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On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data

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  • Marijke Welvaert
  • Yves Rosseel

Abstract

Signal-to-noise ratio, the ratio between signal and noise, is a quantity that has been well established for MRI data but is still subject of ongoing debate and confusion when it comes to fMRI data. fMRI data are characterised by small activation fluctuations in a background of noise. Depending on how the signal of interest and the noise are identified, signal-to-noise ratio for fMRI data is reported by using many different definitions. Since each definition comes with a different scale, interpreting and comparing signal-to-noise ratio values for fMRI data can be a very challenging job. In this paper, we provide an overview of existing definitions. Further, the relationship with activation detection power is investigated. Reference tables and conversion formulae are provided to facilitate comparability between fMRI studies.

Suggested Citation

  • Marijke Welvaert & Yves Rosseel, 2013. "On the Definition of Signal-To-Noise Ratio and Contrast-To-Noise Ratio for fMRI Data," PLOS ONE, Public Library of Science, vol. 8(11), pages 1-10, November.
    • Handle: RePEc:plo:pone00:0077089
    DOI: 10.1371/journal.pone.0077089
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    1. Lee, Seonjoo & Shen, Haipeng & Truong, Young & Lewis, Mechelle & Huang, Xuemei, 2011. "Independent Component Analysis Involving Autocorrelated Sources With an Application to Functional Magnetic Resonance Imaging," Journal of the American Statistical Association, American Statistical Association, vol. 106(495), pages 1009-1024.
    2. Cabella, Brenno C.T. & Sturzbecher, Marcio J. & de Araujo, Draulio B. & Neves, Ubiraci P.C., 2009. "Generalized relative entropy in functional magnetic resonance imaging," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(1), pages 41-50.
    3. Nathan W Churchill & Grigori Yourganov & Anita Oder & Fred Tam & Simon J Graham & Stephen C Strother, 2012. "Optimizing Preprocessing and Analysis Pipelines for Single-Subject fMRI: 2. Interactions with ICA, PCA, Task Contrast and Inter-Subject Heterogeneity," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-15, February.
    4. Welvaert, Marijke & Durnez, Joke & Moerkerke, Beatrijs & Berdoolaege, Geert & Rosseel, Yves, 2011. "neuRosim: An R Package for Generating fMRI Data," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 44(i10).
    5. Zhang, Chunming, 2010. "Statistical inference of minimum BD estimators and classifiers for varying-dimensional models," Journal of Multivariate Analysis, Elsevier, vol. 101(7), pages 1574-1593, August.
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    Cited by:

    1. Sam Efromovich & Jiayi Wu, 2018. "Wavelet Analysis of Big Data Contaminated by Large Noise in an fMRI Study of Neuroplasticity," Methodology and Computing in Applied Probability, Springer, vol. 20(4), pages 1381-1402, December.
    2. Hoang-Dung Nguyen & Keum-Shik Hong & Yong-Il Shin, 2016. "Bundled-Optode Method in Functional Near-Infrared Spectroscopy," PLOS ONE, Public Library of Science, vol. 11(10), pages 1-23, October.
    3. Etay Hay & Petra Ritter & Nancy J Lobaugh & Anthony R McIntosh, 2017. "Multiregional integration in the brain during resting-state fMRI activity," PLOS Computational Biology, Public Library of Science, vol. 13(3), pages 1-20, March.
    4. Daniel Spencer & Rajarshi Guhaniyogi & Raquel Prado, 2020. "Joint Bayesian Estimation of Voxel Activation and Inter-regional Connectivity in fMRI Experiments," Psychometrika, Springer;The Psychometric Society, vol. 85(4), pages 845-869, December.
    5. Robert C Wilson & Yael Niv, 2015. "Is Model Fitting Necessary for Model-Based fMRI?," PLOS Computational Biology, Public Library of Science, vol. 11(6), pages 1-21, June.

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