Rainbow Course 3 – The Flexibility of fMRI Results

Lecturer: Nina Demšar
Fields: Neuroscience


The course is a combination of a theoretical and practical look into functional magnetic resonance imaging (fMRI) analysis, with an emphasis on comparing the different approaches via various software tools.

Functional magnetic resonance imaging is the most commonly used method for imaging brain activity today. The method is based on the BOLD signal that then goes through a complex analysis process to create images locating brain activity. There are various tools that can be used for this data analysis; the most commonly used being Analysis of Functional Images – AFNI (Cox, 1996), FMRIB Software Library – FSL (Smith et al., 2004) and Statistical Parametric Mapping – SPM (Friston et al., 1995; Penny et al., 2006). The whole process of analysis is made up of many steps, decisions concerning the order of said steps and specific parameter values. Since each tool uses different settings and code, it is possible for the results to be different depending on the tool used.

The first part of the course is an introduction to fMRI and, specifically, analysis of the data – going through the most common steps of preprocessing, first-level and second-level analysis. This is followed by a short demonstration of a small dataset being analyzed using the three most common software tools. The demonstration allows the participants to better understand the process of analysis, while also seeing the different ways researchers can approach an fMRI experiment. This practical understanding allows for the final part of the course to be more easily understandable. This part focuses on a few studies which made comparisons of various analyses and showed the differences between the approaches and the results that came out of them (e.g. Botvinik-Nezer et al., 2020; Carp, 2012a; Carp, 2012b; Poline et al., 2006) . The course is concluded with an open question, whether the analytical flexibility of fMRI studies undermines its results and what that means for neuroscience.


  • Botvinik-Nezer, R., et al. (2020). Variability in the analysis of a single neuroimaging dataset by many teams. Nature 583, 84-88. https://doi.org/10.1038/s41586-020-2314-9
  • Carp, J. (2012a). On the plurality of (methodological) worlds: estimating the analytic flexibility of fMRI experiments. Frontiers in Neuroscience, 6(149). https://doi.org/10.3389/fnins.2012.00149
  • Carp, J. (2012b). The secret lives of experiments: methods reporting in the fMRI literature. Neuroimage, 63(1), 289-300. https://doi.org/10.1016/j.neuroimage.2012.07.004
  • Cox, R. W. (1996). AFNI: Software for analysis and visualization of functional magnetic resonance neuroimages. Computers and Biomedical Research, 29(3), 162-173. https://doi.org/10.1006/cbmr.1996.0014
  • Friston, K. J., Holmes, A. P., Worsley, K. J., Poline, J. P., Frith, C. D. & Frackowiak, R. S. J. (1995). Statistical parametric maps in functional neuroimaging: a general linear approach. Human Brain Mapping, 4(2), 189-210. https://www.fil.ion.ucl.ac.uk/~karl/Statisticalparametricmapsinfunctionalimaging.pdf
  • Penny, W. D., Friston, K. J. Ashburner, J. T., Kiebel, S. J. & Nichols, T. E. (2006). Statistical Parametric Mapping: The Analysis of Functional Brain Images. Academic press.
  • Poline, J., Strother, S. C., Dehaene-Lambertz, G., Egan, G. F. & Lancaster, J. L. (2006). Motivation and synthesis of the FIAC experiment: Reproducibility of fMRI results across expert analyses. Human Brain Mapping, 27(5), 351-359. https://doi.org/10.1002/hbm.20268
  • Smith, S. M., Jenkinson, M., Woolrich, M. W., Beckmann, C. F., Behrens, T. E., Johansen-Berg, H., Bannister, P. R., De Luca, M., Drobnjak, I., Flitney, D. E., Niazy, R. K., Saunders, J., Vickers, J., Zhang, Y., De Stefano, N., Brady, J. M. & Matthews, P. M. (2004). Advances in functional and structural MR image analysis and implementation as FSL. NeuroImage, 23(1), 208-219. https://doi.org/10.1016/j.neuroimage.2004.07.051


Nina Demšar

Nina Demšar received her BSc in Biopsychology at the University of Primorska in 2016 and then completed her MSc in Cognitive Science in 2020 at the University of Ljubljana, University of Vienna, Comenius University in Bratislava and Eötvös Loránd University Budapest. During that time she focused on fMRI methodology and wrote her thesis on the Results of functional magnetic resonance imaging analysis with different software tools – a comparison. She is now working on her PhD in Biomedicine (Neuroscience) at the University of Ljubljana and working as a young researcher at the Center for Clinical Physiology at the Faculty of Medicine there.

Affiliation: Faculty of Medicine, University of Ljubljana