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© 2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

Significance: Isolating task-evoked brain signals from background physiological noise (e.g., cardiac, respiratory, and blood pressure fluctuations) poses a major challenge for the analysis of functional near-infrared spectroscopy (fNIRS) data.

Aim: The performance of several analytic methods to separate background physiological noise from brain activity including spatial and temporal filtering, regression, component analysis, and the use of short-separation (SS) measurements were quantitatively compared.

Approach: Using experimentally recorded background signals (breath-hold task), receiver operating characteristics simulations were performed by adding various levels of additive synthetic “brain” responses in order to examine the sensitivity and specificity of several previously proposed analytic approaches.

Results: We found that the use of SS fNIRS channels as regressors of no-interest within a linear regression model was the best performing approach examined. Furthermore, we found that the addition of all available SS data, including all recorded channels and both hemoglobin species, improved the method performance despite the additional degrees-of-freedom of the models. When SS data were not available, we found that principal component filtering using a separate baseline scan was the best alternative.

Conclusions: The use of multiple SS measurements as regressors of no interest implemented in a robust, iteratively prewhitened, general linear model has the best performance of the tested existing methods.

Details

Title
Quantitative comparison of correction techniques for removing systemic physiological signal in functional near-infrared spectroscopy studies
Author
Santosa, Hendrik; Zhai, Xuetong; Fishburn, Frank; Sparto, Patrick J; Huppert, Theodore J
First page
35009
Section
Research Papers
Publication year
2020
Publication date
Jul 2020
Publisher
S P I E - International Society for
ISSN
2329423X
e-ISSN
23294248
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2862347783
Copyright
© 2020. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.