ArticleJournal of neuroscience methods2021
Automated analyses for single-fiber electrophysiological recordings using a newly developed Microsoft Excel application and graphical user interface.
Article in Journal of neuroscience methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundElectrophysiological recordings of isolated sensory afferents are commonly used in the field of pain research to investigate peripheral mechanisms of nociception in various pain models. The method involves skillful and tedious recordings of teased fibers from nerve preparations as well as time-consuming post-recording analyses. To increase efficiency and productivity of data analyses of recorded action potentials, we developed and validated a novel, easy-to-use Microsoft Excel-based application using Visual Basic Programming. NEW
methodA code for the novel program, shigraspike1.0, was written to create a module to include customizable subroutines for analyses for electrical and mechanical responses. Using previously recorded action potentials with tongue-lingual nerve preparations, the program was validated for appropriate execution, ease-of-use, accuracy of the output data and time taken for analyses.
resultsWe observed appropriate execution of shigraspike1.0 on Windows and iOS desktop platforms that included computation of response latency of the spike of interest using electrical stimulus as well as estimation of the number of impulses at each force with a step-and-hold mechanical ramp of 10-200mN. Output data obtained by shigrapsike1.0 for both stimulus types were accurate and statistically insignificant from manual analyses. COMPARISON WITH EXISTING
methodThe novel application shigraspike1.0, allows for rapid analyses for single-fiber recordings and takes less than half the time to analyze electrical and mechanical responses compared to manual analyses.
conclusionsThe newly developed shigraspike1.0 application can be a very productive tool to be routinely used for efficient analyses of single-fiber electrophysiology in pain research.
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