Significance of Non-phase Locked Oscillatory Brain Activity in Response to Noxious Stimuli

Brain-Computer Interface to Deliver Individualized Multisensory Intervention for Neuropathic Pain

To unravel the complexity of the neuropathic pain experience, researchers have tried to identify reliable pain signatures (biomarkers) using electroencephalography (EEG) and skin conductance (SC). Nevertheless, their use as a clinical aid to design personalized therapies remains scarce and patients are prescribed with common and inefficient painkillers. To address this need, novel non-pharmacological interventions, such as transcutaneous electrical nerve stimulation (TENS) to activate peripheral pain relief via neuromodulation and virtual reality (VR) to modulate patients' attention, have emerged. However, all present treatments suffer from the inherent bias of the patient's self-reported pain intensity, depending on their predisposition and tolerance, together with unspecific, pre-defined scheduling of sessions which does not consider the timing of pain episodes onset. Here, we show a Brain-Computer Interface (BCI) detecting in real-time neurophysiological signatures of neuropathic pain from EEG combined with SC and accordingly triggering a multisensory intervention combining TENS and VR. After validating that the multisensory intervention effectively decreased experimentally induced pain, the BCI was tested with thirteen healthy subjects by electrically inducing pain and showed 82% recall in decoding pain in real time. Such constructed BCI was then validated with eight neuropathic patients reaching 75% online pain precision, and consequently releasing the intervention inducing a significant decrease (50% NPSI score) in neuropathic patients' pain perception. Our results demonstrate the feasibility of real-time pain detection from objective neurophysiological signals, and the effectiveness of a triggered combination of VR and TENS to decrease neuropathic pain. This paves the way towards personalized, data-driven pain therapies using fully portable technologies.

In search of a composite biomarker for chronic pain by way of EEG and machine learning: where do we currently stand?

Machine learning is becoming an increasingly common component of routine data analyses in clinical research. The past decade in pain research has witnessed great advances in human neuroimaging and machine learning. With each finding, the pain research community takes one step closer to uncovering fundamental mechanisms underlying chronic pain and at the same time proposing neurophysiological biomarkers. However, it remains challenging to fully understand chronic pain due to its multidimensional representations within the brain. By utilizing cost-effective and non-invasive imaging techniques such as electroencephalography (EEG) and analyzing the resulting data with advanced analytic methods, we have the opportunity to better understand and identify specific neural mechanisms associated with the processing and perception of chronic pain. This narrative literature review summarizes studies from the last decade describing the utility of EEG as a potential biomarker for chronic pain by synergizing clinical and computational perspectives.

EEG Recordings as Biomarkers of Pain Perception: Where Do We Stand and Where to Go?

Introduction

The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception.

Methods

A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review.

Results

Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception.

Conclusion

Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40122-022-00372-2.

The Development of Nociceptive Network Activity in the Somatosensory Cortex of Freely Moving Rat Pups

Cortical perception of noxious stimulation is an essential component of pain experience but it is not known how cortical nociceptive activity emerges during brain development. Here we use continuous telemetric electrocorticogram (ECoG) recording from the primary somatosensory cortex (S1) of awake active rat pups to map functional nociceptive processing in the developing brain over the first 4 weeks of life. Cross-sectional and longitudinal recordings show that baseline S1 ECoG energy increases steadily with age, with a distinctive beta component replaced by a distinctive theta component in week 3. Event-related potentials were evoked by brief noxious hindpaw skin stimulation at all ages tested, confirming the presence of functional nociceptive spinothalamic inputs in S1. However, hindpaw incision, which increases pain sensitivity at all ages, did not increase S1 ECoG energy until week 3. A significant increase in gamma (20–50 Hz) energy occurred in the presence of skin incision at week 3 accompanied by a longer-lasting increase in theta (4–8 Hz) energy at week 4. Continuous ECoG recording demonstrates specific postnatal functional stages in the maturation of S1 cortical nociception. Somatosensory cortical coding of an ongoing pain “state” in awake rat pups becomes apparent between 2 and 4 weeks of age.