Literature DB >> 30711538

Event-related potential arithmetic to analyze offset potentials from conscious mice.

Jamie A O'Reilly1.   

Abstract

BACKGROUND: This paper presents a method for isolating time-dependent event-related potential (ERP) components which are superimposed on the gross ERP waveform. The experimental data that inspired this approach was recorded from the auditory cortex of conscious laboratory mice in response to presentation of ten different duration pure-tone auditory stimuli. NEW
METHOD: The grand-average ERP for each individual stimulus displayed a relatively low amplitude deflection following stimulus offset. In order to isolate this component for analysis, a series of simple arithmetic operations were performed, involving averaging of multiple stimuli ERPs and subtracting this from each individual ERP.
RESULTS: Offset potentials were isolated and quantified. Peak latency was determined by auditory stimulus duration; peak amplitude did not reach the threshold for statistical significance, over the range of durations tested. COMPARISON WITH EXISTING METHOD(S): To the best of my knowledge there are no alternative methods for isolating offset potentials from the gross ERP waveform at present. This novel approach may introduce less subjective bias to analyses than manually selecting measurement windows and performing custom baseline corrections.
CONCLUSIONS: A similar method may be applied to other human or non-human datasets to identify and characterize time-dependent sensory-cognitive processes obscured by gross waveform morphology.
Copyright © 2019 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  ERP analysis; ERP arithmetic; ERP component isolation; ERP operations; Offset response

Mesh:

Year:  2019        PMID: 30711538     DOI: 10.1016/j.jneumeth.2019.01.018

Source DB:  PubMed          Journal:  J Neurosci Methods        ISSN: 0165-0270            Impact factor:   2.390


  2 in total

1.  Decoding violated sensory expectations from the auditory cortex of anaesthetised mice: Hierarchical recurrent neural network depicts separate 'danger' and 'safety' units.

Authors:  Jamie A O'Reilly; Thanate Angsuwatanakul; Jordan Wehrman
Journal:  Eur J Neurosci       Date:  2022-06-22       Impact factor: 3.698

2.  Joint control of visually guided actions involves concordant increases in behavioural and neural coupling.

Authors:  David R Painter; Jeffrey J Kim; Angela I Renton; Jason B Mattingley
Journal:  Commun Biol       Date:  2021-06-29
  2 in total

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