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Data-analytical Stability in Second-Level fMRI inference

Sanne P. Roels1*, Tom Loeys1 and Beatrijs Moerkerke1
  • 1 Ghent University, Data analysis, Belgium

We investigate the impact of decisions in the second-level (i.e. over subjects) inferential process in functional Magnetic Resonance Imaging (fMRI) on 1) the balance between false positives and false negatives and on 2) the data-analytical stability (Qiu et al., 2006; Roels et al., 2015), both proxies for the reproducibility of results. Second-level analysis based on a mass univariate approach typically consists of 3 phases. First, one proceeds via a general linear model for a test image that consists of pooled information from different subjects (Beckmann et al., 2003). We evaluate models that take into account first-level (within-subjects) variability and models that do not take into account this variability. Second, one proceeds via permutation-based inference or via inference based on parametrical assumptions (Holmes et al., 1996). Third, we evaluate 3 commonly used procedures to address the multiple testing problem: family-wise error rate correction, false discovery rate correction and a two-step procedure with minimal cluster size (Lieberman and Cunningham, 2009; Bennett et al., 2009). Based on a simulation study and on real data we find that the two-step procedure with minimal cluster-size results in most stable results, followed by the family- wise error rate correction. The false discovery rate results in most variable results, both for permutation-based inference and parametrical inference. Modeling the subject-specific variability yields a better balance between false positives and false negatives when using parametric inference.

Acknowledgements

The computational resources (Stevin Supercomputer Infrastructure) and services used in this work were provided by the VSC (Flemish Supercomputer Center), funded by Ghent Univer- sity, the Hercules Foundation and the Flemish Government department EWI.

References

Beckmann, C. F., Jenkinson, M., and Smith, S. M. (2003). General multilevel linear modeling for group analysis in FMRI. NeuroImage, 20(2):1052–63.
Bennett, C. M., Wolford, G. L., and Miller, M. B. (2009). The principled control of false positives in neuroimaging. Social cognitive and affective neuroscience, 4(4):417–22.
Holmes, a. P., Blair, R. C., Watson, J. D., and Ford, I. (1996). Nonparametric analysis of statistic images from functional mapping experiments. Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism, 16(1):7–22.
Lieberman, M. D. and Cunningham, W. a. (2009). Type I and Type II error concerns in fMRI research: re-balancing the scale. Social cognitive and affective neuroscience, 4(4):423–8.
Qiu, X., Xiao, Y., Gordon, A., and Yakovlev, A. (2006). Assessing stability of gene selection in microarray data analysis. BMC Bioinformatics, 7.
Roels, S. P., Bossier, H., Loeys, T., and Moerkerke, B. (2015a). Data-analytical stability of cluster-wise and peak-wise inference in fMRI data analysis. Journal of Neuroscience Methods, 240:37–47.

Keywords: fMRI, Modelling and simulation, Reproducibility of Results, inference, Validity and Reliability

Conference: Second Belgian Neuroinformatics Congress, Leuven, Belgium, 4 Dec - 4 Dec, 2015.

Presentation Type: Poster Presentation

Topic: Methods and Modeling

Citation: Roels SP, Loeys T and Moerkerke B (2015). Data-analytical Stability in Second-Level fMRI inference. Front. Neuroinform. Conference Abstract: Second Belgian Neuroinformatics Congress. doi: 10.3389/conf.fninf.2015.19.00022

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Received: 13 Nov 2015; Published Online: 17 Nov 2015.

* Correspondence: Mr. Sanne P Roels, Ghent University, Data analysis, Gent, 9000, Belgium, sanne.roels@ugent.be