Effects of Lockdown Restrictions and Impact of Anxiety and Depression Symptoms in People With Chronic Pain During the Covid-19 Pandemic: A 13-Wave Longitudinal Study.

In early 2020, countries across the world imposed lockdown restrictions to curb the spread of the Covid-19 coronavirus. Lockdown conditions, including social and physical distancing measures and recommended self-isolation for clinically vulnerable groups, were proposed to disproportionately affect those living with chronic pain, who already report reduced access to social support and increased isolation. Yet, empirical evidence from longitudinal studies tracking the effects of prolonged and fluctuating lockdown conditions, and potential psychological factors mediating the effects of such restrictions on outcomes in chronic pain populations, is lacking. Accordingly, in the present 13-wave longitudinal study, we surveyed pain intensity, pain interference, and tiredness in people with chronic pain over the course of 11 months of the Covid-19 pandemic (April 2020-March 2021). Of N = 431 participants at baseline, average completion rate was ∼50% of time points, and all available data points were included in linear mixed models. We examined the impact of varying levels of lockdown restrictions on these outcomes and investigated whether psychological distress levels mediated effects. We found that a full national lockdown was related to greater pain intensity, and these effects were partially mediated by depressive symptoms. No effects of lockdown level were found for pain interference and tiredness, which were instead predicted by higher levels of depression, anxiety, pain catastrophising, and reduced exercise. Our findings are relevant for improving patient care in current and future crises. Offering remote management options for low mood could be particularly beneficial for this vulnerable population in the event of future implementation of lockdown restrictions. PERSPECTIVE: This longitudinal study demonstrates the impact of Covid-19 lockdown restrictions on people with chronic pain. Findings suggest a complex interaction of psychosocial factors that impacted various aspects of pain experience in patients, which offer the potential to inform clinical strategies for remote medicine and future crises.

No evidence of compensatory changes in energy balance, despite reductions in body weight and liver fat, during dapagliflozin treatment in type 2 diabetes mellitus: A randomized, double-blind, placebo-controlled, cross-over trial (ENERGIZE).

AIM: This study assessed the impact of dapagliflozin on food intake, eating behaviour, energy expenditure, magnetic resonance imaging (MRI)-determined brain response to food cues and body composition in patients with type 2 diabetes mellitus (T2D). MATERIALS AND METHODS: Patients were given dapagliflozin 10 mg once daily in a randomized, double-blind, placebo-controlled trial with short-term (1 week) and long-term (12 weeks) cross-over periods. The primary outcome was the difference in test meal food intake between long-term dapagliflozin and placebo treatment. Secondary outcomes included short-term differences in test meal food intake, short- and long-term differences in appetite and eating rate, energy expenditure and functional MRI brain activity in relation to food images. We determined differences in glycated haemoglobin, weight, liver fat (by 1 H magnetic resonance spectroscopy) and subcutaneous/visceral adipose tissue volumes (by MRI). RESULTS: In total, 52 patients (43% were women) were randomized; with the analysis of 49 patients: median age 58 years, weight 99.1 kg, body mass index 35 kg/m2 , glycated haemoglobin 49 mmol/mol. Dapagliflozin reduced glycated haemoglobin by 9.7 mmol/mol [95% confidence interval (CI) 3.91-16.27, p = .004], and body weight (-2.84 vs. -0.87 kg) versus placebo. There was no short- or long-term difference in test meal food intake between dapagliflozin and placebo [mean difference 5.7 g (95% CI -127.9 to 139.3, p = .933); 15.8 g (95% CI -147.7 to 116.1, p = .813), respectively] nor in the rate of eating, energy expenditure, appetite, or brain responses to food cues. Liver fat (median reduction -4.7 vs. 1.95%), but not subcutaneous/visceral adipose tissue, decreased significantly with 12 weeks of dapagliflozin. CONCLUSIONS: The reduction in body weight and liver fat with dapagliflozin was not associated with compensatory adaptations in food intake or energy expenditure.

Neural correlates of naturalistic single-trial appetitive conditioning.

BACKGROUND: Prior research suggests naturalistic single-trial appetitive conditioning may be a potent phenomenon in humans, capable of modulating both motivation and attention. In this study, we aimed to characterise the neural correlates of this phenomenon using functional Magnetic Resonance Imaging (fMRI) paradigms METHODS: Twenty-three healthy adults (12 males) underwent conditioning during which they ate a novel 3D object made from white chocolate (CS+) and handled a similar object made from plastic (CS-). Brain activity was recorded before and after conditioning during a passive viewing paradigm RESULTS: A naturalistic CS+ was rated as more highly craved, better-liked and elicited greater expectancies for chocolate than the CS- after conditioning. An exploration of the interaction between time (pre- and post-conditioning) and CS type (CS+, CS-) during the passive viewing task suggested enhanced activation from pre- to post-conditioning in the right superior frontal gyrus (R.SFG) in response to the CS-. CONCLUSION: Results reveal neural correlates of single-trial appetitive conditioning and highlight a possible role of response inhibition during learning about non-rewards, perhaps optimizing motivated behaviour. These findings contribute to our understanding of the neural mechanisms underpinning rapid reward and non-reward learning, and may inform development of behavioural interventions for reward-driven overeating.

Machine learning and EEG can classify passive viewing of discrete categories of visual stimuli but not the observation of pain.

Previous studies have demonstrated the potential of machine learning (ML) in classifying physical pain from non-pain states using electroencephalographic (EEG) data. However, the application of ML to EEG data to categorise the observation of pain versus non-pain images of human facial expressions or scenes depicting pain being inflicted has not been explored. The present study aimed to address this by training Random Forest (RF) models on cortical event-related potentials (ERPs) recorded while participants passively viewed faces displaying either pain or neutral expressions, as well as action scenes depicting pain or matched non-pain (neutral) scenarios. Ninety-one participants were recruited across three samples, which included a model development group (n = 40) and a cross-subject validation group (n = 51). Additionally, 25 participants from the model development group completed a second experimental session, providing a within-subject temporal validation sample. The analysis of ERPs revealed an enhanced N170 component in response to faces compared to action scenes. Moreover, an increased late positive potential (LPP) was observed during the viewing of pain scenes compared to neutral scenes. Additionally, an enhanced P3 response was found when participants viewed faces displaying pain expressions compared to neutral expressions. Subsequently, three RF models were developed to classify images into faces and scenes, neutral and pain scenes, and neutral and pain expressions. The RF model achieved classification accuracies of 75%, 64%, and 69% for cross-validation, cross-subject, and within-subject classifications, respectively, along with reasonably calibrated predictions for the classification of face versus scene images. However, the RF model was unable to classify pain versus neutral stimuli above chance levels when presented with subsequent tasks involving images from either category. These results expand upon previous findings by externally validating the use of ML in classifying ERPs related to different categories of visual images, namely faces and scenes. The results also indicate the limitations of ML in distinguishing pain and non-pain connotations using ERP responses to the passive viewing of visually similar images.

The neural correlates of texture perception: A systematic review and activation likelihood estimation meta-analysis of functional magnetic resonance imaging studies.

INTRODUCTION: Humans use discriminative touch to perceive texture through dynamic interactions with surfaces, activating low-threshold mechanoreceptors in the skin. It was largely assumed that texture was processed in primary somatosensory regions in the brain; however, imaging studies indicate heterogeneous patterns of brain activity associated with texture processing. METHODS: To address this, we conducted a coordinate-based activation likelihood estimation meta-analysis of 13 functional magnetic resonance imaging studies (comprising 15 experiments contributing 228 participants and 275 foci) selected by a systematic review. RESULTS: Concordant activations for texture perception occurred in the left primary somatosensory and motor regions, with bilateral activations in the secondary somatosensory, posterior insula, and premotor and supplementary motor cortices. We also evaluated differences between studies that compared touch processing to non-haptic control (e.g., rest or visual control) or those that used haptic control (e.g., shape or orientation perception) to specifically investigate texture encoding. Studies employing a haptic control revealed concordance for texture processing only in the left secondary somatosensory cortex. Contrast analyses demonstrated greater concordance of activations in the left primary somatosensory regions and inferior parietal cortex for studies with a non-haptic control, compared to experiments accounting for other haptic aspects. CONCLUSION: These findings suggest that texture processing may recruit higher order integrative structures, and the secondary somatosensory cortex may play a key role in encoding textural properties. The present study provides unique insight into the neural correlates of texture-related processing by assessing the influence of non-textural haptic elements and identifies opportunities for a future research design to understand the neural processing of texture.

Economic value in the Brain: A meta-analysis of willingness-to-pay using the Becker-DeGroot-Marschak auction.

Forming and comparing subjective values (SVs) of choice options is a critical stage of decision-making. Previous studies have highlighted a complex network of brain regions involved in this process by utilising a diverse range of tasks and stimuli, varying in economic, hedonic and sensory qualities. However, the heterogeneity of tasks and sensory modalities may systematically confound the set of regions mediating the SVs of goods. To identify and delineate the core brain valuation system involved in processing SV, we utilised the Becker-DeGroot-Marschak (BDM) auction, an incentivised demand-revealing mechanism which quantifies SV through the economic metric of willingness-to-pay (WTP). A coordinate-based activation likelihood estimation meta-analysis analysed twenty-four fMRI studies employing a BDM task (731 participants; 190 foci). Using an additional contrast analysis, we also investigated whether this encoding of SV would be invariant to the concurrency of auction task and fMRI recordings. A fail-safe number analysis was conducted to explore potential publication bias. WTP positively correlated with fMRI-BOLD activations in the left ventromedial prefrontal cortex with a sub-cluster extending into anterior cingulate cortex, bilateral ventral striatum, right dorsolateral prefrontal cortex, right inferior frontal gyrus, and right anterior insula. Contrast analysis identified preferential engagement of the mentalizing-related structures in response to concurrent scanning. Together, our findings offer succinct empirical support for the core structures participating in the formation of SV, separate from the hedonic aspects of reward and evaluated in terms of WTP using BDM, and show the selective involvement of inhibition-related brain structures during active valuation.

Tactile estimation of hedonic and sensory properties during active touch: An electroencephalography study.

Perceptual judgements about our physical environment are informed by somatosensory information. In real-world exploration, this often involves dynamic hand movements to contact surfaces, termed active touch. The current study investigated cortical oscillatory changes during active exploration to inform the estimation of surface properties and hedonic preferences of two textured stimuli: smooth silk and rough hessian. A purpose-built touch sensor quantified active touch, and oscillatory brain activity was recorded from 129-channel electroencephalography. By fusing these data streams at a single trial level, oscillatory changes within the brain were examined while controlling for objective touch parameters (i.e., friction). Time-frequency analysis was used to quantify changes in cortical oscillatory activity in alpha (8-12 Hz) and beta (16-24 Hz) frequency bands. Results reproduce findings from our lab, whereby active exploration of rough textures increased alpha-band event-related desynchronisation in contralateral sensorimotor areas. Hedonic processing of less preferred textures resulted in an increase in temporoparietal beta-band and frontal alpha-band event-related desynchronisation relative to most preferred textures, suggesting that higher order brain regions are involved in the hedonic processing of texture. Overall, the current study provides novel insight into the neural mechanisms underlying texture perception during active touch and how this process is influenced by cognitive tasks.

Neural correlates of perceptual texture change during active touch.

Introduction: Texture changes occur frequently during real-world haptic explorations, but the neural processes that encode perceptual texture change remain relatively unknown. The present study examines cortical oscillatory changes during transitions between different surface textures during active touch. Methods: Participants explored two differing textures whilst oscillatory brain activity and finger position data were recorded using 129-channel electroencephalography and a purpose-built touch sensor. These data streams were fused to calculate epochs relative to the time when the moving finger crossed the textural boundary on a 3D-printed sample. Changes in oscillatory band power in alpha (8-12 Hz), beta (16-24 Hz) and theta (4-7 Hz) frequency bands were investigated. Results: Alpha-band power reduced over bilateral sensorimotor areas during the transition period relative to ongoing texture processing, indicating that alpha-band activity is modulated by perceptual texture change during complex ongoing tactile exploration. Further, reduced beta-band power was observed in central sensorimotor areas when participants transitioned from rough to smooth relative to transitioning from smooth to rough textures, supporting previous research that beta-band activity is mediated by high-frequency vibrotactile cues. Discussion: The present findings suggest that perceptual texture change is encoded in the brain in alpha-band oscillatory activity whilst completing continuous naturalistic movements across textures.

External validation of binary machine learning models for pain intensity perception classification from EEG in healthy individuals.

Discrimination of pain intensity using machine learning (ML) and electroencephalography (EEG) has significant potential for clinical applications, especially in scenarios where self-report is unsuitable. However, existing research is limited due to a lack of external validation (assessing performance using novel data). We aimed for the first external validation study for pain intensity classification with EEG. Pneumatic pressure stimuli were delivered to the fingernail bed at high and low pain intensities during two independent EEG experiments with healthy participants. Study one (n = 25) was utilised for training and cross-validation. Study two (n = 15) was used for external validation one (identical stimulation parameters to study one) and external validation two (new stimulation parameters). Time-frequency features of peri-stimulus EEG were computed on a single-trial basis for all electrodes. ML training and analysis were performed on a subset of features, identified through feature selection, which were distributed across scalp electrodes and included frontal, central, and parietal regions. Results demonstrated that ML models outperformed chance. The Random Forest (RF) achieved the greatest accuracies of 73.18, 68.32 and 60.42% for cross-validation, external validation one and two, respectively. Importantly, this research is the first to externally validate ML and EEG for the classification of intensity during experimental pain, demonstrating promising performance which generalises to novel samples and paradigms. These findings offer the most rigorous estimates of ML's clinical potential for pain classification.

Pulse Intensity Effects of Burst and Tonic Spinal Cord Stimulation on Neural Responses to Brushing in Patients With Neuropathic Pain.

OBJECTIVES: Tonic spinal cord stimulation (SCS) is accompanied by paresthesia in affected body regions. Comparatively, the absence of paresthesia with burst SCS suggests different involvement of the dorsal column system conveying afferent impulses from low-threshold mechanoreceptors. This study evaluated cortical activation changes during gentle brushing of a pain-free leg during four SCS pulse intensities to assess the effect of intensity on recruitment of dorsal column system fibers during burst and tonic SCS. MATERIALS AND METHODS: Twenty patients using SCS (11 burst, nine tonic) for neuropathic leg pain participated. Brushing was administered to a pain-free area of the leg during four SCS intensities: therapeutic (100%), medium (66%), low (33%), and no stimulation. Whole-brain electroencephalography was continuously recorded. Changes in spectral power during brushing were evaluated using the event-related desynchronization (ERD) method in theta (4-7 Hz), alpha (8-13 Hz), and beta (16-24 Hz) frequency bands. RESULTS: Brushing was accompanied by a suppression of cortical oscillations in the range 4-24 Hz. Stronger intensities of burst and tonic SCS led to less suppression of 4-7 Hz and 8-13 Hz bands in parietal electrodes, and in central electrodes in the 16-24 Hz band, with the strongest, statistically significant suppression at medium intensity. Tonic SCS showed a stronger reduction in 4-7 Hz oscillations over right sensorimotor electrodes, and over right frontal and left sensorimotor electrodes in the 8-13 Hz band, compared to burst SCS. CONCLUSIONS: Results suggest that burst and tonic SCS are mediated by both different and shared mechanisms. Attenuated brushing-related ERD with tonic SCS suggests a gating of cortical activation by afferent impulses in the dorsal column, whereas burst may engage different pathways. Diminished brushing-related ERD at medium and therapeutic intensities of burst and tonic SCS points towards a nonlinear effect of SCS on somatosensory processing.