Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences

With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in se...

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Detalles Bibliográficos
Autores principales: Hocke, Lia M., Oni, Ibukunoluwa K., Duszynski, Chris C., Corrigan, Alex V., Frederick, Blaise deB., Dunn, Jeff F.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428450/
https://www.ncbi.nlm.nih.gov/pubmed/30906511
http://dx.doi.org/10.3390/a11050067
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author Hocke, Lia M.
Oni, Ibukunoluwa K.
Duszynski, Chris C.
Corrigan, Alex V.
Frederick, Blaise deB.
Dunn, Jeff F.
author_facet Hocke, Lia M.
Oni, Ibukunoluwa K.
Duszynski, Chris C.
Corrigan, Alex V.
Frederick, Blaise deB.
Dunn, Jeff F.
author_sort Hocke, Lia M.
collection PubMed
description With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site.
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spelling pubmed-64284502019-03-21 Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences Hocke, Lia M. Oni, Ibukunoluwa K. Duszynski, Chris C. Corrigan, Alex V. Frederick, Blaise deB. Dunn, Jeff F. Algorithms Article With the rapid increase in new fNIRS users employing commercial software, there is a concern that many studies are biased by suboptimal processing methods. The purpose of this study is to provide a visual reference showing the effects of different processing methods, to help inform researchers in setting up and evaluating a processing pipeline. We show the significant impact of pre- and post-processing choices and stress again how important it is to combine data from both hemoglobin species in order to make accurate inferences about the activation site. 2018-05-08 2018-05 /pmc/articles/PMC6428450/ /pubmed/30906511 http://dx.doi.org/10.3390/a11050067 Text en Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Hocke, Lia M.
Oni, Ibukunoluwa K.
Duszynski, Chris C.
Corrigan, Alex V.
Frederick, Blaise deB.
Dunn, Jeff F.
Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title_full Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title_fullStr Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title_full_unstemmed Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title_short Automated Processing of fNIRS Data—A Visual Guide to the Pitfalls and Consequences
title_sort automated processing of fnirs data—a visual guide to the pitfalls and consequences
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6428450/
https://www.ncbi.nlm.nih.gov/pubmed/30906511
http://dx.doi.org/10.3390/a11050067
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