Event Abstract

Signal-to-noise rationale in fMRI

  • 1 Ghent University, Department of Data Analysis, Belgium

fMRI data are often characterized by their signal-to-noise ratio (SNR). In general, the SNR compares the level of a desired signal to the level of background noise. While this concept is quite straightforward for MRI data, this is not trivial for fMRI data. In the case of MRI data, the determination of SNR is image-based and can be conceptualized by comparing the mean signal of the MRI image to the background noise of the image (Edelstein et al., 1986; Parrish et al., 2000). As Parrish et al. (2000) pointed out, for fMRI data it is not at all clear whether the SNR is image-based or time-based. In essence, the question is how to capture the information related to the signal and the noise based on the 4D fMRI dataset. In the literature, several definitions have been proposed. In these definitions the desired signal is represented for example by the amplitude of the signal (e.g. Joel et al. 2011), or the standard deviation of the activation (e.g. Penny, 2011). The use of these ad-hoc definitions decreases the comparability of fMRI studies. During our presentation we will give an overview of existing definitions based on the fMRI literature. By constructing reference tables, we provide insight in the relations among the definitions. In addition, we will present some simulation results that reveal the connection with the power to detect activation in fMRI data.
To conclude, a unified SNR definition might be unreasonable because the measurement depends greatly on how the signal of interest is defined. Still, for fMRI data, a minimal requirement should be that the definition is directly related to the activation signal.


Edelstein, W.A., Glover, G.H., Hardy, C.J. and Redington, R.W. (1986). The intrinsic signal-to-noise ratio in NMR imaging, Magnetic Resonance in Medicine, 3, 604--618.
Joel, S.E., Caffo, B.S., Van Zijl, P.C.M. and Pekar, J.J. (2011). On the relationship between seed-based and ICA-based measures of functional connectivity, Magnetic Resonance in Medicine, 66(3), 644-657.
Parrish, T.B., Gitelman, D.R., LaBar, K.S. and Mesulam M.M. (2000). Impact of signal-to-noise on functional fMRI, Magnetic Resonance in Medicine, 44(6), 925--932.
Penny, W.D. (2011). Comparing Dynamic Causal Models using AIC, BIC and Free Energy, NeuroImage, 59(1), 319--330.

Keywords: fMRI BOLD, simulation, signal-to-noise ratio, Reliability, analysis

Conference: Neuroinformatics 2013, Stockholm, Sweden, 27 Aug - 29 Aug, 2013.

Presentation Type: Poster

Topic: Neuroimaging

Citation: Welvaert M and Welvaert Y (2013). Signal-to-noise rationale in fMRI. Front. Neuroinform. Conference Abstract: Neuroinformatics 2013. doi: 10.3389/conf.fninf.2013.09.00087

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Received: 08 Apr 2013; Published Online: 11 Jul 2013.

* Correspondence: Miss. Marijke Welvaert, Ghent University, Department of Data Analysis, Gent, Belgium, marijke.welvaert@ugent.be