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The NIRS Analysis Package: Noise Reduction and Statistical Inference

Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences bet...

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Autores principales: Fekete, Tomer, Rubin, Denis, Carlson, Joshua M., Mujica-Parodi, Lilianne R.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166314/
https://www.ncbi.nlm.nih.gov/pubmed/21912687
http://dx.doi.org/10.1371/journal.pone.0024322
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author Fekete, Tomer
Rubin, Denis
Carlson, Joshua M.
Mujica-Parodi, Lilianne R.
author_facet Fekete, Tomer
Rubin, Denis
Carlson, Joshua M.
Mujica-Parodi, Lilianne R.
author_sort Fekete, Tomer
collection PubMed
description Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS.
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spelling pubmed-31663142011-09-12 The NIRS Analysis Package: Noise Reduction and Statistical Inference Fekete, Tomer Rubin, Denis Carlson, Joshua M. Mujica-Parodi, Lilianne R. PLoS One Research Article Near infrared spectroscopy (NIRS) is a non-invasive optical imaging technique that can be used to measure cortical hemodynamic responses to specific stimuli or tasks. While analyses of NIRS data are normally adapted from established fMRI techniques, there are nevertheless substantial differences between the two modalities. Here, we investigate the impact of NIRS-specific noise; e.g., systemic (physiological), motion-related artifacts, and serial autocorrelations, upon the validity of statistical inference within the framework of the general linear model. We present a comprehensive framework for noise reduction and statistical inference, which is custom-tailored to the noise characteristics of NIRS. These methods have been implemented in a public domain Matlab toolbox, the NIRS Analysis Package (NAP). Finally, we validate NAP using both simulated and actual data, showing marked improvement in the detection power and reliability of NIRS. Public Library of Science 2011-09-02 /pmc/articles/PMC3166314/ /pubmed/21912687 http://dx.doi.org/10.1371/journal.pone.0024322 Text en Fekete et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
spellingShingle Research Article
Fekete, Tomer
Rubin, Denis
Carlson, Joshua M.
Mujica-Parodi, Lilianne R.
The NIRS Analysis Package: Noise Reduction and Statistical Inference
title The NIRS Analysis Package: Noise Reduction and Statistical Inference
title_full The NIRS Analysis Package: Noise Reduction and Statistical Inference
title_fullStr The NIRS Analysis Package: Noise Reduction and Statistical Inference
title_full_unstemmed The NIRS Analysis Package: Noise Reduction and Statistical Inference
title_short The NIRS Analysis Package: Noise Reduction and Statistical Inference
title_sort nirs analysis package: noise reduction and statistical inference
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3166314/
https://www.ncbi.nlm.nih.gov/pubmed/21912687
http://dx.doi.org/10.1371/journal.pone.0024322
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