Electroencephalography During Nociceptive Stimulation in Chronic Pain Patients: A Systematic Review

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals

Low Back Pain (LBP) is the most prevalent musculoskeletal condition worldwide and a leading cause of disability, significantly affecting mobility, work productivity, and overall quality of life. Due to its high prevalence and substantial economic burden, LBP presents a critical global public health challenge that demands innovative diagnostic and therapeutic solutions. This study introduces a novel deep-learning approach for diagnosing LBP intensity using electroencephalography (EEG) signals and surface electromyography (sEMG) signals from back muscles. A GAN-Convolution-Transformer-based model, named GLEAM (GAN-ConvoLution-sElf Attention-ETLSTM), is designed to classify LBP intensity into four categories: no LBP, mild LBP, moderate LBP, and intolerable LBP. A denoising GAN is central to the model's functionality, playing a pivotal role in enhancing the quality of EEG and sEMG signals by removing noise, resulting in cleaner and more accurate input data. Various features are extracted from the GAN-denoised EEG and sEMG signals, and the combined features from both EEG and sEMG are used for LBP detection. After the feature extraction, the CNN is employed to capture local temporal patterns within the data, allowing the model to focus on smaller, region-specific trends in the signals. Subsequently, the self-attention module identifies global correlations among these locally extracted features, enhancing the model's ability to recognize broader patterns. The proposed ETLSTM network performs the final classification, which achieves an impressive LBP detection accuracy of 98.95%. This research presents several innovative contributions: (i) the development of a novel denoising GAN for cleaning EEG and sEMG signals, (ii) the design and integration of a new ETLSTM architecture as a classifier within the GLEAM model, and (iii) the introduction of the GLEAM hybrid deep learning framework, which enables robust and reliable LBP intensity assessment.

Non-invasive electroencephalography in awake cats: Feasibility and application to sensory processing in chronic pain

BACKGROUND

Feline osteoarthritis (OA) leads to chronic pain and somatosensory sensitisation. In humans, sensory exposure can modulate chronic pain. Recently, electroencephalography (EEG) revealed a specific brain signature to human OA. However, EEG pain characterisation or its modulation does not exist in OA cats, and all EEG were conducted in sedated cats, using intradermal electrodes, which could alter sensory (pain) perception.

NEW METHOD

Cats (n=11) affected by OA were assessed using ten gold-plated surface electrodes. Sensory stimuli were presented in random orders: response to mechanical temporal summation, grapefruit scent and mono-chromatic wavelengths (500 nm-blue, 525 nm-green and 627 nm-red light). The recorded EEG was processed to identify event-related potentials (ERP) and to perform spectral analysis (z-score).

RESULTS

The procedure was well-tolerated. The ERPs were reported for both mechanical (F3, C3, Cz, P3, Pz) and olfactory stimuli (Cz, Pz). The main limitation was motion artifacts. Spectral analysis revealed a significant interaction between the power of EEG frequency bands and light wavelengths (p<0.001). All wavelengths considered, alpha band proportion was higher than that of delta and gamma bands (p<0.044), while the latter was lower than the beta band (p<0.016). Compared to green and red, exposure to blue light elicited distinct changes in EEG power over time (p<0.001).

COMPARISON WITH EXISTING METHOD

This is the first demonstration of EEG feasibility in conscious cats with surface electrodes recording brain activity while exposing them to sensory stimulations.

CONCLUSION

The identification of ERPs and spectral patterns opens new avenues for investigating feline chronic pain and its potential modulation through sensory interventions.

Advances and challenges in neuroimaging-based pain biomarkers

Identifying neural biomarkers of pain has long been a central theme in pain neuroscience. Here, we review the state-of-the-art candidates for neural biomarkers of acute and chronic pain. We classify these potential neural biomarkers into five categories based on the nature of their target variables, including neural biomarkers of (1) within-individual perception, (2) between-individual sensitivity, and (3) discriminability for acute pain, as well as (4) assessment and (5) prospective neural biomarkers for chronic pain. For each category, we provide a synthesized review of candidate biomarkers developed using neuroimaging techniques including functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and electroencephalography (EEG). We also discuss the conceptual and practical challenges in developing neural biomarkers of pain. Addressing these challenges, optimal biomarkers of pain can be developed to deepen our understanding of how the brain represents pain and ultimately help alleviate patients' suffering and improve their well-being.

Dock4 contributes to neuropathic pain by regulating spinal synaptic plasticity in mice

Introduction

Neuropathic pain (NP) conditions arising from injuries to the nervous system due to trauma, disease, or neurotoxins are chronic, severe, debilitating, and exceedingly difficult to treat. However, the mechanisms of NP are not yet clear. Here we explored the role of Dock4, an atypical Rac1 GEF, in the development of NP.

Methods

Mechanical allodynia was assessed as paw withdrawal threshold by a dynamic plantar aesthesiometer. Immunofluorescence staining was conducted to investigate the expression and localization of Dock4, Rac1 and GluN2B. Quantitative analysis of Dock4, Rac1 and GluN2B were determined by qRT-PCR and Western blot assay. Spontaneous excitatory and inhibitory postsynaptic currents in spinal cord slices were examined using whole cell patch clam. Dendritic spine remodeling and synaptogenesis were detected in cultured dorsal spinal neurons.

Results and discussion

We found that SNL caused markedly mechanical allodynia accompanied by increase of Dock4, GTP-Rac1and GluN2B, which was prevented by knockdown of Dock4. Electrophysiological tests showed that SNL facilitated excitatory synaptic transmission, however, this was also inhibited by Dock RNAi-LV. Moreover, knockdown of Dock4 prevented dendritic growth and synaptogenesis.

Conclusion

In summary, our data indicated that Dock4 facilitated excitatory synaptic transmission by promoting the expression of GluN2B at the synaptic site and synaptogenesis, leading to the occurrence of NP.

The relationship between event-related potential components and suicide risk in major depressive disorder

BACKGROUND

Suicide is a serious global issue, with major depressive disorder (MDD) being a significant risk factor for suicidal thoughts and behaviors. There is an urgent need to determine whether event-related potential components (ERPs) could be used as an indicator to assess suicidal risk.

METHODS

From 2020 to 2023, 258 participants in total were recruited into the study. All participants were divided into four groups: MDD patients at high (n = 66), moderate (n = 66), and low risk (n = 56) of suicide, and healthy controls (HCs)(n = 70). Each participant provided socio-demographic information and underwent evaluations using clinical psychological scales such as 7-item Generalized Anxiety Disorder (GAD-7), Health Questionnaire-9 items (PHQ-9), and Nurses' Global Assessment of Suicide Risk (NGASR). The auditory brainstem response test and ERP examination were performed for all subjects.

RESULTS

Our study found that the amplitude of P2-P3 and N2-P3 was significantly reduced in MDD patients at moderate and high risk of suicide, and these were negatively correlated with NGASR total score (all P < 0.05). Point B latency was positively correlated with NGASR total score (P < 0.05). Patients with MDD patients at low risk for suicide had a lower A-B amplitude compared to HCs (P < 0.05). No differences were found in MMN or P50 components between the four groups (all P > 0.05).

CONCLUSIONS

MDD patients at higher risk of suicide exhibited severe impairment of cognitive function. ERP indices, such as the amplitude of P2-P3 and N2-P3, could be associated with the risk of suicide in MDD patients.

EEG-Based Cortical Alterations in Individuals With Chronic Knee Pain Secondary to Osteoarthritis: A Cross-sectional Investigation

Chronic painful knee osteoarthritis (OA) is a disabling physical health condition. Alterations in brain responses to arthritic changes in the knee may explain persistent pain. This study investigated source localized, resting-state electroencephalography activity and functional connectivity in people with knee OA, compared to healthy controls. Adults aged 44 to 85 years with knee OA (n = 37) and healthy control (n = 39) were recruited. Resting-state electroencephalography was collected for 10 minutes and decomposed into infraslow frequency (ISF) to gamma frequency bands. Standard low-resolution electromagnetic brain tomography statistical nonparametric maps were conducted, current densities of regions of interest were compared between groups and correlation analyses were performed between electroencephalography (EEG) measures and clinical pain and functional outcomes in the knee OA group. Standard low-resolution electromagnetic brain tomography nonparametric maps revealed higher (P = .006) gamma band activity over the right insula (RIns) in the knee OA group. A significant (P < .0001) reduction in ISF band activity at the pregenual anterior cingulate cortex, whereas higher theta, alpha, beta, and gamma band activity at the dorsal anterior cingulate cortex, pregenual anterior cingulate cortex, the somatosensory cortex, and RIns in the knee OA group were identified. ISF activity of the dorsal anterior cingulate cortex was positively correlated with pain measures and psychological distress scores. Theta and alpha activity of RIns were negatively correlated with pain interference. In conclusion, aberrations in infraslow and faster frequency EEG oscillations at sensory discriminative, motivational-affective, and descending inhibitory cortical regions were demonstrated in people with chronic painful knee OA. Moreover, EEG oscillations were correlated with pain and functional outcome measures. PERSPECTIVE: This study confirms alterations in the rsEEG oscillations and its relationship with pain experience in people with knee OA. The study provides potential cortical targets and the EEG frequency bands for neuromodulatory interventions for managing chronic pain experience in knee OA.

Structural and functional brain changes in people with knee osteoarthritis: a scoping review

Background

Knee osteoarthritis is a highly prevalent disease worldwide that leads to functional disability and chronic pain. It has been shown that not only changes are generated at the joint level in these individuals, but also neuroplastic changes are produced in different brain areas, especially in those areas related to pain perception, therefore, the objective of this research was to identify and compare the structural and functional brain changes in knee OA versus healthy subjects.

Methodology

Searches in MEDLINE (PubMed), EMBASE, WOS, CINAHL, SCOPUS, Health Source, and Epistemonikos databases were conducted to explore the available evidence on the structural and functional brain changes occurring in people with knee OA. Data were recorded on study characteristics, participant characteristics, and brain assessment techniques. The methodological quality of the studies was analysed with Newcastle Ottawa Scale.

Results

Sixteen studies met the inclusion criteria. A decrease volume of the gray matter in the insular region, parietal lobe, cingulate cortex, hippocampus, visual cortex, temporal lobe, prefrontal cortex, and basal ganglia was found in people with knee OA. However, the opposite occurred in the frontal lobe, nucleus accumbens, amygdala region and somatosensory cortex, where an increase in the gray matter volume was evidenced. Moreover, a decreased connectivity to the frontal lobe from the insula, cingulate cortex, parietal, and temporal areas, and an increase in connectivity from the insula to the prefrontal cortex, subcallosal area, and temporal lobe was shown.

Conclusion

All these findings are suggestive of neuroplastic changes affecting the pain matrix in people with knee OA.

From pulse width modulated TENS to cortical modulation: based on EEG functional connectivity analysis

Modulation in the temporal pattern of transcutaneous electrical nerve stimulation (TENS), such as Pulse width modulated (PWM), has been considered a new dimension in pain and neurorehabilitation therapy. Recently, the potentials of PWM TENS have been studied on sensory profiles and corticospinal activity. However, the underlying mechanism of PWM TENS on cortical network which might lead to pain alleviation is not yet investigated. Therefore, we recorded cortical activity using electroencephalography (EEG) from 12 healthy subjects and assessed the alternation of the functional connectivity at the cortex level up to an hour following the PWM TENS and compared that with the effect of conventional TENS. The connectivity between eight brain regions involved in sensory and pain processing was calculated based on phase lag index and spearman correlation. The alteration in segregation and integration of information in the network were investigated using graph theory. The proposed analysis discovered several statistically significant network changes between PWM TENS and conventional TENS, such as increased local strength and efficiency of the network in high gamma-band in primary and secondary somatosensory sources one hour following stimulation. Our findings regarding the long-lasting desired effects of PWM TENS support its potential as a therapeutic intervention in clinical research.

Alteration in Cortical Activity and Perceived Sensation Following Modulated TENS

Over the last decades, conventional transcutaneous electrical nerve stimulation (TENS) has been utilized as an efficient rehabilitation intervention for alleviation of chronic pain, including phantom limb pain (PLP). However, recently the literature has increasingly focused on alternative temporal stimulation patterns such as pulse width modulation (PWM). While the effect of non-modulated high frequency (NMHF) TENS on somatosensory (SI) cortex activity and sensory perception has been studied, the possible alteration following PWM TENS at the SI has not yet been explored. Therefore, we investigated the cortical modulation by PWM TENS for the first time and conducted a comparative analysis with the conventional TENS pattern. We recorded sensory evoked potentials (SEP) from 14 healthy subjects before, immediately, and 60 min after TENS interventions (PWM and NMHF). The results revealed suppression of SEP components, theta, and alpha band power simultaneously associated with the perceived intensity reduction when the single sensory pulses applied ipsilaterally to the TENS side. The reduction of N1 amplitude, theta, and alpha band activity occurred immediately after both patterns remained at least 60 min. However, the P2 wave was suppressed right after PWM TENS, while NMHF could not induce significant reduction immediately after the intervention phase. As such, since PLP relief has been shown to be correlated with inhibition at somatosensory cortex, we, therefore, believe that the result of this study provides further evidence that PWM TENS may also be potential therapeutic intervention for PLP reduction. Future studies on PLP patients with PWM TENS sessions is needed to validate our result.

Classification of Tonic Pain Experience based on Phase Connectivity in the Alpha Frequency Band of the Electroencephalogram using Convolutional Neural Networks

The complexity of brain activity involved in the generation of the experience of pain makes it hard to identify neural markers able to predict pain states. The within and between subjects variability of pain hinders the predictive potential of machine learning models trained across participants. This challenge can be tackled by implementing deep learning classifiers based on convolutional neural networks (CNNs). We targeted phase-based connectivity in the alpha band recorded with electroencephalography (EEG) during resting states and sensory conditions (eyes open [O] and closed [C] as resting states, and warm [W] and hot [H] water as sensory conditions). Connectivity features were extracted and re-organized as square matrices, because CNNs are effective in detecting the patterns from 2D data. To assess the classifier performance we implemented two complementary approaches: we 1) trained and tested the classifier with data from all participants, and 2) using a leave-one-out approach, that is excluding one participant at a time during training while using their data as a test set. The accuracy of binary classification between pain condition (H) and eyes open resting state (O) was 94.16% with the first approach, and 61.01 % with the leave-one-out approach. Clinical relevance-Further validation of the CNN classifier may help caregivers track the rehabilitation of chronic pain patients and dynamically modify the therapy. Further refinement of the model may allow its application in critical care setting with unresponsive patients to identify pain-like states otherwise incommunicable to medical personnel.

Resting-state electroencephalography (EEG) biomarkers of chronic neuropathic pain. A systematic review

Diagnosis and management of chronic neuropathic pain are challenging, leading to current efforts to characterize 'objective' biomarkers of pain using imaging or neurophysiological techniques, such as electroencephalography (EEG). A systematic literature review was conducted in PubMed-Medline and Web-of-Science until October 2021 to identify EEG biomarkers of chronic neuropathic pain in humans. The risk of bias was assessed by the Newcastle-Ottawa-Scale. Experimental, provoked, or chronic non-neuropathic pain studies were excluded. We identified 14 studies, in which resting-state EEG spectral analysis was compared between patients with pain related to a neurological disease and patients with the same disease but without pain or healthy controls. From these heterogeneous exploratory studies, some conclusions can be drawn, even if they must be weighted by the fact that confounding factors, such as medication and association with anxio-depressive disorders, are generally not taken into account. Overall, EEG signal power was increased in the θ band (4-7Hz) and possibly in the high-β band (20-30Hz), but decreased in the high-α-low-β band (10-20Hz) in the presence of ongoing neuropathic pain, while increased γ band oscillations were not evidenced, unlike in experimental pain. Consequently, the dominant peak frequency was decreased in the θ-α band and increased in the whole-β band in neuropathic pain patients. Disappointingly, pain intensity correlated with various EEG changes across studies, with no consistent trend. This review also discusses the location of regional pain-related EEG changes in the pain connectome, as the perspectives offered by advanced techniques of EEG signal analysis (source location, connectivity, or classification methods based on artificial intelligence). The biomarkers provided by resting-state EEG are of particular interest for optimizing the treatment of chronic neuropathic pain by neuromodulation techniques, such as transcranial alternating current stimulation or neurofeedback procedures.

Low Back Pain Assessment Based on Alpha Oscillation Changes in Spontaneous Electroencephalogram (EEG)

Objectively and accurately assessing pain in clinical settings is challenging. Previous studies showed that alpha oscillations of electroencephalogram data are correlated with subjective perceived pain. Based on this finding, this study is aimed at assessing chronic low back pain based on alpha oscillations. Multichannel electroencephalogram data were recorded from 27 subjects with chronic low back pain under the simple conditions of closing eyes or opening eyes. Spectral analyses were conducted to extract the alpha band responses, and the alpha powers were calculated for the two conditions, respectively. Normalized alpha power was calculated by subtracting the alpha power in the eyes-open condition from that in the eyes-closed condition. The correlation between the alpha power and the subjective pain intensity was evaluated in frontal, central, and posterior regions. The normalized alpha power in the central region was negatively correlated with the subjective pain intensity (R = -0.50, P = 0.01), with the strongest correlation occurring at the Cz electrode (R = -0.59, P = 0.04). The correlation analysis results demonstrated the possibility of using the differences of alpha spectral power between eyes-closed and eyes-open conditions as a measure for assessing chronic low back pain. The findings suggest that the normalized alpha power in the central region may be used as a measurable and quantitative indicator of chronic pain for clinical applications.

Measuring Farm Animal Emotions-Sensor-Based Approaches

Understanding animal emotions is a key to unlocking methods for improving animal welfare. Currently there are no 'benchmarks' or any scientific assessments available for measuring and quantifying the emotional responses of farm animals. Using sensors to collect biometric data as a means of measuring animal emotions is a topic of growing interest in agricultural technology. Here we reviewed several aspects of the use of sensor-based approaches in monitoring animal emotions, beginning with an introduction on animal emotions. Then we reviewed some of the available technological systems for analyzing animal emotions. These systems include a variety of sensors, the algorithms used to process biometric data taken from these sensors, facial expression, and sound analysis. We conclude that a single emotional expression measurement based on either the facial feature of animals or the physiological functions cannot show accurately the farm animal's emotional changes, and hence compound expression recognition measurement is required. We propose some novel ways to combine sensor technologies through sensor fusion into efficient systems for monitoring and measuring the animals' compound expression of emotions. Finally, we explore future perspectives in the field, including challenges and opportunities.