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

Naturalistic acute pain states decoded from neural and facial dynamics

Pain remains poorly understood in task-free contexts, limiting our understanding of its neurobehavioral basis in naturalistic settings. Here, we use a multimodal, data-driven approach with intracranial electroencephalography, pain self-reports, and facial expression analysis to study acute pain in twelve epilepsy patients under continuous neural and audiovisual monitoring. Using machine learning, we successfully decode individual participants' high versus low pain states from distributed neural activity, involving mesolimbic regions, striatum, and temporoparietal cortex. Neural representation of pain remains stable for hours and is modulated by pain onset and relief. Objective facial expressions also classify pain states, concordant with neural findings. Importantly, we identify transient periods of momentary pain as a distinct naturalistic acute pain measure, which can be reliably discriminated from affect-neutral periods using neural and facial features. These findings reveal reliable neurobehavioral markers of acute pain across naturalistic contexts, underscoring the potential for monitoring and personalizing pain interventions in real-world settings.

Pathological features of post-stroke pain: a comprehensive analysis for subtypes

Post-stroke pain is heterogeneous and includes both nociceptive and neuropathic pain. These subtypes can be comprehensively assessed using several clinical tools, such as pain-related questionnaires, quantitative somatosensory tests and brain imaging. In the present study, we conducted a comprehensive assessment of patients with central post-stroke pain and non-central post-stroke pain and analysed their clinical features. We also performed a detailed analysis of the relationships between brain lesion areas or structural disconnection of the white matter and somatosensory dysfunctions. In this multicentre cross-sectional study, 70 patients were divided into 24 with central post-stroke pain, 26 with non-central post-stroke pain and 20 with no-pain groups. Multiple logistic regression analysis was used to summarize the relationships between each pathological feature (for the central post-stroke pain and non-central post-stroke pain groups) and pain-related factors or the results of quantitative somatosensory tests. Relationships between somatosensory dysfunctions and brain lesion areas were analysed using voxel-based lesion-symptom mapping and voxel-based disconnection-symptom mapping. All pathology feature models indicated that central post-stroke pain was associated with cold hypoesthesia at 8°C (β = 2.98, odds ratio = 19.6, 95% confidence interval = 2.7-141.8), cold hyperalgesia at 8°C (β = 2.61, odds ratio = 13.6, 95% confidence interval = 1.13-163.12) and higher Neuropathic Pain Symptom Inventory scores (for spontaneous and evoked pain items only; β = 0.17, odds ratio = 1.19, 95%, confidence interval = 1.07-1.32), whereas non-central post-stroke pain was associated with joint pain (β = 5.01, odds ratio = 149.854, 95%, confidence interval = 19.93-1126.52) and lower Neuropathic Pain Symptom Inventory scores (β = -0.17, odds ratio = 0.8, 95%, confidence interval = 0.75-0.94). In the voxel-based lesion-symptom mapping, the extracted lesion areas indicated mainly voxels significantly associated with cold hyperalgesia, allodynia at 8°C and 22°C and heat hypoesthesia at 45°C. These extracted areas were mainly in the putamen, insular cortex, hippocampus, Rolandic operculum, retrolenticular part of internal and external capsules and sagittal stratum. In voxel-based disconnection-symptom mapping, the extracted disconnection maps were significantly associated with cold hyperalgesia at 8°C, and heat hypoesthesia at 37°C and 45°C. These structural disconnection patterns were mainly in the cingulum frontal parahippocampal tract, the reticulospinal tract and the superior longitudinal fasciculus with a widespread interhemispheric disconnection of the corpus callosum. These findings serve as important indicators to facilitate decision-making and optimize precision treatments through data dimensionality reduction when diagnosing post-stroke pain using clinical assessments, such as bedside quantitative sensory testing, pain-related factors, pain questionnaires and brain imaging.

Anterior prefrontal EEG theta activities indicate memory and executive functions in patients with epilepsy

OBJECTIVE

Cognitive deficits are one of the most debilitating comorbidities in epilepsy and other neurodegenerative, neuropsychiatric, and neurodevelopmental brain disorders. Current diagnostic and therapeutic options are limited and lack objective measures of the underlying neural activities. In this study, electrophysiological biomarkers that reflect cognitive functions in clinically validated batteries were determined to aid diagnosis and treatment in specific brain regions.

METHODS

We employed the Cambridge Neuropsychological Test Automated Battery (CANTAB) tasks to probe memory and executive functions in 86 patients with epilepsy undergoing clinical electroencephalography (EEG) monitoring. EEG electrode signals during performance of particular battery tasks were decomposed to identify specific frequency bands and cortical areas that differentiated patients with impaired, normal, and good standardized performance according to their age and gender.

RESULTS

The anterior prefrontal cortical EEG power in the theta frequency band was consistently lower in patients with impaired memory and executive function performance (z-score < -1). This effect was evident in all four behavioral measures of executive, visual, spatial, and working memory functions and was confined to the cortical area of all four frontal pole electrodes (Nz, Fpz, Fp1, and Fp2).

SIGNIFICANCE

Theta EEG power in the anterior prefrontal cortex provides simple, accessible, and objective electrophysiological measure of memory and executive functions in epilepsy. Our results suggest a feasible clinical biomarker for diagnosis, monitoring, and treatment of cognitive deficits with emerging targeted neuromodulation approaches.

Opioidergic pain relief in humans is mediated by beta and high-gamma modulation in limbic regions

The nature of the neurophysiological effects of opioids, especially those responsible for their analgesic properties, are unknown, hindering efforts to develop non-addictive alternatives. Fentanyl and hydromorphone were administered to patients experiencing semi-chronic, clinically-relevant pain after surgical implantation of electrodes for the localization of seizure onset. Opioids suppressed beta oscillations in lateral amygdala, ventral and dorsolateral prefrontal cortices, and increased beta in medial amygdala and hippocampus. Opioids also suppressed high gamma oscillations in insula and lateral amygdala, and increased high gamma in cingulate cortex and hippocampus. The amplitude of these beta effects in the ventral prefrontal cortex, medial amygdala and hippocampus, and of gamma effects in the insula, were positively correlated with the magnitude of pain relief in response to a constant dose. These findings identify electrophysiological events in a network of limbic structures that may participate in opioidergic pain relief through nociceptive gating and a decreased concerned fixation on pain, providing insights into the neural basis of pain relief and suggesting possible biomarkers for developing non-addictive opioid alternatives.

Spontaneous pain dynamics characterized by stochasticity in neural recordings of awake humans with chronic pain

ABSTRACT

Chronic pain is characterized by spontaneous fluctuations in pain intensity, a phenomenon that remains poorly understood. The aim of this study is to elucidate the neural mechanisms underlying pain fluctuations in patients with chronic pain undergoing deep brain stimulation surgery. We recorded local field potentials (LFPs) from pain-processing hub structures, including the ventral posteromedial nucleus of the thalamus, subgenual cingulate cortex, and periventricular and periaqueductal gray, while patients continuously reported their pain levels. Using novel auto-mutual information metrics to analyze LFP stochastic patterns, we found that pain intensity correlated with both increased regularity of spike-like events and greater past-dependency of neural oscillations in the 4- to 15-Hz frequency band. In addition, during periods of higher pain states, we observed enhanced functional connectivity between the examined hub structures and the prefrontal cortex, suggesting a more focused flow of pain-related information within the pain circuit. By characterizing the dynamic nature of pain fluctuations, this study bridges the gap in understanding moment-to-moment pain variations and their underlying neural mechanisms, paving the way for improved chronic pain management strategies.

Machine learning predicts spinal cord stimulation surgery outcomes and reveals novel neural markers for chronic pain

Spinal cord stimulation (SCS) is a well-accepted therapy for refractory chronic pain. However, predicting responders remain a challenge due to a lack of objective pain biomarkers. The present study applies machine learning to predict which patients will respond to SCS based on intraoperative electroencephalogram (EEG) data and recognized outcome measures. The study included 20 chronic pain patients who were undergoing SCS surgery. During intraoperative monitoring, EEG signals were recorded under SCS OFF (baseline) and ON conditions, including tonic and high density (HD) stimulation. Once spectral EEG features were extracted during offline analysis, principal component analysis (PCA) and a recursive feature elimination approach were used for feature selection. A subset of EEG features, clinical characteristics of the patients and preoperative patient reported outcome measures (PROMs) were used to build a predictive model. Responders and nonresponders were grouped based on 50% reduction in 3-month postoperative Numeric Rating Scale (NRS) scores. The two groups had no statistically significant differences with respect to demographics (including age, diagnosis, and pain location) or PROMs, except for the postoperative NRS (worst pain: p = 0.028; average pain: p < 0.001) and Oswestry Disability Index scores (ODI, p = 0.030). Alpha-theta peak power ratio differed significantly between CP3-CP4 and T3-T4 (p = 0.019), with the lowest activity in CP3-CP4 during tonic stimulation. The decision tree model performed best, achieving 88.2% accuracy, an F1 score of 0.857, and an area under the curve (AUC) of the receiver operating characteristic (ROC)  of 0.879. Our findings suggest that combination of subjective self-reports, intraoperatively obtained EEGs, and well-designed machine learning algorithms might be potentially used to distinguish responders and nonresponders. Machine and deep learning hold enormous potential to predict patient responses to SCS therapy resulting in refined patient selection and improved patient outcomes.

At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface

Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed into an external computer for state classification. These brain-computer interfaces are limited to short-term studies in laboratory settings that may not reflect behavior and neural states in the real world. The development of implantable devices with sensing capabilities is revolutionizing the study and treatment of brain circuits. However, it is unknown whether these devices can decode natural movement state from recorded neural activity or accurately classify states in real-time using on-board algorithms. Here, using a totally implanted sensing-enabled neurostimulator to perform long-term, at-home recordings from the motor cortex and pallidum of four subjects with Parkinson's disease, we successfully identified highly sensitive and specific personalized signatures of gait state, as determined by wearable sensors. Additionally, we demonstrated the feasibility of using at-home data to generate biomarkers compatible with the classifier embedded on-board the neurostimulator. These findings offer a pipeline for ecologically valid movement biomarker identification that can advance therapy across a variety of diseases.

Moving towards the use of artificial intelligence in pain management

BACKGROUND AND OBJECTIVE

While the development of artificial intelligence (AI) technologies in medicine has been significant, their application to acute and chronic pain management has not been well characterized. This systematic review aims to provide an overview of the current state of AI in acute and chronic pain management.

DATABASES AND DATA TREATMENT

This review was registered with PROSPERO (ID# CRD42022307017), the international registry for systematic reviews. The search strategy was prepared by a librarian and run in four electronic databases (Embase, Medline, Central, and Web of Science). Collected articles were screened by two reviewers. Included studies described the use of AI for acute and chronic pain management.

RESULTS

From the 17,601 records identified in the initial search, 197 were included in this review. Identified applications of AI were described for treatment planning as well as treatment delivery. Described uses include prediction of pain, forecasting of individualized responses to treatment, treatment regimen tailoring, image-guidance for procedural interventions and self-management tools. Multiple domains of AI were used including machine learning, computer vision, fuzzy logic, natural language processing and expert systems.

CONCLUSION

There is growing literature regarding applications of AI for pain management, and their clinical use holds potential for improving patient outcomes. However, multiple barriers to their clinical integration remain including lack validation of such applications in diverse patient populations, missing infrastructure to support these tools and limited provider understanding of AI.

SIGNIFICANCE

This review characterizes current applications of AI for pain management and discusses barriers to their clinical integration. Our findings support continuing efforts directed towards establishing comprehensive systems that integrate AI throughout the patient care continuum.

Decoding the mechanism of proanthocyanidins in central analgesia: redox regulation and KCNK3 blockade

Neuropathic pain causes enduring physical discomfort and emotional distress. Conventional pharmacological treatments often provide restricted relief and may result in undesirable side effects, posing a substantial clinical challenge. Peripheral and spinal redox homeostasis plays an important role in pain processing and perception. However, the roles of oxidative stress and antioxidants in pain and analgesia on the cortical region during chronic pain remains obscure. Here we focus on the ventrolateral orbital cortex (VLO), a brain region associated with pain severity and involved in pain inhibition. Using a spared nerve injury mouse model, we observed the notable reactive oxygen species (ROS)-mediated suppression of the excitability of pyramidal cells (PYRVLO) in the VLO. Nasal application or microinjection of the natural antioxidants proanthocyanidins (PACs) to the VLO specifically increased the activity of PYRVLO and induced a significant analgesic effect. Mechanistically, PACs activate PYRVLO by inhibiting distinct potassium channels in different ways: (1) by scavenging ROS to reduce ROS-sensitive voltage-gated potassium currents and (2) by acting as a channel blocker through direct binding to the cap structure of KCNK3 to inhibit the leak potassium current (Ileak). These results reveal the role of cortical oxidative stress in central hyperalgesia and elucidate the mechanism and potential translational significance of PACs in central analgesia. These findings suggest that the effects of PACs extend beyond their commonly assumed antioxidant or anti-inflammatory effects.

The intersectional implications of a quantitative epistemology in pain care and research

Background

There is a growing interest in understanding the long-standing tension between subjective experience and objective measurement, with a focus on better understanding personal or lived experience. However, quantitative pain measurement is itself a complicated practice that is rarely examined. The method does not exist in a vacuum but along a historical trajectory that we believe to be worth unpacking.

Aims

We seek to highlight (1) the problematics associated with a systemic reliance on quantitative tools that are themselves validated via statistical methods; (2) what alternatives already exist, regardless of their logistical shortcomings; and (3) the actual and possible consequences of continuing a trajectory of data-based pain rating.

Methods

We present historical and contemporary case studies through theoretical frames that help the reader understand the social construction of pain as a phenomenon whose quantification has been justified with statistical approaches.

Results

Relying on quantitative data for a pain rating that is perceived as more valid, reliable, and efficient-a triad that has come to represent the ideal pain measurement instrument-risks entrenching both patient/participant and clinician/researcher in systems of computation and control. This is detrimental to society's most vulnerable populations.

Conclusions

Patients, practitioners, and social scientists all have an opportunity to reframe their understanding of pain measurement as medical practice to build more equitable spaces in pain medicine.

Localizing electrophysiologic cue-reactivity within the nucleus accumbens guides deep brain stimulation for opioid use disorder

Substance use disorder (SUD) is a significant public health concern, with over 30% of the affected population not responding to available treatments. Severe SUD is characterized by drug-cue reactivity that has been reported to predict treatment-failure. We leveraged this pathophysiological feature to optimize deep brain stimulation (DBS) of the nucleus accumbens region (NAc) in an adult with SUD. A personalized drug cue-reactivity task was administered while recording NAc region electrophysiology from a lead externalized for clinical purposes. We identified a drug cue-evoked signal in the ventral NAc associated with intensification of opioid-related cravings, which attenuated subsequent to stimulation delivered to the same area. DBS was then programmed to engage this focal region, which resulted in sustained suppression of drug-related cravings. This finding heralds the potential for personalized strategies to optimize DBS for SUD.

When neuromodulation met control theory

The brain is a highly complex physical system made of assemblies of neurons that work together to accomplish elaborate tasks such as motor control, memory and perception. How these parts work together has been studied for decades by neuroscientists using neuroimaging, psychological manipulations, and neurostimulation. Neurostimulation has gained particular interest, given the possibility to perturb the brain and elicit a specific response. This response depends on different parameters such as the intensity, the location and the timing of the stimulation. However, most of the studies performed so far used previously established protocols without considering the ongoing brain activity and, thus, without adaptively targeting the stimulation. In control theory, this approach is called open-loop control, and it is always paired with a different form of control called closed-loop, in which the current activity of the brain is used to establish the next stimulation. Recently, neuroscientists are beginning to shift from classical fixed neuromodulation studies to closed-loop experiments. This new approach allows the control of brain activity based on responses to stimulation and thus to personalize individual treatment in clinical conditions. Here, we review this new approach by introducing control theory and focusing on how these aspects are applied in brain studies. We also present the different stimulation techniques and the control approaches used to steer the brain. Finally, we explore how the closed-loop framework will revolutionize the way the human brain can be studied, including a discussion on open questions and an outlook on future advances.

Neuromodulation of Dopamine D2 Receptors Alters Orbitofrontal Neuronal Activity and Reduces Risk-Prone Behavior in Male Rats with Inflammatory Pain

Dopamine (DA) is believed to play a crucial role in maintaining the integrity of the rodent orbitofrontal cortex (OFC) networks during risk-based decision-making processes. Chronic pain conditions can lead to impaired DAergic signaling, which, in turn, may affect the motivational control of risk-based responses. Nevertheless, the neural mechanisms underlying this instability are poorly understood. In this study, we aimed to investigate whether this impairment is dependent on the activity of the DA D2 receptor (D2r). To address this hypothesis, we implanted bilateral matrices of multielectrodes into the OFC of male rats and recorded the neural activity while they performed a food-reinforced rodent gambling task (rGT). We evaluated behavioral performance and neural activity patterns before and after inducing a model of inflammatory pain - complete Freund's adjuvant (CFA) model. Our findings revealed that rats treated with CFA exhibited an abnormal preference for the large/uncertain reward during rGT performance. This altered behavioral choice profile could be reversed by prior systemic administration of D2r ligands (0.05 mg/kg, quinpirole or raclopride), indicating a potential role of D2r in the decision-making process required for this task. The administration of these ligands at the specified dosages did not affect pain responses, but lead to a significant alteration of OFC neuronal activity that support goal-directed choice responses in the rGT. Finally, we found evidence that CFA-treated rats exhibit OFC functional changes, namely an upregulation of DA D1 receptor (D1r) and a downregulation of DA beta-hydroxylase (DH). These results demonstrate that the disruption of DAergic balance in the brain networks is crucial for the development of high-risk decision profiles during painful conditions.

Safety and feasibility of deep brain stimulation of the anterior cingulate and thalamus in chronic refractory neuropathic pain: a pilot and randomized study

BACKGROUND

Deep Brain Stimulation (DBS) of the anterior cingulum has been recently proposed to treat refractory chronic pain but its safety and its efficacy have not been evaluated in controlled conditions. Our objective was to evaluate the respective feasibility and safety of sensory thalamus (Thal-DBS) combined with anterior cingulate (ACC-DBS) DBS in patients suffering from chronic neuropathic pain.

METHODS

We conducted a bicentric study (clinicaltrials.gov NCT03399942) in patients suffering from medically-refractory chronic unilateral neuropathic pain surgically implanted with both unilateral Thal-DBS and bilateral ACC-DBS, to evaluate successively: Thal-DBS only; combined Thal-DBS and ACC-DBS; ACC-DBS "on" and "off" stimulation periods in randomized cross-over double-blinded conditions; and a 1-year open phase. Safety and efficacy were evaluated by repeated neurological examination, psychiatric assessment, comprehensive assessment of cognitive and affective functioning. Changes on pain intensity (Visual Analogic Scale) and quality of life (EQ-5D scale) were used to evaluate DBS efficacy.

RESULTS

All the patients (2 women, 6 men, mean age 52,1) completed the study. Adverse events were: epileptic seizure (2), transient motor or attention (2), persistent gait disturbances (1), sleep disturbances (1). No patient displayed significant cognitive or affective change. Compared to baseline, the quality of life (EQ-5D utility score) was significantly improved during the ACC-DBS "On" stimulation period (p = 0,039) and at the end of the study (p = 0,034).

CONCLUSION

This pilot study confirmed the safety of anterior cingulate DBS alone or in combination with thalamic stimulation and suggested that it might improve quality of life of patients with chronic refractory neuropathic pain.

TRIAL REGISTRATION

The study has been registered on 20,180,117 (clinicaltrials.gov NCT03399942).

Materials and devices for high-density, high-throughput micro-electrocorticography arrays

The pursuit of precisely recording and localizing neural activities in brain cortical regions drives the development of advanced electrocorticography (ECoG) devices. Remarkable progress has led to the emergence of micro-ECoG (µECoG) devices with sub-millimeter resolutions. This review presents the current research status, development directions, potential innovations and applications of high-density, high-throughput µECoG devices. First, we summarize the challenges associated with accurately recording single or multiple neurons using existing µECoG devices, including passive multielectrode and active transistor arrays. Second, we focus on cutting-edge advancements in passive µECoG devices by discussing the design principles and fabrication strategies to optimize three key parameters: impedance, mechanical flexibility, and biocompatibility. Furthermore, recent findings highlight the need for further research and development in active transistor arrays, including silicon, metal oxide, and solution-gated transistors. These active transistor arrays have the potential to unlock the capabilities of high-density, high-throughput µECoG devices and overcome the limitations of passive multielectrode arrays. The review explores the potential innovations and applications of µECoG devices, showcasing their effectiveness for both brain science research and clinical applications.

Challenges and opportunities of acquiring cortical recordings for chronic adaptive deep brain stimulation

Deep brain stimulation (DBS), a proven treatment for movement disorders, also holds promise for the treatment of psychiatric and cognitive conditions. However, for DBS to be clinically effective, it may require DBS technology that can alter or trigger stimulation in response to changes in biomarkers sensed from the patient's brain. A growing body of evidence suggests that such adaptive DBS is feasible, it might achieve clinical effects that are not possible with standard continuous DBS and that some of the best biomarkers are signals from the cerebral cortex. Yet capturing those markers requires the placement of cortex-optimized electrodes in addition to standard electrodes for DBS. In this Perspective we argue that the need for cortical biomarkers in adaptive DBS and the unfortunate convergence of regulatory and financial factors underpinning the unavailability of cortical electrodes for chronic uses threatens to slow down or stall research on adaptive DBS and propose public-private partnerships as a potential solution to such a critical technological gap.

Neurosurgery for mental conditions and pain: An historical perspective on the limits of biological determinism

Neurosurgical operations treat involuntary movement disorders (MvDs), spasticity, cranial neuralgias, cancer pain, and other selected disorders, and implantable neurostimulation or drug delivery devices relieve MvDs, epilepsy, cancer pain, and spasticity. In contrast, studies of surgery or device implantations to treat chronic noncancer pain or mental conditions have not shown consistent evidence of efficacy and safety in formal, randomized, controlled trials. The success of particular operations in a finite set of disorders remains at odds with disconfirming results in others. Despite expectations that surgery or device implants would benefit particular patients, the normalization of unproven procedures could jeopardize the perceived legitimacy of functional neurosurgery in general. An unacknowledged challenge in functional neurosurgery is the limitation of biological determinism, wherein network activity is presumed to exclusively or predominantly mediate nociception, affect, and behavior. That notion regards certain pain states and mental conditions as disorders or dysregulation of networks, which, by implication, make them amenable to surgery. Moreover, implantable devices can now detect and analyze neural activity for observation outside the body, described as the extrinsic or micro perspective. This fosters a belief that automated analyses of physiological and imaging data can unburden the treatment of selected mental conditions and pain states from psychological subjectivity and complexity and the inherent sematic ambiguity of self-reporting. That idea is appealing; however, it discounts all other influences. Attempts to sway public opinion and regulators to approve deep brain stimulation for unproven indications could, if successful, harm the public interest, making demands for regulatory approval beside the point.

Signatures of chronic pain in multiple sclerosis: a machine learning approach to investigate trigeminal neuralgia

ABSTRACT

Chronic pain is a pervasive, disabling, and understudied feature of multiple sclerosis (MS), a progressive demyelinating and neurodegenerative disease. Current focus on motor components of MS disability combined with difficulties assessing pain symptoms present a challenge for the evaluation and management of pain in MS, highlighting the need for novel methods of assessment of neural signatures of chronic pain in MS. We investigate chronic pain in MS using MS-related trigeminal neuralgia (MS-TN) as a model condition focusing on gray matter structures as predictors of chronic pain. T1 imaging data from people with MS (n = 75) and MS-TN (n = 77) using machine learning (ML) was analyzed to derive imaging predictors at the level of cortex and subcortical gray matter. The ML classifier compared imaging metrics of patients with MS and MS-TN and distinguished between these conditions with 93.4% individual average testing accuracy. Structures within default-mode, somatomotor, salience, and visual networks (including hippocampus, primary somatosensory cortex, occipital cortex, and thalamic subnuclei) were identified as significant imaging predictors of trigeminal neuralgia pain. Our results emphasize the multifaceted nature of chronic pain and demonstrate the utility of imaging and ML in assessing and understanding MS-TN with greater objectivity.

Beta Oscillations in the Sensory Thalamus During Severe Facial Neuropathic Pain Using Novel Sensing Deep Brain Stimulation

OBJECTIVE

Advancements in deep brain stimulation (DBS) devices provide a unique opportunity to record local field potentials longitudinally to improve the efficacy of treatment for intractable facial pain. We aimed to identify potential electrophysiological biomarkers of pain in the ventral posteromedial nucleus (VPM) of the thalamus and periaqueductal gray (PAG) using a long-term sensing DBS system.

MATERIALS AND METHODS

We analyzed power spectra of ambulatory pain-related events from one patient implanted with a long-term sensing generator, representing different pain intensities (pain >7, pain >9) and pain qualities (no pain, burning, stabbing, and shocking pain). Power spectra were parametrized to separate oscillatory and aperiodic features and compared across the different pain states.

RESULTS

Overall, 96 events were marked during a 16-month follow-up. Parameterization of spectra revealed a total of 62 oscillatory peaks with most in the VPM (77.4%). The pain-free condition did not show any oscillations. In contrast, β peaks were observed in the VPM during all episodes (100%) associated with pain >9, 56% of episodes with pain >7, and 50% of burning pain events (center frequencies: 28.4 Hz, 17.8 Hz, and 20.7 Hz, respectively). Episodes of pain >9 indicated the highest relative β band power in the VPM and decreased aperiodic exponents (denoting the slope of the power spectra) in both the VPM and PAG.

CONCLUSIONS

For this patient, an increase in β band activity in the sensory thalamus was associated with severe facial pain, opening the possibility for closed-loop DBS in facial pain.

Is chronic pain caused by central sensitization? A review and critical point of view

Chronic pain causes disability and loss of health worldwide. Yet, a mechanistic explanation for it is still missing. Frequently, neural phenomena, and among them, Central Sensitization (CS), is presented as causing chronic pain. This narrative review explores the evidence substantiating the relationship between CS and chronic pain: four expert researchers were divided in two independent teams that reviewed the available evidence. Three criteria were established for a study to demonstrate a causal relationship: (1) confirm presence of CS, (2) study chronic pain, and (3) test sufficiency or necessity of CS over chronic pain symptoms. No study met those criteria, failing to demonstrate that CS can cause chronic pain. Also, no evidence reporting the occurrence of CS in humans was found. Worryingly, pain assessments are often confounded with CS measures in the literature, omitting that the latter is a neurophysiological and not a perceptual phenomenon. Future research should avoid this misconception to directly interrogate what is the causal contribution of CS to chronic pain to better comprehend this problematic condition.

Transforming personalized chronic pain management with artificial intelligence: A commentary on the current landscape and future directions

Artificial intelligence (AI) has the potential to revolutionize chronic pain management by guiding the development of effective treatment strategies that are tailored to individual patient needs. This potential comes from AI's ability to analyze large and heterogeneous datasets to identify hidden patterns. When applied to clinical datasets of a particular patient population, AI can be used to identify pain subtypes among patients, predict treatment responses, and guide the clinical decision-making process. However, integrating AI into the clinical practice requires overcoming challenges such as data quality, the complexity of human pain physiology, and validation against diverse patient populations. Targeted, collaborative efforts among clinicians, researchers, and AI specialists will be needed to maximize AI's capabilities and advance current management and treatment of chronic pain conditions.

Multimodal integrated flexible neural probe for in situ monitoring of EEG and lactic acid

In physiological activities, the brain's electroencephalogram (EEG) signal and chemical concentration change are crucial for diagnosing and treating neurological disorders. Despite the advantages of flexible neural probes, such as their flexibility and biocompatibility, it remains a challenge to achieve in situ monitoring of electrophysiological and chemical signals on a small scale simultaneously. This study developed a new method to construct an efficient dual-sided multimodal integrated flexible neural probe, which combines a density electrode array for EEG recordings and an electrochemical sensor for detecting lactic acid. The EEG electrode array includes a 6-channel recording electrode array with each electrode 30 × 50 μm in size, and the lactic acid sensor with overall contact is approximately 100 μm wide. The EEG electrodes have an average impedance of 2.57 kΩ at 1 kHz and remained stable after immersing in NS (normal saline) for 3 months. The lactic acid sensor showed a sensitivity of 52.8 nA mM-1. The in vivo experiments demonstrated that the probe can reliably monitor electrophysiological signals. The probe is able to be implanted into the desired site with the help of a guide port. This flexible neural probe can provide more comprehensive insights into brain activity in the field of neuroscience and clinical practices.

Transformer-based classification of visceral pain-related local field potential patterns in the brain

Neuronal ensemble activity entrained by local field potential (LFP) patterns underlies a variety of brain functions, including emotion, cognition, and pain perception. Recent advances in machine learning approaches may enable more effective methods for analyzing LFP patterns across multiple brain areas than conventional time-frequency analysis. In this study, we tested the performance of two machine learning algorithms, AlexNet and the Transformer models, to classify LFP patterns in eight pain-related brain regions before and during acetic acid-induced visceral pain behaviors. Over short time windows lasting several seconds, applying AlexNet to LFP power datasets, but not to raw time-series LFP traces from multiple brain areas, successfully achieved superior classification performance compared with simple LFP power analysis. Furthermore, applying the Transformer directly to the raw LFP traces achieved significantly superior classification performance than AlexNet when using LFP power datasets. These results demonstrate the utility of the Transformer in the analysis of neurophysiological signals, and pave the way for its future applications in the decoding of more complex neuronal activity patterns.

Closed-loop neural interfaces for pain: Where do we stand?

Advances in closed-loop neural interfaces and neuromodulation have offered a potentially effective and non-addictive treatment for chronic pain. These interfaces link neural sensors with device outputs to provide temporally precise stimulation. We discuss challenges and trends of state-of-the-art neural interfaces for treating pain in animal models and human pilot trials.

Clinical pain management: Current practice and recent innovations in research

Chronic pain affects one in five adults. It is not only a major cause of disability for individual patients but also a driver of costs for entire healthcare systems. Treatment of pain remains a challenge, and the use of opioids has further led to a concurrent opioid epidemic. In this review, we discuss current standard treatment options for chronic pain, including pharmacological, behavioral, and interventional treatments. In addition, we review ongoing research in different areas that will potentially unlock new therapies.

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.

Nanorobot-Based Direct Implantation of Flexible Neural Electrode for BCI

Brain-Computer Interface (BCI) has gained remarkable prominence in biomedical community. While BCI holds vast potential across diverse domains, the implantation of neural electrodes poses multifaceted challenges to fully explore the power of BCI. Conventional rigid electrodes face the problem of foreign body reaction induced by mechanical mismatch to biological tissue, while flexible electrodes, though more preferential, lack controllability during implantation. Researchers have explored various strategies, from assistive shuttle to biodegradable coatings, to strike a balance between implantation rigidity and post-implantation flexibility. Yet, these approaches may introduce complications, including immune response, inflammations, and raising intracranial pressure. To this end, this paper proposes a novel nanorobot-based technique for direct implantation of flexible neural electrodes, leveraging the high controllability and repeatability of robotics to enhance the implantation quality. This approach features a dual-arm nanorobotic system equipped with stereo microscope, by which a flexible electrode is first visually aligned to the target neural tissue to establish contact and thereafter implanted into brain with well controlled insertion direction and depth. The key innovation is, through dual-arm coordination, the flexible electrode maintains straight along the implantation direction. With this approach, we implanted CNTf electrodes into cerebral cortex of mouse, and captured standard spiking neural signals.

Stereoelectroencephalography Electrode Implantation for Inpatient Workup of Treatment-Resistant Depression

BACKGROUND AND OBJECTIVES

Treatment-resistant depression is a leading cause of disability. Our center's trial for neurosurgical intervention for treatment-resistant depression involves a staged workup for implantation of a personalized, closed-loop neuromodulation device for refractory depression. The first stage ("stage 1") of workup involves implantation of 10 stereoelectroencephalography (SEEG) electrodes bilaterally into 5 anatomically defined brain regions and involves a specialized preoperative imaging and planning workup and a frame-based operating protocol.

METHODS

We rely on diffusion tractography when planning stereotactic targets for 3 of 5 anatomic areas. We outline the rationale and fiber tracts that we focus on for targeting amygdala, ventral striatum and ventral capsule, and subgenual cingulate. We also outline frame-based stereotactic considerations for implantation of SEEG electrodes.

EXPECTED OUTCOMES

Our method has allowed us to safely target all 5 brain areas in 3 of 3 trial participants in this ongoing study, with adequate fiber bundle contact in each of the 3 areas targeted using tractography. Furthermore, we ultimately used tractography data from our stage 1 workup to guide targeting near relevant fiber bundles for stage 2 (implantation of a responsive neuromodulation device). On completion of our data set, we will determine the overlap between volume of tissue activated for all electrodes and areas of interest defined by anatomy and tractography.

DISCUSSION

Our protocol outlined for SEEG electrode implantation incorporates tractography and frame-based stereotaxy.

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.

Genome-wide association reveals a locus in neuregulin 3 associated with gabapentin efficacy in women with chronic pelvic pain

Chronic pelvic pain (CPP) in women with no obvious pelvic pathology has few evidence-based treatment options. Our recent multicenter randomized controlled trial (GaPP2) in women with CPP and no obvious pelvic pathology showed that gabapentin did not relieve pain overall and was associated with more side effects than placebo. We conducted an exploratory genome-wide association study using eligible GaPP2 participants aiming to identify genetic variants associated with gabapentin response. One genome-wide significant association with gabapentin analgesic response was identified, rs4442490, an intron variant located in Neuregulin 3 (NRG3) (p = 2·11×10-8; OR = 18·82 (95% CI 4·86-72·83). Analysis of a large sample of UK Biobank participants demonstrated phenome-wide significant brain imaging features of rs4442490, particularly implicating the orbitofrontal cortex. NRG3 is expressed predominantly in central nervous system tissues and plays a critical role in nervous system development, maintenance, and repair, suggesting a neurobiologically plausible role in gabapentin efficacy and potential for personalized analgesic treatment.

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.