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.

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.

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.

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

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

The cortical oscillatory patterns associated with varying levels of reward during an effortful vigilance task.

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.

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.

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.

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.

Investigating the effect of losses and gains on effortful engagement during an incentivized Go/NoGo task through anticipatory cortical oscillatory changes.

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.

Neural correlates of texture perception during active touch.

Previous studies have shown attenuation of cortical oscillations over bilateral sensorimotor cortex areas during passive perception of smooth textures applied to the skin. However, humans typically explore surfaces using dynamic hand movements. As movements may both modulate texture-related cortical activity and induce movement-related cortical activation, data from passive texture perception cannot be extrapolated to active texture perception. In the present study, we used electroencephalography to investigate cortical oscillatory changes during texture perception throughout active touch exploration. Three natural textured stimuli were selected: smooth silk, soft brushed cotton, and rough hessian. Texture samples were mounted on a purpose-built touch sensor which measured the load and position of the index finger, whilst electroencephalography from 129 channels recorded oscillatory brain activity. The data were fused to investigate oscillatory changes relating to active touch. Changes in oscillatory band power, event-related desynchronisation/synchronisation (ERD/ERS), were investigated in alpha (8-12 Hz) and beta (16-24 Hz) frequency bands. Active texture exploration revealed bilateral activation patterns over sensorimotor cortical areas. Beta-band ERD increased over contralateral sensorimotor regions for soft and smooth textures, and over ipsilateral sensorimotor areas for the smoothest texture. Analysis of covariance revealed that individual differences in perception of softness and smoothness were related to variations in cortical oscillatory activity. Differences may be due to increased high frequency vibrations for smooth and soft textures compared to rough. For the first time, active touch was quantified and fused with electroencephalography data streams, contributing to the understanding of the neural correlates of texture perception during active touch.

Bid outcome processing in Vickrey auctions: An ERP study.

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.

Inhibition of cortical somatosensory processing during and after low frequency peripheral nerve stimulation in humans.

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 electrodes 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 during and after LFS.