PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Standard

PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI. / Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn; Rasmussen, Peter Mondrup; Lee, Wayne; Ween, Jon E.; Strother, Stephen C.

In: NeuroImage, Vol. 59, No. 2, 2012, p. 1299-1314.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Harvard

Churchill, NW, Yourganov, G, Spring, R, Rasmussen, PM, Lee, W, Ween, JE & Strother, SC 2012, 'PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI' NeuroImage, vol 59, no. 2, pp. 1299-1314., 10.1016/j.neuroimage.2011.08.021

APA

Churchill, N. W., Yourganov, G., Spring, R., Rasmussen, P. M., Lee, W., Ween, J. E., & Strother, S. C. (2012). PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI. NeuroImage, 59(2), 1299-1314. 10.1016/j.neuroimage.2011.08.021

CBE

Churchill NW, Yourganov G, Spring R, Rasmussen PM, Lee W, Ween JE, Strother SC. 2012. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI. NeuroImage. 59(2):1299-1314. Available from: 10.1016/j.neuroimage.2011.08.021

MLA

Vancouver

Churchill NW, Yourganov G, Spring R, Rasmussen PM, Lee W, Ween JE et al. PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI. NeuroImage. 2012;59(2):1299-1314. Available from: 10.1016/j.neuroimage.2011.08.021

Author

Churchill, Nathan W.; Yourganov, Grigori; Spring, Robyn; Rasmussen, Peter Mondrup; Lee, Wayne; Ween, Jon E.; Strother, Stephen C. / PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI.

In: NeuroImage, Vol. 59, No. 2, 2012, p. 1299-1314.

Publication: Research - peer-reviewJournal article – Annual report year: 2011

Bibtex

@article{0eecedb11069423ea95942974f0d808f,
title = "PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI",
publisher = "Academic Press",
author = "Churchill, {Nathan W.} and Grigori Yourganov and Robyn Spring and Rasmussen, {Peter Mondrup} and Wayne Lee and Ween, {Jon E.} and Strother, {Stephen C.}",
year = "2012",
doi = "10.1016/j.neuroimage.2011.08.021",
volume = "59",
number = "2",
pages = "1299--1314",
journal = "NeuroImage",
issn = "1053-8119",

}

RIS

TY - JOUR

T1 - PHYCAA: Data-driven measurement and removal of physiological noise in BOLD fMRI

A1 - Churchill,Nathan W.

A1 - Yourganov,Grigori

A1 - Spring,Robyn

A1 - Rasmussen,Peter Mondrup

A1 - Lee,Wayne

A1 - Ween,Jon E.

A1 - Strother,Stephen C.

AU - Churchill,Nathan W.

AU - Yourganov,Grigori

AU - Spring,Robyn

AU - Rasmussen,Peter Mondrup

AU - Lee,Wayne

AU - Ween,Jon E.

AU - Strother,Stephen C.

PB - Academic Press

PY - 2012

Y1 - 2012

N2 - The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals.

AB - The effects of physiological noise may significantly limit the reproducibility and accuracy of BOLD fMRI. However, physiological noise evidences a complex, undersampled temporal structure and is often non-orthogonal relative to the neuronally-linked BOLD response, which presents a significant challenge for identifying and removing such artifact. This paper presents a multivariate, data-driven method for the characterization and removal of physiological noise in fMRI data, termed PHYCAA (PHYsiological correction using Canonical Autocorrelation Analysis). The method identifies high frequency, autocorrelated physiological noise sources with reproducible spatial structure, using an adaptation of Canonical Correlation Analysis performed in a split-half resampling framework. The technique is able to identify physiological effects with vascular-linked spatial structure, and an intrinsic dimensionality that is task- and subject-dependent. We also demonstrate that increasing dimensionality of such physiological noise is correlated with increasing variability in externally-measured respiratory and cardiac processes. Using PHYCAA as a denoising technique significantly improves simulated signal detection with physiological noise, and real data-driven model prediction and reproducibility, for both block and event-related task designs. This is demonstrated compared to no physiological noise correction, and to the widely used RETROICOR (Glover et al., 2000) physiological denoising algorithm, which uses externally measured cardiac and respiration signals.

KW - Multivariate

KW - BOLD fMRI

KW - Image processing

KW - Physiological noise

KW - Data-driven

U2 - 10.1016/j.neuroimage.2011.08.021

DO - 10.1016/j.neuroimage.2011.08.021

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

VL - 59

SP - 1299

EP - 1314

ER -