Methods for frequency and correlation analyses of neurograms

Ginette Horcholle-Bossavit,Brigitte Quenet

doi:10.1016/j.mex.2021.101258

Ginette Horcholle-Bossavit, Brigitte Quenet

Open Access

https://doi.org/10.1016/j.mex.2021.101258

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Abstract

Many physiological functions are based on motor rhythmic activities, among them breathing is a vital issue. The method presented here, or ‘temporal grid extraction’, aims at characterizing the temporal organization of such an activity. Beyond the measurement of the fundamental frequency, defining the successive cycles, some signal processing tools are helpful in order to look for the presence of higher frequency components that potentially structure these cycles. The method is applied to neurograms recorded from frog brainstem preparations, where two cycle types, buccal and lung cycles, may alternate. It relies on:•Continues Wavelet Transform (CWT) for time-frequency maps and frequency profiles•Crosscorrelation analysis for amplitude maps and amplitude profiles•Cycle-by-cycle autocorrelation analysis for autocorrelation maps and autocorrelation profilesUsing this method, the maps and profiles have revealed that a common high frequency clock drives both buccal and lung cycles.

Highlights

Most of rhythmic motor behaviors are generated by central pattern-generators (CPG) whose mechanisms must be investigated in details in order to support the hypotheses implemented in computational models ([3,6])
Short term cycle-by-cycle feedback signals allows a fast adaptation to the environmental conditions and for the respiratory rhythms, the CPG is sensitive to hypercapnia and hypoxia
We present computational methods used to define several descriptors characterizing the rhythmic patterns displayed in neurograms recorded from brainstem preparations of amphibians ([8,9])

Summary

Method Article

A PSL Research University, ESPCI-Paris Équipe de Statistique Appliquée, Paris F-75005, France b Sorbonne Université, INSERM, UMRS1158 Neurophysiologie respiratoire expérimentale et clinique, Paris F-75013, France abstract. Method name: Temporal grid extraction Keywords: Rhythmic activity, Neurogram processing, CWT analysis, Crosscorrelation, Autocorrelation, Frequency profile, Amplitude profile, Oscillation profile, Cycle-by-cycle variability Article history: Received 20 May 2020; Accepted 31 January 2021; Available online 4 February 2021. Subject Area: More specific subject area: Method name: Name and reference of original method: Neurosciences Methods Rhythms analysis Temporal grid extraction Quenet et al 2014, 2020.

Introduction

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