First-in-human prediction of chronic pain state using intracranial neural biomarkers

Orbitostriatal encoding of reward delayed gratification and impulsivity in chronic pain

Central robust network functional rearrangement is a characteristic of several neurological conditions, including chronic pain. Preclinical and clinical studies have shown the importance of pain-induced dysfunction in both orbitofrontal cortex (OFC) and nucleus accumbens (NAc) brain regions for the emergence of cognitive deficits. Outcome information processing recruits the orbitostriatal circuitry, a pivotal pathway regarding context-dependent reward value encoding. The current literature reveals the existence of structural and functional changes in the orbitostriatal crosstalk in chronic pain conditions, which have emerged as a possible underlying cause for reward and time discrimination impairments observed in individuals affected by such disturbances. However, more comprehensive investigations are needed to elucidate the underlying disturbances that underpin disease development. In this review article, we aim to provide a comprehensive view of the orbitostriatal mechanisms underlying time-reward dependent behaviors, and integrate previous findings on local and network malplasticity under the framework of the chronic pain sphere.

Mental state decoders: game-changers or wishful thinking?

Decoding mental and perceptual states using fMRI has become increasingly popular over the past two decades, with numerous highly-cited studies published in high-profile journals. Nevertheless, what have we learned from these decoders? In this opinion, we argue that fMRI-based decoders are not neurophysiologically informative and are not, and likely cannot be, applicable to real-world decision-making. The former point stems from the fact that decoding models cannot disentangle neural mechanisms from their epiphenomena. The latter point stems from both logical and ethical constraints. Constructing decoders requires precious time and resources that should instead be directed toward scientific endeavors more likely to yield meaningful scientific progress.

Tear Proteins Altered in Patients with Persistent Eye Pain after Refractive Surgery: Biomarker Candidate Discovery

Some patients develop persistent eye pain after refractive surgery, but factors that cause or sustain pain are unknown. We tested whether tear proteins of patients with pain 3 months after surgery differ from those of patients without pain. Patients undergoing refractive surgery (laser in situ keratomileusis or photorefractive keratectomy ) were recruited from 2 clinics, and tears were collected 3 months after surgery. Participants rated their eye pain using a numerical rating scale (NRS, 0-10; no pain-worst pain) at baseline, 1 day, and 3 months after surgery. Using tandem mass tag proteomic analysis, we examined tears from patients with pain [NRS ≥ 3 at 3 months (n = 16)] and patients with no pain [NRS ≤ 1 at 3 months (n = 32)] after surgery. A subset of proteins (83 of 2748 detected, 3.0%) were associated with pain 3 months after surgery. High-dimensional statistical models showed that the magnitude of differential expression was not the only important factor in classifying tear samples from pain patients. Models utilizing 3 or 4 proteins had better classification performance than single proteins and represented differences in both directions (higher or lower in pain). Thus, patterns of protein differences may serve as biomarkers of postsurgical eye pain as well as potential therapeutic targets.

Thalamic stimulation modulated neural oscillations in central post-stroke pain: A case report

The characterization of neural signatures within the somatosensory pathway is essential for elucidating the pathogenic mechanisms of central post-stroke pain (CPSP) and developing more effective treatments such as deep brain stimulation (DBS). We explored the characteristics of thalamic neural oscillations in response to varying pain levels under multi-day local field potential (LFP) recordings and examined the influences of continuous DBS on these thalamic activities. We recorded LFPs from the left ventral posterolateral thalamus (VPL) of a patient with CPSP in the resting state under both off- and on-stimulation conditions. We observed significant differences in the power spectral density (PSD) of different pain levels in the delta, theta and gamma frequency bands of the left VPL; 75Hz DBS significantly increased the PSD of delta and decreased the PSD of low-beta, while 130Hz DBS significantly reduced the PSD of theta and low-beta. Thalamic stimulation modulated the neural oscillations related to pain, and the changes in neural activities in response to stimulation could serve as quantitative indicators for pain relief.

Brain-machine interactive neuromodulation research tool with edge AI computing

Closed-loop neuromodulation with intelligence methods has shown great potentials in providing novel neuro-technology for treating neurological and psychiatric diseases. Development of brain-machine interactive neuromodulation strategies could lead to breakthroughs in precision and personalized electronic medicine. The neuromodulation research tool integrating artificial intelligent computing and performing neural sensing and stimulation in real-time could accelerate the development of closed-loop neuromodulation strategies and translational research into clinical application. In this study, we developed a brain-machine interactive neuromodulation research tool (BMINT), which has capabilities of neurophysiological signals sensing, computing with mainstream machine learning algorithms and delivering electrical stimulation pulse by pulse in real-time. The BMINT research tool achieved system time delay under 3 ms, and computing capabilities in feasible computation cost, efficient deployment of machine learning algorithms and acceleration process. Intelligent computing framework embedded in the BMINT enable real-time closed-loop neuromodulation developed with mainstream AI ecosystem resources. The BMINT could provide timely contribution to accelerate the translational research of intelligent neuromodulation by integrating neural sensing, edge AI computing and stimulation with AI ecosystems.

Naturalistic acute pain states decoded from neural and facial dynamics

Pain is a complex experience that remains largely unexplored in naturalistic contexts, hindering our understanding of its neurobehavioral representation in ecologically valid settings. To address this, we employed a multimodal, data-driven approach integrating intracranial electroencephalography, pain self-reports, and facial expression quantification to characterize the neural and behavioral correlates of naturalistic acute pain in twelve epilepsy patients undergoing continuous monitoring with neural and audiovisual recordings. High self-reported pain states were associated with elevated blood pressure, increased pain medication use, and distinct facial muscle activations. Using machine learning, we successfully decoded individual participants' high versus low self-reported pain states from distributed neural activity patterns (mean AUC = 0.70), involving mesolimbic regions, striatum, and temporoparietal cortex. High self-reported pain states exhibited increased low-frequency activity in temporoparietal areas and decreased high-frequency activity in mesolimbic regions (hippocampus, cingulate, and orbitofrontal cortex) compared to low pain states. This neural pain representation remained stable for hours and was modulated by pain onset and relief. Objective facial expression changes also classified self-reported pain states, with results concordant with electrophysiological predictions. Importantly, we identified transient periods of momentary pain as a distinct naturalistic acute pain measure, which could be reliably differentiated from affect-neutral periods using intracranial and facial features, albeit with neural and facial patterns distinct from self-reported pain. These findings reveal reliable neurobehavioral markers of naturalistic acute pain across contexts and timescales, underscoring the potential for developing personalized pain interventions in real-world settings.

Event detection and classification from multimodal time series with application to neural data

The detection of events in time-series data is a common signal-processing problem. When the data can be modeled as a known template signal with an unknown delay in Gaussian noise, detection of the template signal can be done with a traditional matched filter. However, in many applications, the event of interest is represented in multimodal data consisting of both Gaussian and point-process time series. Neuroscience experiments, for example, can simultaneously record multimodal neural signals such as local field potentials (LFPs), which can be modeled as Gaussian, and neuronal spikes, which can be modeled as point processes. Currently, no method exists for event detection from such multimodal data, and as such our objective in this work is to develop a method to meet this need. Here we address this challenge by developing the multimodal event detector (MED) algorithm which simultaneously estimates event times and classes. To do this, we write a multimodal likelihood function for Gaussian and point-process observations and derive the associated maximum likelihood estimator of simultaneous event times and classes. We additionally introduce a cross-modal scaling parameter to account for model mismatch in real datasets. We validate this method in extensive simulations as well as in a neural spike-LFP dataset recorded during an eye-movement task, where the events of interest are eye movements with unknown times and directions. We show that the MED can successfully detect eye movement onset and classify eye movement direction. Further, the MED successfully combines information across data modalities, with multimodal performance exceeding unimodal performance. This method can facilitate applications such as the discovery of latent events in multimodal neural population activity and the development of brain-computer interfaces for naturalistic settings without constrained tasks or prior knowledge of event times.

Opportunities and challenges for the use of deep brain stimulation in the treatment of refractory major depression

Major Depressive Disorder continues to remain one of the most prevalent psychiatric diseases globally. Despite multiple trials of conventional therapies, a subset of patients fail to have adequate benefit to treatment. Deep brain stimulation (DBS) is a promising treatment in this difficult to treat population and has shown strong antidepressant effects across multiple cohorts. Nearly two decades of work have provided insights into the potential for chronic focal stimulation in precise brain targets to modulate pathological brain circuits that are implicated in the pathogenesis of depression. In this paper we review the rationale that prompted the selection of various brain targets for DBS, their subsequent clinical outcomes and common adverse events reported. We additionally discuss some of the pitfalls and challenges that have prevented more widespread adoption of this technology as well as future directions that have shown promise in improving therapeutic efficacy of DBS in the treatment of depression.

Bioelectronic Medicine: a multidisciplinary roadmap from biophysics to precision therapies

Bioelectronic Medicine stands as an emerging field that rapidly evolves and offers distinctive clinical benefits, alongside unique challenges. It consists of the modulation of the nervous system by precise delivery of electrical current for the treatment of clinical conditions, such as post-stroke movement recovery or drug-resistant disorders. The unquestionable clinical impact of Bioelectronic Medicine is underscored by the successful translation to humans in the last decades, and the long list of preclinical studies. Given the emergency of accelerating the progress in new neuromodulation treatments (i.e., drug-resistant hypertension, autoimmune and degenerative diseases), collaboration between multiple fields is imperative. This work intends to foster multidisciplinary work and bring together different fields to provide the fundamental basis underlying Bioelectronic Medicine. In this review we will go from the biophysics of the cell membrane, which we consider the inner core of neuromodulation, to patient care. We will discuss the recently discovered mechanism of neurotransmission switching and how it will impact neuromodulation design, and we will provide an update on neuronal and glial basis in health and disease. The advances in biomedical technology have facilitated the collection of large amounts of data, thereby introducing new challenges in data analysis. We will discuss the current approaches and challenges in high throughput data analysis, encompassing big data, networks, artificial intelligence, and internet of things. Emphasis will be placed on understanding the electrochemical properties of neural interfaces, along with the integration of biocompatible and reliable materials and compliance with biomedical regulations for translational applications. Preclinical validation is foundational to the translational process, and we will discuss the critical aspects of such animal studies. Finally, we will focus on the patient point-of-care and challenges in neuromodulation as the ultimate goal of bioelectronic medicine. This review is a call to scientists from different fields to work together with a common endeavor: accelerate the decoding and modulation of the nervous system in a new era of therapeutic possibilities.

A pilot study on a patient with refractory headache: Personalized deep brain stimulation through stereoelectroencephalography

The integration of stereoelectroencephalography with therapeutic deep brain stimulation (DBS) holds immense promise as a viable approach for precise treatment of refractory disorders, yet it has not been explored in the domain of headache or pain management. Here, we implanted 14 electrodes in a patient with refractory migraine and integrated clinical assessment and electrophysiological data to investigate personalized targets for refractory headache treatment. Using statistical analyses and cross-validated machine-learning models, we identified high-frequency oscillations in the right nucleus accumbens as a critical headache-related biomarker. Through a systematic bipolar stimulation approach and blinded sham-controlled survey, combined with real-time electrophysiological data, we successfully identified the left dorsal anterior cingulate cortex as the optimal target for the best potential treatment. In this pilot study, the concept of the herein-proposed data-driven approach to optimizing precise and personalized treatment strategies for DBS may create a new frontier in the field of refractory headache and even pain disorders.

Modulations in high-density EEG during the suppression of phantom-limb pain with neurostimulation in upper limb amputees

Phantom limb pain (PLP) is a distressing and persistent sensation that occurs after the amputation of a limb. While medication-based treatments have limitations and adverse effects, neurostimulation is a promising alternative approach whose mechanism of action needs research, including electroencephalographic (EEG) recordings for the assessment of cortical manifestation of PLP relieving effects. Here we collected and analyzed high-density EEG data in 3 patients (P01, P02, and P03). Peripheral nerve stimulation suppressed PLP in P01 but was ineffective in P02. In contrast, transcutaneous electrical nerve stimulation was effective in P02. In P03, spinal cord stimulation was used to suppress PLP. Changes in EEG oscillatory components were analyzed using spectral analysis and Petrosian fractal dimension. With these methods, changes in EEG spatio-spectral components were found in the theta, alpha, and beta bands in all patients, with these effects being specific to each individual. The changes in the EEG patterns were found for both the periods when PLP level was stationary and the periods when PLP was gradually changing after neurostimulation was turned on or off. Overall, our findings align with the proposed roles of brain rhythms in thalamocortical dysrhythmia or disruption of cortical excitation and inhibition which has been linked to neuropathic pain. The individual differences in the observed effects could be related to the specifics of each patient's treatment and the unique spectral characteristics in each of them. These findings pave the way to the closed-loop systems for PLP management where neurostimulation parameters are adjusted based on EEG-derived markers.

Closed-loop neurostimulation for the treatment of psychiatric disorders

Despite increasing prevalence and huge personal and societal burden, psychiatric diseases still lack treatments which can control symptoms for a large fraction of patients. Increasing insight into the neurobiology underlying these diseases has demonstrated wide-ranging aberrant activity and functioning in multiple brain circuits and networks. Together with varied presentation and symptoms, this makes one-size-fits-all treatment a challenge. There has been a resurgence of interest in the use of neurostimulation as a treatment for psychiatric diseases. Initial studies using continuous open-loop stimulation, in which clinicians adjusted stimulation parameters during patient visits, showed promise but also mixed results. Given the periodic nature and fluctuations of symptoms often observed in psychiatric illnesses, the use of device-driven closed-loop stimulation may provide more effective therapy. The use of a biomarker, which is correlated with specific symptoms, to deliver stimulation only during symptomatic periods allows for the personalized therapy needed for such heterogeneous disorders. Here, we provide the reader with background motivating the use of closed-loop neurostimulation for the treatment of psychiatric disorders. We review foundational studies of open- and closed-loop neurostimulation for neuropsychiatric indications, focusing on deep brain stimulation, and discuss key considerations when designing and implementing closed-loop neurostimulation.

Brain structural and functional abnormalities in patients with tension-type headache: A systematic review of magnetic resonance imaging studies

Tension-type headache (TTH) stands as the most prevalent form of headache, yet an adequate understanding of its underlying mechanisms remains elusive. This article endeavors to comprehensively review structural and functional magnetic resonance imaging (MRI) studies investigating TTH patients, to gain valuable insights into the pathophysiology of TTH, and to explore new avenues for enhanced treatment strategies. We conducted a systematic search to identify relevant articles examining brain MRI disparities between TTH individuals and headache-free controls (HFC). Fourteen studies, encompassing 312 diagnosed TTH patients, were selected for inclusion. Among these, eight studies utilized conventional MRI, one employed diffusion tensor imaging, and five implemented various functional MRI modalities. Consistent findings across these studies revealed a notable increase in white matter hyperintensity (WMH) in TTH patients. Furthermore, the potential involvement of the specific brain areas recognized to be involved in different dimensions of pain perception including cortical regions (anterior and posterior cingulate cortex, prefrontal cortex, anterior and posterior insular cortex), subcortical regions (thalamus, caudate, putamen, and parahippocampus), cerebellum in TTH pathogenesis was identified. However, no significant association was established between TTH and intracranial abnormalities or total intracranial volume. In conclusion, these findings support the hypotheses regarding the role of central mechanisms in TTH pathophysiology and offer probable brain regions implicated in these mechanisms. Due to the scarce data on the precise role of these regions in the TTH, further preclinical and clinical investigations should be done to advance our knowledge and enhance targeted therapeutic options of TTH.

Central neuropathic pain

Central neuropathic pain arises from a lesion or disease of the central somatosensory nervous system such as brain injury, spinal cord injury, stroke, multiple sclerosis or related neuroinflammatory conditions. The incidence of central neuropathic pain differs based on its underlying cause. Individuals with spinal cord injury are at the highest risk; however, central post-stroke pain is the most prevalent form of central neuropathic pain worldwide. The mechanisms that underlie central neuropathic pain are not fully understood, but the pathophysiology likely involves intricate interactions and maladaptive plasticity within spinal circuits and brain circuits associated with nociception and antinociception coupled with neuronal hyperexcitability. Modulation of neuronal activity, neuron-glia and neuro-immune interactions and targeting pain-related alterations in brain connectivity, represent potential therapeutic approaches. Current evidence-based pharmacological treatments include antidepressants and gabapentinoids as first-line options. Non-pharmacological pain management options include self-management strategies, exercise and neuromodulation. A comprehensive pain history and clinical examination form the foundation of central neuropathic pain classification, identification of potential risk factors and stratification of patients for clinical trials. Advanced neurophysiological and neuroimaging techniques hold promise to improve the understanding of mechanisms that underlie central neuropathic pain and as predictive biomarkers of treatment outcome.

Precision, integrative medicine for pain management in sickle cell disease

Sickle cell disease (SCD) is a prevalent and complex inherited pain disorder that can manifest as acute vaso-occlusive crises (VOC) and/or chronic pain. Despite their known risks, opioids are often prescribed routinely and indiscriminately in managing SCD pain, because it is so often severe and debilitating. Integrative medicine strategies, particularly non-opioid therapies, hold promise in safe and effective management of SCD pain. However, the lack of evidence-based methods for managing SCD pain hinders the widespread implementation of non-opioid therapies. In this review, we acknowledge that implementing personalized pain treatment strategies in SCD, which is a guideline-recommended strategy, is currently fraught with limitations. The full implementation of pharmacological and biobehavioral pain approaches targeting mechanistic pain pathways faces challenges due to limited knowledge and limited financial and personnel support. We recommend personalized medicine, pharmacogenomics, and integrative medicine as aspirational strategies for improving pain care in SCD. As an organizing model that is a comprehensive framework for classifying pain subphenotypes and mechanisms in SCD, and for guiding selection of specific strategies, we present evidence updating pain research pioneer Richard Melzack's neuromatrix theory of pain. We advocate for using the updated neuromatrix model to subphenotype individuals with SCD, to better select personalized multimodal treatment strategies, and to identify research gaps fruitful for exploration. We present a fairly complete list of currently used pharmacologic and non-pharmacologic SCD pain therapies, classified by their mechanism of action and by their hypothesized targets in the updated neuromatrix model.

Changes in alpha, theta, and gamma oscillations in distinct cortical areas are associated with altered acute pain responses in chronic low back pain patients

Introduction

Chronic pain negatively impacts a range of sensory and affective behaviors. Previous studies have shown that the presence of chronic pain not only causes hypersensitivity at the site of injury but may also be associated with pain-aversive experiences at anatomically unrelated sites. While animal studies have indicated that the cingulate and prefrontal cortices are involved in this generalized hyperalgesia, the mechanisms distinguishing increased sensitivity at the site of injury from a generalized site-nonspecific enhancement in the aversive response to nociceptive inputs are not well known.

Methods

We compared measured pain responses to peripheral mechanical stimuli applied to a site of chronic pain and at a pain-free site in participants suffering from chronic lower back pain (n = 15) versus pain-free control participants (n = 15) by analyzing behavioral and electroencephalographic (EEG) data.

Results

As expected, participants with chronic pain endorsed enhanced pain with mechanical stimuli in both back and hand. We further analyzed electroencephalographic (EEG) recordings during these evoked pain episodes. Brain oscillations in theta and alpha bands in the medial orbitofrontal cortex (mOFC) were associated with localized hypersensitivity, while increased gamma oscillations in the anterior cingulate cortex (ACC) and increased theta oscillations in the dorsolateral prefrontal cortex (dlPFC) were associated with generalized hyperalgesia.

Discussion

These findings indicate that chronic pain may disrupt multiple cortical circuits to impact nociceptive processing.

Anatomical and Functional Connectivity of Critical Deep Brain Structures and Their Potential Clinical Application in Brain Stimulation

Subcortical structures, such as the hippocampus, amygdala, and nucleus accumbens (NAcc), play crucial roles in human cognitive, memory, and emotional processing, chronic pain pathophysiology, and are implicated in various psychiatric and neurological diseases. Interventions modulating the activities of these deep brain structures hold promise for improving clinical outcomes. Recently, non-invasive brain stimulation (NIBS) has been applied to modulate brain activity and has demonstrated its potential for treating psychiatric and neurological disorders. However, modulating the above deep brain structures using NIBS may be challenging due to the nature of these stimulations. This study attempts to identify brain surface regions as source targets for NIBS to reach these deep brain structures by integrating functional magnetic resonance imaging (fMRI) and diffusion tensor imaging (DTI). We used resting-state functional connectivity (rsFC) and probabilistic tractography (PTG) analysis to identify brain surface stimulation targets that are functionally and structurally connected to the hippocampus, amygdala, and NAcc in 119 healthy participants. Our results showed that the medial prefrontal cortex (mPFC) is functionally and anatomically connected to all three subcortical regions, while the precuneus is connected to the hippocampus and amygdala. The mPFC and precuneus, two key hubs of the default mode network (DMN), as well as other cortical areas distributed at the prefrontal cortex and the parietal, temporal, and occipital lobes, were identified as potential locations for NIBS to modulate the function of these deep structures. The findings may provide new insights into the NIBS target selections for treating psychiatric and neurological disorders and chronic pain.