Incorporating synaptic time-dependent plasticity and dynamic synapse into a computational model of wind-up

Predicting Wide-Dynamic Range Neuron Activity from Peripheral Nerve Stimulation using Linear Parameter Varying Models

Neuromodulation treatments for chronic pain are programmed with limited knowledge of how electrical stimulation of nerve fibers affects the dynamic response of pain-processing neurons in the spinal cord and the brain. By modeling these effects with tractable representations, we may be able to improve efficacy of stimulation therapy. However, pain transmitting neurons in the dorsal horn of the spinal cord, the first pain relay station in the nervous system, have complex responses to peripheral nerve stimulation (PNS) with nonlinearities and history effects. Wide-dynamic range (WDR) neurons are well studied in pain models and respond to peripheral noxious and non-noxious stimuli. We propose to use linear parameter varying (LPV) models to capture PNS responses of WDR neurons of the deep lamina in the dorsal horn in the spinal cord. Here we show that LPV models perform better than a single linear time-invariant (LTI) model in representing the responses of the WDR neurons to widely varying amplitudes of PNS current. In the future, we can use these models alongside LPV control techniques to design closed-loop PNS stimulation that may accomplish optimal pain treatment goals.Clinical Relevance- Electrical nerve stimulation as a therapy for chronic pain is in need of a more informed approach to programming. By describing the effects of stimulation on the pain system with tractable mathematical models, we may be able to titrate the stimulation to more effectively treat chronic pain.

Presenting a Neuroid model of wind-up based on dynamic synapse

The treatment of chronic pain depends mainly on our understanding of the mechanisms such as central sensitization which is involved in it. Wind-up of spinal cord is one of the most important phenomena in the study of central sensitization which has received considerable attention in recent years. Wind-up is a form of short-term synaptic plasticity (STP) that can lead to central sensitivity. Although several models have been proposed for wind-up, none of them are based on the experimental evidence. In this study, a new network model is introduced according to the gate control theory of pain. Neuroids are used as neuron models in which their parameters are captured from available experimental data. Adjusting the weights of the network is based on the short-term synaptic plasticity. The results of the time and frequency domain show that the model can well simulate wind-up behavior. This model can be used for analyzing, predicting and controlling chronic pain in the future.

Computational modeling of Adelta-fiber-mediated nociceptive detection of electrocutaneous stimulation

Sensitization is an example of malfunctioning of the nociceptive pathway in either the peripheral or central nervous system. Using quantitative sensory testing, one can only infer sensitization, but not determine the defective subsystem. The states of the subsystems may be characterized using computational modeling together with experimental data. Here, we develop a neurophysiologically plausible model replicating experimental observations from a psychophysical human subject study. We study the effects of single temporal stimulus parameters on detection thresholds corresponding to a 0.5 detection probability. To model peripheral activation and central processing, we adapt a stochastic drift-diffusion model and a probabilistic hazard model to our experimental setting without reaction times. We retain six lumped parameters in both models characterizing peripheral and central mechanisms. Both models have similar psychophysical functions, but the hazard model is computationally more efficient. The model-based effects of temporal stimulus parameters on detection thresholds are consistent with those from human subject data.

Computational modeling of peripheral pain: a commentary

This commentary is intended to find possible explanations for the low impact of computational modeling on pain research. We discuss the main strategies that have been used in building computational models for the study of pain. The analysis suggests that traditional models lack biological plausibility at some levels, they do not provide clinically relevant results, and they cannot capture the stochastic character of neural dynamics. On this basis, we provide some suggestions that may be useful in building computational models of pain with a wider range of applications.

Mechanisms and models of spinal cord stimulation for the treatment of neuropathic pain

Spinal cord stimulation (SCS) is an established and cost-effective therapy for treating severe chronic pain. However, despite over 40 years of clinical practice and the development of novel electrode designs and treatment protocols, increases in clinical success, defined as the proportion of patients that experience 50% or greater self-reported pain relief, have stalled. An incomplete knowledge of the neural circuits and systems underlying chronic pain and the interaction of SCS with these circuits may underlie this plateau in clinical efficacy. This review summarizes prior work and identifies gaps in our knowledge regarding the neural circuits related to pain and SCS in the dorsal horn, supraspinal structures, and the Pain Matrix. In addition, this review discusses and critiques current experimental and computational models used to investigate and optimize SCS. Further research into the interactions between SCS and pain pathways in the nervous system using animal and computational models is a fruitful approach to improve this promising therapy.

Ionic mechanisms in peripheral pain

Chronic pain constitutes an important and growing problem in society with large unmet needs with respect to treatment and clear implications for quality of life. Computational modeling is used to complement experimental studies to elucidate mechanisms involved in pain states. Models representing the peripheral nerve ending often address questions related to sensitization or reduction in pain detection threshold. In models of the axon or the cell body of the unmyelinated C-fiber, a large body of work concerns the role of particular sodium channels and mutations of these. Furthermore, in central structures: spinal cord or higher structures, sensitization often refers not only to enhanced synaptic efficacy but also to elevated intrinsic neuronal excitability. One of the recent developments in computational neuroscience is the emergence of computational neuropharmacology. In this area, computational modeling is used to study mechanisms of pathology with the objective of finding the means of restoring healthy function. This research has received increased attention from the pharmaceutical industry as ion channels have gained increased interest as drug targets. Computational modeling has several advantages, notably the ability to provide mechanistic links between molecular and cellular levels on the one hand and functions at the systems level on the other hand. These characteristics make computational modeling an additional tool to be used in the process of selecting pharmaceutical targets. Furthermore, large-scale simulations can provide a framework to systematically study the effects of several interacting disease parameters or effects from combinations of drugs.

Dynamic afferent synapses to decision-making networks improve performance in tasks requiring stimulus associations and discriminations

Animals must often make opposing responses to similar complex stimuli. Multiple sensory inputs from such stimuli combine to produce stimulus-specific patterns of neural activity. It is the differences between these activity patterns, even when small, that provide the basis for any differences in behavioral response. In the present study, we investigate three tasks with differing degrees of overlap in the inputs, each with just two response possibilities. We simulate behavioral output via winner-takes-all activity in one of two pools of neurons forming a biologically based decision-making layer. The decision-making layer receives inputs either in a direct stimulus-dependent manner or via an intervening recurrent network of neurons that form the associative layer, whose activity helps distinguish the stimuli of each task. We show that synaptic facilitation of synapses to the decision-making layer improves performance in these tasks, robustly increasing accuracy and speed of responses across multiple configurations of network inputs. Conversely, we find that synaptic depression worsens performance. In a linearly nonseparable task with exclusive-or logic, the benefit of synaptic facilitation lies in its superlinear transmission: effective synaptic strength increases with presynaptic firing rate, which enhances the already present superlinearity of presynaptic firing rate as a function of stimulus-dependent input. In linearly separable single-stimulus discrimination tasks, we find that facilitating synapses are always beneficial because synaptic facilitation always enhances any differences between inputs. Thus we predict that for optimal decision-making accuracy and speed, synapses from sensory or associative areas to decision-making or premotor areas should be facilitating.

Rate and pulse based plasticity governed by local synaptic state variables

Classically, action-potential-based learning paradigms such as the Bienenstock–Cooper–Munroe (BCM) rule for pulse rates or spike timing-dependent plasticity for pulse pairings have been experimentally demonstrated to evoke long-lasting synaptic weight changes (i.e., plasticity). However, several recent experiments have shown that plasticity also depends on the local dynamics at the synapse, such as membrane voltage, Calcium time course and level, or dendritic spikes. In this paper, we introduce a formulation of the BCM rule which is based on the instantaneous postsynaptic membrane potential as well as the transmission profile of the presynaptic spike. While this rule incorporates only simple local voltage- and current dynamics and is thus neither directly rate nor timing based, it can replicate a range of experiments, such as various rate and spike pairing protocols, combinations of the two, as well as voltage-dependent plasticity. A detailed comparison of current plasticity models with respect to this range of experiments also demonstrates the efficacy of the new plasticity rule. All experiments can be replicated with a limited set of parameters, avoiding the overfitting problem of more involved plasticity rules.

Multiple firing patterns in deep dorsal horn neurons of the spinal cord: computational analysis of mechanisms and functional implications

Deep dorsal horn relay neurons (dDHNs) of the spinal cord are known to exhibit multiple firing patterns under the control of local metabotropic neuromodulation: tonic firing, plateau potential, and spontaneous oscillations. This work investigates the role of interactions between voltage-gated channels and the occurrence of different firing patterns and then correlates these two phenomena with their functional role in sensory information processing. We designed a conductance-based model using the NEURON software package, which successfully reproduced the classical features of plateau in dDHNs, including a wind-up of the neuronal response after repetitive stimulation. This modeling approach allowed us to systematically test the impact of conductance interactions on the firing patterns. We found that the expression of multiple firing patterns can be reproduced by changes in the balance between two currents (L-type calcium and potassium inward rectifier conductances). By investigating a possible generalization of the firing state switch, we found that the switch can also occur by varying the balance of any hyperpolarizing and depolarizing conductances. This result extends the control of the firing switch to neuromodulators or to network effects such as synaptic inhibition. We observed that the switch between the different firing patterns occurs as a continuous function in the model, revealing a particular intermediate state called the accelerating mode. To characterize the functional effect of a firing switch on information transfer, we used correlation analysis between a model of peripheral nociceptive afference and the dDHN model. The simulation results indicate that the accelerating mode was the optimal firing state for information transfer.

On the relation between bursts and dynamic synapse properties: a modulation-based ansatz

When entering a synapse, presynaptic pulse trains are filtered according to the recent pulse history at the synapse and also with respect to their own pulse time course. Various behavioral models have tried to reproduce these complex filtering properties. In particular, the quantal model of neurotransmitter release has been shown to be highly selective for particular presynaptic pulse patterns. However, since the original, pulse-iterative quantal model does not lend itself to mathematical analysis, investigations have only been carried out via simulations. In contrast, we derive a comprehensive explicit expression for the quantal model. We show the correlation between the parameters of this explicit expression and the preferred spike train pattern of the synapse. In particular, our analysis of the transmission of modulated pulse trains across a dynamic synapse links the original parameters of the quantal model to the transmission efficacy of two major spiking regimes, that is, bursting and constant-rate ones.

rTMS for suppressing neuropathic pain: a meta-analysis

This pooled individual data (PID)-based meta-analysis collectively assessed the analgesic effect of repetitive transcranial magnetic stimulation (rTMS) on various neuropathic pain states based on their neuroanatomical hierarchy. Available randomized controlled trials (RCTs) were screened. PID was coded for age, gender, pain neuroanatomical origins, pain duration, and treatment parameters analyses. Coded pain neuroanatomical origins consist of peripheral nerve (PN); nerve root (NR); spinal cord (SC); trigeminal nerve or ganglion (TGN); and post-stroke supraspinal related pain (PSP). Raw data of 149 patients were extracted from 5 (1 parallel, 4 cross-over) selected (from 235 articles) RCTs. A significant (P < .001) overall analgesic effect (mean percent difference in pain visual analog scale (VAS) score reduction with 95% confidence interval) was detected with greater reduction in VAS with rTMS in comparison to sham. Including the parallel study (Khedr et al), the TGN subgroup was found to have the greatest analgesic effect (28.8%), followed by PSP (16.7%), SC (14.7%), NR (10.0%), and PN (1.5%). The results were similar when we excluded the parallel study with the greatest analgesic effect observed in TGN (33.0%), followed by SC (14.7%), PSP (10.5%), NR (10.0%), and PN (1.5%). In addition, multiple (vs single, P = .003) sessions and lower (>1 and < or =10 Hz) treatment frequency range (vs >10 Hz) appears to generate better analgesic outcome. In short, rTMS appears to be more effective in suppressing centrally than peripherally originated neuropathic pain states.

PERSPECTIVE

This is the first PID-based meta-analysis to assess the differential analgesic effect of rTMS on neuropathic pain based on the neuroanatomical origins of the pain pathophysiology and treatment parameters. The derived information serves as a useful resource in regards to treatment parameters and patient population selection for future rTMS-pain studies.