Dataset Information

Comparison of intensity-dependent inhibition of spinal wide-dynamic range neurons by dorsal column and peripheral nerve stimulation in a rat model of neuropathic pain.

ABSTRACT: Spinal cord stimulation (SCS) and peripheral nerve stimulation (PNS) are thought to reduce pain by activating a sufficient number of large myelinated (A?) fibres, which in turn initiate spinal segmental mechanisms of analgesia. However, the volume of neuronal activity and how this activity is associated with different treatment targets is unclear under neuropathic pain conditions.We sought to delineate the intensity-dependent mechanisms of SCS and PNS analgesia by in vivo extracellular recordings from spinal wide-dynamic range neurons in nerve-injured rats. To mimic therapeutic SCS and PNS, we used bipolar needle electrodes and platinum hook electrodes to stimulate the dorsal column and the tibial nerve, respectively. Compound action potentials were recorded to calibrate the amplitude of conditioning stimulation required to activate A-fibres and thus titrate the volume of activation.Dorsal column stimulation (50?Hz, five intensities) inhibited the windup (a short form of neuronal sensitization) and the C-component response of wide-dynamic range neurons to graded intracutaneous electrical stimuli in an intensity-dependent manner. Tibial nerve stimulation (50?Hz, three intensities) also suppressed the windup in an intensity-dependent fashion but did not affect the acute C-component response.SCS and PNS may offer similar inhibition of short-term neuronal sensitization. However, only SCS attenuates spinal transmission of acute noxious inputs under neuropathic pain conditions. Our findings begin to differentiate peripheral from spinal-targeted neuromodulation therapies and may help to select the best stimulation target and optimum therapeutic intensity for pain treatment.

SUBMITTER: Yang F

PROVIDER: S-EPMC4558098 | biostudies-literature | 2014 Aug

REPOSITORIES: biostudies-literature

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Comparison of intensity-dependent inhibition of spinal wide-dynamic range neurons by dorsal column and peripheral nerve stimulation in a rat model of neuropathic pain.

Yang F F Xu Q Q Cheong Y-K YK Shechter R R Sdrulla A A He S-Q SQ Tiwari V V Dong X X Wacnik P W PW Meyer R R Raja S N SN Guan Y Y

European journal of pain (London, England) 20140106 7

<h4>Background</h4>Spinal cord stimulation (SCS) and peripheral nerve stimulation (PNS) are thought to reduce pain by activating a sufficient number of large myelinated (Aβ) fibres, which in turn initiate spinal segmental mechanisms of analgesia. However, the volume of neuronal activity and how this activity is associated with different treatment targets is unclear under neuropathic pain conditions.<h4>Methods</h4>We sought to delineate the intensity-dependent mechanisms of SCS and PNS analgesia ...[more]

PMID: 24390782

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Project description:The brainstem dorsal column nuclei (DCN) are essential to inform the brain of tactile and proprioceptive events experienced by the body. However, little is known about how ascending somatosensory information is represented in the DCN. Our objective was to investigate the usefulness of high-frequency (HF) and low-frequency (LF) DCN signal features (SFs) in predicting the nerve from which signals were evoked. We also aimed to explore the robustness of DCN SFs and map their relative information content across the brainstem surface. DCN surface potentials were recorded from urethane-anesthetized Wistar rats during sural and peroneal nerve electrical stimulation. Five salient SFs were extracted from each recording electrode of a seven-electrode array. We used a machine learning approach to quantify and rank information content contained within DCN surface-potential signals following peripheral nerve activation. Machine-learning of SF and electrode position combinations was quantified to determine a hierarchy of information importance for resolving the peripheral origin of nerve activation. A supervised back-propagation artificial neural network (ANN) could predict the nerve from which a response was evoked with up to 96.8 ± 0.8% accuracy. Guided by feature-learnability, we maintained high prediction accuracy after reducing ANN algorithm inputs from 35 (5 SFs from 7 electrodes) to 6 (4 SFs from one electrode and 2 SFs from a second electrode). When the number of input features were reduced, the best performing input combinations included HF and LF features. Feature-learnability also revealed that signals recorded from the same midline electrode can be accurately classified when evoked from bilateral nerve pairs, suggesting DCN surface activity asymmetry. Here we demonstrate a novel method for mapping the information content of signal patterns across the DCN surface and show that DCN SFs are robust across a population. Finally, we also show that the DCN is functionally asymmetrically organized, which challenges our current understanding of somatotopic symmetry across the midline at sub-cortical levels.

Dataset Information

Comparison of intensity-dependent inhibition of spinal wide-dynamic range neurons by dorsal column and peripheral nerve stimulation in a rat model of neuropathic pain.

Publications

Comparison of intensity-dependent inhibition of spinal wide-dynamic range neurons by dorsal column and peripheral nerve stimulation in a rat model of neuropathic pain.

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