Encoding of menstrual pain experience with theta oscillations in women with primary dysmenorrhea

Dysmenorrhea and psychological distress: a meta-analysis

Dysmenorrhea is characterized by pelvic pain associated with menstruation. Similar to people with other pain conditions, females who experience dysmenorrhea report increased psychological distress. However, the pooled magnitude of this association has not been quantified across studies. Accordingly, this meta-analytic review quantifies the magnitude of the associations between dysmenorrhea severity and psychological distress. We conducted a systematic search of the literature using PsycINFO, PubMed, CINHAL, Embase, and Web of Science. Analyzed studies provided observational data on dysmenorrhea severity and anxiety symptoms, depressive symptoms, and/or global psychological distress. A total of 44 studies were included, and three random-effects meta-analyses were conducted, with average pooled effect sizes calculated using Person's r. We found significant, positive associations between measures of dysmenorrhea severity and measures of depressive symptoms (r = 0.216), anxiety symptoms (r = 0.207), and global psychological distress (r = 0.311). Our review suggests that females with greater dysmenorrhea severity experience greater psychological distress. Future directions include defining a clinically meaningful dysmenorrhea severity threshold, understanding the mechanisms and directionality underlying the dysmenorrhea-psychological distress relationship, and designing and testing interventions to jointly address dysmenorrhea and psychological distress.

Changes in alpha, theta, and gamma oscillations in distinct cortical areas are associated with altered acute pain responses in chronic low back pain patients

Introduction

Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known.

Methods

We compared measured pain responses to peripheral mechanical stimuli applied to a site of chronic pain and at a pain-free site in participants suffering from chronic lower back pain (n = 15) versus pain-free control participants (n = 15) by analyzing behavioral and electroencephalographic (EEG) data.

Results

As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia.

Discussion

These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.

Scientific Knowledge Graph of Dysmenorrhea: A Bibliometric Analysis from 2001 to 2021

Purpose

This study aims to help researchers master the most active hotspots and trends quickly through bibliometric analysis in the field of dysmenorrhea.

Methods

We retrieved literature on Web of Science from 2001 to 2021, and bibliometric analysis software CiteSpace was used in combination with VOSviewer.

Results

We finally acquired 944 papers and an upward trend in articles continued in this field overall. Through the map, China contributed the most, followed by the USA and Turkey. For institutions, Beijing University of Chinese Medicine in China contributed the most, followed by National Yang-Ming University in Taiwan, China. Hsieh JC and Hellman KM were both the most prolific authors with 14 articles. Five major research groups, respectively, with Hsieh JC, Hellman KM, Zhu J, Liang F and Dun W were the key group. Dawood MY was the most dominant author and most frequently cited author. The Cochrane Database of Systematic Reviews Journal was the most productive, and the Fertility and Sterility Journal was the most cited. Advances in pathogenesis and management for primary dysmenorrhea written by Dawood MY was most cited and influential. Pathophysiology, the potential central mechanism, syndrome, evaluation index, diagnosis of adenomyosis-associated dysmenorrhea, treatment, etc., were the main trends and hotspots.

Conclusion

Dysmenorrhea research has received a lot of attention from scholars. Strengthening international cooperation may promote the development of this field. The pathophysiology of dysmenorrhea, its impact on public health and its treatment are current research hotspots and are likely to be the focus of future study.

Brain structural and functional changes during menstrual migraine: Relationships with pain

Objectives

Menstrual migraine (MM) is a special type of migraine associated with the ovarian cycle, which imposes a marked burden on female patients. However, the pathogenesis of MM is not completely understood. We investigated gray matter volume (GMV) and functional connectivity (FC) alterations in patients with MM to explore whether there are changes in resting-state FC (rsFC) in brain regions with structural GMV abnormalities and investigated their relevance to pain and concomitant symptoms.

Methods

Seventy-five patients with MM and 54 female healthy controls underwent functional magnetic resonance imaging and examination. The patients completed a patient’s headache diary, which included the frequency of migraine attacks, a visual analog scale for pain, a self-rating anxiety scale, and a self-rating depression scale. We used voxel-based morphometry (VBM) to examine the GMV differences between the MM and healthy control groups. The identified brain areas were selected as seeds to assess functional changes in the MM group. Correlation analysis between the altered VBM/rsFC and clinical outcomes was performed.

Results

Compared with healthy controls, patients with MM showed decreased GMV in the right anterior cingulum cortex (ACC) and increased GMV in the right superior parietal cortex. Pearson’s correlation analysis illustrated that only GMV in the right ACC was associated with visual analogue scale pain scores in the MM group. RsFC with the ACC as the seed showed that patients with MM exhibited increased FC between the ACC and the left inferior temporal gyrus, bilateral angular gyrus, and right precuneus. Correlation analysis showed that the change in FC between the right ACC and the right precuneus was positively correlated with headache frequency, and the change in FC between the right ACC and the right angular gyrus was positively correlated with the depression score.

Conclusion

Our results suggested that the ACC may be an important biomarker in MM, and its structural and functional impairments are significantly associated with the severity of pain and pain-related impairment of emotion in patients with MM. These findings demonstrated that headache-associated structural and functional abnormalities in the ACC may can provide integrative evidence on the physiological mechanisms of MM.

Prevalence of chronic pelvic pain and primary dysmenorrhea in women of reproductive age in Ecuador

Background

Chronic pelvic pain (CPP) and primary dysmenorrhoea are debilitating conditions that can impair the quality of life of affected women. These conditions are frequently neglected, delaying proper diagnosis and healthcare provision. This study aimed to estimate the prevalence of CPP and primary dysmenorrhoea in Ecuador and identify potential variables associated with their occurrence.

Methods

We conducted a cross-sectional survey in an urban neighbourhood of Quito, the capital of Ecuador. A total of 2397 participants of 14–49 years of age were included. The data were collected through questionnaires administered by trained interviewers.The crude and adjusted prevalence ratios were calculated using a log-binomial regression model. The correlation between pain intensity catastrophising of symptoms were statistically analysed.

Results

The prevalence of CPP and primary dysmenorrhoea was 9.8% and 8.9%, respectively. Irritative urinary symptoms, primary dysmenorrhoea, and underlying mental disorders were associated with CPP, while smoking, irritable bowel syndrome, sleep disturbance, dyspareunia, and mental disorders were associated with primary dysmenorrhoea.

Conclusions

The prevalence of CPP and primary dysmenorrhoea in Ecuador was similar to that in other Latin American countries. Primary dysmenorrhoea is a risk factor of CPP, and less than a quarter of women are undergoing treatment for the condition. Our findings reinforce the importance of healthcare interventions in anticipating the diagnosis of these conditions in women of reproductive age.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12905-022-01948-y.

The Effect of Long-Term Menstrual Pain on Large-Scale Brain Network in Primary Dysmenorrhea Patients

Purpose

Primary dysmenorrhea (PD) is a common gynecological disease, characterized by crampy and suprapubic pain occurring with menses. Growing evidences demonstrated that PD patients were associated with abnormalities in brain function and structure. However, little is known regarding whether the large-scale brain network changes in PD patients. The purpose of this study was to investigate the effect of long-term menstrual pain on large-scale brain network in PD patients using independent component analysis (ICA) method.

Methods

Twenty-eight PD patients (female, mean age, 24.25±1.00 years) and twenty-eight healthy controls (HCs) (mean age, 24.46±1.31 years), closely matched for age, sex, and education, underwent resting-state magnetic resonance imaging scans. ICA was applied to extract the resting-state networks (RSNs) in two groups. Then, two-sample t-tests were conducted to investigate different intranetwork FCs within RSNs and interactions among RSNs between two groups.

Results

Compared to the HC group, PD patients showed significant increased intra-network FCs within the auditory network (AN), sensorimotor network (SMN), right executive control network (RECN). However, PD patients showed significant decreased intra-network FCs within ventral default mode network (vDMN) and salience network (SN). Moreover, FNC analysis showed increased VN-AN and decreased VN-SMN functional connectivity between two groups.

Conclusion

Our study highlighted that PD patients had abnormal brain networks related to auditory, sensorimotor and higher cognitive network. Our results offer important insights into the altered large-scale brain network neural mechanisms of pain in PD patients.

Intensity-dependent modulation of cortical somatosensory processing during external, low-frequency peripheral nerve stimulation in humans

External low-frequency peripheral nerve stimulation (LFS) has been proposed as a novel method for neuropathic pain relief. Previous studies have reported that LFS elicits long-term depression-like effects on human pain perception when delivered at noxious intensities, whereas lower intensities are ineffective. To shed light on cortical regions mediating the effects of LFS, we investigated changes in somatosensory-evoked potentials (SEPs) during four LFS intensities. LFS was applied to the radial nerve (600 pulses, 1 Hz) of 24 healthy participants at perception (1 times), low (5 times), medium (10 times), and high intensities (15 times detection threshold). SEPs were recorded during LFS, and averaged SEPs in 10 consecutive 1-min epochs of LFS were analyzed using source dipole modeling. Changes in resting electroencephalography (EEG) were investigated after each LFS block. Source activity in the midcingulate cortex (MCC) decreased linearly during LFS, with greater attenuation at stronger LFS intensities, and in the ipsilateral operculo-insular cortex during the two lowest LFS stimulus intensities. Increased LFS intensities resulted in greater augmentation of contralateral primary sensorimotor cortex (SI/MI) activity. Stronger LFS intensities were followed by increased α (alpha, 9-11 Hz) band power in SI/MI and decreased θ (theta, 3-5 Hz) band power in MCC. Intensity-dependent attenuation of MCC activity with LFS is consistent with a state of long-term depression. Sustained increases in contralateral SI/MI activity suggests that effects of LFS on somatosensory processing may also be dependent on satiation of SI/MI. Further research could clarify if the activation of SI/MI during LFS competes with nociceptive processing in neuropathic pain.NEW & NOTEWORTHY Somatosensory-evoked potentials during low-frequency stimulation of peripheral nerves were examined at graded stimulus intensities. Low-frequency stimulation was associated with decreased responsiveness in the midcingulate cortex and increased responsiveness in primary sensorimotor cortex. Greater intensities were associated with increased midcingulate cortex θ band power and decreased sensorimotor cortex α band power. Results further previous evidence of an inhibition of somatosensory processing during and after low-frequency stimulation and point toward a potential augmentation of activity in somatosensory processing regions.

Machine Learning Analysis Reveals Abnormal Static and Dynamic Low-Frequency Oscillations Indicative of Long-Term Menstrual Pain in Primary Dysmenorrhea Patients

BACKGROUND

Previous neuroimaging studies demonstrated that patients with primary dysmenorrhea (PD) exhibited dysfunctional resting-state brain activity. However, alterations of dynamic brain activity in PD patients have not been fully characterized.

PURPOSE

Our study aimed to assess the effect of long-term menstrual pain on changes in static and dynamic neural activity in PD patients.

MATERIAL AND METHODS

Twenty-eight PD patients and 28 healthy controls (HCs) underwent resting-state magnetic resonance imaging scans. The amplitude of low-frequency fluctuations (ALFF) and dynamic ALFF was used as classification features in a machine learning approach involving a support vector machine (SVM) classifier.

RESULTS

Compared with the HC group, PD patients showed significantly increased ALFF values in the right cerebellum_crus2, right rectus, left supplementary motor area, right superior frontal gyrus, right supplementary motor area, and left superior frontal medial gyrus. Additionally, PD patients showed significantly decreased ALFF values in the right middle temporal gyrus and left thalamus. PD patients also showed significantly increased dALFF values in the right fusiform, Vermis_10, right middle temporal gyrus, right putamen, right insula, left thalamus, right precentral gyrus, and right postcentral gyrus. Based on ALFF and dALFF values, the SVM classifier achieved respective overall accuracies of 96.36% and 85.45% and respective areas under the curve of 1.0 and 0.95.

CONCLUSION

PD patients demonstrated abnormal static and dynamic brain activities that involved the default mode network, sensorimotor network, and pain-related subcortical nuclei. Moreover, ALFF and dALFF may offer sensitive biomarkers for distinguishing patients with PD from HCs.

A novel beamformer-based imaging of phase-amplitude coupling (BIPAC) unveiling the inter-regional connectivity of emotional prosody processing in women with primary dysmenorrhea

Objective. Neural communication or the interactions of brain regions play a key role in the formation of functional neural networks. A type of neural communication can be measured in the form of phase-amplitude coupling (PAC), which is the coupling between the phase of low-frequency oscillations and the amplitude of high-frequency oscillations. This paper presents a beamformer-based imaging method, beamformer-based imaging of PAC (BIPAC), to quantify the strength of PAC between a seed region and other brain regions.Approach. A dipole is used to model the ensemble of neural activity within a group of nearby neurons and represents a mixture of multiple source components of cortical activity. From ensemble activity at each brain location, the source component with the strongest coupling to the seed activity is extracted, while unrelated components are suppressed to enhance the sensitivity of coupled-source estimation.Main results. In evaluations using simulation data sets, BIPAC proved advantageous with regard to estimation accuracy in source localization, orientation, and coupling strength. BIPAC was also applied to the analysis of magnetoencephalographic signals recorded from women with primary dysmenorrhea in an implicit emotional prosody experiment. In response to negative emotional prosody, auditory areas revealed strong PAC with the ventral auditory stream and occipitoparietal areas in the theta-gamma and alpha-gamma bands, which may respectively indicate the recruitment of auditory sensory memory and attention reorientation. Moreover, patients with more severe pain experience appeared to have stronger coupling between auditory areas and temporoparietal regions.Significance. Our findings indicate that the implicit processing of emotional prosody is altered by menstrual pain experience. The proposed BIPAC is feasible and applicable to imaging inter-regional connectivity based on cross-frequency coupling estimates. The experimental results also demonstrate that BIPAC is capable of revealing autonomous brain processing and neurodynamics, which are more subtle than active and attended task-driven processing.

Pelvic Pain Alters Functional Connectivity Between Anterior Cingulate Cortex and Hippocampus in Both Humans and a Rat Model

The anterior cingulate cortex (ACC) and hippocampus (HIPP) are two key brain regions associated with pain and pain-related affective processing. However, whether and how pelvic pain alters the neural activity and connectivity of the ACC and HIPP under baseline and during social pain, and the underlying cellular and molecular mechanisms, remain unclear. Using functional magnetic resonance imaging (fMRI) combined with electrophysiology and biochemistry, we show that pelvic pain, particularly, primary dysmenorrhea (PDM), causes an increase in the functional connectivity between ACC and HIPP in resting-state fMRI, and a smaller reduction in connectivity during social exclusion in PDM females with periovulatory phase. Similarly, model rats demonstrate significantly increased ACC-HIPP synchronization in the gamma band, associating with reduced modulation by ACC-theta on HIPP-gamma and increased levels of receptor proteins and excitation. This study brings together human fMRI and animal research and enables improved therapeutic strategies for ameliorating pain and pain-related affective processing.

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.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Biological and behavioral markers of pain following nerve injury in humans

The evolution of peripheral and central changes following a peripheral nerve injury imply the onset of afferent signals that affect the brain. Changes to inflammatory processes may contribute to peripheral and central alterations such as altered psychological state and are not well characterized in humans. We focused on four elements that change peripheral and central nervous systems following ankle injury in 24 adolescent patients and 12 age-sex matched controls. Findings include (a) Changes in tibial, fibular, and sciatic nerve divisions consistent with neurodegeneration; (b) Changes within the primary motor and somatosensory areas as well as higher order brain regions implicated in pain processing; (c) Increased expression of fear of pain and pain reporting; and (d) Significant changes in cytokine profiles relating to neuroinflammatory signaling pathways. Findings address how changes resulting from peripheral nerve injury may develop into chronic neuropathic pain through changes in the peripheral and central nervous system.

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

To develop a machine learning model to investigate the discriminative power of whole-brain gray-matter (GM) images derived from primary dysmenorrhea (PDM) women and healthy controls (HCs) during the pain-free phase and further evaluate the predictive ability of contributing features in predicting the variance in menstrual pain intensity. Sixty patients with PDM and 54 matched female HCs were recruited from the local university. All participants underwent the head and pelvic magnetic resonance imaging scans to calculate GM volume and myometrium-apparent diffusion coefficient (ADC) during their periovulatory phase. Questionnaire assessment was also conducted. A support vector machine algorithm was used to develop the classification model. The significance of model performance was determined by the permutation test. Multiple regression analysis was implemented to explore the relationship between discriminative features and intensity of menstrual pain. Demographics and myometrium ADC-based classifications failed to pass the permutation tests. Brain-based classification results demonstrated that 75.44% of subjects were correctly classified, with 83.33% identification of the patients with PDM (P < 0.001). In the regression analysis, demographical indicators and myometrium ADC accounted for a total of 29.37% of the variance in pain intensity. After regressing out these factors, GM features explained 60.33% of the remaining variance. Our results suggested that GM volume can be used to discriminate patients with PDM and HCs during the pain-free phase, and neuroimaging features can further predict the variance in the intensity of menstrual pain, which may provide a potential imaging marker for the assessment of menstrual pain intervention.

Chronic stress increases pain sensitivity via activation of the rACC-BLA pathway in rats

Exposure to chronic stress can produce maladaptive neurobiological changes in pathways associated with pain processing, which may cause stress-induced hyperalgesia (SIH). However, the underlying mechanisms still remain largely unknown. In previous studies, we have reported that the amygdala is involved in chronic forced swim (FS) stress-induced depressive-like behaviors and the exacerbation of neuropathic pain in rats, of which, the basolateral amygdala (BLA) and the central nucleus of the amygdala (CeA) are shown to play important roles in the integration of affective and sensory information including nociception. Here, using in vivo multichannel recording from rostal anterior cingulate cortex (rACC) and BLA, we found that chronic FS stress (CFSS) could increase the pain sensitivity of rats in response to low intensity innoxious stimuli (LIS) and high intensity noxious stimuli (HNS) imposed upon the hindpaw, validating the occurrence of SIH in stressed rats. Moreover, we discovered that CFSS not only induced an increased activity of rACC neuronal population but also produced an augmented field potential power (FPP) of rACC local field potential (LFP), especially in low frequency theta band as well as in high frequency low gamma band ranges, both at the baseline state and under LIS and HNS conditions. In addition, by using a cross-correlation method and a partial directed coherence (PDC) algorithm to analyze the LFP oscillating activity in rACC and BLA, we demonstrated that CFSS could substantially promote the synchronization between rACC and BLA regions, and also enhanced the neural information flow from rACC to BLA. We conclude that exposure of chronic FS stress to rats could result in an increased activity of rACC neuronal population and promote the functional connectivity and the synchronization between rACC and BLA regions, and also enhance the pain-related neural information flow from rACC to BLA, which likely underlie the pathogenesis of SIH.

Interactions of BDNF Val66Met Polymorphism and Menstrual Pain on Brain Complexity

The irregularity and uncertainty of neurophysiologic signals across different time scales can be regarded as neural complexity, which is related to the adaptability of the nervous system and the information processing between neurons. We recently reported general loss of brain complexity, as measured by multiscale sample entropy (MSE), at pain-related regions in females with primary dysmenorrhea (PDM). However, it is unclear whether this loss of brain complexity is associated with inter-subject genetic variations. Brain-derived neurotrophic factor (BDNF) is a widely expressed neurotrophin in the brain and is crucial to neural plasticity. The BDNF Val66Met single-nucleotide polymorphism (SNP) is associated with mood, stress, and pain conditions. Therefore, we aimed to examine the interactions of BDNF Val66Met polymorphism and long-term menstrual pain experience on brain complexity. We genotyped BDNF Val66Met SNP in 80 PDM females (20 Val/Val, 31 Val/Met, 29 Met/Met) and 76 healthy female controls (25 Val/Val, 36 Val/Met, 15 Met/Met). MSE analysis was applied to neural source activity estimated from resting-state magnetoencephalography (MEG) signals during pain-free state. We found that brain complexity alterations were associated with the interactions of BDNF Val66Met polymorphism and menstrual pain experience. In healthy female controls, Met carriers (Val/Met and Met/Met) demonstrated lower brain complexity than Val/Val homozygotes in extensive brain regions, suggesting a possible protective role of Val/Val homozygosity in brain complexity. However, after experiencing long-term menstrual pain, the complexity differences between different genotypes in healthy controls were greatly diminished in PDM females, especially in the limbic system, including the hippocampus and amygdala. Our results suggest that pain experience preponderantly affects the effect of BDNF Val66Met polymorphism on brain complexity. The results of the present study also highlight the potential utilization of resting-state brain complexity for the development of new therapeutic strategies in patients with chronic pain.

Local field potential decoding of the onset and intensity of acute pain in rats

Pain is a complex sensory and affective experience. The current definition for pain relies on verbal reports in clinical settings and behavioral assays in animal models. These definitions can be subjective and do not take into consideration signals in the neural system. Local field potentials (LFPs) represent summed electrical currents from multiple neurons in a defined brain area. Although single neuronal spike activity has been shown to modulate the acute pain, it is not yet clear how ensemble activities in the form of LFPs can be used to decode the precise timing and intensity of pain. The anterior cingulate cortex (ACC) is known to play a role in the affective-aversive component of pain in human and animal studies. Few studies, however, have examined how neural activities in the ACC can be used to interpret or predict acute noxious inputs. Here, we recorded in vivo extracellular activity in the ACC from freely behaving rats after stimulus with non-noxious, low-intensity noxious, and high-intensity noxious stimuli, both in the absence and chronic pain. Using a supervised machine learning classifier with selected LFP features, we predicted the intensity and the onset of acute nociceptive signals with high degree of precision. These results suggest the potential to use LFPs to decode acute pain.