptoms are dependent on the frequency bands in patients with PDM during the pain-free periovulation phase. In this study, we used an energy landscape analysis to examine the interactions over time across the large-scale network in a sample of 59 patients with PDM and 57 healthy controls (HCs) at different frequency bands. Compared with HCs, patients with PDM exhibit aberrant brain dynamics, with more significant differences in the slow-4 frequency band. Patients with PDM show more indirect neural transition counts due to an unstable intermediate state, whereas neurotypical brain activity frequently transitions between 2 major states. This data-driven approach further revealed that the brains of individuals with PDM have more abnormal brain dynamics than HCs. Our results suggested that unstable brain dynamics were associated with the strength of brain functional segregation and the Pain Catastrophizing Scale score. Our findings provide preliminary evidence that atypical dynamics in the functional network may serve as a potential key feature and biological marker of patients with PDM during the pain-free phase. PERSPECTIVE: We applied energy landscape analysis on brain-imaging data to identify relatively stable and dominant brain activity patterns for patients with PDM. More atypical brain dynamics were found in the slow-4 band and were related to the strength of functional segregation, providing new insights into the dysfunction brain dynamics.

Degree centrality-based resting-state functional magnetic resonance imaging explores central mechanisms in lumbar disc herniation patients with chronic low back pain

Objective

To investigate the central mechanism of lumbar disc herniation in patients with chronic low back pain (LDHCP) using resting-state functional magnetic resonance imaging (rs-fMRI) utilizing the Degree Centrality (DC) method.

Methods

Twenty-five LDHCP and twenty-two healthy controls (HCs) were enrolled, and rs-fMRI data from their brains were collected. We compared whole-brain DC values between the LDHCP and HC groups, and examined correlations between DC values within the LDHCP group and the Visual Analogue Score (VAS), Oswestry Dysfunction Index (ODI), and disease duration. Diagnostic efficacy was evaluated using receiver operating characteristic (ROC) curve analysis.

Results

LDHCP patients exhibited increased DC values in the bilateral cerebellum and brainstem, whereas decreased DC values were noted in the left middle temporal gyrus and right post-central gyrus when compared with HCs. The DC values of the left middle temporal gyrus were positively correlated with VAS (r = 0.416, p = 0.039) and ODI (r = 0.405, p = 0.045), whereas there was no correlation with disease duration (p > 0.05). Other brain regions showed no significant correlations with VAS, ODI, or disease duration (p > 0.05). Furthermore, the results obtained from ROC curve analysis demonstrated that the Area Under the Curve (AUC) for the left middle temporal gyrus was 0.929.

Conclusion

The findings indicated local abnormalities in spontaneous neural activity and functional connectivity in the bilateral cerebellum, bilateral brainstem, left middle temporal gyrus, and right postcentral gyrus among LDHCP patients.

Integration of graph network with kernel SVM and logistic regression for identification of biomarkers in SCA12 and its diagnosis

Spinocerebellar ataxia type 12 is a hereditary and neurodegenerative illness commonly found in India. However, there is no established noninvasive automatic diagnostic system for its diagnosis and identification of imaging biomarkers. This work proposes a novel four-phase machine learning-based diagnostic framework to find spinocerebellar ataxia type 12 disease-specific atrophic-brain regions and distinguish spinocerebellar ataxia type 12 from healthy using a real structural magnetic resonance imaging dataset. Firstly, each brain region is represented in terms of statistics of coefficients obtained using 3D-discrete wavelet transform. Secondly, a set of relevant regions are selected using a graph network-based method. Thirdly, a kernel support vector machine is used to capture nonlinear relationships among the voxels of a brain region. Finally, the linear relationship among the brain regions is captured to build a decision model to distinguish spinocerebellar ataxia type 12 from healthy by using the regularized logistic regression method. A classification accuracy of 95% and a harmonic mean of precision and recall, i.e. F1-score of 94.92%, is achieved. The proposed framework provides relevant regions responsible for the atrophy. The importance of each region is captured using Shapley Additive exPlanations values. We also performed a statistical analysis to find volumetric changes in spinocerebellar ataxia type 12 group compared to healthy. The promising result of the proposed framework shows that clinicians can use it for early and timely diagnosis of spinocerebellar ataxia type 12.

Revealing the Central Mechanism of Acupuncture for Primary Dysmenorrhea Based on Neuroimaging: A Narrative Review

Objective

The central mechanism of acupuncture for primary dysmenorrhea was explored by summarizing the changes in different regional networks of the brain induced by acupuncture stimulation by analyzing the existing studies.

Methods

The original studies were collected and selected from three English databases such as PubMed and four Chinese databases as China Knowledge Network (CNKI). The main keyword clusters are neuroimaging, acupuncture, and primary dysmenorrhea.

Results

The literature review yielded 130 possibly qualified studies, and 23 articles fulfilled the criteria for inclusion. Regarding the type of acupuncture studies, 6 moxibustion studies and 17 manual acupuncture studies for primary dysmenorrhea were included. Based on functional magnetic resonance imaging (fMRI), perfusion-weighted imaging (PWI), and positron emission tomography-computer tomography techniques (PET-CT), one or more analysis methods such as amplitude of low-frequency fluctuations (ALFF), regional homogeneity (ReHo), functional connectivity (FC), and independent components analysis (ICA) were used. The results are summarized. To summarize the high-frequency brain area alterations observed in patients with acupuncture-induced primary dysmenorrhea were the anterior cingulate gyrus, thalamus, insula, precentral gyrus, middle frontal gyrus, postcentral gyrus, putamen, and cerebellum.

Conclusion

The results suggest that the mechanism of acupuncture in the treatment of primary dysmenorrhea is the involvement of networks regulating different areas of the brain in the analgesic effects of acupuncture. The brain regions involved in primary dysmenorrhea acupuncture analgesia were mainly located in the pain matrix, default mode network, salience network, and limbic system.

Altered brain activities in mesocorticolimbic pathway in primary dysmenorrhea patients of long-term menstrual pain

Background

Patients with primary dysmenorrhea (PDM) often present with abnormalities other than dysmenorrhea including co-occurrence with other chronic pain conditions and central sensitization. Changes in brain activity in PDM have been demonstrated; however, the results are not consistent. Herein, this study probed into altered intraregional and interregional brain activity in patients with PDM and expounded more findings.

Methods

A total of 33 patients with PDM and 36 healthy controls (HCs) were recruited and underwent a resting-state functional magnetic resonance imaging scan. Regional homogeneity (ReHo) and mean amplitude of low-frequency fluctuation (mALFF) analysis were applied to compare the difference in intraregional brain activity between the two groups, and the regions with ReHo and mALFF group differences were used as seeds for functional connectivity (FC) analysis to explore the difference of interregional activity. Pearson's correlation analysis was conducted between rs-fMRI data and clinical symptoms in patients with PDM.

Results

Compared with HCs, patients with PDM showed altered intraregional activity in a series of brain regions, including the hippocampus, the temporal pole superior temporal gyrus, the nucleus accumbens, the pregenual anterior cingulate cortex, the cerebellum_8, the middle temporal gyrus, the inferior temporal gyrus, the rolandic operculum, the postcentral gyrus and the middle frontal gyrus (MFG), and altered interregional FC mainly between regions of the mesocorticolimbic pathway and regions associated with sensation and movement. The anxiety symptoms are correlated with the intraregional activity of the right temporal pole superior temporal gyrus and FC between MFG and superior frontal gyrus.

Conclusion

Our study showed a more comprehensive method to explore changes in brain activity in PDM. We found that the mesocorticolimbic pathway might play a key role in the chronic transformation of pain in PDM. We, therefore, speculate that the modulation of the mesocorticolimbic pathway may be a potential novel therapeutic mechanism for PDM.

The progestin-only pills drospirenone 4 mg and desogestrel 0.075 mg as an option for the management of dysmenorrhea and mastodynia

INTRODUCTION

Dysmenorrhea and mastodynia are the most common gynecologic pain causes in women of all ages and races during their reproductive life. The following study aimed to show the influence of two POP´s in the development of dysmenorrhea and mastodynia after nine months of use.

MATERIAL AND METHODS

A total of 858 women with 6691 drospirenone (DRSP) cycles and 332 women with 2487 desogestrel (DSG) cycles were analyzed. Women included in this study were all child-bearing potentials, at risk of pregnancy, agreeing to use only the study medication for contraception for the duration of the study medication treatment, aged 18 to 45.

RESULTS

At screening, 168 (19.6%) of the 858 patients using DRSP and 64 (19,3%) of the DSG patients reported that they had suffered from dysmenorrhea within six cycles prior to the first visit before starting with the medication. 20,2% of the DRSP and 10,9% of the DSG group had a sever dysmenorrhea. After 9 cycles this was reduced to 0,6% and 3,1% respectively. In total, 96 women (11.2%) in the DRSP and 49 (14,8%) experienced mastodynia within six cycles before the screening. Of these 91.6% in the DRSP group and 91,8% in the DSG group had no or mild mastodynoa at follow-up.

DISCUSSION

The progestins 4 mg and desogestrel 0,075 mg showed a marked effect in the non-contraceptive aspects of dysmenorrhea and mastodynia so that new possibilities are opened for these two benign gynecological diseases. Future studies must reaffirm these first data.

Neuroimaging Assessment of Pain

Pain is an unpleasant sensory and emotional experience. Understanding the neural mechanisms of acute and chronic pain and the brain changes affecting pain factors is important for finding pain treatment methods. The emergence and progress of non-invasive neuroimaging technology can help us better understand pain at the neural level. Recent developments in identifying brain-based biomarkers of pain through advances in advanced imaging can provide some foundations for predicting and detecting pain. For example, a neurologic pain signature (involving brain regions that receive nociceptive afferents) and a stimulus intensity-independent pain signature (involving brain regions that do not show increased activity in proportion to noxious stimulus intensity) were developed based on multivariate modeling to identify processes related to the pain experience. However, an accurate and comprehensive review of common neuroimaging techniques for evaluating pain is lacking. This paper reviews the mechanism, clinical application, reliability, strengths, and limitations of common neuroimaging techniques for assessing pain to promote our further understanding of pain.

Are menstrual symptoms associated with central sensitization inventory? A cross-sectional study

BACKGROUND

Dysmenorrhea is a prevalent pain condition that affects women of reproductive age, who are monthly exposed to this pain, usually until they reach the adult age, or even after that, which can predispose them to Central Sensitization. The present study aimed to observe the association between menstrual characteristics and central sensitivity symptoms in women.

METHODS

Cross-sectional study. Brazilian women (n=10,402) answered an online form comprised of questions regarding their gynecological history, the Numerical Rating Scale for pain and the Central Sensitization Inventory, part A. For the analysis, we separated women into two groups: Central Sensitivity Symptoms group (n=5,200) and No-Central Sensitivity Symptoms group (n=5,202). We performed a binary logistic regression with the backward insertion method for the variables with p<0.05 in the bivariate analysis between groups. The significance level was set at 5%.

RESULTS

Prevalence of dysmenorrhea was 67.3%, and 32.2% of women in the Central Sensitivity Symptoms group reported pain >8 during their menstrual period. The logistic regression showed that greater levels of menstrual pain (Odds Ratio 1.12), gynecological diseases (Odds Ratio 1.51), presence of dysmenorrhea since adolescence (Odds Ratio 1.20) and irregular menstrual cycles (Odds Ratio 1.47) increased the likelihood of women presenting with Central Sensitivity Symptoms (p<0.05 for all comparisons).

CONCLUSIONS

The present study shows that Central Sensitivity Symptoms are present in about 50% of women and are associated with menstrual characteristics such as dysmenorrhea-related pain intensity, cycle regularity, presence of dysmenorrhea since adolescence accompanied by gynecological diseases.

SIGNIFICANCE

Central sensitivity symptoms occur in 50% of women, and are more present in women with dysmenorrhea. They are associated with cycle regularity, presence of dysmenorrhea since adolescence, and gynecological diseases.

LIMITATIONS

Women that suffer from dysmenorrhea and of higher socioeconomic and educational levels may have been more propense to respond to the invitation; as such, the findings of the present study should be carefully interpreted.

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.

The effects of long-term menstrual pain on pain empathy in women with primary dysmenorrhea

ABSTRACT

Primary dysmenorrhea (PDM) is not only a painful experience but also affects the psychological and affective states of women. Neuroimaging studies have revealed shared neural substrates for somatic and empathic pains in healthy subjects. However, little is known about the relationship between pain intensity and pain empathy in pain disorders. The cyclic nature of PDM makes it a unique model for investigating this issue during a patients' pain phase. To study how long-term pain modulates empathy for pain, T1-weighted magnetic resonance imaging scans were obtained in 39 PDM patients and 41 matched female healthy controls during menstruation. Subjects viewed static visual stimuli of the limbs submitted to painful and nonpainful stimulation to solicit empathy. The visual analogue scale for pain intensity and the Interpersonal Reactivity Index for empathic ability were also obtained. We found that women with PDM exhibited higher pain empathy compared with controls. The anterior insula and brain regions related to sensory discrimination with decreased gray matter volumes were not only shared but also acted as a mediator between pain intensity and pain empathy in PDM patients. In addition, the general linear modeling analysis revealed that long-term pain experience was a more important factor to pain empathy compared with pain intensity. This indicated that long-term pain may cause maladaptive brain structural plasticity, which may further affect psychological adjustment to bring patients more vivid pain when they witness suffering and distress in others.

Altered Posterior Cerebellar Lobule Connectivity With Perigenual Anterior Cingulate Cortex in Women With Primary Dysmenorrhea

Objectives: This study aimed to investigate the potential connectivity mechanism between the cerebellum and anterior cingulate cortex (ACC) and the cerebellar structure in primary dysmenorrhea (PDM).

Methods: We applied the spatially unbiased infratentorial template (SUIT) of the cerebellum to obtain anatomical details of cerebellar lobules, upon which the functional connectivity (FC) between the cerebellar lobules and ACC subregions was analyzed and the gray matter (GM) volume of cerebellar lobules was measured by using voxel-based morphometry (VBM) in 35 PDM females and 38 age-matched healthy females. The potential relationship between the altered FC or GM volume and clinical information was also evaluated in PDM females.

Results: PDM females showed higher connectivity between the left perigenual ACC (pACC) and lobule vermis_VI, between the left pACC and left lobule IX, and between right pACC and right cerebellar lobule VIIb than did the healthy controls. Compared with healthy controls, no altered GM volume was found in PDM females. No significant correlation was found between altered cerebellum–ACC FC and the clinical variables in the PDM females.

Conclusion: PDM females have abnormal posterior cerebellar connectivity with pACC but no abnormal structural changes. ACC–cerebellar circuit disturbances might be involved in the PDM females.

Immediate Analgesic Effect of Acupuncture in Patients With Primary Dysmenorrhea: A fMRI Study

Primary dysmenorrhea (PDM) is a common gynecological disease characterized by lower abdominal pain. Acupuncture is considered a good alternative therapy for PDM. However, the central mechanism of the analgesic effect of acupuncture is largely unknown. In this study, eligible patients were randomized into the real and sham acupuncture groups using a computer-generated, permuted block randomization method. The study cohort comprised 34 patients: 19 in the real acupuncture group and 15 in the sham acupuncture group. The clinical characteristics of the patients during their menstrual period were collected, and imaging scans were performed during the first 3 days of the patients' menstrual period. We analyzed task and resting functional magnetic resonance imaging (fMRI) data to investigate the potential central mechanism of the immediate effect of acupuncture intervention on the intensity of PDM pain. The task fMRI study found that the rostral anterior cingulate cortex (rACC) and right supplemental motor area were activated during real acupuncture. Using the resting-state functional connectivity (FC) method, we found a post- versus pre-treatment change in the FC of the rACC and left precentral gyrus in the comparison of real acupuncture versus sham acupuncture. In addition, the FC of the rACC-left precentral gyrus at baseline was negatively correlated with short-term analgesia, while the change in the FC of the rACC-left precentral gyrus was positively correlated with short-term analgesia after acupuncture treatment. These findings support the importance of rACC-left precentral gyrus resting-state FC in the modulation of the intensity of PDM pain through acupuncture, which may shed light on the central mechanism of acupuncture in the treatment of PDM.

Dynamic network topological properties for classifying primary dysmenorrhoea in the pain-free phase

BACKGROUND

Primary dysmenorrhoea (PDM) is known to alter brain static functional activity. This study aimed to explore the dynamic topological properties (DTP) of dynamic brain functional network in women with PDM in the pain-free phase and their performance in distinguishing PDM in the pain-free phase from healthy controls.

METHODS

Thirty-five women with PDM and 38 healthy women without PDM were included. A dynamic brain functional network was constructed using the slide-window approach. The stability (TP-Stab) and variability (TP-Var) of the DTP of the dynamic functional network were computed using the graph-theory method. A support vector machine (SVM) was used to evaluate the performance of DTP in identifying PDM in the pain-free phase.

RESULTS

Compared with healthy controls, women with PDM had not only lower TP-Stab in global DTP, which included cluster clustering coefficient (C_p ), characteristic path length (L_p ), global efficiency (E_g ) and local efficiency (E_loc ), but also lower TP-Stab and higher TP-Var in nodal DTP (nodal efficiency, E_nod ), mainly in the prefrontal cortex, anterior cingulate cortex, parahippocampal regions and insula. The TP-Stab and TP-Var were significantly correlated with psychological variables, that is positive emotions, sense of control and meaningful existence. SVM analysis showed that the DTP could identify PDM in the pain-free phase from healthy controls with an accuracy of 79.31%, sensitivity of 82.61% and specificity of 76%.

CONCLUSIONS

Women with PDM in the pain-free phase have altered global DTP and nodal DTP, mainly involving pain-related neurocircuits. The highly variable brain network is helpful for identifying PDM in the pain-free phase.

SIGNIFICANCE

This study shows that women with primary dysmenorrhoea (PDM) have decreased stability of dynamic network topological properties (DTP) and increased DTP variability in the pain-free phase. The altered DTP can be used to identify PDM in the pain-free phase. These findings demonstrate the presence of unstable characteristics in the whole network and disrupted pain-related neurocircuits, which might be used as potential classifiers for PDM in the pain-free phase. This study improves our knowledge of the brain mechanisms underlying PDM.

Data Science Methods for Nursing-Relevant Patient Outcomes and Clinical Processes: The 2019 Literature Year in Review

Data science continues to be recognized and used within healthcare due to the increased availability of large data sets and advanced analytics. It can be challenging for nurse leaders to remain apprised of this rapidly changing landscape. In this article, we describe our findings from a scoping literature review of papers published in 2019 that use data science to explore, explain, and/or predict 15 phenomena of interest to nurses. Fourteen of the 15 phenomena were associated with at least one paper published in 2019. We identified the use of many contemporary data science methods (eg, natural language processing, neural networks) for many of the outcomes. We found many studies exploring Readmissions and Pressure Injuries. The topics of Artificial Intelligence/Machine Learning Acceptance, Burnout, Patient Safety, and Unit Culture were poorly represented. We hope that the studies described in this article help readers: (1) understand the breadth and depth of data science's ability to improve clinical processes and patient outcomes that are relevant to nurses and (2) identify gaps in the literature that are in need of exploration.

Ecological Momentary Assessment of Non-Menstrual Pelvic Pain: Potential Pathways of Central Sensitization in Adolescents and Young Adults with and without Primary Dysmenorrhea

Purpose

Primary dysmenorrhea (PD; menstrual pain without an identified organic cause) has been proposed as a possible risk factor for the development of chronic pelvic pain, but the mechanism through which this process occurs is unknown. One possible mechanism is central sensitization – alterations in the central nervous system that increase responsiveness to pain leading to hypersensitivity. Repeated episodes of pain, such as those experienced over time with PD, may alter how the brain processes pain. Ecological momentary assessment (EMA; collection of data in real time in participants’ natural environments) is a novel data collection method that may help elucidate pain occurring during non-menstrual cycle phases.

Patients and Methods

The current observational study assessed the feasibility and acceptability of using EMA via text messages to collect pelvic pain data during menstrual and non-menstrual cycle phases in a community sample of adolescents and young adults (AYA) aged 16–24 years with and without PD and explored occurrence rates and intensity of non-menstrual pelvic pain (NMPP) in each of these groups.

Results

Thirty-nine AYA with PD and 53 healthy controls reported pelvic pain level via nightly text message. Global response rate was 98.5%, and all participants reported that the EMA protocol was acceptable. AYA with PD reported higher intensity (2.0 vs 1.6 on 0–10 numeric rating scale; p=0.003) and frequency (8.7% vs 3.1% of days; p=0.004) of NMPP compared to healthy controls.

Conclusion

The EMA protocol was feasible and acceptable. Though both the intensity and frequency of NMPP were low and at levels that would not typically warrant clinical assessment or intervention, these repeated nociceptive events may represent a potential mechanism contributing to the transition from cyclical to chronic pelvic pain in some individuals.

Effect of traditional Chinese medicine formula GeGen decoction on primary dysmenorrhea: A randomized controlled trial study

ETHNOPHARMACOLOGICAL RELEVANCE

GeGen Decoction, a well-known Chinese herbal formula, is widely used in China and other Asian countries to treat gynecological diseases, including primary dysmenorrhea. Pharmacological studies have confirmed that GeGen Decoction is able to inhibit spasmodic contractions of the uterus in vivo and in vitro.

AIM OF THE STUDY

The objective of this study is to examine the efficacy and safety of GeGen Decoction on primary dysmenorrheic patients.

METHODS

This was a randomized, double-blinded, placebo-controlled trial. GeGen Decoction or placebo was administered a week before the expected start of each cycle for three consecutive menstrual periods. Between-group differences in pain intensity were detected by visual analogue scale (VAS). In addition, serum levels of arginine vasopressin (AVP) and estrogen (E) were examined by enzyme-linked immunosorbent assay. Metabolomic analysis was further used to evaluate the influence of GeGen Decoction on the metabolomics of primary dysmenorrheic patients.

RESULTS

A total of 71 primary dysmenorrheic women were recruited and 30 participants met the criteria were randomized into GeGen Decoction or placebo group. After three consecutive menstrual cycles' treatments, the VAS score of the GeGen Decoction group was significantly lower than that of the placebo group. Both serum levels of AVP and E decreased after GeGen Decoction administration, while the placebo seemed to have little effect on either of the index. Moreover, after GeGen Decoction treatment, seven important metabolites were identified by metabolomic analysis compared to the placebo group. No abnormalities in blood biochemical and routine physical examination pre and post GeGen Decoction intervention were observed.

CONCLUSIONS

GeGen Decoction can remarkably relieve the severity of menstrual pain without obvious adverse effects. Its therapeutic effect on primary dysmenorrhea might be related to the regulation of pituitary hypothalamic ovarian hormones, and interfering with the metabolic change.

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.

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

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

Imaging the aged brain: pertinence and methods

The global population is ageing at an accelerating speed. The ability to perform working memory tasks together with rapid processing becomes increasingly difficult with increases in age. With increasing national average life spans and a rise in the prevalence of age-related disease, it is pertinent to discuss the unique perspectives that can be gained from imaging the aged brain. Differences in structure, function, blood flow, and neurovascular coupling are present in both healthy aged brains and in diseased brains and have not yet been explored to their full depth in contemporary imaging studies. Imaging methods ranging from optical imaging to magnetic resonance imaging (MRI) to newer technologies such as photoacoustic tomography each offer unique advantages and challenges in imaging the aged brain. This paper will summarize first the importance and challenges of imaging the aged brain and then offer analysis of potential imaging modalities and their representative applications. The potential breakthroughs in brain imaging are also envisioned.