Pain phenotypes classified by machine learning using electroencephalography features.

Pain is a multidimensional experience mediated by distributed neural networks in the brain. To study this phenomenon, EEGs were collected from 20 subjects with chronic lumbar radiculopathy, 20 age and gender matched healthy subjects, and 17 subjects with chronic lumbar pain scheduled to receive an implanted spinal cord stimulator. Analysis of power spectral density, coherence, and phase-amplitude coupling using conventional statistics showed that there were no significant differences between the radiculopathy and control groups after correcting for multiple comparisons. However, analysis of transient spectral events showed that there were differences between these two groups in terms of the number, power, and frequency-span of events in a low gamma band. Finally, we trained a binary support vector machine to classify radiculopathy versus healthy subjects, as well as a 3-way classifier for subjects in the 3 groups. Both classifiers performed significantly better than chance, indicating that EEG features contain relevant information pertaining to sensory states, and may be used to help distinguish between pain states when other clinical signs are inconclusive.

Beyond pain privacy and pain meters: a new vision for pain biomarkers.

To an individual, pain is unambiguously real. To a caregiver, assessing pain in others is a challenging process shrouded in doubt. To explain this challenge, many assume that pain "belongs" exclusively to the bearer of that experience and accept the dogma that pain is private. However, privacy also entails that it is not possible to identify, share, or communicate that experience with others. Obviously, this is not true and the consequences of pain privacy would be devastating for healthcare. Pain is indeed unique and subjective, but not necessarily private. Pain is in fact readily communicable, though perhaps not as effectively and reliably as caregivers would like. On the other hand, healthcare systems mandate objective metrics in pain diagnosis. Smiley face caricatures are a staple of clinical practice and a universal standard for reporting pain levels. These conditions create a double paradox: Assess a private experience that is inaccessible, and use numerical scales to measure subjective attributes. Navigating this stressful environment, medical professionals experience intellectual dissonance, patients are frustrated, and value-based care is undermined. Offering a way out, first, we refute the privacy and objectification of pain citing philosophical, behavioral, and neuroscientific arguments. We discuss Wittgensteinian views against privacy, explore the clear evolutionary advantage of communicating pain to others, and identify neural circuits in the mammalian brain that contribute to empathy. Second, we highlight the subjectivity of pain, embracing the complexity and uniqueness of an individual's pain. We also provide compelling evidence for brain mechanisms that actively shape the pain experience according to predictive coding principles. Third, we offer a vision for the development of biomarker technologies that assess pain fairly without engendering bias against the patient's narrative. Our recommendations are based on the overwhelming appreciation that "medicine by emoji" is inadequate for capturing the multidimensional nature of pain. Our view is that the most promising candidates for pain biomarkers consist of self-reports as ground truth augmented by physiological signatures of biological relevance to pain. Integration of subjective and objective multimodal features will be key for the development of comprehensive pain assessment models.

Machine learning algorithm for predicting seizure control after temporal lobe resection using peri-ictal electroencephalography.

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure ("peri-ictal") represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.

Toward a brighter constellation: multiorgan neuroimaging of neural and vascular dynamics in the spinal cord and brain.

Significance: Pain comprises a complex interaction between motor action and somatosensation that is dependent on dynamic interactions between the brain and spinal cord. This makes understanding pain particularly challenging as it involves rich interactions between many circuits (e.g., neural and vascular) and signaling cascades throughout the body. As such, experimentation on a single region may lead to an incomplete and potentially incorrect understanding of crucial underlying mechanisms. Aim: We aimed to develop and validate tools to enable detailed and extended observation of neural and vascular activity in the brain and spinal cord. The first key set of innovations was targeted to developing novel imaging hardware that addresses the many challenges of multisite imaging. The second key set of innovations was targeted to enabling bioluminescent (BL) imaging, as this approach can address limitations of fluorescent microscopy including photobleaching, phototoxicity, and decreased resolution due to scattering of excitation signals. Approach: We designed 3D-printed brain and spinal cord implants to enable effective surgical implantations and optical access with wearable miniscopes or an open window (e.g., for one- or two-photon microscopy or optogenetic stimulation). We also tested the viability for BL imaging and developed a novel modified miniscope optimized for these signals (BLmini). Results: We describe "universal" implants for acute and chronic simultaneous brain-spinal cord imaging and optical stimulation. We further describe successful imaging of BL signals in both foci and a new miniscope, the "BLmini," which has reduced weight, cost, and form-factor relative to standard wearable miniscopes. Conclusions: The combination of 3D-printed implants, advanced imaging tools, and bioluminescence imaging techniques offers a coalition of methods for understanding spinal cord-brain interactions. Our work has the potential for use in future research into neuropathic pain and other sensory disorders and motor behavior.

A channelopathy contributes to cerebellar dysfunction in a model of multiple sclerosis.

OBJECTIVE: Cerebellar dysfunction in multiple sclerosis (MS) contributes significantly to disability, is relatively refractory to symptomatic therapy, and often progresses despite treatment with disease-modifying agents. We previously observed that sodium channel Nav1.8, whose expression is normally restricted to the peripheral nervous system, is present in cerebellar Purkinje neurons in a mouse model of MS (experimental autoimmune encephalomyelitis [EAE]) and in humans with MS. Here, we tested the hypothesis that upregulation of Nav1.8 in cerebellum in MS and EAE has functional consequences contributing to symptom burden. METHODS: Electrophysiology and behavioral assessment were performed in a new transgenic mouse model overexpressing Nav1.8 in Purkinje neurons. We also measured EAE symptom progression in mice lacking Nav1.8 compared to wild-type littermates. Finally, we administered the Nav1.8-selective blocker A803467 in the context of previously established EAE to determine reversibility of MS-like deficits. RESULTS: We report that, in the context of an otherwise healthy nervous system, ectopic expression of Nav1.8 in Purkinje neurons alters their electrophysiological properties, and disrupts coordinated motor behaviors. Additionally, we show that Nav1.8 expression contributes to symptom development in EAE. Finally, we demonstrate that abnormal patterns of Purkinje neuron firing and MS-like deficits in EAE can be partially reversed by pharmacotherapy using a Nav1.8-selective blocker. INTERPRETATION: Our results add to the evidence that a channelopathy contributes to cerebellar dysfunction in MS. Our data suggest that Nav1.8-specific blockers, when available for humans, merit study in MS.

Transient gamma events delineate somatosensory modality in S1.

Gamma band activity localized to the primary somatosensory cortex (S1) in humans and animals is implicated in the higher order neural processing of painful and tactile stimuli. However, it is unclear if gamma band activity differs between these distinct somatosensory modalities. Here, we coupled a novel behavioral approach with chronic extracellular electrophysiology to investigate differences in S1 gamma band activity elicited by noxious and innocuous hind paw stimulation in transgenic mice. Like prior studies, we found that trial-averaged gamma power in S1 increased following both noxious and innocuous stimuli. However, on individual trials, we noticed that evoked gamma band activity was not a continuous oscillatory signal but a series of transient spectral events. Upon further analysis we found that there was a significantly higher incidence of these gamma band events following noxious stimulation than innocuous stimulation. These findings suggest that somatosensory stimuli may be represented by specific features of gamma band activity at the single trial level, which may provide insight to mechanisms underlying acute pain.

Machine learning polysomnographically-derived electroencephalography biomarkers predictive of epworth sleepiness scale.

Excessive daytime sleepiness (EDS) causes difficulty in concentrating and continuous fatigue during the day. In the clinical setting, the assessment and diagnosis of EDS rely mostly on subjective questionnaires and verbal reports, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of previously collected encephalography (EEG) data to identify surrogate biomarkers for EDS, thereby defining the quantitative EEG changes in individuals with high Epworth Sleepiness Scale (ESS) (n = 31), compared to a group of individuals with low ESS (n = 41) at the Cleveland Clinic. The epochs of EEG analyzed were extracted from a large overnight polysomnogram registry during the most proximate period of wakefulness. Signal processing of EEG showed significantly different EEG features in the low ESS group compared to high ESS, including enhanced power in the alpha and beta bands and attenuation in the delta and theta bands. Our machine learning (ML) algorithms trained on the binary classification of high vs. low ESS reached an accuracy of 80.2%, precision of 79.2%, recall of 73.8% and specificity of 85.3%. Moreover, we ruled out the effects of confounding clinical variables by evaluating the statistical contribution of these variables on our ML models. These results indicate that EEG data contain information in the form of rhythmic activity that could be leveraged for the quantitative assessment of EDS using ML.

SARS-CoV-2 alters neural synchronies in the brain with more severe effects in younger individuals.

Coronavirus disease secondary to infection by SARS-CoV-2 (COVID19 or C19) causes respiratory illness, as well as severe neurological symptoms that have not been fully characterized. In a previous study, we developed a computational pipeline for the automated, rapid, high-throughput and objective analysis of electroencephalography (EEG) rhythms. In this retrospective study, we used this pipeline to define the quantitative EEG changes in patients with a PCR-positive diagnosis of C19 (n = 31) in the intensive care unit (ICU) of Cleveland Clinic, compared to a group of age-matched PCR-negative (n = 38) control patients in the same ICU setting. Qualitative assessment of EEG by two independent teams of electroencephalographers confirmed prior reports with regards to the high prevalence of diffuse encephalopathy in C19 patients, although the diagnosis of encephalopathy was inconsistent between teams. Quantitative analysis of EEG showed distinct slowing of brain rhythms in C19 patients compared to control (enhanced delta power and attenuated alpha-beta power). Surprisingly, these C19-related changes in EEG power were more prominent in patients below age 70. Moreover, machine learning algorithms showed consistently higher accuracy in the binary classification of patients as C19 versus control using EEG power for subjects below age 70 compared to older ones, providing further evidence for the more severe impact of SARS-CoV-2 on brain rhythms in younger individuals irrespective of PCR diagnosis or symptomatology, and raising concerns over potential long-term effects of C19 on brain physiology in the adult population and the utility of EEG monitoring in C19 patients.

Towards a Brighter Constellation: Multi-Organ Neuroimaging of Neural and Vascular Dynamics in the Spinal Cord and Brain.

Significance: Pain is comprised of a complex interaction between motor action and somatosensation that is dependent on dynamic interactions between the brain and spinal cord. This makes understanding pain particularly challenging as it involves rich interactions between many circuits (e.g., neural and vascular) and signaling cascades throughout the body. As such, experimentation on a single region may lead to an incomplete and potentially incorrect understanding of crucial underlying mechanisms. Aim: Here, we aimed to develop and validate new tools to enable detailed and extended observation of neural and vascular activity in the brain and spinal cord. The first key set of innovations were targeted to developing novel imaging hardware that addresses the many challenges of multi-site imaging. The second key set of innovations were targeted to enabling bioluminescent imaging, as this approach can address limitations of fluorescent microscopy including photobleaching, phototoxicity and decreased resolution due to scattering of excitation signals. Approach: We designed 3D-printed brain and spinal cord implants to enable effective surgical implantations and optical access with wearable miniscopes or an open window (e.g., for one- or two-photon microscopy or optogenetic stimulation). We also tested the viability for bioluminescent imaging, and developed a novel modified miniscope optimized for these signals (BLmini). Results: Here, we describe novel 'universal' implants for acute and chronic simultaneous brain-spinal cord imaging and optical stimulation. We further describe successful imaging of bioluminescent signals in both foci, and a new miniscope, the 'BLmini,' which has reduced weight, cost and form-factor relative to standard wearable miniscopes. Conclusions: The combination of 3D printed implants, advanced imaging tools, and bioluminescence imaging techniques offers a new coalition of methods for understanding spinal cord-brain interactions. This work has the potential for use in future research into neuropathic pain and other sensory disorders and motor behavior.

Remote neuroimmune signaling: a long-range mechanism of nociceptive network plasticity.

Chronic pain secondary to neuronal injury is actively and continuously modulated at multiple locations along the sensory neuraxis. Here, we describe how nociceptive neurons of the spinal cord and thalamus process and communicate nociceptive information in terms of precisely calibrated firing patterns. We then discuss how several cell types with immunogenic properties (e.g. blood cells and glia) cause system-wide interference in nociceptive processing through novel signaling schema, thus contributing to nociceptive network plasticity and chronic pain.

Automated and rapid self-report of nociception in transgenic mice.

There are currently no rapid, operant pain behaviors in rodents that use a self-report to directly engage higher-order brain circuitry. We have developed a pain detection assay consisting of a lick behavior in response to optogenetic activation of predominantly nociceptive peripheral afferent nerve fibers in head-restrained transgenic mice expressing ChR2 in TRPV1 containing neurons. TRPV1-ChR2-EYFP mice (n = 5) were trained to provide lick reports to the detection of light-evoked nociceptive stimulation to the hind paw. Using simultaneous video recording, we demonstrate that the learned lick behavior may prove more pertinent in investigating brain driven pain processes than the reflex behavior. Within sessions, the response bias of transgenic mice changed with respect to lick behavior but not reflex behavior. Furthermore, response similarity between the lick and reflex behaviors diverged near perceptual threshold. Our nociceptive lick-report detection assay will enable a host of investigations into the millisecond, single cell, neural dynamics underlying pain processing in the central nervous system of awake behaving animals.

Time-dynamic pulse modulation of spinal cord stimulation reduces mechanical hypersensitivity and spontaneous pain in rats.

Enhancing the efficacy of spinal cord stimulation (SCS) is needed to alleviate the burden of chronic pain and dependence on opioids. Present SCS therapies are characterized by the delivery of constant stimulation in the form of trains of tonic pulses (TPs). We tested the hypothesis that modulated SCS using novel time-dynamic pulses (TDPs) leads to improved analgesia and compared the effects of SCS using conventional TPs and a collection of TDPs in a rat model of neuropathic pain according to a longitudinal, double-blind, and crossover design. We tested the effects of the following SCS patterns on paw withdrawal threshold and resting state EEG theta power as a biomarker of spontaneous pain: Tonic (conventional), amplitude modulation, pulse width modulation, sinusoidal rate modulation, and stochastic rate modulation. Results demonstrated that under the parameter settings tested in this study, all tested patterns except pulse width modulation, significantly reversed mechanical hypersensitivity, with stochastic rate modulation achieving the highest efficacy, followed by the sinusoidal rate modulation. The anti-nociceptive effects of sinusoidal rate modulation on EEG outlasted SCS duration on the behavioral and EEG levels. These results suggest that TDP modulation may improve clinical outcomes by reducing pain intensity and possibly improving the sensory experience.

Discovery and validation of biomarkers to aid the development of safe and effective pain therapeutics: challenges and opportunities.

Pain medication plays an important role in the treatment of acute and chronic pain conditions, but some drugs, opioids in particular, have been overprescribed or prescribed without adequate safeguards, leading to an alarming rise in medication-related overdose deaths. The NIH Helping to End Addiction Long-term (HEAL) Initiative is a trans-agency effort to provide scientific solutions to stem the opioid crisis. One component of the initiative is to support biomarker discovery and rigorous validation in collaboration with industry leaders to accelerate high-quality clinical research into neurotherapeutics and pain. The use of objective biomarkers and clinical trial end points throughout the drug discovery and development process is crucial to help define pathophysiological subsets of pain, evaluate target engagement of new drugs and predict the analgesic efficacy of new drugs. In 2018, the NIH-led Discovery and Validation of Biomarkers to Develop Non-Addictive Therapeutics for Pain workshop convened scientific leaders from academia, industry, government and patient advocacy groups to discuss progress, challenges, gaps and ideas to facilitate the development of biomarkers and end points for pain. The outcomes of this workshop are outlined in this Consensus Statement.

Alarm or curse? The pain of neuroinflammation.

The nociceptive nervous system and the immune system serve to defend and alarm the host of imminent or actual damage. However, persistent or recurring exposure of neurons to activated immune cells is associated with an increase in painful behavior following experimental neuropathic injuries. Our understanding of the functional consequences of immune cell-neuron interaction is still incomplete. The purpose of this review is to focus on a seriously detrimental consequence of chronic activation of these two systems, by discussing the contributions of microglia and polymorphonuclear neutrophils to neuropathic pain following experimental spinal cord injury or peripheral nerve injury. Identification of molecules mediating pro-nociceptive signaling between immune cells and neurons, as well as the distinction between neuroprotective versus neuroexcitatory effects of activated immune cells, may be useful in the development of pharmacotherapy for the management of chronic pain and restoration of the beneficial alarm function of pain.

What does a pain 'biomarker' mean and can a machine be taught to measure pain?

Artificial intelligence allows machines to predict human faculties such as image and voice recognition. Can machines be taught to measure pain? We argue that the two fundamental requirements for a device with 'pain biomarker' capabilities are hardware and software. We discuss the merits and limitations of electroencephalography (EEG) as the hardware component of a putative embodiment of the device, and advances in the application of machine learning approaches to EEG for predicting pain.

Sub-paresthesia spinal cord stimulation reverses thermal hyperalgesia and modulates low frequency EEG in a rat model of neuropathic pain.

Paresthesia, a common feature of epidural spinal cord stimulation (SCS) for pain management, presents a challenge to the double-blind study design. Although sub-paresthesia SCS has been shown to be effective in alleviating pain, empirical criteria for sub-paresthesia SCS have not been established and its basic mechanisms of action at supraspinal levels are unknown. We tested our hypothesis that sub-paresthesia SCS attenuates behavioral signs of neuropathic pain in a rat model, and modulates pain-related theta (4-8 Hz) power of the electroencephalogram (EEG), a previously validated correlate of spontaneous pain in rodent models. Results show that sub-paresthesia SCS attenuates thermal hyperalgesia and power amplitude in the 3-4 Hz range, consistent with clinical data showing significant yet modest analgesic effects of sub-paresthesia SCS in humans. Therefore, we present evidence for anti-nociceptive effects of sub-paresthesia SCS in a rat model of neuropathic pain and further validate EEG theta power as a reliable 'biosignature' of spontaneous pain.

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation. NEW METHOD: We have developed a quantitative and automated support vector machine (SVM)-based algorithm to accurately classify artifactual EEG epochs in awake rodent, canine and humans subjects. An embodiment of this method also enables the determination of 'eyes open/closed' states in human subjects. RESULTS: The levels of SVM accuracy for artifact classification in humans, Sprague Dawley rats and beagle dogs were 94.17%, 83.68%, and 85.37%, respectively, whereas 'eyes open/closed' states in humans were labeled with 88.60% accuracy. Each of these results was significantly higher than chance. COMPARISON WITH EXISTING METHODS: Other existing methods, like those dependent on Independent Component Analysis, have not been tested in non-human subjects, and require full EEG montages, instead of only single channels, as this method does. CONCLUSIONS: We conclude that our EEG artifact detection algorithm provides a valid and practical solution to a common problem in the quantitative analysis and assessment of EEG in pre-clinical research settings across evolutionary spectra.

An Electroencephalography Bioassay for Preclinical Testing of Analgesic Efficacy.

We present a multimodal method combining quantitative electroencephalography (EEG), behavior and pharmacology for pre-clinical screening of analgesic efficacy in vivo. The method consists of an objective and non-invasive approach for realtime assessment of spontaneous nociceptive states based on EEG recordings of theta power over primary somatosensory cortex in awake rats. Three drugs were chosen: (1) pregabalin, a CNS-acting calcium channel inhibitor; (2) EMA 401, a PNS-acting angiotensin II type 2 receptor inhibitor; and (3) minocycline, a CNS-acting glial inhibitor. Optimal doses were determined based on pharmacokinetic studies and/or published data. The effects of these drugs at single or multiple doses were tested on the attenuation of theta power and paw withdrawal latency (PWL) in a rat model of neuropathic pain. We report mostly parallel trends in the reversal of theta power and PWL in response to administration of pregabalin and EMA 401, but not minocycline. We also note divergent trends at non-optimal doses and following prolonged drug administration, suggesting that EEG theta power can be used to detect false positive and false negative outcomes of the withdrawal reflex behavior, and yielding novel insights into the analgesic effects of these drugs on spontaneous nociceptive states in rats.

Long-term protection of central axons with phenytoin in monophasic and chronic-relapsing EAE.

Axonal degeneration is a major contributor to non-remitting deficits in multiple sclerosis, and there is thus considerable current interest in the development of strategies that might prevent axonal loss in neuroinflammatory disease. Dysregulation of sodium ion homeostasis has been implicated in mechanisms leading to axonal degeneration, and several studies have shown that blockade of sodium channels can ameliorate axon damage following anoxic, traumatic and nitric oxide-induced CNS injury. Two sodium channel blockers, phenytoin and flecainide, have been reported to protect axons in experimental autoimmune encephalomyelitis (EAE) for 30 days, but long-term protective effects have not been studied. We demonstrate here that oral administration of phenytoin provides long-term (up to 180 days) protection for spinal cord corticospinal tract (CST) and dorsal column (DC) axons in both monophasic (C57/BL6 mice) and chronic-relapsing (Biozzi mice) murine EAE. Untreated C57/BL6 mice exhibit a 40-50% loss of CST and DF axons at 90 and 180 days post-EAE induction via myelin-oligodendrocyte glycoprotein (MOG) injection. In contrast, only 4% of DF axons are lost at 90 days, and only 8% are lost at 180 days in phenytoin-treated C57/BL6 mice with EAE; only 21-29% of CST axons are lost at 90 and 180 days in phenytoin-treated C57/BL6 mice with EAE. Attenuation of dorsal column compound action potentials was ameliorated and clinical status was also significantly enhanced with phenytoin treatment at 90 and 180 days in this model. In addition, inflammatory cell infiltration into the dorsal columns was reduced in phenytoin-treated mice with EAE compared with untreated mice with EAE. Similar results were obtained in Biozzi mice with chronic-relapsing EAE followed for 120 days post-injection. These observations demonstrate that phenytoin provides long-term protection of CNS axons and improves clinical status in both monophasic and chronic-relapsing models of neuroinflammation.