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Experiment 1. Interaction of within-subject and between-subject pain intensity effects. Features of the EEG responses were compared using a twoway mixed-design ANOVA, with a within-subject factor (two levels: low-pain and high-pain trials) and a between-subject factor (two levels: low-pain and high-pain subjects). (A) Participants were sorted by mean pain rating across trials and median-split into low-pain subjects (green/cyan, left) and high-pain subjects (blue/red, right) (n = 48 each). In each subject, trials were sorted by pain ratings and median-split into low-pain trials (green/blue, bottom) and highpain trials (cyan/red, top) (n = 20 each). (B) Time-domain analysis. Group-level ERPs were markedly different between low-pain and high-pain trials, but not between low-pain and high-pain subjects. (C) Time-frequency analysis. Group-level TFDs for low-pain (Bottom) and high-pain (Top) trials of low-pain (Left) and high-pain (Right) subjects, respectively. All TFD features were markedly different between low-pain and high-pain trials. Only the γ-ERS was clearly different between low-pain and high-pain subjects. (D) Statistical comparisons and scalp topographies of neural responses. N2-wave and P2-wave amplitudes, as well as LEP magnitude, were significantly larger in high-pain than in low-pain trials, but not different between low-pain and high-pain subjects. α-ERD magnitude was not significantly different between low-pain and high-pain trials, or between low-pain and high-pain subjects. γ-ERS magnitude was larger both in high-pain than low-pain trials (P < 0.001), and in high-pain than low-pain subjects (P = 0.005). The lack of interaction between trial type and subject type indicates that the ability of γ-ERS to predict pain both within-and between-subjects was not driven by a small number of highly pain-sensitive individuals.
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Significance
While several features of brain activity can be used to predict the variability of painful percepts within a given individual, it is much more difficult to predict pain variability across individuals. Here, we used electrophysiology to sample brain activity of humans and rodents, and demonstrated that laser-induced gamma oscillations s...
Contexts in source publication
Context 1
... all explored features of the EEG response elicited by transient laser stimuli (experiment 1, n = 96), both in the time domain and in the time-frequency domain, reflected within- subject pain reports (SI Appendix, Fig. S1A). Peak amplitudes of all main EEG waves in the time domain (i.e., N1 wave, 120 to 200 ms; N2 wave, 180 to 300 ms; P2 wave, 250 to 500 ms), as well as magnitudes of stimulus-induced modulations of EEG oscil- lations [i.e., "laser-evoked potential" (LEP), 100 to 400 ms, 1 to 10 Hz; "alpha-band event-related desynchronization" (α-ERD), 600 to 900 ms, 7 to 13 Hz; and "gamma-band event-related synchronization" (γ-ERS), 180 to 260 ms, 60 to 85 Hz], were significantly correlated with subjective ratings of pain perception (Figs. 2 and 3 and Table 1). ...
Context 2
... tional properties of the γ-ERS. Permutation testing (5,000 times) indicated that, within the γ-ERS cluster identified in experiment 1, the t values and r values that respectively reflected subjective ratings within and across individuals were significantly different from chance (P < 0.001 and P = 0.005, respectively) ( Fig. 2C and SI Appendix, Fig. S3). In other words, the magnitude of gamma oscillations within this γ-ERS cluster was significantly corre- lated with ratings of pain perception not only within-subject (mean r = 0.16 ± 0.23, P < 0.001, one-sample t test), but also Interaction of within-subject and between-subject pain intensity effects. Features of the EEG responses ...
Context 3
... and Between-Subject Effects. To explore the possible interaction of within-subject and between-subject effects and thus assess whether the main effect of reported pain intensity was only being driven by high (or, less likely, low) pain-sensitive individuals, we compared features of the EEG responses elicited by laser stimuli (experi- ment 1) ( Fig. 3 and SI Appendix, Table S2) using a two-way mixed-design analysis of variance (ANOVA) (statistical proce- dures are detailed in SI Appendix), with a within-subject factor (two levels: low-pain and high-pain trials) and a between-subject factor (two levels: low-pain and high-pain subjects). Results are summarized in Fig. 3 and SI ...
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... (experi- ment 1) ( Fig. 3 and SI Appendix, Table S2) using a two-way mixed-design analysis of variance (ANOVA) (statistical proce- dures are detailed in SI Appendix), with a within-subject factor (two levels: low-pain and high-pain trials) and a between-subject factor (two levels: low-pain and high-pain subjects). Results are summarized in Fig. 3 and SI Appendix, Table S3. All EEG re- sponse features, except the magnitude of α-ERD, were signifi- cantly modulated by the within-subject factor: i.e., they were significantly larger in high-pain trials than in low-pain trials. In contrast, the between-subject factor did not explain the vari- ability of all EEG response features, ...
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... In the future, the modulation effect of tACS on pain at different frequencies (e.g., α-wave, γ-wave) can be further explored to study the specific effects of different frequencies on the neural network. Comparing the effects of tACS at different stimulation locations (e.g., prefrontal, parietal) to optimize the selection strategy of stimulation target areas (Tu et al., 2016;Vodovozov et al., 2018;Hu and Iannetti, 2019), Additionally, the combined effects of tACS with other non-invasive brain stimulation techniques, including transcranial direct current stimulation (tDCS) and transcranial magnetic stimulation (TMS), warrant further exploration. The potential synergistic mechanisms of tACS in conjunction with pharmacological interventions, psychotherapy (e.g., cognitive behavioral therapy, CBT), and physical therapy (e.g., visceral acupressure) should also be examined. ...
Neural oscillations play a critical role in the regulation of brain functions, with theta waves (4–8 Hz) in the sensorimotor cortex significantly influencing pain perception and modulation. These oscillations can modulate pain signal transmission, emotional cognition, and neuroplasticity. Post-stroke chronic pain is a common and complex symptom that imposes significant physiological and psychological burdens on patients. Transcranial alternating current stimulation (tACS), a non-invasive brain stimulation technique, can synchronize specific frequency neural activities, reorganize brain networks, and modulate neuroplasticity by adjusting specific frequency neural oscillations. In recent years, tACS has been widely applied in the research and treatment of various neurological and psychiatric disorders. This study aims to systematically summarize the current research progress on the regulation of θ oscillations in sensorimotor cortex by tACS. By reviewing relevant experimental and clinical studies, we explore the specific mechanisms of θ oscillations in pain perception and modulation and analyze the mechanisms and effects of tACS modulation of θ oscillations. Additionally, we examine the central and peripheral neural mechanisms of post-stroke chronic pain, emphasizing the critical role of the sensorimotor cortex in pain processing. In conclusion, tACS shows potential for modulating sensorimotor cortex θ oscillations and alleviating post-stroke chronic pain. This research provides new insights into the neural modulation mechanisms related to pain and offers potential new directions for developing novel therapies. Future clinical studies and technological optimizations are necessary to ensure the effectiveness and feasibility of tACS in clinical practice.
... These surprising changes of sensory energy elicit large and widespread cortical responses in several species, measurable using invasive and non-invasive electrophysiology ( Figure 1; see Section 3 for a detailed phenomenological description of these responses), as well as blood-flow based neuroimaging (Velasco et al. 1980(Velasco et al. , 1985Velasco and Velasco 1986;Velasco, Velasco, and Velasco III 1989;Mouraux and Iannetti 2009;Wang et al. 2010;Iannetti and Mouraux 2011;Valentini et al. 2011;Legrain et al. 2011;Torta et al. 2012;Liang et al. 2013;Hu et al. 2015;Novembre et al. 2018Novembre et al. , 2023Somervail et al. 2021Somervail et al. , 2022. The fact that these responses are phenomenologically similar across different species suggests their importance for survival (Hu et al. 2015;Hu and Iannetti 2019;Somervail et al. 2021;Novembre et al. 2023). ...
... In scalp electrophysiology the VP is typically observed after averaging a number of single-trial responses in the time domain. When performing a time-frequency analysis of the amplitude of electrocortical oscillations in single trials , it becomes clear that the stimuli eliciting a VP also induce several widespread non-phase-locked responses, such as a transient increase of gamma power and a longer-lasting decrease of alpha power (Mouraux et al. 2003;Hu and Iannetti 2019). ...
... Responses similar to the VP have also been observed with invasive recordings from cortical and subcortical structures of rodents Knight 1993a, 1993b;Hu et al. 2015;Hu and Iannetti 2019;Somervail et al. 2021), cats (Albe-Fessard and Rougeul 1958), monkeys (Novembre et al. 2023) and humans (Velasco et al. 1985;Velasco and Velasco 1986;Velasco, Velasco, and Velasco III 1989). Some of these investigations also confirmed the invariance of this response with respect to stimulus modality or stimulus location (Albe-Fessard and Rougeul 1958;Velasco et al. 1984Velasco et al. , 1985Velasco and Velasco 1986;Velasco, Velasco, and Velasco III 1989;Hu et al. 2015;Hu and Iannetti 2019). ...
Sudden and unexpected environmental stimuli likely signal events demanding immediate behavioural responses. These stimuli also trigger some of the largest and most widespread electrocortical responses in the awake mammalian brain. Many researchers implicitly interpret these sensory-evoked brain responses as mainly reflecting modality-specific sensory processing mediated by high-fidelity 'lemniscal' thalamocortical pathways to primary sensory cortices. Here we contend that this interpretation is unjustified. We provide evidence that virtually all electrocortical responses elicited by the sudden sensory stimuli used in systems and cognitive neuroscience are strongly contributed to by non-modality-specific processes mediated by diffuse 'extralemniscal' thalamocortical projections, reflected in the scalp vertex potential (VP). We conclude by suggesting a mechanism through which transient extralemniscal responses affect ongoing brain activity and promote swift reactions to sudden environmental changes.
... The magnitudes of time-frequency features, including low-frequency LEP responses (LEP) and high-frequency gamma band oscillations (GBO), were extracted with specific time-frequency regions-of-interest (ROIs) [Aδ-LEP, 100-400 ms, 1-13 Hz; C-LEP: 400-800 ms, 1-13 Hz; Aδ-GBO, 100-400 ms, 60-95 Hz; C-GBO: 400-800 ms, 60-95 Hz] (Fig. 1F). These magnitudes were quantified by computing the top 20% of all time-frequency points within each ROI [47,48]. ...
... Numerous studies suggest that cortical oscillations in the gamma frequency (i.e., GBOs) are one of the most promising neural markers of pain across species [47,[62][63][64]. GBOs could reliably correlate with pain perception intensity within individuals and pain sensitivity across different individuals, in both humans [47] and rodents [63]. ...
... Numerous studies suggest that cortical oscillations in the gamma frequency (i.e., GBOs) are one of the most promising neural markers of pain across species [47,[62][63][64]. GBOs could reliably correlate with pain perception intensity within individuals and pain sensitivity across different individuals, in both humans [47] and rodents [63]. GBO magnitude could also track the timevarying fluctuations of the intensity of pain [65][66][67]. ...
Autistic individuals carrying mutations in SHANK3 (encoding a synaptic scaffolding protein) have been consistently reported to exhibit reduced pain sensitivity. However, the neural mechanisms underlying impaired pain processing remain unclear. To investigate the role of SHANK3 in pain processing, we conducted behavioral, electrophysiological, and pharmacological tests upon nociceptive stimulation in a Shank3 mutant dog model. Behaviorally, Shank3 mutant dogs showed reduced nocifensive sensitivity compared to wild-type (WT) dogs. Electrophysiologically, Shank3 mutant dogs exhibited reduced neural responses elicited by the activations of both Aδ- and C-fiber nociceptors. Additionally, Shank3 mutants showed a lower level of aperiodic exponents, which serve as a marker for the excitatory-inhibitory balance of neural activity. The aperiodic exponents mediated the relationship between genotype and nocifensive sensitivity as well as between genotype and neural responses elicited by nociceptive stimuli. Pharmacologically, the reduced nocifensive sensitivity and atypical excitatory-inhibitory balance were rescued by a GABAAR antagonist pentylenetetrazole. These findings highlight the critical role of Shank3 in pain processing and suggest that an impaired excitatory-inhibitory balance may be responsible for the reduced nocifensive reactivity in autism.
... Previous amplitude-based research has greatly contributed to revealing the neural encoding of pain [11][12][13][14]. Pain variations at intraindividual and interindividual levels have been associated with the amplitudes of N2, P2, and gamma-band oscillations [3,[15][16][17]. ...
... However, many previous studies faced a critical issue: a lack of pain selectivity. EEG responses often correlate with perceptual variation in nonpain sensory modalities as well [15,18]. Interestingly, pain discriminability, the ability to distinguish between two or more painful stimuli, appears to be selectively encoded by the amplitude of event-related potentials (ERPs) [19,20]. ...
... Impaired pain discriminability has been observed in patients with chronic pain [23,24], and higher pain discriminability can predict a better effect of pain treatment [25,26]. In reality, however, pain discriminability remains severely underinvestigated [15,16,20,27,28]. ...
Neural activity varies dramatically across time. While such variability has been associated with cognition, its relationship with pain remains largely unexplored. Here, we systematically investigated the relationship between neural variability and pain, particularly pain discriminability, in five large electroencephalography (EEG) datasets (total N = 489), collected from healthy individuals (Datasets 1–4) and patients with postherpetic neuralgia (PHN; Dataset 5) who had received painful or nonpainful sensory stimuli. We found robust correlations between neural variability and interindividual pain discriminability. These correlations were (1) replicable in multiple datasets, (2) pain selective, as no significant correlations were observed in nonpain modalities, and (3) clinically relevant, as they were partly disrupted in patients with PHN. Importantly, variability and amplitude of EEG signals were mutually independent and had distinct temporal and oscillatory profiles in encoding pain discriminability. These findings demonstrate that neural variability is a replicable and selective indicator of pain discriminability above and beyond amplitude, thereby enhancing the understanding of neural encoding of pain discriminability and underscoring the value of neural variability in pain studies.
... A positive correlation exists between the amplitude of gamma (γ) oscillations and pain perception intensity in both humans and rodents. 25 Enhanced γ-band activity is consistently observed in the PFC, IC, somatosensory cortex, and orbitofrontal cortex during both acute and chronic pain states. 26 Parvalbumin-positive interneurons (PVINs), a subtype of GABAergic interneurons, generate γ oscillations and locally regulate the activity of principal neurons. ...
... Stimulus duration was set to 4 ms and stimulus diameter to 7 mm. Laser intensity was set to 3.5 J, which induces stable brain responses while being well tolerated [46]. To avoid tissue damage and minimize habituation/ sensitization effects, stimulation sites were changed slightly after each stimulus. ...
... In addition, noxious stimuli suppress neuronal oscillations in the alpha (8 to <13 Hz) and beta (13 to 30 Hz) frequency bands and induce oscillations in the gamma (30 to100 Hz) frequency band [1,2]. Single-trial evoked and oscillatory brain responses to noxious stimuli were quantified using established procedures [46,58] which have been validated on a published data set ( [59]; Fig F in S1 Text). To examine evoked brain responses, preprocessed data from the neurofeedback runs was bandpass filtered between 1 and 30 Hz (fourth-order Butterworth). ...
... First, individual peak latencies of evoked responses were determined based on averages across all trials of all neurofeedback conditions. To this end, local minima/maxima of the averaged waveform were determined at predefined channels (N1: C4, N2: Cz; P2: Cz) [46,58] and in predefined time-windows (N1: 120-200 ms; N2: 180-300 ms; P2: 250-500 ms) [46]. Second, single-trial amplitudes were obtained by averaging across a 30 ms window [58] centered at the previously defined peak latency. ...
Pain is closely linked to alpha oscillations (8 < 13 Hz) which are thought to represent a supra-modal, top-down mediated gating mechanism that shapes sensory processing. Consequently, alpha oscillations might also shape the cerebral processing of nociceptive input and eventually the perception of pain. To test this mechanistic hypothesis, we designed a sham-controlled and double-blind electroencephalography (EEG)-based neurofeedback study. In a short-term neurofeedback training protocol, healthy participants learned to up- and down-regulate somatosensory alpha oscillations using attention. Subsequently, we investigated how this manipulation impacts experimental pain applied during neurofeedback. Using Bayesian statistics and mediation analysis, we aimed to test whether alpha oscillations mediate attention effects on pain perception. The results showed that attention and neurofeedback successfully up- and down-regulated the asymmetry of somatosensory alpha oscillations. However, attention and neurofeedback did not modulate pain ratings or related brain responses. Accordingly, somatosensory alpha oscillations did not mediate attention effects on pain perception. Thus, our results challenge the hypothesis that somatosensory alpha oscillations shape pain perception. A causal relationship between alpha oscillations and pain perception might not exist or be more complex than hypothesized.
Trial registration: Following Stage 1 acceptance, the study was registered at ClinicalTrials.gov NCT05570695.
... [8][9][10] Hu et al found that gamma-band event-related synchronization can serve as a unique electrophysiological indicator for differentiating between responses to equally salient auditory, visual, and somatosensory stimuli versus those activated by nociceptive stimuli. 11,12 The presence of pain is frequently accompanied by physiological responses, encompassing enhanced heart rate, dilated pupil size, increased respiratory rate, and the occurrence of facial grimaces. [13][14][15][16] In clinical and experimental pain research, facial expression has garnered significant focus and offers the most specific and sensitive nonverbal indicators for pain. ...
... Ma et al previously established as pertinent to variations in stimulus intensity and pain perception. 12,34 For each ROI, the power was averaged across both time and frequency dimensions. ...
Purpose
Pain is a multidimensional, unpleasant emotional and sensory experience, and accurately assessing its intensity is crucial for effective management. However, individuals with cognitive impairments or language deficits may struggle to accurately report their pain. EEG provides insight into the neurological aspects of pain, while facial EMG captures the sensory and peripheral muscle responses. Our objective is to explore the relationship between individual pain perception, brain activity, and facial expressions through a combined analysis of EEG and facial EMG, aiming to provide an objective and multidimensional approach to pain assessment.
Methods
We investigated pain perception in response to electrical stimulation of the middle finger in 26 healthy subjects. The 32-channel EEG and 3-channel facial EMG signals were simultaneously recorded during a pain rating task. Group difference and correlation analysis were employed to investigate the relationship between individual pain perception, EEG, and facial EMG. The general linear model (GLM) was used for multidimensional pain assessment.
Results
The EEG analysis revealed that painful stimuli induced N2-P2 complex waveforms and gamma oscillations, with substantial variability in response to different stimuli. The facial EMG signals also demonstrated significant differences and variability correlated with subjective pain ratings. A combined analysis of EEG and facial EMG data using a general linear model indicated that both N2-P2 complex waveforms and the zygomatic muscle responses significantly contributed to pain assessment.
Conclusion
Facial EMG signals provide pain descriptions which are not sufficiently captured by EEG signals, and integrating both signals offers a more comprehensive understanding of pain perception. Our study underscores the potential of multimodal neurophysiological measurements in pain perception, offering a more comprehensive framework for evaluating pain.
... Since the amplitude of gamma band oscillation (GBO) is directly related to subjective pain intensity, studies suggest that these oscillations reflect cortical activity directly linked to pain perception. In pain-free patients or those with mild pain, gamma oscillations recorded in the awake state before surgery can encode individual pain sensitivity and predict postoperative changes in subjective pain intensity [29]. Most studies on gamma activity during pain stimulation have shown a significant increase in energy [30]. ...
... Beta 13-30 Hz Increased during states of alertness and cognitive processing; can be elevated in anticipation of pain [32]. Gamma 30-100 Hz Increased during acute pain; associated with the perception of pain and processing of nociceptive information [28][29][30]. ...
Pain is a subjective and complex symptom, making its prediction, management, and treatment a significant challenge in clinical research. To address these challenges, the search for reliable and objective pain biomarkers has become a focal point in pain studies. Electroencephalography (EEG), a non-invasive clinical tool, has emerged as the most widely used method for assessing brain regions associated with pain due to its temporal resolution, accuracy, and comprehensive nature. Multichannel EEG is now a primary technique in the study of pain biomarkers. This review discusses the current status and future prospects of EEG biomarkers in pain research, synthesizing evidence on the potential of EEG recordings as reliable biomarkers for pain perception. This will contribute to establishing a more solid foundation for the prediction, diagnosis, and intervention of pain in future research and management.
... Specifically, the decrease in alpha and beta band power reflects the disinhibition ability of the attentional networks [14], [15], [16] and motor networks [13], [17], [18] respectively. Conversely, an increase in gamma band power was observed, indicating a heightened demand for information processing in the brain [19], [20], [21], [22], [23]. Although theta oscillation has been shown to increase following acute pain stimulation in animal studies [24], [25], there is currently no evidence of this phenomenon in humans. ...
Subacute low back pain (sLBP) is a critical transitional phase between acute and chronic stages and is key in determining the progression to chronic pain. While persistent pain has been linked to changes in brain activity, studies have focused mainly on acute and chronic phases, leaving neural changes during the subacute phase—especially during movement—under-researched. This cross-sectional study aimed to investigate changes in brain activity and the impact of pain intensity in individuals with sLBP during rest and reaching movements. Using a 28-electrode EEG, we measured motor-related brain waves, including theta, alpha, beta, and gamma oscillations. Transitioning from rest to movement phases resulted in significant reductions (> 80%) in mean power across all frequency bands, indicating dynamic brain activation in response to movement. Furthermore, pain intensity was significantly correlated with brain wave activity. During rest, pain intensity was positively correlated with alpha oscillation activity in the central brain area (r = 0.40, p < 0.05). In contrast, during movement, pain intensity was negatively correlated with changes in brain activity (r = -0.36 to -0.40, p < 0.05). These findings suggest that pain influences brain activity differently during rest and movement, underscoring the impact of pain levels on neural networks related to the sensorimotor system in sLBP and highlighting the importance of understanding neural changes during this critical transitional phase.
... After EEG preprocessing, the PSD of EEG signals was calculated using the Welch's method (2-s windows, 50% overlap) from signals of each electrode separately for each patient (Donoghue et al., 2020;Hu & Iannetti, 2019;Li et al., 2020). This operation yielded an EEG power spectrum ranging from 1 to 45 Hz, in steps of 0.25 Hz, for each electrode and each patient. ...
Background
The prevalence of postoperative pain is notably high among the elderly population, which poses significant challenges for their postoperative recovery. In this study, we aimed to identify preoperative predictors for acute and chronic postoperative pain in patients undergoing lumbar spinal surgery through a longitudinal investigation.
Methods
We recruited 75 patients (mean age 68.29 ± 5.60 years) and collected their resting‐state electroencephalography (EEG) data two hours before the surgery. The aperiodic and periodic signal components were extracted from the resting‐state EEG using the Fitting Oscillations and One‐Over‐F algorithm. We also collected the preoperative pain ratings, demographic information and the Hospital Anxiety and Depression Scale from all patients. The postoperative pain ratings were collected ten times from Day 1 to Week 12 after surgery.
Results
We observed a high incidence of postoperative acute and chronic pain among older patients. Preoperative pain and peak alpha frequency in resting‐state EEG were the primary predictors of acute postoperative pain. Although age is a significant predictor of chronic postoperative pain, its predictive performance is poor.
Conclusions
Overall, our study provides valuable insights into the complex pattern of preoperative EEG features, preoperative pain and age in predicting postoperative pain at different stages. Our findings highlight the significance of exploring preoperative features to identify patients who are at a higher risk of developing severe postoperative pain, which can aid in the development of more personalized and effective pain management strategies.
Significance
The heightened occurrence of postoperative pain among the elderly presents formidable obstacles to their recuperation. This study delves into identifying preoperative factors influencing acute and chronic postoperative pain. Our findings indicate that preoperative pain and peak alpha frequency are crucial predictors of acute postoperative pain. However, the predictive performance for chronic postoperative pain is limited, although age was a significant predictor of chronic postoperative pain. These insights contribute to the identification of patients at elevated risk for severe acute and chronic postoperative pain, offering valuable guidance for pre‐surgical risk assessment.
