ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

Ludovica Griffanti, Gholamreza Salimi-Khorshidi, Christian Beckmann, Edward J. Auerbach, Gwenaelle Douaud, Claire E. Sexton, Eniko Zsoldos, Klaus P. Ebmeier, Nicola Filippini, Clare E. Mackay, Steen Moeller, Junqian Xu, Essa Yacoub, Giuseppe Baselli, Kamil Ugurbil, Karla L. Miller, Steven M. Smith

Research output: Contribution to journalArticleAcademicpeer-review

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Abstract

The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.
Original languageEnglish
Pages (from-to)232-247
JournalNeuroImage
Volume95
DOIs
Publication statusPublished - 2014

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Artifacts
Magnetic Resonance Imaging
Spatial Analysis
Noise

Keywords

  • IR-96744
  • METIS-311170

Cite this

Griffanti, L., Salimi-Khorshidi, G., Beckmann, C., Auerbach, E. J., Douaud, G., Sexton, C. E., ... Smith, S. M. (2014). ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. NeuroImage, 95, 232-247. https://doi.org/10.1016/j.neuroimage.2014.03.034
Griffanti, Ludovica ; Salimi-Khorshidi, Gholamreza ; Beckmann, Christian ; Auerbach, Edward J. ; Douaud, Gwenaelle ; Sexton, Claire E. ; Zsoldos, Eniko ; Ebmeier, Klaus P. ; Filippini, Nicola ; Mackay, Clare E. ; Moeller, Steen ; Xu, Junqian ; Yacoub, Essa ; Baselli, Giuseppe ; Ugurbil, Kamil ; Miller, Karla L. ; Smith, Steven M. / ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. In: NeuroImage. 2014 ; Vol. 95. pp. 232-247.
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Griffanti, L, Salimi-Khorshidi, G, Beckmann, C, Auerbach, EJ, Douaud, G, Sexton, CE, Zsoldos, E, Ebmeier, KP, Filippini, N, Mackay, CE, Moeller, S, Xu, J, Yacoub, E, Baselli, G, Ugurbil, K, Miller, KL & Smith, SM 2014, 'ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging' NeuroImage, vol. 95, pp. 232-247. https://doi.org/10.1016/j.neuroimage.2014.03.034

ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging. / Griffanti, Ludovica; Salimi-Khorshidi, Gholamreza; Beckmann, Christian; Auerbach, Edward J.; Douaud, Gwenaelle; Sexton, Claire E.; Zsoldos, Eniko; Ebmeier, Klaus P.; Filippini, Nicola; Mackay, Clare E.; Moeller, Steen; Xu, Junqian; Yacoub, Essa; Baselli, Giuseppe; Ugurbil, Kamil; Miller, Karla L.; Smith, Steven M.

In: NeuroImage, Vol. 95, 2014, p. 232-247.

Research output: Contribution to journalArticleAcademicpeer-review

TY - JOUR

T1 - ICA-based artefact removal and accelerated fMRI acquisition for improved resting state network imaging

AU - Griffanti, Ludovica

AU - Salimi-Khorshidi, Gholamreza

AU - Beckmann, Christian

AU - Auerbach, Edward J.

AU - Douaud, Gwenaelle

AU - Sexton, Claire E.

AU - Zsoldos, Eniko

AU - Ebmeier, Klaus P.

AU - Filippini, Nicola

AU - Mackay, Clare E.

AU - Moeller, Steen

AU - Xu, Junqian

AU - Yacoub, Essa

AU - Baselli, Giuseppe

AU - Ugurbil, Kamil

AU - Miller, Karla L.

AU - Smith, Steven M.

PY - 2014

Y1 - 2014

N2 - The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

AB - The identification of resting state networks (RSNs) and the quantification of their functional connectivity in resting-state fMRI (rfMRI) are seriously hindered by the presence of artefacts, many of which overlap spatially or spectrally with RSNs. Moreover, recent developments in fMRI acquisition yield data with higher spatial and temporal resolutions, but may increase artefacts both spatially and/or temporally. Hence the correct identification and removal of non-neural fluctuations is crucial, especially in accelerated acquisitions. In this paper we investigate the effectiveness of three data-driven cleaning procedures, compare standard against higher (spatial and temporal) resolution accelerated fMRI acquisitions, and investigate the combined effect of different acquisitions and different cleanup approaches. We applied single-subject independent component analysis (ICA), followed by automatic component classification with FMRIB's ICA-based X-noiseifier (FIX) to identify artefactual components. We then compared two first-level (within-subject) cleaning approaches for removing those artefacts and motion-related fluctuations from the data. The effectiveness of the cleaning procedures was assessed using time series (amplitude and spectra), network matrix and spatial map analyses. For time series and network analyses we also tested the effect of a second-level cleaning (informed by group-level analysis). Comparing these approaches, the preferable balance between noise removal and signal loss was achieved by regressing out of the data the full space of motion-related fluctuations and only the unique variance of the artefactual ICA components. Using similar analyses, we also investigated the effects of different cleaning approaches on data from different acquisition sequences. With the optimal cleaning procedures, functional connectivity results from accelerated data were statistically comparable or significantly better than the standard (unaccelerated) acquisition, and, crucially, with higher spatial and temporal resolution. Moreover, we were able to perform higher dimensionality ICA decompositions with the accelerated data, which is very valuable for detailed network analyses.

KW - IR-96744

KW - METIS-311170

U2 - 10.1016/j.neuroimage.2014.03.034

DO - 10.1016/j.neuroimage.2014.03.034

M3 - Article

VL - 95

SP - 232

EP - 247

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

ER -