Visualization of Group Inference Data in Functional Neuroimaging
- First Online:
- 997 Downloads
While thresholded statistical parametric maps can convey an accurate account for the location and spatial extent of an effect in functional neuroimaging studies, their use is somewhat limited for characterizing more complex experimental effects, such as interactions in a factorial design. The resulting necessity for plotting the underlying data has long been recognized. Statistical Parametric Mapping (SPM) is a widely used software package for analyzing functional neuroimaging data that offers a variety of options for visualizing data from first level analyses. However, nowadays, the thrust of the statistical inference lies at the second level thus allowing for population inference. Unfortunately, the options for visualizing data from second level analyses are quite sparse. rfxplot is a new toolbox designed to alleviate this problem by providing a comprehensive array of options for plotting data from within second level analyses in SPM. These include graphs of average effect sizes (across subjects), averaged fitted responses and event-related blood oxygen level-dependent (BOLD) time courses. All data are retrieved from the underlying first level analyses and voxel selection can be tailored to the maximum effect in each subject within a defined search volume. All plot configurations can be easily configured via a graphical user-interface as well as non-interactively via a script. The large variety of plot options renders rfxplot suitable both for data exploration as well as producing high-quality figures for publications.
KeywordsSPM Toolbox Functional neuroimaging Data plotting Second level analyses Parametric modulation Fitted response Parameter estimate Event-related BOLD response Peri-stimulus time histogram
- Brett, M., Anton, J-L., Valabregue, R., & Poline, J-B. (2001). Region of interest analysis using an SPM toolbox [abstract] Presented at the 8th International Conference on Functional Mapping of the Human Brain, June 2-6, 2002, Sendai, Japan. Avaible on CD-ROM in NeoroImage, Vol 16, No 2.Google Scholar
- Glascher, J., Hampton, A. N., & O’Doherty, J. P. (2008). Determining a role for ventromedial prefrontal cortex in encoding action-based value signals during reward-related decision making. June 11, 2008. doi:10.1093/cercor/bhn098.
- Kiebel, S. J., & Holmes, A. J. (2007). The general linear model. In R. S. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, S. Zeki, J. Ashburner, & W. D. Penny (Eds.), Human brain function (pp. 101–126). San Diego: Elsevier.Google Scholar
- Penny, W. D., & Holmes, A. J. (2007). Random-effects analysis. In R. S. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, S. Zeki, J. Ashburner, & W. D. Penny (Eds.), Human brain function (pp. 156–165). San Diego: Elsevier.Google Scholar
- Poline, J. B., Kherif, F., & Penny, W. D. (2007). Contrasts and classical inference. In R. S. Frackowiak, K. J. Friston, C. D. Frith, R. J. Dolan, C. J. Price, S. Zeki, J. Ashburner, & W. D. Penny (Eds.), Human brain function (pp. 126–140). San Diego: Elsevier.Google Scholar
- Yacubian, J., Sommer, T., Schroeder, K., Glascher, J., Kalisch, R., Leuenberger, B., Braus, D. F., & Buchel, C. (2007). Gene–gene interaction associated with neural reward sensitivity. Proceedings of the National Academy of Sciences of the United States of America, 104, 8125–8130. doi:10.1073/pnas.0702029104.PubMedCrossRefGoogle Scholar