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dc.contributor.authorvon Lühmann, Alexanderen_US
dc.contributor.authorLi, Xingeen_US
dc.contributor.authorMüller, Klaus-Roberten_US
dc.contributor.authorBoas, David A.en_US
dc.contributor.authorYücel, Meryem A.en_US
dc.coverage.spatialUnited Statesen_US
dc.date2019-12-17
dc.date.accessioned2020-05-14T18:15:00Z
dc.date.available2020-05-14T18:15:00Z
dc.date.issued2020-03
dc.identifierhttps://www.ncbi.nlm.nih.gov/pubmed/31870944
dc.identifier.citationAlexander von Lühmann, Xinge Li, Klaus-Robert Müller, David A Boas, Meryem A Yücel. 2020. "Improved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysis." Neuroimage, Volume 208, 17 pages. https://doi.org/10.1016/j.neuroimage.2019.116472
dc.identifier.issn1095-9572
dc.identifier.urihttps://hdl.handle.net/2144/40868
dc.description.abstractFor the robust estimation of evoked brain activity from functional Near-Infrared Spectroscopy (fNIRS) signals, it is crucial to reduce nuisance signals from systemic physiology and motion. The current best practice incorporates short-separation (SS) fNIRS measurements as regressors in a General Linear Model (GLM). However, several challenging signal characteristics such as non-instantaneous and non-constant coupling are not yet addressed by this approach and additional auxiliary signals are not optimally exploited. We have recently introduced a new methodological framework for the unsupervised multivariate analysis of fNIRS signals using Blind Source Separation (BSS) methods. Building onto the framework, in this manuscript we show how to incorporate the advantages of regularized temporally embedded Canonical Correlation Analysis (tCCA) into the supervised GLM. This approach allows flexible integration of any number of auxiliary modalities and signals. We provide guidance for the selection of optimal parameters and auxiliary signals for the proposed GLM extension. Its performance in the recovery of evoked HRFs is then evaluated using both simulated ground truth data and real experimental data and compared with the GLM with short-separation regression. Our results show that the GLM with tCCA significantly improves upon the current best practice, yielding significantly better results across all applied metrics: Correlation (HbO max. +45%), Root Mean Squared Error (HbO max. -55%), F-Score (HbO up to 3.25-fold) and p-value as well as power spectral density of the noise floor. The proposed method can be incorporated into the GLM in an easily applicable way that flexibly combines any available auxiliary signals into optimal nuisance regressors. This work has potential significance both for conventional neuroscientific fNIRS experiments as well as for emerging applications of fNIRS in everyday environments, medicine and BCI, where high Contrast to Noise Ratio is of importance for single trial analysis.en_US
dc.format.extent17 pagesen_US
dc.languageeng
dc.language.isoen_US
dc.publisherElsevier Inc.en_US
dc.relation.ispartofNeuroimage
dc.rightsCopyright © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.en_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectCanonical correlation analysisen_US
dc.subjectFunctional near-infrared spectroscopyen_US
dc.subjectGeneral linear modelen_US
dc.subjectMultimodalityen_US
dc.subjectPhysiological noise/nuisance regressionen_US
dc.subjectTemporal embeddingen_US
dc.subjectMedical and health sciencesen_US
dc.subjectPsychology and cognitive sciencesen_US
dc.subjectNeurology & neurosurgeryen_US
dc.titleImproved physiological noise regression in fNIRS: a multimodal extension of the General Linear Model using temporally embedded Canonical Correlation Analysisen_US
dc.typeArticleen_US
dc.description.versionPublished versionen_US
dc.identifier.doi10.1016/j.neuroimage.2019.116472
pubs.elements-sourcepubmeden_US
pubs.notesEmbargo: Not knownen_US
pubs.organisational-groupBoston Universityen_US
pubs.organisational-groupBoston University, College of Engineeringen_US
pubs.organisational-groupBoston University, College of Engineering, Department of Biomedical Engineeringen_US
pubs.publication-statusPublisheden_US
dc.identifier.orcid0000-0002-6709-7711 (Boas, David A)
dc.identifier.mycv501956


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Copyright © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.
Except where otherwise noted, this item's license is described as Copyright © 2019 Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license.