Modeling the daily rhythm of human pain processing in the dorsal horn

Mechanisms for dysregulation of excitatory-inhibitory balance underlying allodynia in dorsal horn neural subcircuits

Chronic pain is a wide-spread condition that is debilitating and expensive to manage, costing the United States alone around $600 billion in 2010. In a common type of chronic pain called allodynia, non-painful stimuli produce painful responses with highly variable presentations across individuals. While the specific mechanisms remain unclear, allodynia is hypothesized to be caused by the dysregulation of excitatory-inhibitory (E-I) balance in pain-processing neural circuitry in the dorsal horn of the spinal cord. In this work, we analyze biophysically-motivated subcircuit structures that represent common motifs in neural circuits in layers I-II of the dorsal horn. These circuits are hypothesized to be part of the neural pathways that mediate two different types of allodynia: static and dynamic. We use neural firing rate models to describe the activity of populations of excitatory and inhibitory interneurons within each subcircuit. By accounting for experimentally-observed responses under healthy conditions, we specify model parameters defining populations of subcircuits that yield typical behavior under normal conditions. Then, we implement a sensitivity analysis approach to identify the mechanisms most likely to cause allodynia-producing dysregulation of the subcircuit's E-I signaling. We find that disruption of E-I balance generally occurs either due to downregulation of inhibitory signaling so that excitatory neurons are "released" from inhibitory control, or due to upregulation of excitatory neuron responses so that excitatory neurons "escape" their inhibitory control. Which of these mechanisms is most likely to occur, the subcircuit components involved in the mechanism, and the proportion of subcircuits exhibiting the mechanism can vary depending on the subcircuit structure. These results suggest specific hypotheses about diverse mechanisms that may be most likely responsible for allodynia, thus offering predictions for the high interindividual variability observed in allodynia and identifying targets for further experimental studies on the underlying mechanisms of this chronic pain condition.

Author summary

While chronic pain affects roughly 20% of the US adult population [1], symptoms and presentations of the condition are highly variable across individuals and its causes remain largely unknown. A prevailing hypothesis for the cause of a type of chronic pain called allodynia is that the balance between excitatory and inhibitory signaling pathways between neuron populations in the spinal cord dorsal horn may be disrupted. To help better understand neural mechanisms underlying allodynia, we analyze biologically-motivated mathematical models of subcircuits of neuron populations that are part of the pain processing signaling pathway in the dorsal horn of the spinal cord. We use a novel sensitivity analysis approach to identify mechanisms of subcircuit dysregulation that may contribute to two different types of allodynia. The model results identify specific subcircuit components that are most likely to contribute to each type of allodynia. These mechanisms suggest targets for further experimental study, as well as for pharmacological intervention for better pain treatments.

Modeling homeostatic and circadian modulation of human pain sensitivity

Introduction

Mathematical modeling has played a significant role in understanding how homeostatic sleep pressure and the circadian rhythm interact to influence sleep-wake behavior. Pain sensitivity is also affected by these processes, and recent experimental results have measured the circadian and homeostatic components of the 24 h rhythm of thermal pain sensitivity in humans. To analyze how rhythms in pain sensitivity are affected by disruptions in sleep behavior and shifts in circadian rhythms, we introduce a dynamic mathematical model for circadian and homeostatic regulation of sleep-wake states and pain intensity.

Methods

The model consists of a biophysically based, sleep-wake regulation network model coupled to data-driven functions for the circadian and homeostatic modulation of pain sensitivity. This coupled sleep-wake-pain sensitivity model is validated by comparison to thermal pain intensities in adult humans measured across a 34 h sleep deprivation protocol.

Results

We use the model to predict dysregulation of pain sensitivity rhythms across different scenarios of sleep deprivation and circadian rhythm shifts, including entrainment to new environmental light and activity timing as occurs with jet lag and chronic sleep restriction. Model results show that increases in pain sensitivity occur under conditions of increased homeostatic sleep drive with nonlinear modulation by the circadian rhythm, leading to unexpected decreased pain sensitivity in some scenarios.

Discussion

This model provides a useful tool for pain management by predicting alterations in pain sensitivity due to varying or disrupted sleep schedules.

The Circadian Clocks, Oscillations of Pain-Related Mediators, and Pain

The circadian clock is a biochemical oscillator that is synchronized with solar time. Normal circadian rhythms are necessary for many physiological functions. Circadian rhythms have also been linked with many physiological functions, several clinical symptoms, and diseases. Accumulating evidence suggests that the circadian clock appears to modulate the processing of nociceptive information. Many pain conditions display a circadian fluctuation pattern clinically. Thus, the aim of this review is to summarize the existing knowledge about the circadian clocks involved in diurnal rhythms of pain. Possible cellular and molecular mechanisms regarding the connection between the circadian clocks and pain are discussed.

Measuring pain and nociception: Through the glasses of a computational scientist. Transdisciplinary overview of methods

In a healthy state, pain plays an important role in natural biofeedback loops and helps to detect and prevent potentially harmful stimuli and situations. However, pain can become chronic and as such a pathological condition, losing its informative and adaptive function. Efficient pain treatment remains a largely unmet clinical need. One promising route to improve the characterization of pain, and with that the potential for more effective pain therapies, is the integration of different data modalities through cutting edge computational methods. Using these methods, multiscale, complex, and network models of pain signaling can be created and utilized for the benefit of patients. Such models require collaborative work of experts from different research domains such as medicine, biology, physiology, psychology as well as mathematics and data science. Efficient work of collaborative teams requires developing of a common language and common level of understanding as a prerequisite. One of ways to meet this need is to provide easy to comprehend overviews of certain topics within the pain research domain. Here, we propose such an overview on the topic of pain assessment in humans for computational researchers. Quantifications related to pain are necessary for building computational models. However, as defined by the International Association of the Study of Pain (IASP), pain is a sensory and emotional experience and thus, it cannot be measured and quantified objectively. This results in a need for clear distinctions between nociception, pain and correlates of pain. Therefore, here we review methods to assess pain as a percept and nociception as a biological basis for this percept in humans, with the goal of creating a roadmap of modelling options.

Selective electrical stimulation of low versus high diameter myelinated fibers and its application in pain relief: a modeling study

Electrical stimulation of peripheral nerve fibers has always been an attractive field of research. Due to the higher activation threshold, the stimulation of small fibers is accompanied by the stimulation of larger ones. It is therefore necessary to design a specific stimulation theme in order to only activate narrow fibers. There is evidence that stimulating Aδ fibers can activate endogenous pain-relieving mechanisms. However, both selective stimulation and reducing pain by activating small nociceptive fibers are still poorly investigated. In this study, using high-frequency stimulation waveforms (5-20 kHz), computational modeling provides a simple framework for activating narrow nociceptive fibers. Additionally, a model of myelinated nerve fibers is modified by including sodium-potassium pump and investigating its effects on neuronal stimulation. Besides, a modified mathematical model of pain processing circuits in the dorsal horn is presented that consists of supraspinal pain control mechanisms. Hence, by employing this pain-modulating model, the mechanism of the reduction of pain by activating nociceptive fibers is explored. In the case of two fibers with the same distance from the point source electrode, a single stimulation waveform is capable of blocking one large fiber and stimulating another small fiber. Noteworthy, the Na/K pump model demonstrated that it does not have a significant effect on the activation threshold and firing frequency of fiber. Ultimately, results suggest that the descending pathways of Locus coeruleus may effectively contribute to pain relief through stimulation of nociceptive fibers, which will be beneficial for clinical interventions.

Multiscale Computer Model of the Spinal Dorsal Horn Reveals Changes in Network Processing Associated with Chronic Pain

Pain-related sensory input is processed in the spinal dorsal horn (SDH) before being relayed to the brain. That processing profoundly influences whether stimuli are correctly or incorrectly perceived as painful. Significant advances have been made in identifying the types of excitatory and inhibitory neurons that comprise the SDH, and there is some information about how neuron types are connected, but it remains unclear how the overall circuit processes sensory input or how that processing is disrupted under chronic pain conditions. To explore SDH function, we developed a computational model of the circuit that is tightly constrained by experimental data. Our model comprises conductance-based neuron models that reproduce the characteristic firing patterns of spinal neurons. Excitatory and inhibitory neuron populations, defined by their expression of genetic markers, spiking pattern, or morphology, were synaptically connected according to available qualitative data. Using a genetic algorithm, synaptic weights were tuned to reproduce projection neuron firing rates (model output) based on primary afferent firing rates (model input) across a range of mechanical stimulus intensities. Disparate synaptic weight combinations could produce equivalent circuit function, revealing degeneracy that may underlie heterogeneous responses of different circuits to perturbations or pathologic insults. To validate our model, we verified that it responded to the reduction of inhibition (i.e., disinhibition) and ablation of specific neuron types in a manner consistent with experiments. Thus validated, our model offers a valuable resource for interpreting experimental results and testing hypotheses in silico to plan experiments for examining normal and pathologic SDH circuit function.SIGNIFICANCE STATEMENT We developed a multiscale computer model of the posterior part of spinal cord gray matter (spinal dorsal horn), which is involved in perceiving touch and pain. The model reproduces several experimental observations and makes predictions about how specific types of spinal neurons and synapses influence projection neurons that send information to the brain. Misfiring of these projection neurons can produce anomalous sensations associated with chronic pain. Our computer model will not only assist in planning future experiments, but will also be useful for developing new pharmacotherapy for chronic pain disorders, connecting the effect of drugs acting at the molecular scale with emergent properties of neurons and circuits that shape the pain experience.

Analysis of Airway Vagal Neurons

Internal organs, including the airway, are innervated by neurons of the autonomic and sensory nervous systems. The airway-innervating sensory neurons primarily originate from the vagus nerve, whose cell bodies are found, in rodents, in the jugular and nodose ganglia complex (JNC). About half of these sensory neurons expressed the heat-sensing ion channel TRPV1 and evolved to limit tissue damage by detecting chemical, mechanical, or thermal threats and to initiate protective airway reflexes such as coughing and bronchoconstriction. They also help monitor the host homeostasis by sensing nutrients, pressure, and O₂ levels and help mount airway defenses by controlling immune and goblet cell activity. To better appreciate the scope of the physiological role and pathological contributions of these neurons, we will review gain and loss-of-function approaches geared at controlling the activity of these neurons. We will also present a method to study transcriptomic changes in airway-innervating neurons and a co-culture approach designed to understand how nociceptors modulate immune responses.

A scoping review of the diurnal variation in the intensity of neuropathic pain

BACKGROUND

Recent studies suggest that neuropathic pain exhibit a daily diurnal pattern with peak levels usually in the late afternoon to evening and trough in the morning hours, although literature on this topic has been sparse. This scoping review examines current evidence on the chronobiology of neuropathic pain in both animal models and in humans with neuropathic pain.

METHOD

Literature search was conducted on major medical databases for relevant articles on chronobiology of neuropathic pain in both animal models and in humans with neuropathic pain. Data extracted include details of specific animal models or specific neuropathic pain conditions in humans, methods and timing of assessing pain severity, and specific findings of diurnal variation in pain intensity or its surrogate markers.

RESULTS

Thirteen animal and eight human studies published between 1976 to 2020 were included in the analysis. Seven out of 13 animal studies reported specific diurnal variation in pain intensity, with five of the seven studies reporting a trend towards increased sensitivity to mechanical allodynia or thermal hyperalgesia in the late light to dark phase. All eight studies on human subjects reported a diurnal variation in the intensity of neuropathic pain where there was an increase in pain intensity through the day with peaks in late evening and early night hours.

CONCLUSIONS

Studies included in this review demonstrated a diurnal variation in the pattern of neuropathic pain that is distinct from the pattern for nociceptive pain. These findings have implications for potential therapeutic strategies for neuropathic pain.

Circadian Clock-Controlled Checkpoints in the Pathogenesis of Complex Disease

The circadian clock coordinates physiology, metabolism, and behavior with the 24-h cycles of environmental light. Fundamental mechanisms of how the circadian clock regulates organ physiology and metabolism have been elucidated at a rapid speed in the past two decades. Here we review circadian networks in more than six organ systems associated with complex disease, which cluster around metabolic disorders, and seek to propose critical regulatory molecules controlled by the circadian clock (named clock-controlled checkpoints) in the pathogenesis of complex disease. These include clock-controlled checkpoints such as circadian nuclear receptors in liver and muscle tissues, chemokines and adhesion molecules in the vasculature. Although the progress is encouraging, many gaps in the mechanisms remain unaddressed. Future studies should focus on devising time-dependent strategies for drug delivery and engagement in well-characterized organs such as the liver, and elucidating fundamental circadian biology in so far less characterized organ systems, including the heart, blood, peripheral neurons, and reproductive systems.

Systems and Circuits Linking Chronic Pain and Circadian Rhythms

Research over the last 20 years regarding the link between circadian rhythms and chronic pain pathology has suggested interconnected mechanisms that are not fully understood. Strong evidence for a bidirectional relationship between circadian function and pain has been revealed through inflammatory and immune studies as well as neuropathic ones. However, one limitation of many of these studies is a focus on only a few molecules or cell types, often within only one region of the brain or spinal cord, rather than systems-level interactions. To address this, our review will examine the circadian system as a whole, from the intracellular genetic machinery that controls its timing mechanism to its input and output circuits, and how chronic pain, whether inflammatory or neuropathic, may mediate or be driven by changes in these processes. We will investigate how rhythms of circadian clock gene expression and behavior, immune cells, cytokines, chemokines, intracellular signaling, and glial cells affect and are affected by chronic pain in animal models and human pathologies. We will also discuss key areas in both circadian rhythms and chronic pain that are sexually dimorphic. Understanding the overlapping mechanisms and complex interplay between pain and circadian mediators, the various nuclei they affect, and how they differ between sexes, will be crucial to move forward in developing treatments for chronic pain and for determining how and when they will achieve their maximum efficacy.

Orofacial musculoskeletal pain: An evidence-based bio-psycho-social matrix model

Pain is a multidimensional experience comprising sensory-discriminative, affective-motivational, and cognitive-evaluative dimensions. Clinical and research findings have demonstrated a complex interplay between social burdens, individual coping strategies, mood states, psychological disorders, sleep disturbances, masticatory muscle tone, and orofacial musculoskeletal pain. Accordingly, current classification systems for orofacial pain require psychosocial assessments to be an integral part of the multidimensional diagnostic process. Here, we review evidence on how psychosocial and biological factors may generate and perpetuate musculoskeletal orofacial pain. Specifically, we discuss studies investigating a putative causal relationship between stress, bruxism, and pain in the masticatory system. We present findings that attribute brain structures various roles in modulating pain perception and pain-related behavior. We also examine studies investigating how the nervous and immune system on cellular and molecular levels may account for orofacial nociceptive signaling. Furthermore, we review evidence pointing towards associations between orofacial musculoskeletal pain and neuroendocrine imbalances, sleep disturbances, and alterations of the circadian timing system. We conclude with several proposals that may help to alleviate orofacial pain in the future.

Mathematical and computational models for pain: a systematic review

OBJECTIVE

There is no single prevailing theory of pain that explains its origin, qualities, and alleviation. Although many studies have investigated various molecular targets for pain management, few have attempted to examine the etiology or working mechanisms of pain through mathematical or computational model development. In this systematic review, we identified and classified mathematical and computational models for characterizing pain.

METHODS

The databases queried were Science Direct and PubMed, yielding 560 articles published prior to January 1st, 2020. After screening for inclusion of mathematical or computational models of pain, 31 articles were deemed relevant.

RESULTS

Most of the reviewed articles utilized classification algorithms to categorize pain and no-pain conditions. We found the literature heavily focused on the application of existing models or machine learning algorithms to identify the presence or absence of pain, rather than to explore features of pain that may be used for diagnostics and treatment.

CONCLUSIONS

Although understudied, the development of mathematical models may augment the current understanding of pain by providing directions for testable hypotheses of its underlying mechanisms. Additional focus is needed on developing models that seek to understand the underlying mechanisms of pain, as this could potentially lead to major breakthroughs in its treatment.

A Computational Model for Pain Processing in the Dorsal Horn Following Axonal Damage to Receptor Fibers

Computational modeling of the neural activity in the human spinal cord may help elucidate the underlying mechanisms involved in the complex processing of painful stimuli. In this study, we use a biologically-plausible model of the dorsal horn circuitry as a platform to simulate pain processing under healthy and pathological conditions. Specifically, we distort signals in the receptor fibers akin to what is observed in axonal damage and monitor the corresponding changes in five quantitative markers associated with the pain response. Axonal damage may lead to spike-train delays, evoked potentials, an increase in the refractoriness of the system, and intermittent blockage of spikes. We demonstrate how such effects applied to mechanoreceptor and nociceptor fibers in the pain processing circuit can give rise to dramatically distinct responses at the network/population level. The computational modeling of damaged neuronal assemblies may help unravel the myriad of responses observed in painful neuropathies and improve diagnostics and treatment protocols.