The importance of physiological noise regression in high temporal resolution fMRI

Norman Scheel; Catie Chang; Amir Madany Mamlouk

doi:10.1007/978-3-319-11179-7_104

The importance of physiological noise regression in high temporal resolution fMRI

Norman Scheel, Catie Chang, Amir Madany Mamlouk

National Institutes of Health

5 Zitate (Scopus)

Abstract

Recently a new technique called multiband imaging was introduced, it allows extremely low repetition times for functional magnetic resonance imaging (fMRI). As these ultra fast imaging scans can increase the Nyquist rate by an order of magnitude, there are many new effects, that have to be accounted for. As more frequencies can now be sampled directly, we want to analyze especially those that are due to physiological noise, such as cardiac and respiratory signals. Here, we adapted RETROICOR [4] to handle multiband fMRI data. We show the importance of physiological noise regression for standard temporal resolution fMRI and compare it to the high temporal resolution case. Our results show that especially for multiband fMRI scans, it is of the utmost importance to apply physiological noise regression, as residuals of these noises are clearly detectable in non noise independent components if no prior physiological noise has been applied.

Originalsprache	Englisch
Titel	Artificial Neural Networks and Machine Learning - ICANN 2014
Redakteure/-innen	Stefan Wermter, Cornelius Weber, Wlodzislaw Duch, Timo Honkela, Petia Koprinkova-Hristova, Sven Magg, Günther Palm, Allessandro E.P. Villa
Seitenumfang	8
Band	8681
Herausgeber (Verlag)	Springer Verlag
Erscheinungsdatum	01.01.2014
Auflage	1
Seiten	829-836
ISBN (Print)	978-3-319-11178-0
ISBN (elektronisch)	978-3-319-11179-7
DOIs	https://doi.org/10.1007/978-3-319-11179-7_104
Publikationsstatus	Veröffentlicht - 01.01.2014
Veranstaltung	24th International Conference on Artificial Neural Networks - Depat. of Informatics, Knowledge Technology, University of hamburg, Hamburg, Deutschland Dauer: 15.09.2014 → 19.09.2014 http://icann2014.org

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10.1007/978-3-319-11179-7_104

https://link.springer.com/chapter/10.1007/978-3-319-11179-7_104

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Scheel, N., Chang, C., & Mamlouk, A. M. (2014). The importance of physiological noise regression in high temporal resolution fMRI. in S. Wermter, C. Weber, W. Duch, T. Honkela, P. Koprinkova-Hristova, S. Magg, G. Palm, & A. E. P. Villa (Hrsg.), Artificial Neural Networks and Machine Learning - ICANN 2014 (1 Aufl., Band 8681, S. 829-836). (Lecture Notes in Computer Science). Springer Verlag. https://doi.org/10.1007/978-3-319-11179-7_104

Scheel, Norman ; Chang, Catie ; Mamlouk, Amir Madany. / The importance of physiological noise regression in high temporal resolution fMRI. Artificial Neural Networks and Machine Learning - ICANN 2014. Hrsg. / Stefan Wermter ; Cornelius Weber ; Wlodzislaw Duch ; Timo Honkela ; Petia Koprinkova-Hristova ; Sven Magg ; Günther Palm ; Allessandro E.P. Villa. Band 8681 1. Aufl. Springer Verlag, 2014. S. 829-836 (Lecture Notes in Computer Science).

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title = "The importance of physiological noise regression in high temporal resolution fMRI",

abstract = "Recently a new technique called multiband imaging was introduced, it allows extremely low repetition times for functional magnetic resonance imaging (fMRI). As these ultra fast imaging scans can increase the Nyquist rate by an order of magnitude, there are many new effects, that have to be accounted for. As more frequencies can now be sampled directly, we want to analyze especially those that are due to physiological noise, such as cardiac and respiratory signals. Here, we adapted RETROICOR [4] to handle multiband fMRI data. We show the importance of physiological noise regression for standard temporal resolution fMRI and compare it to the high temporal resolution case. Our results show that especially for multiband fMRI scans, it is of the utmost importance to apply physiological noise regression, as residuals of these noises are clearly detectable in non noise independent components if no prior physiological noise has been applied.",

author = "Norman Scheel and Catie Chang and Mamlouk, \{Amir Madany\}",

year = "2014",

month = jan,

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doi = "10.1007/978-3-319-11179-7\_104",

language = "English",

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series = "Lecture Notes in Computer Science",

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pages = "829--836",

editor = "Stefan Wermter and Cornelius Weber and Wlodzislaw Duch and Timo Honkela and Petia Koprinkova-Hristova and Sven Magg and G{\"u}nther Palm and Villa, \{Allessandro E.P.\}",

booktitle = "Artificial Neural Networks and Machine Learning - ICANN 2014",

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Scheel, N, Chang, C & Mamlouk, AM 2014, The importance of physiological noise regression in high temporal resolution fMRI. in S Wermter, C Weber, W Duch, T Honkela, P Koprinkova-Hristova, S Magg, G Palm & AEP Villa (Hrsg.), Artificial Neural Networks and Machine Learning - ICANN 2014. 1 Aufl., Bd. 8681, Lecture Notes in Computer Science, Springer Verlag, S. 829-836, 24th International Conference on Artificial Neural Networks, Hamburg, Deutschland, 15.09.14. https://doi.org/10.1007/978-3-319-11179-7_104

The importance of physiological noise regression in high temporal resolution fMRI. / Scheel, Norman; Chang, Catie; Mamlouk, Amir Madany.
Artificial Neural Networks and Machine Learning - ICANN 2014. Hrsg. / Stefan Wermter; Cornelius Weber; Wlodzislaw Duch; Timo Honkela; Petia Koprinkova-Hristova; Sven Magg; Günther Palm; Allessandro E.P. Villa. Band 8681 1. Aufl. Springer Verlag, 2014. S. 829-836 (Lecture Notes in Computer Science).

Publikation: Kapitel in Büchern/Berichten/Konferenzbänden › Konferenzbeitrag › Begutachtung

TY - GEN

T1 - The importance of physiological noise regression in high temporal resolution fMRI

AU - Scheel, Norman

AU - Chang, Catie

AU - Mamlouk, Amir Madany

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Recently a new technique called multiband imaging was introduced, it allows extremely low repetition times for functional magnetic resonance imaging (fMRI). As these ultra fast imaging scans can increase the Nyquist rate by an order of magnitude, there are many new effects, that have to be accounted for. As more frequencies can now be sampled directly, we want to analyze especially those that are due to physiological noise, such as cardiac and respiratory signals. Here, we adapted RETROICOR [4] to handle multiband fMRI data. We show the importance of physiological noise regression for standard temporal resolution fMRI and compare it to the high temporal resolution case. Our results show that especially for multiband fMRI scans, it is of the utmost importance to apply physiological noise regression, as residuals of these noises are clearly detectable in non noise independent components if no prior physiological noise has been applied.

AB - Recently a new technique called multiband imaging was introduced, it allows extremely low repetition times for functional magnetic resonance imaging (fMRI). As these ultra fast imaging scans can increase the Nyquist rate by an order of magnitude, there are many new effects, that have to be accounted for. As more frequencies can now be sampled directly, we want to analyze especially those that are due to physiological noise, such as cardiac and respiratory signals. Here, we adapted RETROICOR [4] to handle multiband fMRI data. We show the importance of physiological noise regression for standard temporal resolution fMRI and compare it to the high temporal resolution case. Our results show that especially for multiband fMRI scans, it is of the utmost importance to apply physiological noise regression, as residuals of these noises are clearly detectable in non noise independent components if no prior physiological noise has been applied.

U2 - 10.1007/978-3-319-11179-7_104

DO - 10.1007/978-3-319-11179-7_104

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SN - 978-3-319-11178-0

VL - 8681

T3 - Lecture Notes in Computer Science

SP - 829

EP - 836

BT - Artificial Neural Networks and Machine Learning - ICANN 2014

A2 - Wermter, Stefan

A2 - Weber, Cornelius

A2 - Duch, Wlodzislaw

A2 - Honkela, Timo

A2 - Koprinkova-Hristova, Petia

A2 - Magg, Sven

A2 - Palm, Günther

A2 - Villa, Allessandro E.P.

PB - Springer Verlag

T2 - 24th International Conference on Artificial Neural Networks

Y2 - 15 September 2014 through 19 September 2014

ER -

Scheel N, Chang C, Mamlouk AM. The importance of physiological noise regression in high temporal resolution fMRI. in Wermter S, Weber C, Duch W, Honkela T, Koprinkova-Hristova P, Magg S, Palm G, Villa AEP, Hrsg., Artificial Neural Networks and Machine Learning - ICANN 2014. 1 Aufl. Band 8681. Springer Verlag. 2014. S. 829-836. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-11179-7_104

The importance of physiological noise regression in high temporal resolution fMRI

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