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Brain Structure and Function

, Volume 222, Issue 3, pp 1447–1468 | Cite as

Resting-state test–retest reliability of a priori defined canonical networks over different preprocessing steps

  • Deepthi P. Varikuti
  • Felix Hoffstaedter
  • Sarah Genon
  • Holger Schwender
  • Andrew T. Reid
  • Simon B. Eickhoff
Original Article

Abstract

Resting-state functional connectivity analysis has become a widely used method for the investigation of human brain connectivity and pathology. The measurement of neuronal activity by functional MRI, however, is impeded by various nuisance signals that reduce the stability of functional connectivity. Several methods exist to address this predicament, but little consensus has yet been reached on the most appropriate approach. Given the crucial importance of reliability for the development of clinical applications, we here investigated the effect of various confound removal approaches on the test–retest reliability of functional-connectivity estimates in two previously defined functional brain networks. Our results showed that gray matter masking improved the reliability of connectivity estimates, whereas denoising based on principal components analysis reduced it. We additionally observed that refraining from using any correction for global signals provided the best test–retest reliability, but failed to reproduce anti-correlations between what have been previously described as antagonistic networks. This suggests that improved reliability can come at the expense of potentially poorer biological validity. Consistent with this, we observed that reliability was proportional to the retained variance, which presumably included structured noise, such as reliable nuisance signals (for instance, noise induced by cardiac processes). We conclude that compromises are necessary between maximizing test–retest reliability and removing variance that may be attributable to non-neuronal sources.

Keywords

Test–retest fMRI Resting-state functional connectivity Reliability Confound removal 

Notes

Acknowledgments

This study was supported by the Deutsche Forschungsgemeinschaft (DFG, EI 816/4-1, LA 3071/3-1; EI 816/6-1), the National Institute of Mental Health (R01-MH074457), the Helmholtz Portfolio Theme “Supercomputing and Modeling for the Human Brain” and the European Union Seventh Framework Program (FP7/2007–2013) under Grant Agreement No. 604102 (Human Brain Project).

Compliance with ethical standards

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Ethical approval

The original study protocol of the data used here has been approved by the local ethics committees of the university hospital Aachen and informed consent was obtained by all the participants prior to the examination. The current data were analyzed anonymously.

Supplementary material

429_2016_1286_MOESM1_ESM.pdf (80.5 mb)
Supplementary material 1 (PDF 82383 kb)

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Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Deepthi P. Varikuti
    • 1
    • 2
  • Felix Hoffstaedter
    • 1
    • 2
  • Sarah Genon
    • 1
    • 2
  • Holger Schwender
    • 3
  • Andrew T. Reid
    • 2
  • Simon B. Eickhoff
    • 1
    • 2
  1. 1.Institute of Clinical Neuroscience and Medical Psychology, Medical FacultyHeinrich Heine University DüsseldorfDüsseldorfGermany
  2. 2.Institute of Neuroscience and Medicine (INM-1), Research Center JuelichJuelichGermany
  3. 3.Mathematical InstituteHeinrich Heine University DüsseldorfDüsseldorfGermany

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