The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is...

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Autores principales:	Peter E. Clayson, Scott A. Baldwin, Harold A. Rocha, Michael J. Larson
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Event-related potentials (ERPs) Multiverse analysis Multilevel models ERP psychometric reliability Data quality Open science Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
Acceso en línea:	https://doaj.org/article/dad5c9104bd34a4b90e7eee46fe845d1
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spelling	oai:doaj.org-article:dad5c9104bd34a4b90e7eee46fe845d12021-12-04T04:33:16ZThe data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines1095-957210.1016/j.neuroimage.2021.118712https://doaj.org/article/dad5c9104bd34a4b90e7eee46fe845d12021-12-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1053811921009848https://doaj.org/toc/1095-9572In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a 0.10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.Peter E. ClaysonScott A. BaldwinHarold A. RochaMichael J. LarsonElsevierarticleEvent-related potentials (ERPs)Multiverse analysisMultilevel modelsERP psychometric reliabilityData qualityOpen scienceNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENNeuroImage, Vol 245, Iss , Pp 118712- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Event-related potentials (ERPs) Multiverse analysis Multilevel models ERP psychometric reliability Data quality Open science Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571
spellingShingle	Event-related potentials (ERPs) Multiverse analysis Multilevel models ERP psychometric reliability Data quality Open science Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Peter E. Clayson Scott A. Baldwin Harold A. Rocha Michael J. Larson The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
description	In studies of event-related brain potentials (ERPs), numerous decisions about data processing are required to extract ERP scores from continuous data. Unfortunately, the systematic impact of these choices on the data quality and psychometric reliability of ERP scores or even ERP scores themselves is virtually unknown, which is a barrier to the standardization of ERPs. The aim of the present study was to optimize processing pipelines for the error-related negativity (ERN) and error positivity (Pe) by considering a multiverse of data processing choices. A multiverse analysis of a data processing pipeline examines the impact of a large set of different reasonable choices to determine the robustness of effects, such as the impact of different decisions on between-trial standard deviations (i.e., data quality) and between-condition differences (i.e., experimental effects). ERN and Pe data from 298 healthy young adults were used to determine the impact of different methodological choices on data quality and experimental effects (correct vs. error trials) at several key stages: highpass filtering, lowpass filtering, ocular artifact correction, reference, baseline adjustment, scoring sensors, and measurement procedure. This multiverse analysis yielded 3,456 ERN scores and 576 Pe scores per person. An optimized pipeline for ERN included a 15 Hz lowpass filter, ICA-based ocular artifact correction, and a region of interest (ROI) approach to scoring. For Pe, the optimized pipeline included a 0.10 Hz highpass filter, 30 Hz lowpass filter, regression-based ocular artifact correction, a -200 to 0 ms baseline adjustment window, and an ROI approach to scoring. The multiverse approach can be used to optimize pipelines for eventual standardization, which would support efforts toward establishing normative ERP databases. The proposed process of analyzing the data-processing multiverse of ERP scores paves the way for better refinement, identification, and selection of data processing parameters, ultimately improving the precision and utility of ERPs.
format	article
author	Peter E. Clayson Scott A. Baldwin Harold A. Rocha Michael J. Larson
author_facet	Peter E. Clayson Scott A. Baldwin Harold A. Rocha Michael J. Larson
author_sort	Peter E. Clayson
title	The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
title_short	The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
title_full	The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
title_fullStr	The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
title_full_unstemmed	The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines
title_sort	data-processing multiverse of event-related potentials (erps): a roadmap for the optimization and standardization of erp processing and reduction pipelines
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/dad5c9104bd34a4b90e7eee46fe845d1
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The data-processing multiverse of event-related potentials (ERPs): A roadmap for the optimization and standardization of ERP processing and reduction pipelines

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