Multimodal sensory control of motor performance by glycinergic interneurons of the mouse spinal cord deep dorsal horn.

Sensory feedback is integral for contextually appropriate motor output, yet the neural circuits responsible remain elusive. Here, we pinpoint the medial deep dorsal horn of the mouse spinal cord as a convergence point for proprioceptive and cutaneous input. Within this region, we identify a population of tonically active glycinergic inhibitory neurons expressing parvalbumin. Using anatomy and electrophysiology, we demonstrate that deep dorsal horn parvalbumin-expressing interneuron (dPV) activity is shaped by convergent proprioceptive, cutaneous, and descending input. Selectively targeting spinal dPVs, we reveal their widespread ipsilateral inhibition onto pre-motor and motor networks and demonstrate their role in gating sensory-evoked muscle activity using electromyography (EMG) recordings. dPV ablation altered limb kinematics and step-cycle timing during treadmill locomotion and reduced the transitions between sub-movements during spontaneous behavior. These findings reveal a circuit basis by which sensory convergence onto dorsal horn inhibitory neurons modulates motor output to facilitate smooth movement and context-appropriate transitions.

Absence of paresthesia during high-rate spinal cord stimulation reveals importance of synchrony for sensations evoked by electrical stimulation.

Electrically activating mechanoreceptive afferents inhibits pain. However, paresthesia evoked by spinal cord stimulation (SCS) at 40-60 Hz becomes uncomfortable at high pulse amplitudes, limiting SCS "dosage." Kilohertz-frequency SCS produces analgesia without paresthesia and is thought, therefore, not to activate afferent axons. We show that paresthesia is absent not because axons do not spike but because they spike asynchronously. In a pain patient, selectively increasing SCS frequency abolished paresthesia and epidurally recorded evoked compound action potentials (ECAPs). Dependence of ECAP amplitude on SCS frequency was reproduced in pigs, rats, and computer simulations and is explained by overdrive desynchronization: spikes desychronize when axons are stimulated faster than their refractory period. Unlike synchronous spikes, asynchronous spikes fail to produce paresthesia because their transmission to somatosensory cortex is blocked by feedforward inhibition. Our results demonstrate how stimulation frequency impacts synchrony based on axon properties and how synchrony impacts sensation based on circuit properties.

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.

Ionic mechanisms underlying tonic and burst firing behavior in subfornical organ neurons: a combined experimental and modeling study.

Subfornical organ (SFO) neurons exhibit heterogeneity in current expression and spiking behavior, where the two major spiking phenotypes appear as tonic and burst firing. Insight into the mechanisms behind this heterogeneity is critical for understanding how the SFO, a sensory circumventricular organ, integrates and selectively influences physiological function. To integrate efficient methods for studying this heterogeneity, we built a single-compartment, Hodgkin-Huxley-type model of an SFO neuron that is parameterized by SFO-specific in vitro patch-clamp data. The model accounts for the membrane potential distribution and spike train variability of both tonic and burst firing SFO neurons. Analysis of model dynamics confirms that a persistent Na+ and Ca2+ currents are required for burst initiation and maintenance and suggests that a slow-activating K+ current may be responsible for burst termination in SFO neurons. Additionally, the model suggests that heterogeneity in current expression and subsequent influence on spike afterpotential underlie the behavioral differences between tonic and burst firing SFO neurons. Future use of this model in coordination with single neuron patch-clamp electrophysiology provides a platform for explaining and predicting the response of SFO neurons to various combinations of circulating signals, thus elucidating the mechanisms underlying physiological signal integration within the SFO. NEW & NOTEWORTHY Our understanding of how the subfornical organ (SFO) selectively influences autonomic nervous system function remains incomplete but theoretically results from the electrical responses of SFO neurons to physiologically important signals. We have built a computational model of SFO neurons, derived from and supported by experimental data, which explains how SFO neurons produce different electrical patterns. The model provides an efficient system to theoretically and experimentally explore how changes in the essential features of SFO neurons affect their electrical activity.