Affective touch, involving touch-sensitive C-tactile (CT) afferent nerve fibres, is integral to human development and well-being. Despite presumed cultural differences, affective touch research typically includes 'Western', minority-world contexts, with findings extrapolated cross-culturally. We report the first cross-cultural study to experimentally investigate subjective and neurophysiological correlates of affective touch in women in South Africa (SA) and the United Kingdom (UK) using (i) touch ratings and (ii) cortical oscillations for slow, CT-optimal (vs. faster, CT-suboptimal) touch on two body regions (arm and palm). We also controlled for individual differences in touch experiences, attitudes, and attachment style. Cultural context modulated affective touch: SA (vs. UK) participants rated touch as more positive and less intense, with enhanced differentiation in sensorimotor beta band oscillations, especially during palm touch. UK participants differentiated between stroking speeds, with opposite directions of effects at the arm and palm for frontal theta oscillations. Alpha band power showed consistent effects across countries. Results highlight the importance of cultural context in the subjective experience and neural processing of affective touch. Findings suggest that palm touch may hold greater social or emotional significance in SA than in the UK. Future research should further explore potential cultural influences on the meaning and function of touch across contexts.

Brain imaging performed in natural settings is known as mobile brain and body imaging (MoBI). One of the features which distinguishes MoBI and laboratory-based experiments is the body posture. Previous studies pointed to mechanical, autonomic, cortical and cognitive differences between upright stance and sitting or reclining. The purpose of this study was to analyse effects of posture on eye-movement related potentials (EMRP) recorded during free viewing of human faces. A 64-channel wireless EEG was recorded from 14 participants in either standing or reclining postures while they freely viewed pictures of emotional faces displaying fear, anger, sadness, and a neutral emotional state. Eye tracking data was used to insert triggers corresponding to the instant at which the gaze first landed on a face. Spatial filtering of the EEG data was performed using a group independent component analysis (ICA). Grand average EMRPs displayed the post-saccadic lambda component and the face-sensitive N170/vertex positive potential (VPP) complex. The lambda component but not the N170 component was stronger during reclining than upright posture. Emotional expression of faces showed no effects on EMRP components or subjective ratings. Results suggest that posture primarily affects early components of EMRPs recorded using wireless EEG recordings during free viewing of faces. Thus, findings from evoked potential data obtained in seated individuals, e.g., in laboratory experiments, should be interpreted with caution in MoBI experiments with posture affecting primarily the early latency component.

Abstract 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.

Vickrey auctions (VA) and Becker-DeGroot-Marschak auctions (BDM) are strategically equivalent demand-revealing mechanisms, differentiated only by a human opponent in the VA, and a random-number-generator opponent in the BDM. Game parameters are such that players are incentivized to reveal their private subjective values (SV) and behavior should be identical in both tasks. However, this has been repeatedly shown not to be the case. In this study, the neural correlates of outcome feedback processing during VA and BDM were directly compared using electroencephalography. Twenty-eight healthy participants bid for household products which were then divided into high- and low-SV categories. The VA included a human opponent deception to induce a social environment, while in reality a random-number-generator was used in both tasks. A P3 component peaking at 336 ms over midline parietal sites showed more positive amplitudes for high bid values, and for win outcomes in the VA but not the BDM. Both auctions also elicited a Reward Positivity potential, maximal at 275 ms along the central midline electrodes, that was not modulated by auction task or SV. Further, an exploratory N170 potential in the right occipitotemporal electrodes and a vertex positive potential component were stronger in the VA relative to the BDM. Results point to an enhanced cortical response to bid outcomes during VA task in a potential component associated with emotional control, and to the occurrence of face-sensitive potentials in VA but not in BDM auction. These findings suggest modulation of bid outcome processing by the social-competitive aspect of auction tasks. Directly comparing two prominent auction paradigms affords the opportunity to isolate the impact of social environment on competitive, risky decision-making. Findings suggest that feedback processing as early as 176 ms is facilitated by the presence of a human competitor, and later processing is modulated by social context and subjective value.

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.

Previous research has predicted subjective pain intensity from electroencephalographic (EEG) data using machine learning (ML) models. However, there is a paucity of externally validated ML models for pain assessment, particularly for continuous pain prediction (e.g., decoding pain ratings on a 101-point scale). We aimed to conduct the first external validation paradigm for ML regression models for pain intensity prediction from EEG data. Ninety-one subjects were recruited across three samples. Sample one (n = 40) was used for model development, sample two (n = 51) was used as a cross-subject external validation set, whilst sample three (n = 25) was used as a within-subjects temporal external validation set. Pneumatic pressure stimuli were delivered to the left-hand index fingernail bed at 10 graded intensity levels. Single-trial time-frequency features of peri-stimulus EEG were used to train a Random Forest (RF) model and long short-term memory (LSTM) network to predict pain intensity responses. Results demonstrated that both the RF model and LSTM network predicted pain intensity significantly more accurately than a random prediction model, with the mean absolute error (MAE) of the RF (best performing model) at 19.59, 21.29, and 18.90 for internal validation, cross-subject external validation, and within-subject external validation, respectively. However, neither model was able to predict pain intensity better than a baseline dummy model, which predicted the mean behavioural rating of the training set and did not have access to neural data. Moreover, in a replication of our recent work, we developed a RF model for the classification of low and high-pain trials, which demonstrated internal and external validation accuracies up to 64% and 58%, respectively. Taken together, our results suggest that using ML and EEG to predict continuous pain ratings is not currently feasible. However, classification models demonstrate some potential, consistently outperforming chance across validation samples. Further improvements such as composite measures are required to elevate ML performance to a clinically meaningful level.

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.

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.

IntroductionTexture 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.MethodsParticipants 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.ResultsAlpha-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.DiscussionThe present findings suggest that perceptual texture change is encoded in the brain in alpha-band oscillatory activity whilst completing continuous naturalistic movements across textures.

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.

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.

Online retailers often sell products using a socially competitive second-price sealed-bid auction known as a Vickrey auction (VA), an incentivized demand-revealing mechanism used to elicit players' subjective values. The VA presents a situation of risky decision-making, which typically implements value processing and a loss aversion mechanism. Neural outcome processing of VA bids are not known; this study explores this for the first time using EEG. Twenty-eight healthy participants bid on household items against an anonymous, computerized opponent. Bid outcome event-related potentials were predicted to differentiate between three conditions: outbid (no-win), large margin win (bargain), and small margin win (snatch). Individual loss aversion values were evaluated in a separate behavioral experiment offering gains or losses of variable amounts but equal chances against an assured gain. Processing outcomes of VA bids were associated with a feedback-related negativity (FRN) potential with a spatial maximum at the vertex (251-271 ms), where bargain win trials resulted in greater FRN amplitudes than snatch win trials. Additionally, a P300 potential was sensitive to win versus no-win outcomes and to retail price. Individual loss aversion level did not correlate with the strength of FRN or P300. Results show that outcome processing in a VA is associated with FRN that differentiates between relatively advantageous and less advantageous gains, and a P300 that distinguishes between the more and less expensive auction items. Our findings pave the way to an objective exploration of economic decision-making and purchasing behavior involving a widely popular auction.

Somatosensory-evoked potentials during low-frequency stimulation of peripheral nerves were examined at graded stimulus intensities. Low-frequency stimulation was associated with decreased responsiveness in the midcingulate cortex and increased responsiveness in primary sensorimotor cortex. Greater intensities were associated with increased midcingulate cortex θ band-power and decreased sensorimotor cortex α band-power. Results further previous evidence of an inhibition of somatosensory processing during and after low-frequency stimulation and point toward a potential augmentation of activity in somatosensory processing regions.

Losses usually have greater subjective value (SV) than gains of equal nominal value but often cause a relative deterioration in effortful performance. Since losses and gains induce differing approach/avoidance behavioral tendencies, we explored whether incentive type interacted with approach/avoidance motor-sets. Alpha- and beta-band event-related desynchronization (ERD) was hypothesized to be weakest when participants expected a loss and prepared an inhibitory motor-set, and strongest when participants expected a gain and prepared an active motor-set. It was also hypothesized that effort would modulate reward and motor-set-related cortical activation patterns. Participants completed a cued Go/NoGo task while expecting a reward (+10p), avoiding a loss (-10p), or receiving no incentive (0p); and while expecting a NoGo cue with a probability of either .75 or .25. Pre-movement alpha- and beta-band EEG power was analyzed using the ERD method, and the SV of effort was evaluated using a cognitive effort discounting task. Gains incentivized faster RTs and stronger preparatory alpha band ERD compared to loss and no incentive conditions, while inhibitory motor-sets resulted in significantly weaker alpha-band ERD. However, there was no interaction between incentive and motor-sets. Participants were more willing to expend effort in losses compared to gain trials, although the SV of effort was not associated with ERD patterns or RTs. Results suggest that incentive and approach/avoidance motor tendencies modulate cortical activations prior to a speeded RT movement independently, and are not associated with the economic value of effort. The present results favor attentional explanations of the effect of incentive modality on effort.

Objective: Transcutaneous low-frequency stimulation (LFS) elicits long-term depression-like effects on human pain perception. However, the neural mechanisms underlying LFS are poorly understood. We investigated cortical activation changes occurring during LFS and if changes were associated with reduced nociceptive processing and increased amplitude of spontaneous cortical oscillations post-treatment. Methods: LFS was applied to the radial nerve of 25 healthy volunteers over two sessions using active (1 Hz) or sham (0.02 Hz) frequencies. Changes in resting electroencephalography (EEG) and laser-evoked potentials (LEPs) were investigated before and after LFS. Somatosensory-evoked potentials were recorded during LFS and source analysis was carried out. Results: Ipsilateral midcingulate and operculo-insular cortex source activity declined linearly during LFS. Active LFS was associated with attenuated long-latency LEP amplitude in ipsilateral frontocentral elec-trodes and increased resting alpha (8-12 Hz) and beta (16-24 Hz) band power in electrodes overlying operculo-insular, sensorimotor and frontal cortical regions. Reduced ipsilateral operculo-insular cortex source activity during LFS correlated with a smaller post-treatment alpha-band power increase. Conclusions: LFS attenuated somatosensory processing both during and after stimulation. Significance: Results further our understanding of the attenuation of somatosensory processing both dur -ing and after LFS. (c) 2021 International Federation of Clinical Neurophysiology. Published by Elsevier B.V. All rights reserved.

We explored how reward and value of effort shapes performance in a sustained vigilance, reaction time (RT) task. It was posited that reward and value would hasten RTs and increase cognitive effort by boosting activation in the sensorimotor cortex and inhibition in the frontal cortex, similar to the horse-race model of motor actions. Participants performed a series of speeded responses while expecting differing monetary rewards (0 pence (p), 1 p, and 10 p) if they responded faster than their median RT. Amplitudes of cortical alpha, beta, and theta oscillations were analysed using the event-related desynchronization method. In experiment 1 ( N = 29, with 12 females), reward was consistent within block, while in experiment 2 ( N = 17, with 12 females), reward amount was displayed before each trial. Each experiment evaluated the baseline amplitude of cortical oscillations differently. The value of effort was evaluated using a cognitive effort discounting task (COGED). In both experiments, RTs decreased significantly with higher rewards. Reward level sharpened the increased amplitudes of beta oscillations during fast responses in experiment 1. In experiment 2, reward decreased the amplitudes of beta oscillations in the ipsilateral sensorimotor cortex. Individual effort values did not significantly correlate with oscillatory changes in either experiment. Results suggest that reward level and response speed interacted with the task- and baseline-dependent patterns of cortical inhibition in the frontal cortex and with activation in the sensorimotor cortex during the period of motor preparation in a sustained vigilance task. However, neither the shortening of RT with increasing reward nor the value of effort correlated with oscillatory changes. This implies that amplitudes of cortical oscillations may shape upcoming motor responses but do not translate higher-order motivational factors into motor performance.