Differential alteration of fMRI signal variability in the ascending trigeminal somatosensory and pain modulatory pathways in 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.

Brain-wide pleiotropy investigation of alcohol drinking and tobacco smoking behaviors

To investigate the pleiotropic mechanisms linking brain structure and function to alcohol drinking and tobacco smoking, we integrated genome-wide data generated by the GWAS and Sequencing Consortium of Alcohol and Nicotine use (GSCAN; up to 805,431 participants) with information related to 3,935 brain imaging-derived phenotypes (IDPs) available from UK Biobank (N=33,224). We observed global genetic correlation of smoking behaviors with white matter hyperintensities, the morphology of the superior longitudinal fasciculus, and the mean thickness of pole-occipital. With respect to the latter brain IDP, we identified a local genetic correlation with age at which the individual began smoking regularly (hg38 chr2:35,895,678-36,640,246: rho=1, p=1.01×10 -5 ). This region has been previously associated with smoking initiation, educational attainment, chronotype, and cortical thickness. Our genetically informed causal inference analysis using both latent causal variable approach and Mendelian randomization linked the activity of prefrontal and premotor cortex and that of superior and inferior precentral sulci, and cingulate sulci to the number of alcoholic drinks per week (genetic causality proportion, gcp=0.38, p=8.9×10 -4 , rho=-0.18±0.07; inverse variance weighting, IVW beta=-0.04, 95%CI=-0.07 - -0.01). This relationship could be related to the role of these brain regions in the modulation of reward-seeking motivation and the processing of social cues. Overall, our brain-wide investigation highlighted that different pleiotropic mechanisms likely contribute to the relationship of brain structure and function with alcohol drinking and tobacco smoking, suggesting decision-making activities and chemosensory processing as modulators of propensity towards alcohol and tobacco consumption.

Aberrant concordance among dynamics of spontaneous brain activity in patients with migraine without aura: A multivariate pattern analysis study

Background

Alterations in the static and dynamic characteristics of spontaneous brain activity have been extensively studied to investigate functional brain changes in migraine without aura (MwoA). However, alterations in concordance among the dynamics of spontaneous brain activity in MwoA remain largely unknown. This study aimed to determine the possibilities of diagnosis based on the concordance indices.

Methods

Resting-state functional MRI scans were performed on 32 patients with MwoA and 33 matched healthy controls (HCs) in the first cohort, as well as 36 patients with MwoA and 32 HCs in the validation cohort. The dynamic indices including fractional amplitude of low-frequency fluctuation, regional homogeneity, voxel-mirrored homotopic connectivity, degree centrality and global signal connectivity were analyzed. We calculated the concordance of grey matter volume-wise (across voxels) and voxel-wise (across time windows) to quantify the degree of integration among different functional levels represented by these dynamic indices. Subsequently, the voxel-wise concordance alterations were analyzed as features for multi-voxel pattern analysis (MVPA) utilizing the support vector machine.

Results

Compared with that of HCs, patients with MwoA had lower whole-grey matter volume-wise concordance, and the mean value of volume-wise concordance was negatively correlated with the frequency of migraine attacks. The MVPA results revealed that the most discriminative brain regions were the right thalamus, right cerebellar Crus II, left insula, left precentral gyrus, right cuneus, and left inferior occipital gyrus.

Conclusions

Concordance alterations in the dynamics of spontaneous brain activity in brain regions could be an important feature in the identification of patients with MwoA.

High-definition tDCS over primary motor cortex modulates brain signal variability and functional connectivity in episodic migraine

OBJECTIVE

This study investigated how high-definition transcranial direct current stimulation (HD-tDCS) over the primary motor cortex (M1) affects brain signal variability and functional connectivity in the trigeminal pain pathway, and their association with changes in migraine attacks.

METHODS

Twenty-five episodic migraine patients were randomized for ten daily sessions of active or sham M1 HD-tDCS. Resting-state blood-oxygenation-level-dependent (BOLD) signal variability and seed-based functional connectivity were assessed pre- and post-treatment. A mediation analysis was performed to test whether BOLD signal variability mediates the relationship between treatment group and moderate-to-severe headache days.

RESULTS

The active M1 HD-tDCS group showed reduced BOLD variability in the spinal trigeminal nucleus (SpV) and thalamus, but increased variability in the rostral anterior cingulate cortex (rACC) compared to the sham group. Connectivity decreased between medial pulvinar-temporal pole, medial dorsal-precuneus, and the ventral posterior medial nucleus-SpV, but increased between the rACC-amygdala, and the periaqueductal gray-parahippocampal gyrus. Changes in medial pulvinar variability mediated the reduction in moderate-to-severe headache days at one-month post-treatment.

CONCLUSIONS

M1 HD-tDCS alters BOLD signal variability and connectivity in the trigeminal somatosensory and modulatory pain system, potentially alleviating migraine headache attacks.

SIGNIFICANCE

M1 HD-tDCS realigns brain signal variability and connectivity in migraineurs closer to healthy control levels.

Modulation of temporal and occipital cortex by acupuncture in non-menstrual MWoA patients: a rest BOLD fMRI study

OBJECTIVE

To investigate the changes in amplitude of low-frequency fluctuation (ALFF) and degree centrality (DC) values before and after acupuncture in young women with non-menstrual migraine without aura (MWoA) through rest blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI).

METHODS

Patients with non-menstrual MWoA (Group 1, n = 50) and healthy controls (Group 2, n = 50) were recruited. fMRI was performed in Group 1 at 2 time points: before acupuncture (time point 1, TP1); and after the end of all acupuncture sessions (time point 2, TP2), and performed in Group 2 as a one-time scan. Patients in Group 1 were assessed with the Migraine Disability Assessment Questionnaire (MIDAS) and the Short-Form McGill Pain Questionnaire (SF-MPQ) at TP1 and TP2 after fMRI was performed. The ALFF and DC values were compared within Group 1 at two time points and between Group 1 and Group2. The correlation between ALFF and DC values with the statistical differences and the clinical scales scores were analyzed.

RESULTS

Brain activities increased in the left fusiform gyrus and right angular gyrus, left middle occipital gyrus, and bilateral prefrontal cortex and decreased in left inferior parietal lobule in Group 1, which had different ALFF values compared with Group 2 at TP1. The bilateral fusiform gyrus, bilateral inferior temporal gyrus and right middle temporal gyrus increased and right angular gyrus, right superior marginal gyrus, right inferior parietal lobule, right middle occipital gyrus, right superior frontal gyrus, right middle frontal gyrus, right anterior central gyrus, and right supplementary motor area decreased in activity in Group 1 had different DC values compared with Group 2 at TP1. ALFF and DC values of right inferior temporal gyrus, right fusiform gyrus and right middle temporal gyrus were decreased in Group1 at TP1 compared with TP2. ALFF values in the left middle occipital area were positively correlated with the pain degree at TP1 in Group1 (correlation coefficient r, r = 0.827, r = 0.343; P < 0.01, P = 0.015). The DC values of the right inferior temporal area were positively correlated with the pain degree at TP1 in Group 1 (r = 0.371; P = 0.008).

CONCLUSION

Spontaneous brain activity and network changes in young women with non-menstrual MwoA were altered by acupuncture. The right temporal area may be an important target for acupuncture modulated brain function in young women with non-menstrual MwoA.

Brain connectome-based imaging markers for identifiable signature of migraine with and without aura

Background

Cortical spreading depression (CSD) has been considered the prominent theory for migraine with aura (MwA). However, it is also argued that CSD can exist in patients in a silent state, and not manifest as aura. Thus, the MwA classification based on aura may be questionable. This study aimed to capture whole-brain connectome-based imaging markers with identifiable signatures for MwA and migraine without aura (MwoA).

Methods

A total of 88 migraine patients (32 MwA) and 49 healthy controls (HC) underwent a diffusion tensor imaging and resting-state functional magnetic resonance imaging scan. The whole-brain structural connectivity (SC) and functional connectivity (FC) analysis was employed to extract imaging features. The extracted features were subjected to an all-relevant feature selection process within cross-validation loops to pinpoint attributes demonstrating substantial efficacy for patient categorization. Based on the identified features, the predictive ability of the random forest classifiers constructed with the 88 migraine patients' sample was tested using an independent sample of 32 migraine patients (eight MwA).

Results

Compared to MwoA and HC, MwA showed two reduced SC and six FC (five increased and one reduced) features [all P<0.01, after false discovery rate (FDR) correction], involving frontal areas, temporal areas, visual areas, amygdala, and thalamus. A total of four imaging features were significantly correlated with clinical rating scales in all patients (r=-0.38 to 0.47, P<0.01, after FDR correction). The predictive ability of the random forest classifiers achieved an accuracy of 78.1% in the external sample to identify MwA.

Conclusions

The whole-brain connectivity features in our results may serve as connectome-based imaging markers for MwA identification. The alterations of SC and FC strength provide possible evidence in further understanding the heterogeneity and mechanism of MwA which may help for patient-specific decision-making.

Abnormal preoperative fMRI signal variability in the pain ascending pathway is associated with the postoperative axial pain intensity in degenerative cervical myelopathy patients

BACKGROUND CONTEXT

The moment-to-moment variability of resting-state brain activity has been suggested to play an active role in chronic pain.

PURPOSE

To investigate preoperative alterations in regional blood-oxygen-level-dependent signal variability (BOLDsv) and inter-regional dynamic functional connectivity (dFC) in individuals with degenerative cervical myelopathy (DCM), and their potential association with postoperative axial pain severity.

STUDY DESIGN

Cross-sectional study.

PATIENT SAMPLE

Resting-state functional magnetic resonance imaging was obtained in 42 migraine individuals and 40 healthy controls (HCs).

OUTCOME MEASURES

We calculated the standard deviation (SD) of the BOLD time-series at each voxel and the SD and mean of the dynamic conditional correlation between the brain regions which showed significant group differences in BOLDsv.

METHODS

A group comparison was conducted using whole-brain voxel-wise analysis of the standard deviation (SD) of the BOLD time-series which was a measure of the BOLDsv. The brain areas displaying notable group discrepancies in BOLDsv were utilized to outline regions of interest (ROIs). To determine the strength/variability of the dFC, the mean and SD of the dynamic conditional correlation were calculated within these ROIs. Moreover, the postoperative axial pain (PAP) severity of patients was evaluated.

RESULTS

Our results revealed that DCM patients with postoperative axial pain (PAP) demonstrated considerably increased BOLDsv in the bilateral thalamus and right insular, but significantly lower BOLDsv in the right S1. By applying dynamic functional connectivity (dFC) analysis, we found that DCM patients with PAP exhibited greater fluctuation of dFC in the thalamo-cortical pathway (specifically, thalamus-S1), when compared to HCs and patients without PAP (nPAP). Lastly, we established that dysfunctional BOLDsv and dFC in the ascending pain pathway were positively associated with the severity of PAP in DCM patients.

CONCLUSION

Our results indicate a potential correlation between impaired pain ascending pathway and postoperative axial pain in DCM patients. These findings could potentially spark novel treatment approaches for individuals experiencing preoperative axial pain.

Abnormal Thalamo-Cortical Interactions in Overlapping Communities of Migraine: An Edge Functional Connectivity Study

OBJECTIVE

Migraine has been demonstrated to exhibit abnormal functional connectivity of large-scale brain networks, which is closely associated with its pathophysiology and has not yet been explored by edge functional connectivity. We used an edge-centric approach combined with motif analysis to evaluate higher-order communication patterns of brain networks in migraine.

METHODS

We investigated edge-centric metrics in 108 interictal migraine patients and 71 healthy controls. We parcellated the brain into networks using independent component analysis. We applied edge graph construction, k-means clustering, community overlap detection, graph-theory-based evaluations, and clinical correlation analysis. We conducted motif analysis to explore the interactions among regions, and a classification model to test the specificity of edge-centric results.

RESULTS

The normalized entropy of lateral thalamus was significantly increased in migraine, which was positively correlated with the baseline headache duration, and negatively correlated with headache duration reduction following preventive medications at 3-month follow-up. Network-wise entropy of the sensorimotor network was significantly elevated in migraine. The community similarity between lateral thalamus and postcentral gyrus was enhanced in migraine. Migraine patients showed overrepresented L-shape and diverse motifs, and underrepresented forked motifs with lateral thalamus serving as the reference node. Furthermore, migraine patients presented with overrepresented L-shape triads, where the postcentral gyrus shared different edges with the lateral thalamus. The classification model showed that entropy of the lateral thalamus had the highest discriminative power, with an area under the curve of 0.86.

INTERPRETATION

Our findings indicated an abnormal higher-order thalamo-cortical communication pattern in migraine patients. The thalamo-cortical-somatosensory disturbance of concerted working may potentially lead to aberrant information flow and deficit pain processing of migraine. ANN NEUROL 2023;94:1168-1181.

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.

Functional MRI and Diffusion Tensor Imaging in Migraine: A Review of Migraine Functional and White Matter Microstructural Changes

Migraine is a complex and heterogenous disorder whose disease mechanisms remain disputed. This narrative review summarizes functional MRI (fMRI) and diffusion tensor imaging (DTI) findings and interprets their association with migraine symptoms and subtype to support and expand our current understanding of migraine pathophysiology. Our PubMed search evaluated and included fMRI and DTI studies involving comparisons between migraineurs vs healthy controls, migraineurs with vs without aura, and episodic vs chronic migraineurs. Migraineurs demonstrate changes in functional connectivity (FC) and regional activation in numerous pain-related networks depending on migraine phase, presence of aura, and chronicity. Changes to diffusion indices are observed in major cortical white matter tracts extending to the brainstem and cerebellum, more prominent in chronic migraine and associated with FC changes. Reported changes in FC and regional activation likely relate to pain processing and sensory hypersensitivities. Diffuse white matter microstructural changes in dysfunctional cortical pain and sensory pathways complement these functional differences. Interpretations of reported fMRI and DTI measure trends have not achieved a clear consensus due to inconsistencies in the migraine neuroimaging literature. Future fMRI and DTI studies should establish and implement a uniform methodology that reproduces existing results and directly compares migraineurs with different subtypes. Combined fMRI and DTI imaging may provide better pathophysiological explanations for nonspecific FC and white matter microstructural differences.

A state-of-the-art review of functional magnetic resonance imaging technique integrated with advanced statistical modeling and machine learning for primary headache diagnosis

Primary headache is a very common and burdensome functional headache worldwide, which can be classified as migraine, tension-type headache (TTH), trigeminal autonomic cephalalgia (TAC), and other primary headaches. Managing and treating these different categories require distinct approaches, and accurate diagnosis is crucial. Functional magnetic resonance imaging (fMRI) has become a research hotspot to explore primary headache. By examining the interrelationships between activated brain regions and improving temporal and spatial resolution, fMRI can distinguish between primary headaches and their subtypes. Currently the most commonly used is the cortical brain mapping technique, which is based on blood oxygen level-dependent functional magnetic resonance imaging (BOLD-fMRI). This review sheds light on the state-of-the-art advancements in data analysis based on fMRI technology for primary headaches along with their subtypes. It encompasses not only the conventional analysis methodologies employed to unravel pathophysiological mechanisms, but also deep-learning approaches that integrate these techniques with advanced statistical modeling and machine learning. The aim is to highlight cutting-edge fMRI technologies and provide new insights into the diagnosis of primary headaches.

Insights into migraine attacks from neuroimaging

Migraine is one of the most common neurological diseases and it has a huge social and personal impact. Although head pain is the core symptom, individuals with migraine can have a plethora of non-headache symptoms that precede, accompany, or follow the pain. Neuroimaging studies have shown that the involvement of specific brain areas can explain many of the symptoms reported during the different phases of migraine. Recruitment of the hypothalamus, pons, spinal trigeminal nucleus, thalamus, and visual and pain-processing cortical areas starts during the premonitory phase and persists through the headache phase, contributing to the onset of pain and associated symptoms. Once the pain stops, the involvement of most brain areas ends, although the pons, hypothalamus, and visual cortex remain active after acute treatment intake and resolution of migraine symptoms. A better understanding of the correlations between imaging findings and migraine symptomatology can provide new insight into migraine pathophysiology and the mechanisms of novel migraine-specific treatments.

A multimodal meta-analysis of gray matter alterations in trigeminal neuralgia

Background

Brain gray matter alterations in patients with trigeminal neuralgia (TN) have been detected in prior neuroimaging studies, but the results are heterogeneous. The current study conducted coordinate-based meta-analyses across neuroimaging studies, aiming to find the pattern of brain anatomic and functional alterations in patients with TN.

Methods

We performed a systematic literature search of PubMed, Embase, and Web of Science to identify relevant publications. A multimodal meta-analysis for whole-brain voxel-based morphometry (VBM) studies and functional imaging studies in TN was performed using anisotropic effect size-based signed differential mapping.

Results

The meta-analysis comprised 10 VBM studies with 398 TN patients and 275 healthy controls, and 13 functional magnetic resonance imaging studies with 307 TN patients and 264 healthy controls. The multimodal meta-analysis showed conjoint structural and functional brain alterations in the right fusiform gyrus and inferior temporal gyrus, bilateral thalamus, left superior temporal gyrus, left insula, and inferior frontal gyrus. The unimodal meta-analysis showed decreased gray matter volume alone in the left putamen, left postcentral gyrus, and right amygdala as well as only functional abnormalities in the left cerebellum, bilateral precuneus, and left middle temporal gyrus.

Conclusion

This meta-analysis revealed overlapping anatomic and functional gray matter abnormalities in patients with TN, which may help provide new insights into the neuropathology and potential treatment biomarkers of TN.

Impairments to the multisensory integration brain regions during migraine chronification: correlation with the vestibular dysfunction

Objectives

Migraine is often combined with vestibular dysfunction, particularly in patients with chronic migraine (CM). However, the pathogenesis of migraine chronification leading to vestibular dysfunction is not fully understood. The current study investigated whether structural or functional impairments to the brain during migraine chronification could be associated with vestibular dysfunction development.

Methods

The eligible participants underwent clinical assessment and magnetic resonance imaging (MRI) scans. Voxel-based morphometry (VBM) determined structural impairment by evaluating alterations in gray matter volume (GMV). Functional impairment was assessed by the mean amplitude of low-frequency fluctuation (mALFF). Furthermore, the resting-state functional connectivity (rsFC) of regions possessing impairment was examined with a seed-based approach. We also analyzed the correlations between altered neuroimaging features with clinical variables and performed multiple linear regression.

Results

Eighteen CM patients, 18 episodic migraine (EM) patients, and 18 healthy controls (HCs) were included in this study. A one-way ANOVA indicated the group differences in mALFF. These were located within right supramarginal gyrus (SMG), left angular gyrus (AG), middle frontal gyrus (MFG), left middle occipital gyrus (MOG), right rolandic operculum (Rol) and left superior parietal gyrus (SPG). During rsFC analysis, the CM group had more enhanced rsFC of left SPG with left MOG than the EM and HC groups. The EM group revealed enhanced rsFC of left SPG with left AG than the CM and HC groups. In multiple linear regression, after controlling for age, body mass index (BMI) and disease duration, the rsFC of left SPG with left MOG (β = 48.896, p = 0.021) was found to predict the total Dizziness Handicap Inventory (DHI) score with an explained variance of 25.1%. Moreover, the rsFC of left SPG with left MOG (β = 1.253, p = 0.003) and right SMG (β = -1.571, p = 0.049) were significant predictors of migraine frequency, accounting for a total explained variance of 73.8%.

Conclusion

The functional impairments due to migraine chronification are primarily concentrated in the multisensory integration-related brain regions. Additionally, the rsFC of SPG with MOG can predict the frequency of migraine and the degree of vestibular dysfunction. Therefore, these neuroimaging features could be potential mechanisms and therapeutic targets for developing vestibular dysfunction in migraine.

Classifying migraine using PET compressive big data analytics of brain's μ-opioid and D2/D3 dopamine neurotransmission

Introduction: Migraine is a common and debilitating pain disorder associated with dysfunction of the central nervous system. Advanced magnetic resonance imaging (MRI) studies have reported relevant pathophysiologic states in migraine. However, its molecular mechanistic processes are still poorly understood in vivo. This study examined migraine patients with a novel machine learning (ML) method based on their central μ-opioid and dopamine D2/D3 profiles, the most critical neurotransmitters in the brain for pain perception and its cognitive-motivational interface. Methods: We employed compressive Big Data Analytics (CBDA) to identify migraineurs and healthy controls (HC) in a large positron emission tomography (PET) dataset. 198 PET volumes were obtained from 38 migraineurs and 23 HC during rest and thermal pain challenge. 61 subjects were scanned with the selective μ-opioid receptor (μOR) radiotracer [11C]Carfentanil, and 22 with the selective dopamine D2/D3 receptor (DOR) radiotracer [11C]Raclopride. PET scans were recast into a 1D array of 510,340 voxels with spatial and intensity filtering of non-displaceable binding potential (BPND), representing the receptor availability level. We then performed data reduction and CBDA to power rank the predictive brain voxels. Results: CBDA classified migraineurs from HC with accuracy, sensitivity, and specificity above 90% for whole-brain and region-of-interest (ROI) analyses. The most predictive ROIs for μOR were the insula (anterior), thalamus (pulvinar, medial-dorsal, and ventral lateral/posterior nuclei), and the putamen. The latter, putamen (anterior), was also the most predictive for migraine regarding DOR D2/D3 BPND levels. Discussion: CBDA of endogenous μ-opioid and D2/D3 dopamine dysfunctions in the brain can accurately identify a migraine patient based on their receptor availability across key sensory, motor, and motivational processing regions. Our ML-based findings in the migraineur's brain neurotransmission partly explain the severe impact of migraine suffering and associated neuropsychiatric comorbidities.

Migraine: from pathophysiology to treatment

Migraine is an extremely disabling, common neurological disorder characterized by a complex neurobiology, involving a series of central and peripheral nervous system areas and networks. A growing increase in the understanding of migraine pathophysiology in recent years has facilitated translation of that knowledge into novel treatments, which are currently becoming available to patients in many parts of the world and are substantially changing the clinical approach to the disease. In the first part of this review, we will provide an up to date overview of migraine pathophysiology by analyzing the anatomy and function of the main regions involved in the disease, focusing on how these give rise to the plethora of symptoms characterizing the attacks and overall disease. The second part of the paper will discuss the novel therapeutic agents that have emerged for the treatment of migraine, including molecules targeting calcitonin gene-related peptide (gepants and monoclonal antibodies), serotonin 5-HT_1F receptor agonists (ditans) and non-invasive neuromodulation, as well as providing a brief overview of new evidence for classic migraine treatments.

Potential predictors for the efficacy of non-steroidal anti-inflammatory drugs in patients with migraine

Objectives

To explore potential predictors of the efficacy of non-steroidal anti-inflammatory drugs (NSAIDs) in patients with migraine.

Methods

Consecutive migraine patients were recruited and divided into responders and non-responders to NSAIDs according to follow-up for at least three months. Demographic data, migraine-related disabilities and characteristics, and psychiatric comorbidities were evaluated and used to build multivariable logistic regression models. Subsequently, we generated receiver operating characteristic (ROC) curves to explore the performance of these traits in predicting NSAIDs efficacy.

Results

A total of 567 patients with migraine who completed at least three months of follow-up were enrolled. In the multivariate regression analysis, five factors were identified as potential predictors for NSAIDs efficacy in treating migraine. Namely, attack duration (odds ratio (OR) = 0.959; p < 0.001), headache impact (OR = 0.966; p = 0.015), depression (OR = 0.889; p < 0.001), anxiety (OR = 0.748; p < 0.001), and education level (OR = 1.362; p < 0.001) were associated with response to NSAIDs treatment. The area under the curve, sensitivity, and specificity combining these five factors for predicting the efficacy of NSAIDs were 0.834, 0.909 and 0.676.

Conclusions

These findings suggest that migraine-related and psychiatric factors are associated with the response to NSAIDs in migraine management. Identifying such key factors may help to optimize individualized migraine management strategy.

Functional magnetic resonance imaging in migraine: A systematic review

BACKGROUND

Migraine is a highly prevalent primary headache disorder. Despite a high burden of disease, key disease mechanisms are not entirely understood. Functional magnetic resonance imaging is an imaging method using the blood-oxygen-level-dependent signal, which has been increasingly used in migraine research over recent years. This systematic review summarizes recent findings employing functional magnetic resonance imaging for the investigation of migraine.

METHODS

We conducted a systematic search and selection of functional magnetic resonance imaging applications in migraine from April 2014 to December 2021 (PubMed and references of identified articles according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines). Methodological details and main findings were extracted and synthesized.

RESULTS

Out of 224 articles identified, 114 were included after selection. Repeatedly emerging structures of interest included the insula, brainstem, limbic system, hypothalamus, thalamus, and functional networks. Assessment of functional brain changes in response to treatment is emerging, and machine learning has been used to investigate potential functional magnetic resonance imaging-based markers of migraine.

CONCLUSIONS

A wide variety of functional magnetic resonance imaging-based metrics were found altered across the brain for heterogeneous migraine cohorts, partially correlating with clinical parameters and supporting the concept to conceive migraine as a brain state. However, a majority of findings from previous studies have not been replicated, and studies varied considerably regarding image acquisition and analyses techniques. Thus, while functional magnetic resonance imaging appears to have the potential to advance our understanding of migraine pathophysiology, replication of findings in large representative datasets and precise, standardized reporting of clinical data would likely benefit the field and further increase the value of observations.

The anatomy of head pain

Pain-sensitive structures in the head and neck, including the scalp, periosteum, meninges, and blood vessels, are innervated predominantly by the trigeminal and upper cervical nerves. The trigeminal nerve supplies most of the sensation to the head and face, with the ophthalmic division (V1) providing innervation to much of the supratentorial dura mater and vessels. This creates referral patterns for pain that may be misleading to clinicians and patients, as described by studies involving awake craniotomies and stimulation with electrical and mechanical stimuli. Most brain parenchyma and supratentorial vessels refer pain to the ipsilateral V1 territory, and less commonly the V2 or V3 region. The upper cervical nerves provide innervation to the posterior scalp, while the periauricular region and posterior fossa are territories with shared innervation. Afferent fibers that innervate the head and neck send nociceptive input to the trigeminocervical complex, which then projects to additional pain processing areas in the brainstem, thalamus, hypothalamus, and cortex. This chapter discusses the pain-sensitive structures in the head and neck, including pain referral patterns for many of these structures. It also provides an overview of peripheral and central nervous system structures responsible for transmitting and interpreting these nociceptive signals.

Efficacy of repetitive transcranial magnetic stimulation on chronic migraine: A meta-analysis

Introduction

Migraine is a neurovascular disorder that affects the quality of life of more than 1 billion people worldwide. Repetitive transcranial magnetic stimulation (rTMS) is a neuromodulation tool that uses pulsed magnetic fields to modulate the cerebral cortex. This meta-analysis ascertained the therapeutic or preventive effect of rTMS on chronic migraine.

Methods

We performed a database search of PubMed, Web of Science, Embase, and the Cochrane Library from January 2004 to December 2021. Eligible studies included randomized controlled studies of the analgesic effects of rTMS in patients with chronic migraine.

Results

Eight studies were included. Random effects analysis showed an effect size of -1.13 [95% confidence interval (CI): -1.69 to -0.58] on the frequency of migraine attacks, indicating that rTMS was more effective for decreasing migraine attacks than the sham rTMS.

Conclusions

The meta-analysis revealed that rTMS is an effective approach for reducing migraine attack when the dorsolateral prefrontal cortex was stimulated. However, rTMS may not be suggested as a method to reduce the pain level.

Systematic review registration

http://www.crd.york.ac.uk/PROSPERO/, identifier: CRD42021228344.

Altered brain functional activity and connectivity in bone metastasis pain of lung cancer patients: A preliminary resting-state fMRI study

Bone metastasis pain (BMP) is one of the most prevalent symptoms among cancer survivors. The present study aims to explore the brain functional activity and connectivity patterns in BMP of lung cancer patients preliminarily. Thirty BMP patients and 33 healthy controls (HCs) matched for age and sex were recruited from inpatients and communities, respectively. All participants underwent fMRI data acquisition and pain assessment. Low-frequency fluctuations (ALFF) and regional homogeneity (ReHo) were applied to evaluate brain functional activity. Then, functional connectivity (FC) was calculated for the ALFF- and ReHo-identified seed brain regions. A two-sample t-test or Manny–Whitney U-test was applied to compare demographic and neuropsychological data as well as the neuroimaging indices according to the data distribution. A correlation analysis was conducted to explore the potential relationships between neuroimaging indices and pain intensity. Receiver operating characteristic curve analysis was applied to assess the classification performance of neuroimaging indices in discriminating individual subjects between the BMP patients and HCs. No significant intergroup differences in demographic and neuropsychological data were noted. BMP patients showed reduced ALFF and ReHo largely in the prefrontal cortex and increased ReHo in the bilateral thalamus and left fusiform gyrus. The lower FC was found within the prefrontal cortex. No significant correlation between the neuroimaging indices and pain intensity was observed. The neuroimaging indices showed satisfactory classification performance between the BMP patients and HCs, and the combined ALFF and ReHo showed a better accuracy rate (93.7%) than individual indices. In conclusion, altered brain functional activity and connectivity in the prefrontal cortex, fusiform gyrus, and thalamus may be associated with the neuropathology of BMP and may represent a potential biomarker for classifying BMP patients and healthy controls.

Predicting response to tVNS in patients with migraine using functional MRI: A voxels-based machine learning analysis

Background

Migraine is a common disorder, affecting many patients. However, for one thing, lacking objective biomarkers, misdiagnosis, and missed diagnosis happen occasionally. For another, though transcutaneous vagus nerve stimulation (tVNS) could alleviate migraine symptoms, the individual difference of tVNS efficacy in migraineurs hamper the clinical application of tVNS. Therefore, it is necessary to identify biomarkers to discriminate migraineurs as well as select patients suitable for tVNS treatment.

Methods

A total of 70 patients diagnosed with migraine without aura (MWoA) and 70 matched healthy controls were recruited to complete fMRI scanning. In study 1, the fractional amplitude of low-frequency fluctuation (fALFF) of each voxel was calculated, and the differences between healthy controls and MWoA were compared. Meaningful voxels were extracted as features for discriminating model construction by a support vector machine. The performance of the discriminating model was assessed by accuracy, sensitivity, and specificity. In addition, a mask of these significant brain regions was generated for further analysis. Then, in study 2, 33 of the 70 patients with MWoA in study 1 receiving real tVNS were included to construct the predicting model in the generated mask. Discriminative features of the discriminating model in study 1 were used to predict the reduction of attack frequency after a 4-week tVNS treatment by support vector regression. A correlation coefficient between predicted value and actual value of the reduction of migraine attack frequency was conducted in 33 patients to assess the performance of predicting model after tVNS treatment. We vislized the distribution of the predictive voxels as well as investigated the association between fALFF change (post-per treatment) of predict weight brain regions and clinical outcomes (frequency of migraine attack) in the real group.

Results

A biomarker containing 3,650 features was identified with an accuracy of 79.3%, sensitivity of 78.6%, and specificity of 80.0% (p &lt; 0.002). The discriminative features were found in the trigeminal cervical complex/rostral ventromedial medulla (TCC/RVM), thalamus, medial prefrontal cortex (mPFC), and temporal gyrus. Then, 70 of 3,650 discriminative features were identified to predict the reduction of attack frequency after tVNS treatment with a correlation coefficient of 0.36 (p = 0.03). The 70 predictive features were involved in TCC/RVM, mPFC, temporal gyrus, middle cingulate cortex (MCC), and insula. The reduction of migraine attack frequency had a positive correlation with right TCC/RVM (r = 0.433, p = 0.021), left MCC (r = 0.451, p = 0.016), and bilateral mPFC (r = 0.416, p = 0.028), and negative with left insula (r = −0.473, p = 0.011) and right superior temporal gyrus/middle temporal gyrus (r = −0.684, p &lt; 0.001), respectively.

Conclusions

By machine learning, the study proposed two potential biomarkers that could discriminate patients with MWoA and predict the efficacy of tVNS in reducing migraine attack frequency. The pivotal features were mainly located in the TCC/RVM, thalamus, mPFC, and temporal gyrus.

Propagation Structure of Intrinsic Brain Activity in Migraine without Aura

Previous studies have revealed highly reproducible patterns of temporally lagged brain activity in healthy human adults. However, it is unknown whether temporal organization of intrinsic activity is altered in migraines or if it relates to migraine chronification. In this resting-state functional magnetic resonance imaging study, temporal features of intrinsic activity were investigated using resting-state lag analysis, and 39 episodic migraine patients, 17 chronic migraine patients, and 35 healthy controls were assessed. Temporally earlier intrinsic activity in the hippocampal complex was revealed in the chronic migraine group relative to the other two groups. We also found earlier intrinsic activity in the medial prefrontal cortex in chronic compared with episodic migraines. Both migraine groups showed earlier intrinsic activity in the lateral temporal cortex and sensorimotor cortex compared with the healthy control group. Across all patients, headache frequency negatively correlated with temporal lag of the medial prefrontal cortex and hippocampal complex. Disrupted propagation of intrinsic activity in regions involved in sensory, cognitive and affective processing of pain may contribute to abnormal brain function during migraines. Decreased time latency in the lateral temporal cortex and sensorimotor cortex may be common manifestations in episodic and chronic migraines. The temporal features of the medial prefrontal cortex and hippocampal complex were associated with migraine chronification.

Resting-state occipital alpha power is associated with treatment outcome in patients with chronic migraine

ABSTRACT

Preventive treatment is crucial for patients with chronic migraine (CM). This study explored the association between resting-state cortical oscillations and 3-month treatment outcome in patients with CM. Treatment-naïve patients with CM were recruited with their demographic data, psychosocial data, and headache profiles as well as the healthy controls (HCs). Resting-state cortical activities were recorded using an electroencephalogram and analysed using source-based and electrode-based spectral power method. The regions of interest were the bilateral primary somatosensory (S1) and visual (V1) cortices. After 3-month treatment with flunarizine, patients with CM were categorized into responders and nonresponders. Demographic, clinical, and electroencephalogram data from 72 patients with CM and 50 HCs were analysed. Elevated anxiety, depression, and stress were observed in patients with CM. Theta power in bilateral S1 and alpha and gamma powers in the right S1 increased in patients with CM. Nonresponders (n = 34) exhibited larger alpha powers in bilateral V1 than those in responders (n = 38). Alpha powers also exhibited significant correlations with changes of monthly headache days. Notably, in responders and nonresponders, occipital alpha powers did not differ at baseline and in the third month. In conclusion, patients with CM who were not responsive to preventive treatment were associated with augmented resting-state occipital alpha activity. Moreover, changes in migraine attack frequency were associated with baseline occipital alpha power. However, the prognostic feature of visual alpha oscillation seems to be inherent because it is not altered by flunarizine treatment. These findings may be useful for developing personalised migraine treatment plans.

Alteration in Functional Magnetic Resonance Imaging Signal Complexity Across Multiple Time Scales in Patients With Migraine Without Aura

Background

Migraine is a primary neurological disorder associated with complex brain activity. Recently, mounting evidence has suggested that migraine is underpinned by aberrant dynamic brain activity characterized by linear and non-linear changes across a variety of time scales. However, the abnormal dynamic brain activity at different time scales is still unknown in patients with migraine without aura (MWoA). This study aimed to assess the altered patterns of brain activity dynamics over different time scales and the potential pathophysiological mechanisms of alterations in patients with MWoA.

Methods

Multiscale entropy in 50 patients and 20 healthy controls (HCs) was calculated to investigate the patterns and altered brain complexity (BC) across five different time scales. Spearman rank correlation analysis between BC in regions showing significant intergroup differences and clinical scores (i.e., frequency of migraine attacks, duration, headache impact test) was conducted in patients with MWoA.

Results

The spatial distribution of BC varied across different time scales. At time scale1, BC was higher in the posterior default mode network (DMN) across participants. Compared with HCs, patients with MWoA had higher BC in the DMN and sensorimotor network. At time scale2, BC was mainly higher in the anterior DMN across participants. Patients with MWoA had higher BC in the sensorimotor network. At time scale3, BC was mainly higher in the frontoparietal network across participants. Patients with MWoA had increased BC in the parietal gyrus. At time scale4, BC is mainly higher in the sensorimotor network. Patients with MWoA had higher BC in the postcentral gyrus. At time scale5, BC was mainly higher in the DMN. Patients with MWoA had lower BC in the posterior DMN. In particular, BC values in the precuneus and paracentral lobule significantly correlated with clinical symptoms.

Conclusion

Migraine is associated with alterations in dynamic brain activity in the sensorimotor network and DMN over multiple time scales. Time-varying BC within these regions could be linked to instability in pain transmission and modulation. Our findings provide new evidence for the hypothesis of abnormal dynamic brain activity in migraine.

The Changes of Brain Function After Spinal Manipulation Therapy in Patients with Chronic Low Back Pain: A Rest BOLD fMRI Study

OBJECTIVE

To investigate the changes of regional homogeneity (Reho) values before and after spinal manipulative therapy (SMT) in patients with chronic low back pain (CLBP) through rest blood-oxygen-level-dependent functional magnetic resonance imaging (BOLD fMRI).

METHODS

Patients with CLBP (Group 1, n = 20) and healthy control subjects (Group 2, n = 20) were recruited. The fMRI was performed three times in Group 1 before SMT (time point 1, TP1), after the first SMT (time point 2, TP2), after the sixth SMT (time point 3, TP3), and for one time in Group 2, which received no intervention. The clinical scales were finished in Group 1 every time before fMRI was performed. The Reho values were compared among Group 1 at different time points, and between Group 1 and Group 2. The correlation between Reho values with the statistical differences and the clinical scale scores were calculated.

RESULTS

The bilateral precuneus and right mid-frontal gyrus in Group 1 had different Reho values compared with Group 2 at TP1. The Reho values were increased in the left precuneus and decreased in the left superior frontal gyrus in Group 1 at TP2 compared with TP1. The Reho values were increased in the left postcentral gyrus and decreased in the left posterior cingulate cortex and the superior frontal gyrus in Group 1 at TP3 compared with TP1. The ReHo values of the left precuneus in Group 1 at TP1 were negatively correlated with the pain degree at TP1 and TP2 (r = -0.549, -0.453; p = 0.012, 0.045). The Reho values of the middle temporal gyrus in Group 1 at TP3 were negatively correlated with the changes of clinical scale scores between TP3 and TP1 (r = 0.454, 0.559; p = 0.044, 0.01).

CONCLUSION

Patients with CLBP showed abnormal brain function activity, which was altered after SMT. The Reho values of the left precuneus could predict the immediate analgesic effect of SMT.

Altered brain functional network dynamics in classic trigeminal neuralgia: a resting-state functional magnetic resonance imaging study

Background

Accumulating studies have indicated a wide range of brain alterations with respect to the structure and function of classic trigeminal neuralgia (CTN). Given the dynamic nature of pain experience, the exploration of temporal fluctuations in interregional activity covariance may enhance the understanding of pain processes in the brain. The present study aimed to characterize the temporal features of functional connectivity (FC) states as well as topological alteration in CTN.

Methods

Resting-state functional magnetic resonance imaging and three-dimensional T1-weighted images were obtained from 41 CTN patients and 43 matched healthy controls (HCs). After group independent component analysis, sliding window based dynamic functional network connectivity (dFNC) analysis was applied to investigate specific FC states and related temporal properties. Then, the dynamics of the whole brain topological organization were estimated by calculating the coefficient of variation of graph-theoretical properties. Further correlation analyses were performed between all these measurements and clinical data.

Results

Two distinct states were identified. Of these, the state 2, characterized by complicated coupling between default mode network (DMN) and cognitive control network (CC) and tight connections within DMN, was expressed more in CTN patients and presented as increased fractional windows and dwell time. Moreover, patients switched less frequently between states than HCs. Regarding the dynamic topological analysis, disruptions in global graph-theoretical properties (including network efficiency and small-worldness) were observed in patients, coupled with decreased variability in nodal efficiency of anterior cingulate cortex (ACC) in the salience network (SN) and the thalamus and caudate nucleus in the subcortical network (SC). The variation of topological properties showed negative correlation with disease duration and attack frequency.

Conclusions

The present study indicated disrupted flexibility of brain topological organization under persistent noxious stimulation and further highlighted the important role of “dynamic pain connectome” regions (including DMN/CC/SN) in the pathophysiology of CTN from the temporal fluctuation aspect. Additionally, the findings provided supplementary evidence for current knowledge about the aberrant cortical-subcortical interaction in pain development.

Supplementary Information

The online version contains supplementary material available at 10.1186/s10194-021-01354-z.

Structural and functional thalamocortical connectivity study in female fibromyalgia

Dysfunctional thalamocortical interactions have been suggested as putative mechanisms of ineffective pain modulation and also suggested as possible pathophysiology of fibromyalgia (FM). However, it remains unclear which specific thalamocortical networks are altered and whether it is related to abnormal pain perception in people with FM. Here, we conducted combined vertex-wise subcortical shape, cortical thickness, structural covariance, and resting-state functional connectivity analyses to address these questions. FM group exhibited a regional shape deflation of the left posterior thalamus encompassing the ventral posterior lateral and pulvinar nuclei. The structural covariance analysis showed that the extent of regional deflation of the left posterior thalamus was negatively covaried with the left inferior parietal cortical thickness in the FM group, whereas those two regions were positively covaried in the healthy controls. In functional connectivity analysis with the left posterior thalamus as a seed, FM group had less connectivity with the periaqueductal gray compared with healthy controls, but enhanced connectivity between the posterior thalamus and bilateral inferior parietal regions, associated with a lower electrical pain threshold at the hand dorsum (pain-free point). Overall, our findings showed the structural thalamic alteration interacts with the cortical regions in a functionally maladaptive direction, leading the FM brain more responsive to external stimuli and potentially contributing to pain amplification.

Alterations in Effective Connectivity of the Hippocampus in Migraine without Aura

Purpose

Neuroimaging studies on migraine have revealed structural and functional alterations in the hippocampus, a region involved in pain processing and stress response. This study was designed to investigate whether effective connectivity of this region is disrupted in migraine and relates to chronicity of this disease.

Patients and Methods

In 39 episodic migraine (EM) patients, 17 chronic migraine (CM) patients, and 35 healthy controls, we investigated differences in the directional influences between the hippocampus and the rest of the brain by combining resting-state functional magnetic resonance imaging and Granger causality analysis (GCA), with bilateral hippocampus as seed regions. The associations between directional influences and the clinical variables were also examined.

Results

Comparing each patient group to the control group, we found increased and decreased negative influence on the hippocampus exerted by the bilateral visual areas and right dorsolateral prefrontal cortex (dlPFC), respectively. The hippocampus showed increased positive influence on the right posterior insula and medial prefrontal cortex (mPFC), as well as increased negative influence on the left cerebellum in CM patients relative to EM patients and healthy controls. Furthermore, across all patients, the migraine frequency exhibited a positive and negative association with causal influence from the hippocampus to mPFC and left cerebellum, respectively.

Conclusion

Migraine patients have abnormal effective connectivity between the hippocampus and multiple brain regions involved in the sensory and cognitive processing of pain. Disrupted directional influences to the hippocampus exerted by dlPFC and bilateral visual areas were common features of EM and CM patients. Directional influences from the hippocampus to mPFC and left cerebellum may be useful imaging biomarkers for assessing migraine frequency.

Pre-operative Limbic System Functional Connectivity Distinguishes Responders From Non-responders to Surgical Treatment for Trigeminal Neuralgia

Background: Trigeminal neuralgia (TN) is a severe facial pain condition often requiring surgical treatment. Unfortunately, even technically successful surgery fails to achieve durable pain relief in many patients. The purpose of this study was to use resting-state functional magnetic resonance imaging (fMRI) to: (1) compare functional connectivity between limbic and accessory sensory networks in TN patients vs. healthy controls; and (2) determine if pre-operative variability in these networks can distinguish responders and non-responders to surgery for TN. Methods: We prospectively recruited 22 medically refractory classic or idiopathic TN patients undergoing surgical treatment over a 3-year period, and 19 age- and sex-matched healthy control subjects. fMRI was acquired within the month prior to surgery for all TN patients and at any time during the study period for controls. Functional connectivity analysis was restricted to six pain-relevant brain regions selected a priori: anterior cingulate cortex (ACC), posterior cingulate cortex, hippocampus, amygdala, thalamus, and insula. Two comparisons were performed: (1) TN vs. controls; and (2) responders vs. non-responders to surgical treatment for TN. Functional connectivity was assessed with a two-sample t-test, using a statistical significance threshold of p < 0.050 with false discovery rate (FDR) correction for multiple comparisons. Results: Pre-operative functional connectivity was increased in TN patients compared to controls between the right insular cortex and both the left thalamus [t ₍₃₉₎ = 3.67, p = 0.0007] and right thalamus [t ₍₃₉₎ = 3.22, p = 0.0026]. TN patients who were non-responders to surgery displayed increased functional connectivity between limbic structures, including between the left and right hippocampus [t ₍₁₈₎ = 2.85, p = 0.0106], and decreased functional connectivity between the ACC and both the left amygdala [t ₍₁₈₎ = 2.94, p = 0.0087] and right hippocampus [t ₍₁₈₎ = 3.20, p = 0.0049]. Across all TN patients, duration of illness was negatively correlated with connectivity between the ACC and left amygdala (r ² = 0.34, p = 0.00437) as well as the ACC and right hippocampus (r ² = 0.21, p = 0.0318). Conclusions: TN patients show significant functional connectivity abnormalities in sensory-salience regions. However, variations in the strength of functional connectivity in limbic networks may explain why some TN patients fail to respond adequately to surgery.

Functional Magnetic Resonance Imaging Signal Variability Is Associated With Neuromodulation in Fibromyalgia

OBJECTIVES

Although primary motor cortex (M1) transcranial direct current stimulation (tDCS) has an analgesic effect in fibromyalgia (FM), its neural mechanism remains elusive. We investigated whether M1-tDCS modulates a regional temporal variability of blood-oxygenation-level-dependent (BOLD) signals, an indicator of the brain's flexibility and efficiency and if this change is associated with pain improvement.

MATERIALS AND METHODS

In a within-subjects cross-over design, 12 female FM patients underwent sham and active tDCS on five consecutive days, respectively. Each session was performed with an anode placed on the left M1 and a cathode on the contralateral supraorbital region. The subjects also participated in resting-state functional magnetic resonance imaging (fMRI) at baseline and after sham and active tDCS. We compared the BOLD signal variability (SD_BOLD ), defined as the standard deviation of the BOLD time-series, between the tDCS conditions. Baseline SD_BOLD was compared to 15 healthy female controls.

RESULTS

At baseline, FM patients showed reduced SD_BOLD in the ventromedial prefrontal cortex (vmPFC), lateral PFC, and anterior insula and increased SD_BOLD in the posterior insula compared to healthy controls. After active tDCS, compared to sham, we found an increased SD_BOLD in the left rostral anterior cingulate cortex (rACC), lateral PFC, and thalamus. After sham tDCS, compared to baseline, we found a decreased SD_BOLD in the dorsomedial PFC and posterior cingulate cortex/precuneus. Interestingly, after active tDCS compared to sham, pain reduction was correlated with an increased SD_BOLD in the rACC/vmPFC but with a decreased SD_BOLD in the posterior insula.

CONCLUSION

Our findings suggest that M1-tDCS might revert temporal variability of fMRI signals in the rACC/vmPFC and posterior insula linked to FM pain. Changes in neural variability would be part of the mechanisms underlying repetitive M1-tDCS analgesia in FM.