Display options
Cite
Feis RA, Smith SM, Filippini N, et al. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Front Neurosci. 2015;9:395doi: 10.3389/fnins.2015.00395.
Feis, R. A., Smith, S. M., Filippini, N., Douaud, G., Dopper, E. G., Heise, V., Trachtenberg, A. J., van Swieten, J. C., van Buchem, M. A., Rombouts, S. A., & Mackay, C. E. (2015). ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Frontiers in neuroscience, 9395. https://doi.org/10.3389/fnins.2015.00395
Feis, Rogier A, et al. "ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI." Frontiers in neuroscience vol. 9 (2015): 395. doi: https://doi.org/10.3389/fnins.2015.00395
Feis RA, Smith SM, Filippini N, Douaud G, Dopper EG, Heise V, Trachtenberg AJ, van Swieten JC, van Buchem MA, Rombouts SA, Mackay CE. ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI. Front Neurosci. 2015 Oct 27;9:395. doi: 10.3389/fnins.2015.00395. eCollection 2015. PMID: 26578859; PMCID: PMC4621866.
Copy Download .nbib Format: NLM
Share it on

Front Neurosci. 2015 Oct 27;9:395. doi: 10.3389/fnins.2015.00395. eCollection 2015.

ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI.

Frontiers in neuroscience

Rogier A Feis, Stephen M Smith, Nicola Filippini, Gwenaëlle Douaud, Elise G P Dopper, Verena Heise, Aaron J Trachtenberg, John C van Swieten, Mark A van Buchem, Serge A R B Rombouts, Clare E Mackay

Affiliations

  1. Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; FMRIB, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK.
  2. FMRIB, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK.
  3. FMRIB, Nuffield Department of Clinical Neurosciences, Oxford Centre for Functional Magnetic Resonance Imaging of the Brain, University of Oxford Oxford, UK ; Department of Psychiatry, University of Oxford Oxford, UK.
  4. Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; Department of Neurology, Erasmus Medical Centre Rotterdam, Netherlands.
  5. Department of Neurology, Erasmus Medical Centre Rotterdam, Netherlands.
  6. Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; Leiden Institute for Brain and Cognition, Leiden University Leiden, Netherlands.
  7. Department of Radiology, Leiden University Medical Centre Leiden, Netherlands ; Leiden Institute for Brain and Cognition, Leiden University Leiden, Netherlands ; Institute of Psychology, Leiden University Leiden, Netherlands.

PMID: 26578859 PMCID: PMC4621866 DOI: 10.3389/fnins.2015.00395

Abstract

Resting-state fMRI (R-fMRI) has shown considerable promise in providing potential biomarkers for diagnosis, prognosis and drug response across a range of diseases. Incorporating R-fMRI into multi-center studies is becoming increasingly popular, imposing technical challenges on data acquisition and analysis, as fMRI data is particularly sensitive to structured noise resulting from hardware, software, and environmental differences. Here, we investigated whether a novel clean up tool for structured noise was capable of reducing center-related R-fMRI differences between healthy subjects. We analyzed three Tesla R-fMRI data from 72 subjects, half of whom were scanned with eyes closed in a Philips Achieva system in The Netherlands, and half of whom were scanned with eyes open in a Siemens Trio system in the UK. After pre-statistical processing and individual Independent Component Analysis (ICA), FMRIB's ICA-based X-noiseifier (FIX) was used to remove noise components from the data. GICA and dual regression were run and non-parametric statistics were used to compare spatial maps between groups before and after applying FIX. Large significant differences were found in all resting-state networks between study sites before using FIX, most of which were reduced to non-significant after applying FIX. The between-center difference in the medial/primary visual network, presumably reflecting a between-center difference in protocol, remained statistically significant. FIX helps facilitate multi-center R-fMRI research by diminishing structured noise from R-fMRI data. In doing so, it improves combination of existing data from different centers in new settings and comparison of rare diseases and risk genes for which adequate sample size remains a challenge.

Keywords: dual regression; independent component analysis; multi-center analysis; resting-state functional MRI; structured noise reduction

References

  1. J Magn Reson Imaging. 2012 Jul;36(1):39-54 - PubMed
  2. Neuroimage. 2009 Oct 15;48(1):63-72 - PubMed
  3. Neuroimage. 2009 Jan 1;44(1):83-98 - PubMed
  4. Neuroimage. 2002 Oct;17(2):825-41 - PubMed
  5. Mov Disord. 2015 Sep;30(10):1382-90 - PubMed
  6. Neuroimage. 2012 Aug 15;62(2):782-90 - PubMed
  7. Neuroimage. 2011 Jan 1;54(1):602-10 - PubMed
  8. Neuroimage. 2004;23 Suppl 1:S208-19 - PubMed
  9. Mol Psychiatry. 2014 Jun;19(6):659-67 - PubMed
  10. Neuroimage. 2009 Mar;45(1 Suppl):S173-86 - PubMed
  11. Radiology. 2005 Dec;237(3):781-9 - PubMed
  12. Front Syst Neurosci. 2010 Aug 26;4:null - PubMed
  13. Hum Brain Mapp. 2008 Aug;29(8):958-72 - PubMed
  14. Neuroimage. 2014 Apr 15;90:449-68 - PubMed
  15. Proc Natl Acad Sci U S A. 2010 Mar 9;107(10):4734-9 - PubMed
  16. Neurology. 2013 Feb 26;80(9):814-23 - PubMed
  17. Nat Rev Neurosci. 2007 Sep;8(9):700-11 - PubMed
  18. Eur J Neurol. 2008 Feb;15(2):109-10 - PubMed
  19. J Magn Reson Imaging. 2007 Oct;26(4):1122-6 - PubMed
  20. Neuroimage. 2007 May 15;36(1):144-52 - PubMed
  21. Neuroimage. 1998 Oct;8(3):249-61 - PubMed
  22. IEEE Trans Med Imaging. 2004 Feb;23(2):137-52 - PubMed
  23. Med Image Anal. 2001 Jun;5(2):143-56 - PubMed
  24. Neuroinformatics. 2013 Oct;11(4):469-76 - PubMed
  25. Sci Rep. 2015 Jun 04;5:10818 - PubMed
  26. Front Hum Neurosci. 2014 Jul 08;8:503 - PubMed
  27. Neuroimage. 2013 Apr 1;69:21-34 - PubMed
  28. J Neurophysiol. 2005 May;93(5):2864-72 - PubMed
  29. Front Syst Neurosci. 2010 Sep 20;4:null - PubMed
  30. Proc Natl Acad Sci U S A. 2009 Aug 4;106(31):13040-5 - PubMed
  31. Proc Natl Acad Sci U S A. 2005 Jul 5;102(27):9673-8 - PubMed
  32. Neuroimage. 2013 Sep;78:463-73 - PubMed
  33. Neuroimage. 2002 Sep;17(1):479-89 - PubMed
  34. Eur J Neurol. 2008 Feb;15(2):113-22 - PubMed
  35. Neuroimage. 2006 Feb 15;29(4):1359-67 - PubMed
  36. Neuroimage. 2014 May 15;92:381-97 - PubMed
  37. Mol Psychiatry. 2011 Sep;16(9):908-16 - PubMed
  38. PLoS One. 2009 May 29;4(5):e5743 - PubMed
  39. Proc Natl Acad Sci U S A. 2009 Apr 28;106(17):7209-14 - PubMed
  40. Proc Natl Acad Sci U S A. 2003 Jan 7;100(1):253-8 - PubMed
  41. IEEE Trans Neural Netw. 1999;10(3):626-34 - PubMed
  42. Rev Neurol (Paris). 2013 Oct;169(10):820-4 - PubMed
  43. Front Syst Neurosci. 2012 Sep 05;6:62 - PubMed
  44. Neuroimage. 2014 Jul 15;95:232-47 - PubMed
  45. J Neurosci Methods. 2008 Jun 30;171(2):349-55 - PubMed
  46. Front Hum Neurosci. 2014 Aug 20;8:645 - PubMed
  47. Neuroimage. 2012 Jan 2;59(1):565-72 - PubMed
  48. Neurobiol Aging. 2012 Mar;33(3):618.e1-618.e13 - PubMed
  49. J Neurophysiol. 2008 Aug;100(2):922-31 - PubMed

Publication Types

Grant support