Spinal sensory projection neuron responses to spinal cord stimulation are mediated by circuits beyond gate control

Abstract

Spinal cord stimulation (SCS) is a therapy used to treat intractable pain with a putative mechanism of action based on the Gate Control Theory. We hypothesized that sensory projection neuron responses to SCS would follow a single stereotyped response curve as a function of SCS frequency, as predicted by the Gate Control circuit. We recorded the responses of antidromically identified sensory projection neurons in the lumbar spinal cord during 1- to 150-Hz SCS in both healthy rats and neuropathic rats following chronic constriction injury (CCI). The relationship between SCS frequency and projection neuron activity predicted by the Gate Control circuit accounted for a subset of neuronal responses to SCS but could not account for the full range of observed responses. Heterogeneous responses were classifiable into three additional groups and were reproduced using computational models of spinal microcircuits representing other interactions between nociceptive and nonnociceptive sensory inputs. Intrathecal administration of bicuculline, a GABAA receptor antagonist, increased spontaneous and evoked activity in projection neurons, enhanced excitatory responses to SCS, and reduced inhibitory responses to SCS, suggesting that GABAA neurotransmission plays a broad role in regulating projection neuron activity. These in vivo and computational results challenge the Gate Control Theory as the only mechanism underlying SCS and refine our understanding of the effects of SCS on spinal sensory neurons within the framework of contemporary understanding of dorsal horn circuitry.

Keywords: Gate Control Theory; chronic pain; computational modeling; spinal cord stimulation; spinal microcircuits.

Copyright © 2015 the American Physiological Society.

Figures

Fig. 1.

Recording the responses of single lumbar dorsal horn projection neurons to epidural spinal cord stimulation (SCS) in urethane-anesthesized rats. A: rats were implanted with sciatic nerve cuffs for peripheral stimulation and bipolar paddles for SCS. Peripheral stimuli [brush, press, pinch, crush (BPPC)] were applied to the hindpaw ipsilateral to the recording electrode for neuron characterization. A Pt-Ir microelectrode was lowered into the ventral aspect of the contralateral cervical spinal cord for antidromic identification of ascending projections from lumbar dorsal horn neurons. DAQ: data acquisition. B: timeline of experimental recordings: antidromic identification and neuron characterization preceded randomized blocks of SCS.

Fig. 2.

Cord dorsum potentials (CDP) were used to determine SCS amplitudes. A: inverting the polarity of biphasic SCS (100-μs pulse width) inverted the artifact (red) but not the CDP signal (blue) recorded 1 cm caudal to the site of SCS and over the approximate neuron search region. B: Moving the electrode 3 and 6 mm caudal to the original recording site (A) resulted in a dispersed CDP with later peak latencies consistent with compound action potential propagation at ∼10 m/s. C: representative examples of peristimulus responses to SCS at 50 Hz applied at different amplitudes corresponding to different evoked CDP amplitudes. Increasing the amplitude of SCS beyond the threshold required to evoke a ∼50 μV CDP (e.g., to motor threshold) did not significantly affect peristimulus responses to SCS.

Fig. 3.

Characterization of recorded neurons. A–C: all included neurons met 3 criteria required for antidromic identification (Lipski 1981): neurons followed single pulse stimulation of the contralateral ventral cervical spinal cord (A), neurons followed trains of 3 pulses of stimulation at 200–333 Hz (B), and neurons exhibited orthodromic-antidromic collisions during activation of the neuron's peripheral receptive field (C). Axes in B and C are the same as in A. D: neurons fell into established low-threshold (LT), wide dynamic range (WDR), and nociceptive-specific (NS) categories as determined based on firing rates during brush-press-pinch-crush (BPPC) stimulation of peripheral receptive fields. E: neuron locations (right) based on 22 recovered Prussian Blue lesions (example left).

Fig. 4.

Analysis of poststimulus time histograms (PSTHs) was used to differentiate responders to SCS from nonresponders. A: representative peristimulus responses during SCS Off (left) and SCS On (middle) during 10-, 30-, 50-, 100-, and 150-Hz SCS. Normalization of the SCS On response to the SCS Off response using Z-scores (right) was used to determine if a response to SCS was significant. B, left: responder/nonresponder, sorted by incidences of significant peristimulus responses, with individual significant responses highlighted in gray. A neuron was classified as a responder if the Z-score result from 2 or more contiguous frequencies in either the SCS only or SCS + sciatic condition was significant. Neurons for which Z-score analysis could not be conducted due to low firing rates but for which Kolmogorov-Smirnoff tests yielded significant responses were included. B, right: map of neurons that were included in firing rate (Fig. 5), principal component, and clustering analyses (Fig. 6). Only individual frequency-response relationships that met the contiguous frequency criterion were included in principal component analysis (PCA) and clustering. C: distribution of LT, WDR, and NS neurons among nonresponders (top) and responders (bottom).

Fig. 5.

Effect of SCS frequency on firing rate of projection neurons. A and C: median and 25-75th percentiles (boxes) of responder neuron firing rates between SCS Off and SCS On conditions when only SCS was applied (A) or when SCS was applied with sciatic stimulation (C). Outliers beyond 1.5 interquartile ranges (whiskers) are denoted with the “+” symbol. B and D: changes in firing rates of individual responder neurons between SCS On and SCS Off conditions of individual neurons when only SCS was applied (B) or when SCS was applied with sciatic stimulation (D). Each column represents the continuum of responses across neurons for a given SCS frequency. All columns in the color maps are sorted according to response during SCS independently of the neuron from which the response originated.

Fig. 6.

Classification of individual neuron responses to different frequencies of SCS. A: representative examples of increasing (red), nonmonotonic (violet), and decreasing (blue) responses relative to “Off” baselines (dotted line) with increasing SCS frequency. Although the magnitudes of deviations vs. “Off” baselines were heterogeneous, all examples exhibited significant on vs. off PSTH responses to SCS, and the shapes of the responses were stereotyped, motivating classification of normalized responses. B: principal component based k-means clustering of normalized responses. C: aggregate normalized responses (means ± SE of normalized responses shown) formed by averaging individual responses from each cluster in B. D: hierarchal analysis using squared Euclidean distance between points corresponding to the 1st 2 PCA loadings as the proximity measure. *Frequency-response relationship of the neuron (numbering same as in Fig. 4B) during the SCS + Sciatic condition. Discrepancies between k-means and hierarchal clustering are identified in B and D with highlighting using the color corresponding to classification by the other method. E: distribution of response type classification transitions between SCS only and SCS + sciatic stimulation conditions. Groups of slices represent response classifications during SCS only, and individual slices represent response classifications during SCS + sciatic. The number of neurons exhibiting a particular transition is denoted by the white numeral in each inner slice. Neurons that were nonresponsive in either the SCS only or SCS + sciatic condition were grouped according to their response classification in the other condition. F: distribution of response type classifications by physiological response class (LT, WDR, and NS) in the SCS only (left) and SCS + sciatic (right) conditions. Only the 31 neurons that could be classified as LT, WDR, or NS were included. The number of neurons exhibiting a particular response is denoted by the white numeral in each inner slice. Neurons that were nonresponsive in either the SCS only or SCS + sciatic condition were grouped according to their response classification in the other condition.

Fig. 7.

Heterogeneous responses of dorsal horn projection neurons to SCS were also present in animals following constriction injury (CCI). A: CCI produced a significant reduction in paw withdrawal thresholds (n = 8; P < 9.5 × 10−7, Student's t-test). B: representative brush-sensitive WDR neuron, brush-insensitive WDR neuron, and NS neuron indicating that neurons are responsive to stimulation of the peripheral receptive field following CCI. C: normalized responses from 6 responsive projection neurons out of 7 neurons found from CCI animals. C, insets: results of least squares regression classification of the frequency-response curves plotted against the original classification scheme (Fig. 6). Data from one nonresponsive neuron are not shown.

Fig. 8.

Computational models of spinal microcircuits reproduced frequency-response curves and features of PSTHs. A: template circuit that describes all features that contributed to the individual microcircuits. B–D: individual spinal microcircuits, normalized frequency-response relationships, and normalized smoothed PSTHs constructed using a “virtual” 10-Hz SCS train time-aligned onto real 10-, 50-, and 150-Hz SCS comparing model and experimental responses. In PSTH comparisons, the model response (bold) was overlaid onto all experimental PSTH responses (faint) from neurons whose frequency-response relationships were classified into the cluster corresponding to the microcircuit. The colors of the graph lines indicate the cluster to which each microcircuit corresponds (Fig. 6).

Fig. 9.

Spike frequency adaptation mechanisms allowed reproduction of the aggregate response representing neurons in cluster 4 (Fig. 6). A: normalized model projection neuron responses to different frequencies of SCS across different values for gNa, gK, and gKm. In all cases, the Na+ conductance slowly inactivates according to Eqs. 4–6. gNa = 100% refers to a hillock Na+ conductance of 2.19 S/cm2. gK = 100% refers to a hillock K+ conductance of 0.076 S/cm2, a soma K+ conductance of 0.0043 S/cm2, and a dendritic K+ conductance of 0.034 S/cm2. gKm = 100% refers to a soma and dendritic Km conductance of 0.0005 S/cm2. B: normalized individual and average experimental responses (left) juxtaposed with the average of the normalized model responses shown in A (right). C: experimental (left) and all model (right) smoothed normalized PSTHs generated using a virtual 10-Hz train for real 10-, 50-, and 150-Hz SCS responses.

Fig. 10.

Administration of bicuculline (BIC) unmasked or disinhibited responses to peripheral stimulation and SCS. A: BPPC response profiles of 2 example neurons during control conditions (blue) and after application of BIC (red). BIC unmasked responses to BPPC in the top neuron and increased baseline firing/disinhibited evoked responses in the bottom neuron. B: raw frequency-response relationship of neuronal activity vs. SCS frequency before and after the application of BIC in a representative example. C: peristimulus responses to SCS before (blue) and after (red) intrathecal application of BIC for the representative example shown in B. D: BPPC response of neuron to which CGP 35348 (CGP), then CGP + BIC was applied. E: normalized relationship between SCS frequency and neuron response to SCS before and after the application of CGP, then CGP + BIC in 1 neuron. F: PSTHs during SCS after application of CGP (orange) and after application of CGP + BIC (red) in a representative example. Raw neuron firing rate vs. SCS frequency curves are not shown because the application of CGP and CGP + BIC resulted in increased baseline activity (D) that would have distorted the plot axes.