Characterizing the opioidergic mechanisms of repetitive transcranial magnetic stimulation-induced analgesia: a randomized controlled trial.

ABSTRACT: Repetitive transcranial magnetic stimulation (rTMS) is a promising technology to reduce chronic pain. Investigating the mechanisms of rTMS analgesia holds the potential to improve treatment efficacy. Using a double-blind and placebo-controlled design at both stimulation and pharmacologic ends, this study investigated the opioidergic mechanisms of rTMS analgesia by abolishing and recovering analgesia in 2 separate stages across brain regions and TMS doses. A group of 45 healthy participants were equally randomized to the primary motor cortex (M1), the dorsolateral prefrontal cortex (DLPFC), and the Sham group. In each session, participants received an intravenous infusion of naloxone or saline before the first rTMS session. Participants then received a second dose of rTMS session after the drugs were metabolized at 90 minutes. M1-rTMS-induced analgesia was abolished by naloxone compared with saline and was recovered by the second rTMS run when naloxone was metabolized. In the DLPFC, double but not the first TMS session induced significant pain reduction in the saline condition, resulting in less pain compared with the naloxone condition. In addition, TMS over the M1 or DLPFC selectively increased plasma concentrations of ß-endorphin or encephalin, respectively. Overall, we present causal evidence that opioidergic mechanisms are involved in both M1-induced and DLPFC-rTMS-induced analgesia; however, these are shaped by rTMS dosage and the release of different endogenous opioids.

Uncovering a stability signature of brain dynamics associated with meditation experience using massive time-series feature extraction.

Previous research has examined resting electroencephalographic (EEG) data to explore brain activity related to meditation. However, previous research has mostly examined power in different frequency bands. The practical objective of this study was to comprehensively test whether other types of time-series analysis methods are better suited to characterize brain activity related to meditation. To achieve this, we compared >7000 time-series features of the EEG signal to comprehensively characterize brain activity differences in meditators, using many measures that are novel in meditation research. Eyes-closed resting-state EEG data from 49 meditators and 46 non-meditators was decomposed into the top eight principal components (PCs). We extracted 7381 time-series features from each PC and each participant and used them to train classification algorithms to identify meditators. Highly differentiating individual features from successful classifiers were analysed in detail. Only the third PC (which had a central-parietal maximum) showed above-chance classification accuracy (67 %, pFDR = 0.007), for which 405 features significantly distinguished meditators (all pFDR < 0.05). Top-performing features indicated that meditators exhibited more consistent statistical properties across shorter subsegments of their EEG time-series (higher stationarity) and displayed an altered distributional shape of values about the mean. By contrast, classifiers trained with traditional band-power measures did not distinguish the groups (pFDR > 0.05). Our novel analysis approach suggests the key signatures of meditators' brain activity are higher temporal stability and a distribution of time-series values suggestive of longer, larger, or more frequent non-outlying voltage deviations from the mean within the third PC of their EEG data. The higher temporal stability observed in this EEG component might underpin the higher attentional stability associated with meditation. The novel time-series properties identified here have considerable potential for future exploration in meditation research and the analysis of neural dynamics more broadly.

How to Interpret Effect Sizes for Biopsychosocial Outcomes and Implications for Current Research.

Biopsychosocial factors are associated with pain, but they can be difficult to compare. One way of comparing them is to use standardized mean differences. Previously, these effects sizes have been termed as small, medium, or large, if they are bigger than or equal to, respectively, .2, .5, or .8. These cut-offs are arbitrary and recent evidence showed that they need to be reconsidered. We argue it is necessary to determine cut-offs for each biopsychosocial factor. To achieve this, we propose 3 potential approaches: 1) examining, for each factor, how the effect size differs depending upon disease severity; 2) using an existing minimum clinically important difference to anchor the large effect size; and 3) define cut-offs by comparing data from people with and without pain. This is important for pain research, as exploring these methodologies has potential to improve comparability of biopsychosocial factors and lead to more directed treatments. We note assumptions and limitations of these methods that should also be considered. PERSPECTIVE: Standardized mean differences can estimate effect sizes between groups and could theoretically allow for comparison of biopsychosocial factors. However, common thresholds to define effect sizes are arbitrary and likely differ based on outcome. We propose methods that could overcome this and be used to derive biopsychosocial outcome-specific effect sizes.

Comparisons of Accelerated Continuous and Intermittent Theta Burst Stimulation for Treatment-Resistant Depression and Suicidal Ideation.

BACKGROUND: Suicidal ideation is a substantial clinical challenge in treatment-resistant depression (TRD). Recent work demonstrated promising antidepressant effects in TRD patients with no or mild suicidal ideation using a specific protocol termed intermittent theta burst stimulation (iTBS). Here, we examined the clinical effects of accelerated schedules of iTBS and continuous TBS (cTBS) in patients with moderate to severe suicidal ideation. METHODS: Patients with TRD and moderate to severe suicidal ideation (n = 44) were randomly assigned to receive accelerated iTBS or cTBS treatment. Treatments were delivered in 10 daily TBS sessions (1800 pulses/session) for 5 consecutive days (total of 90,000 pulses). Neuronavigation was employed to target accelerated iTBS and cTBS to the left and right dorsolateral prefrontal cortex (DLPFC), respectively. Clinical outcomes were evaluated in a 4-week follow-up period. RESULTS: Accelerated cTBS was superior to iTBS in the management of suicidal ideation (pweek 1 = .027) and anxiety symptoms (pweek 1 = .01). Accelerated iTBS and cTBS were comparable in antidepressant effects (p < .001; accelerated cTBS: mean change at weeks 1, 3, 5 = 49.55%, 54.99%, 53.11%; accelerated iTBS: mean change at weeks 1, 3, 5 = 44.52%, 48.04%, 51.74%). No serious adverse events occurred during the trial. One patient withdrew due to hypomania. The most common adverse event was discomfort at the treatment site (22.73% in both groups). CONCLUSIONS: These findings provide the first evidence that accelerated schedules of left DLPFC iTBS and right DLPFC cTBS are comparably effective in managing antidepressant symptoms and indicate that right DLPFC cTBS is potentially superior in reducing suicidal ideation and anxiety symptoms.

Impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life of Australians: a population-based longitudinal data analysis.

Background: Health risk factors, including smoking, excessive alcohol consumption, overweight, obesity, and insufficient physical activity, are major contributors to many poor health conditions. This study aimed to assess the impact of health risk factors on healthcare resource utilization, work-related outcomes and health-related quality of life (HRQoL) in Australia. Methods: We used two waves of the nationally representative Household, Income, and Labor Dynamics in Australia (HILDA) Survey from 2013 and 2017 for the analysis. Healthcare resource utilization included outpatient visits, hospitalisations, and prescribed medication use. Work-related outcomes were assessed through employment status and sick leave. HRQoL was assessed using the SF-6D scores. Generalized estimating equation (GEE) with logit or log link function and random-effects regression models were used to analyse the longitudinal data on the relationship between health risk factors and the outcomes. The models were adjusted for age, sex, marital status, education background, employment status, equilibrium household income, residential area, country of birth, indigenous status, and socio-economic status. Results: After adjusting for all other health risk factors covariates, physical inactivity had the greatest impact on healthcare resource utilization, work-related outcomes, and HRQoL. Physical inactivity increased the likelihood of outpatient visits (AOR = 1.60, 95% CI = 1.45, 1.76 p < 0.001), hospitalization (AOR = 1.83, 95% CI = 1.66-2.01, p < 0.001), and the probability of taking sick leave (AOR = 1.31, 95% CI = 1.21-1.41, p < 0.001), and decreased the odds of having an above population median HRQoL (AOR = 0.48, 95% CI = 0.45-0.51, p < 0.001) after adjusting for all other health risk factors and covariates. Obesity had the greatest impact on medication use (AOR = 2.02, 95% CI = 1.97-2.29, p < 0.001) after adjusting for all other health risk factors and covariates. Conclusion: Our study contributed to the growing body of literature on the relative impact of health risk factors for healthcare resource utilization, work-related outcomes and HRQoL. Our results suggested that public health interventions aim at improving these risk factors, particularly physical inactivity and obesity, can offer substantial benefits, not only for healthcare resource utilization but also for productivity.

Evidence- and data-driven classification of low back pain via artificial intelligence: Protocol of the PREDICT-LBP study.

In patients presenting with low back pain (LBP), once specific causes are excluded (fracture, infection, inflammatory arthritis, cancer, cauda equina and radiculopathy) many clinicians pose a diagnosis of non-specific LBP. Accordingly, current management of non-specific LBP is generic. There is a need for a classification of non-specific LBP that is both data- and evidence-based assessing multi-dimensional pain-related factors in a large sample size. The "PRedictive Evidence Driven Intelligent Classification Tool for Low Back Pain" (PREDICT-LBP) project is a prospective cross-sectional study which will compare 300 women and men with non-specific LBP (aged 18-55 years) with 100 matched referents without a history of LBP. Participants will be recruited from the general public and local medical facilities. Data will be collected on spinal tissue (intervertebral disc composition and morphology, vertebral fat fraction and paraspinal muscle size and composition via magnetic resonance imaging [MRI]), central nervous system adaptation (pain thresholds, temporal summation of pain, brain resting state functional connectivity, structural connectivity and regional volumes via MRI), psychosocial factors (e.g. depression, anxiety) and other musculoskeletal pain symptoms. Dimensionality reduction, cluster validation and fuzzy c-means clustering methods, classification models, and relevant sensitivity analyses, will classify non-specific LBP patients into sub-groups. This project represents a first personalised diagnostic approach to non-specific LBP, with potential for widespread uptake in clinical practice. This project will provide evidence to support clinical trials assessing specific treatments approaches for potential subgroups of patients with non-specific LBP. The classification tool may lead to better patient outcomes and reduction in economic costs.

Towards data-driven biopsychosocial classification of non-specific chronic low back pain: a pilot study.

The classification of non-specific chronic low back pain (CLBP) according to multidimensional data could guide clinical management; yet recent systematic reviews show this has not been attempted. This was a prospective cross-sectional study of participants with CLBP (n = 21) and age-, sex- and height-matched pain-free controls (n = 21). Nervous system, lumbar spinal tissue and psychosocial factors were collected. Dimensionality reduction was followed by fuzzy c-means clustering to determine sub-groups. Machine learning models (Support Vector Machine, k-Nearest Neighbour, Naïve Bayes and Random Forest) were used to determine the accuracy of classification to sub-groups. The primary analysis showed that four factors (cognitive function, depressive symptoms, general self-efficacy and anxiety symptoms) and two clusters (normal versus impaired psychosocial profiles) optimally classified participants. The error rates in classification models ranged from 4.2 to 14.2% when only CLBP patients were considered and increased to 24.2 to 37.5% when pain-free controls were added. This data-driven pilot study classified participants with CLBP into sub-groups, primarily based on psychosocial factors. This contributes to the literature as it was the first study to evaluate data-driven machine learning CLBP classification based on nervous system, lumbar spinal tissue and psychosocial factors. Future studies with larger sample sizes should validate these findings.

Associations of body size with all-cause and cause-specific mortality in healthy older adults.

In the general population, body mass index (BMI) and waist circumference are recognized risk factors for several chronic diseases and all-cause mortality. However, whether these associations are the same for older adults is less clear. The association of baseline BMI and waist circumference with all-cause and cause-specific mortality was investigated in 18,209 Australian and US participants (mean age: 75.1 ± 4.5 years) from the ASPirin in Reducing Events in the Elderly (ASPREE) study, followed up for a median of 6.9 years (IQR: 5.7, 8.0). There were substantially different relationships observed in men and women. In men, the lowest risk of all-cause and cardiovascular mortality was observed with a BMI in the range 25.0-29.9 kg/m2 [HR25-29.9 vs 21-24.9 kg/m2: 0.85; 95% CI, 0.73-1.00] while the highest risk was in those who were underweight [HRBMI <21 kg/m2 vs BMI 21-24.9 kg/m2: 1.82; 95% CI 1.30-2.55], leading to a clear U-shaped relationship. In women, all-cause mortality was highest in those with the lowest BMI leading to a J-shaped relationship (HRBMI <21 kg/m2 vs BMI 21-24.9 kg/m2: 1.64; 95% CI 1.26-2.14). Waist circumference showed a weaker relationship with all-cause mortality in both men and women. There was little evidence of a relationship between either index of body size and subsequent cancer mortality in men or women, while non-cardiovascular non-cancer mortality was higher in underweight participants. For older men, being overweight was found to be associated with a lower risk of all-cause mortality, while among both men and women, a BMI in the underweight category was associated with a higher risk. Waist circumference alone had little association with all-cause or cause-specific mortality risk.Trial registration ASPREE https://ClinicalTrials.gov number NCT01038583.

Analgesic efficacy of theta-burst stimulation for postoperative pain.

OBJECTIVE: Repetitive transcranial magnetic stimulation (rTMS) may be a relevant method to assist postoperative pain. However, studies to date have only used conventional 10 Hz rTMS and targeted the DLPFC for postoperative pain. A more recent form of rTMS, termed intermittent Theta Burst Stimulation (iTBS), enables to increase cortical excitability in a short period of time. This preliminary double-blind, randomised, sham controlled study was designed to evaluate the efficacy of iTBS in postoperative care across two distinct stimulation targets. METHODS: A group of 45 patients post laparoscopic surgery were randomised to receive a single session of iTBS over either the dorsolateral prefrontal cortex (DLPFC), primary motor cortex (M1), or Sham stimulation (1:1:1 ratio). Outcome measurements were number of pump attempts, total anaesthetic volume used, and self-rated pain experience, assessed at 1 hour, 6 hours, 24 hours, and 48 hours post stimulation. All randomised patients were analysed (n = 15 in each group). RESULTS: Compared to Sham stimulation, DLPFC-iTBS reduced pump attempts at 6 (DLPFC = 0.73 ± 0.88, Sham = 2.36 ± 1.65, P = 0.031), 24 (DLPFC = 1.40 ± 1.24, Sham = 5.03 ± 3.87, P = 0.008), and 48 (DLPFC = 1.47 ± 1.41, Sham = 5.87 ± 4.34, P = 0.014) hours post-surgery, whereby M1 stimulation had no effect. No group effect was observed on total anaesthetics, which was mainly provided through the continuous administration of opioids at a set speed for each group. There was also no group or interaction effect on pain ratings. Pump attempts were positively associated with pain ratings in the DLPFC (r = 0.59, P = 0.02) and M1 (r = 0.56, P = 0.03) stimulation. CONCLUSIONS: Our findings show that iTBS to the DLPFC reduces pump attempts for additional anaesthetics following a laparoscopic surgery. However, reduced pump attempts by DLPFC stimulation did not translate into a significantly smaller volume of total anaesthetic, due to the continuous administration of opioids at a set speed for each group. SIGNIFICANCE: Our findings therefore provide preliminary evidence for iTBS targeting the DLPFC to be used to improve postoperative pain management.

The Interaction Between Psychosocial Factors and Exercise-Induced Hypoalgesia in Pain-Free Nurses.

Purpose: This cross-sectional study aimed to investigate whether psychosocial factors were predictive for exercise-induced hypoalgesia (EIH) in pain-free adults. Methods: A sample of 38 pain-free nurses with a mean (SD) age of 26 (6) years were included in this study. Participants completed psychosocial questionnaires prior to physical tests. Pressure pain threshold (PPT) was assessed bilaterally at the calves (local), lower back (semi-local) and forearm (remote) before and immediately after a maximal graded cycling exercise test. Separate linear mixed effects models were used to determine change in PPT before and after cycling exercise (EIH). Multiple linear regression for all psychosocial variables and best subset regression was used to identify predictors of EIH at all locations. Results: The relative mean increase in PPT at the forearm, lumbar, calf, and globally (all sites pooled) was 6.0% (p<0.001), 10.1% (p<0.001), 13.9% (p<0.001), and 10.2% (p=0.013), respectively. Separate best subset multiple linear regression models at the forearm (predictors; Multidimensional Scale of Perceived Social Support (MSPSS) total), lumbar (predictors; MSPSS total, Pain Catastrophizing Scale (PCS) total, Depression Anxiety Stress Scale (DASS) depression), calf (predictors; MSPSS friends, PCS total), and global (predictors; MSPSS friends, PCS total) accounted for 7.5% (p=0.053), 13% (p=0.052), 24% (p=0.003), and 17% (p=0.015) of the variance, respectively. Conclusion: These findings confirm that cycling exercise produced EIH in young nurses and provided preliminary evidence to support the interaction between perceived social support, pain catastrophizing and EIH. Further investigation is required to better understand psychological and social factors that mediate EIH on a larger sample of adults at high risk of developing chronic musculoskeletal pain.

Provision of a Multidisciplinary Post-Suicidal, Community-Based Aftercare Program: A Longitudinal Study.

Suicide is a global concern with rates in Australia continuing to increase. Effective post-suicidal care is critical for reducing persistent suicidal behaviour. One model of care is that adopted by Alfred Health, delivering a multidisciplinary, hybrid clinical and non-clinical (psycho-social support), assertive outreach approach. This study measured improvements in resilience and wellbeing, changes to distress and suicidal ideation at least 6-months post-discharge from care. Thirty-one consumers participated including a one-on-one interview to gather qualitative feedback. There was a significant change on all outcome measures with large effect sizes. Participants had significantly reduced suicidal ideation and distress and increased coping self-efficacy, hope and well-being. The qualitative findings indicated that a key component to recovery was the staff. Limitations included a low sample size, and broad time range of follow-up data collection. Providing assertive, multidisciplinary, collaborative and outreach-focused post-suicidal care can increase and sustain protective psychological factors and reduced suicidal ideation in most individuals.

Chronic back pain sub-grouped via psychosocial, brain and physical factors using machine learning.

Chronic back pain (CBP) is heterogenous and identifying sub-groups could improve clinical decision making. Machine learning can build upon prior sub-grouping approaches by using a data-driven approach to overcome clinician subjectivity, however, only binary classification of pain versus no-pain has been attempted to date. In our cross-sectional study, age- and sex-matched participants with CBP (n = 4156) and pain-free controls (n = 14,927) from the UkBioBank were included. We included variables of body mass index, depression, loneliness/social isolation, grip strength, brain grey matter volumes and functional connectivity. We used fuzzy c-means clustering to derive CBP sub-groups and Support Vector Machine (SVM), Naïve Bayes, k-Nearest Neighbour (kNN) and Random Forest classifiers to determine classification accuracy. We showed that two variables (loneliness/social isolation and depression) and five clusters were optimal for creating sub-groups of CBP individuals. Classification accuracy was greater than 95% for when CBP sub-groups were assessed only, while misclassification in CBP sub-groups increased to 35-53% across classifiers when pain-free controls were added. We showed that individuals with CBP could sub-grouped and accurately classified. Future research should optimise variables by including specific spinal, psychosocial and nervous system measures associated with CBP to create more robust sub-groups that are discernible from pain-free controls.

Severe low back or lower limb pain is associated with recurrent falls among older Australians.

BACKGROUND: Few studies have explored the impact of low back or lower limb pain severity on recurrent (≥2) falls in older adults. OBJECTIVES: Investigate the association between the severity of low back or lower limb pain, and ≥2 falls or falls-related injuries. METHODS: Community-dwelling Australian males and females in the ASPREE Longitudinal Study of Older Persons (ALSOP), aged ≥70 years. Self-reported, cross-sectional questionnaire data regarding number of falls and falls-related injuries in the last 12 months; and sites and severity of pain experienced on most days. Adjusted relative risks (RR) were estimated from multivariable Poisson regression models, for males and females separately. RESULTS: Of 14,892 ALSOP participants, 13% (n = 1983) reported ≥2 falls ('recurrent fallers') in the last 12 months. Males and females who reported severe low back, or severe lower limb pain on most days were more likely to report ≥2 falls in the last 12 months compared to those with mild pain (lower back: males RR = 1.70 and females RR = 1.5, p = 0.001; lower limb: males RR = 2.0, p < 0.001 and females RR = 1.4, p = 0.003). Female recurrent fallers who reported severe low back (RR = 1.3, p = 0.029) or lower limb (RR = 1.2, p = 0.024) pain on most days were more likely to report a falls-related injury in the last 12 months compared to females with mild pain. CONCLUSION: Severe low back or lower limb pain was associated with an increased likelihood of recurrent falls (males/females) or falls-related injuries (females only). Assessment of severe low back and lower limb pain should be considered as a priority when undertaking falls-risk evaluation. SIGNIFICANCE: Severe low back pain, or severe lower limb pain is associated with an increased likelihood of recurrent falls in older males and females, and an increased likelihood of falls-related injuries in older female recurrent fallers. Assessment and management of severe low back and lower limb pain should be prioritized when undertaking falls-risk assessment. Future longitudinal research is required to further interrogate this relationship and its underlying mechanisms.

Factors Influencing Change in Brain-Predicted Age Difference in a Cohort of Healthy Older Individuals.

Background: There is considerable variability in the rate at which we age biologically, and the brain is particularly susceptible to the effects of aging. Objective: We examined the test-retest reliability of brain age at one- and three-year intervals and identified characteristics that predict the longitudinal change in brain-predicted age difference (brain-PAD, defined by deviations of brain age from chronological age). Methods: T1-weighted magnetic resonance images were acquired at three timepoints from 497 community-dwelling adults (73.8±3.5 years at baseline, 48% were female). Brain age was estimated from whole brain volume, using a publicly available algorithm trained on an independent dataset. Linear mixed models were used, adjusting for sex, age, and age2. Results: Excellent retest reliability of brain age was observed over one and three years. We identified a significant sex difference in brain-PAD, where a faster rate of brain aging (worsening in brain age relative to chronological age) was observed in men, and this finding replicated in secondary analyses. The effect size, however, was relatively weak, equivalent to 0.16 years difference per year. A higher score in physical health related quality of life and verbal fluency were associated with a faster rate of brain aging, while depression was linked to a slower rate of brain aging, but these findings were not robust. Conclusion: Our study provides consistent evidence that older men have slightly faster brain atrophy than women. Given the sparsity of longitudinal research on brain age in older populations, future prospective studies are needed to confirm our findings.

Relative contributions of the nervous system, spinal tissue and psychosocial health to non-specific low back pain: Multivariate meta-analysis.

BACKGROUND AND OBJECTIVES: Nervous system, psychosocial and spinal tissue biomarkers are associated with non-specific low back pain (nsLBP), though relative contributions are unclear. DATABASES AND DATA TREATMENT: MEDLINE, EMBASE, CINAHL, PsycINFO and SPORTDiscus were searched up to 25 March 2020. Related reviews and reference lists were also screened. Observational studies examining structural and functional nervous system biomarkers (e.g. quantitative sensory tests, structural and functional brain measures), psychosocial factors (e.g. mental health, catastrophizing) and structural spinal imaging biomarkers (e.g. intervertebral disc degeneration, paraspinal muscle size) between nsLBP and pain-free controls were included. For multivariate meta-analysis, two of three domains were required in each study. Random-effects pairwise and multivariate meta-analyses were performed. GRADE approach assessed evidence certainty. Newcastle-Ottawa scale assessed risk of bias. Main outcomes were the effect size difference of domains between nsLBP and pain-free controls. RESULTS: Of 4519 unique records identified, 33 studies (LBP = 1552, referents = 1322) were meta-analysed. Psychosocial state (Hedges' g [95%CI]: 0.90 [0.69-1.10], p < 0.001) in nsLBP showed larger effect sizes than nervous system (0.31 [0.13-0.49], p < 0.001; difference: 0.61 [0.36-0.86], p < 0.001) and spine imaging biomarkers (0.55 [0.37-0.73], p < 0.001; difference: 0.36 [0.04-0.67], p = 0.027). The relationship between domains changes depending on if pain duration is acute or chronic. CONCLUSIONS: Psychosocial effect sizes in nsLBP are greater than those for spinal imaging and nervous system biomarkers. Limitations include cross-sectional design of studies included and inference of causality. Future research should investigate the clinical relevance of these effect size differences in relation to pain intensity and disability. STUDY REGISTRATION: PROSPERO-CRD42020159188. SIGNIFICANCE: Spinal structural lesions (e.g. intervertebral disc degeneration), psychosocial (e.g. depression) and nervous system factors (detected by e.g. quantitative sensory tests, structural and functional measures) contribute to non-specific low back pain. However, psychosocial factors may be more compromised than nervous system and spinal imaging biomarkers. This relationship depends on if the pain is acute or chronic. These findings underscore that the 'non-specific' label in back pain should be reconsidered, and more specific multidimensional categories evaluated to guide patient management.

Brain structure, psychosocial, and physical health in acute and chronic back pain: a UK Biobank study.

ABSTRACT: Brain structure, psychosocial, and physical factors underpin back pain conditions; however, less is known about how these factors differ based on pain duration and location. We examined, cross-sectionally, 11,106 individuals from the UK Biobank who (1) were pain-free (n = 5616), (2) had acute back pain (n = 1746), (3) had chronic localised back pain (CBP; n = 1872), or (4) had chronic back pain and additional chronic pain sites (CWP; n = 1872). We found differences in structural brain measures in the chronic pain groups alone. Both CBP and CWP groups had lower primary somatosensory cortex {CBP mean difference (MD) (95% confidence interval [CI]): -250 (-393, -107) mm3, P < 0.001; CWP: -170 (-313, -27)mm3, P = 0.011} and higher caudate gray matter volumes (CBP: 127 [38,216]mm3, P = 0.001; CWP: 122 [33,210]mm3, P = 0.002) compared with pain-free controls. The CBP group also had a lower primary motor cortex volume (-215 [-382, -50]mm3, P = 0.005), whereas the CWP group had a lower amygdala gray matter volume (-27 [-52, -3]mm3, P = 0.021) compared with pain-free controls. Differences in gray matter volumes in some regions may be moderated by sex and body mass index. Psychosocial factors and body mass index differed between all groups and affected those with widespread pain the most (all, P < 0.001), whereas grip strength was only compromised in individuals with widespread pain (-1.0 [-1.4, -0.5] kg, P < 0.001) compared with pain-free controls. Longitudinal research is necessary to confirm these interactions to determine the process of pain development in relation to assessed variables and covariates. However, our results suggest that categorised pain duration and the number of pain sites warrant consideration when assessing markers of brain structure, psychosocial, and physical health.

Lessons from an initiative to address gender bias.

How a letter addressing the lack of women invited to speak at a conference in brain stimulation encouraged researchers to take action.

Examining resting-state functional connectivity in key hubs of the default mode network in chronic low back pain.

OBJECTIVES: Changes in brain connectivity have been observed within the default mode network (DMN) in chronic low back pain (CLBP), however the extent of these disruptions and how they may be related to CLBP requires further examination. While studies using seed-based analysis have found disrupted functional connectivity in the medial prefrontal cortex (mPFC), a major hub of the DMN, limited studies have investigated other equally important hubs, such as the posterior cingulate cortex (PCC) in CLBP. METHODS: This preliminary study comprised 12 individuals with CLBP and 12 healthy controls who completed a resting-state functional magnetic resonance imaging (fMRI) scan. The mPFC and PCC were used as seeds to assess functional connectivity. RESULTS: Both groups displayed similar patterns of DMN connectivity, however group comparisons showed that CLBP group had reduced connectivity between the PCC and angular gyrus compared to healthy controls. An exploratory analysis examined whether the alterations observed in mPFC and PCC connectivity were related to pain catastrophizing in CLBP, but no significant associations were observed. CONCLUSIONS: These results may suggest alterations in the PCC are apparent in CLBP, however, the impact and functional role of these disruptions require further investigation.