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Computational toolbox makes microneurography analysis techniques widely available

JUN 14, 2024
Open-source, comprehensive suite of analysis tools provides standardized data and analytics, increases potential for collaboration.
Computational toolbox makes microneurography analysis techniques widely available internal name

Computational toolbox makes microneurography analysis techniques widely available lead image

Microneurographic sensors provide a powerful means of studying peripheral nerve signals for scientific and medical applications. These signals can be used to diagnose neural disorders, understand their causes and effects, and inform possible treatment plans. Minimally invasive microelectrodes provide a simple and reliable means of accessing nerves, and proper analysis methods can correlate that data with other physiological characteristics.

However, that analysis depends on software tools that are broadly capable and freely available to researchers and medical specialists, characteristics lacking in most existing tools. D’Alesio et al. developed a suite of software tools packaged together to form a computational toolbox to make analysis more accessible.

“In addition to the complexity of the technique itself and the difficulty of recording from the peripheral nerve, there is also a lack of data analysis tools,” said author Calogero Maria Oddo. “We developed this toolbox to enhance experimental efficiency and provide the capability to extract data acquired during microneurographic sessions.”

The authors’ toolbox contains software for common operations, such as burst and spike analysis, wavelet transforms, and spectrogram analysis, along with the ability to correlate neural signals with physiological effects, like heart rate or blood pressure. They designed their interface to be open source and easy to use, even for non-experts, with the goal of developing a common data language to further the field of microneurography.

“This toolbox will allow data to be standardized in terms of pre-processing, evaluation, and feature structure,” said Oddo. “We want to nurture collaboration among groups by using these computational tools to allow data and analytics to be shared.”

Source: “An open computational toolbox to analyze multi- and single- unit sympathetic nerve activity in microneurography,” by Giacomo D’Alesio, Lars Ingmar Stumpp, Paolo Sciarrone, Alessandro Navari, Francesco Gentile, Chiara Borrelli, Sara Ballanti, Eleonora Degl’Innocenti, Adrian Carrasco, Ana Catarina Costa, Alexandre Andrade, Andrea Mannini, Vaughan Gary Macefield, Michele Emdin, Claudio Passino, Alberto Mazzoni, Alberto Giannoni, and Calogero Maria Oddo, Biophysics Reviews (2024). The article can be accessed at https://doi.org/10.1063/5.0202385 .

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