Inhibition by general anesthetic propofol of compound action potentials in the frog sciatic nerve and its chemical structure

2018 ◽  
Vol 392 (3) ◽  
pp. 359-369 ◽  
Author(s):  
Nobuya Magori ◽  
Tsugumi Fujita ◽  
Kotaro Mizuta ◽  
Eiichi Kumamoto
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Chemical Structure ◽  
General Anesthetic ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

scholarly journals Inhibition of frog sciatic nerve compound action potentials by aroma-oil compounds in a manner dependent on their chemical structures

PAIN RESEARCH ◽  
2015 ◽  
Vol 30 (1) ◽  
pp. 16-29
Author(s):  
Sena Ohtsubo ◽  
Tsugumi Fujita ◽  
Moe Miyahara ◽  
Akitomo Matsushita ◽  
Chang-Yu Jiang ◽  
...  
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Chemical Structures ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

Traditional Japanese medicines inhibit compound action potentials in the frog sciatic nerve

2016 ◽  
Vol 178 ◽  
pp. 272-280 ◽  
Author(s):  
Akitomo Matsushita ◽  
Tsugumi Fujita ◽  
Sena Ohtsubo ◽  
Eiichi Kumamoto
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

scholarly journals Inhibitory actions of aroma–oil chemicals on compound action potentials in the frog sciatic nerve

PAIN RESEARCH ◽  
2014 ◽  
Vol 29 (1) ◽  
pp. 17-30 ◽  
Author(s):  
Sena Ohtsubo ◽  
Tsugumi Fujita ◽  
Akitomo Matsushita ◽  
Chang–Yu Jiang ◽  
Eiichi Kumamoto
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

Hinokitiol inhibits compound action potentials in the frog sciatic nerve

2018 ◽  
Vol 819 ◽  
pp. 254-260 ◽  
Author(s):  
Nobuya Magori ◽  
Tsugumi Fujita ◽  
Eiichi Kumamoto
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

Inhibition by TRPA1 agonists of compound action potentials in the frog sciatic nerve

2013 ◽  
Vol 434 (1) ◽  
pp. 179-184 ◽  
Author(s):  
Akitomo Matsushita ◽  
Sena Ohtsubo ◽  
Tsugumi Fujita ◽  
Eiichi Kumamoto
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Compound Action Potentials ◽  
Frog Sciatic Nerve ◽  
Compound Action

Effect of Glucose on Isolated Rat Sciatic Nerve Compound Action Potentials

2000 ◽  
Vol 38 (5) ◽  
pp. 871
Author(s):  
Cheong Lee ◽  
Hee Jung Jun ◽  
Jae Hong Park ◽  
Sam Soon Cho ◽  
Yoon Choi
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Rat Sciatic Nerve ◽  
Compound Action Potentials ◽  
Effect Of Glucose ◽  
Isolated Rat ◽  
Compound Action

scholarly journals Classification of naturally evoked compound action potentials in peripheral nerve spatiotemporal recordings

2018 ◽  
Author(s):  
Ryan G. L. Koh ◽  
Adrian I. Nachman ◽  
José Zariffa
Keyword(s):  
Sciatic Nerve ◽  
Action Potentials ◽  
Pearson Correlation ◽  
Neural Signals ◽  
Sensory Stimuli ◽  
Firing Rates ◽  
Nerve Cuff ◽  
Compound Action Potentials ◽  
The Mean ◽  
Compound Action

Peripheral neural signals have the potential to provide the necessary motor, sensory or autonomic information for robust control in many neuroprosthetic and neuromodulation applications. However, developing methods to recover information encoded in these signals is a significant challenge. We introduce the idea of using spatiotemporal signatures extracted from multi-contact nerve cuff electrode recordings to classify naturally evoked compound action potentials (CAP). 9 Long-Evan rats were implanted with a 56-channel nerve cuff on the sciatic nerve. Afferent activity was selectively evoked in the different fascicles of the sciatic nerve (tibial, peroneal, sural) using mechano-sensory stimuli. Spatiotemporal signatures of recorded CAPs were used to train three different classifiers. Performance was measured based on the classification accuracy, F1-score, and the ability to reconstruct original firing rates of neural pathways. The mean classification accuracies, for a 3-class problem, for the best performing classifier was 0.686 ± 0.126 and corresponding mean F1-score was 0.605 ± 0.212. The mean Pearson correlation coefficients between the original firing rates and estimated firing rates found for the best classifier was 0.728 ± 0.276. The proposed method demonstrates the possibility of classifying individual naturally evoked CAPs in peripheral neural signals recorded from extraneural electrodes, allowing for more precise control signals in neuroprosthetic applications.

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