1. So you want to run an MVPA experiment… Lindsay Morgan April 9, 2012

2. Overview Study Design Preprocessing Pattern Estimation Voxel Selection Classifier

5. Study Design Blocked design Smaller # of conditions Better estimate of the average response pattern Event Related Design Larger # of conditions – Similarity analyses Better estimate of the response distribution across exemplars Psychologically less predictable Requires sequence optimization (e.g., OptSeq, de Bruijn)

6. Study Design Suggestions Multiple runs – Independent data sets for training & testing – Many short runs preferable to a few long runs (Coutanche & Thompson-Schill NeuroImage 2012) Equal # of exemplars per stimulus class – Or use subsamples of more numerous class

7. Pre-processing Pre-process each run separately Slice time correction Motion correction Smoothing?

8. To Smooth or Not to Smooth? Op de Beeck NeuroImage 2010

9. Pattern Estimation Raw signal intensity values Suitable for block or slow event-related Betas (parameter estimates) or t values Suitable for all designs Derived from GLM – Accounts for overlap in HRF – Can remove motion effects and linear trends

10. Mur et al., Soc Cog Affective Neurosci, 2009 Data transformation so far…

11. Kriegeskorte et al., Frontiers Sys Neurosci, 2008 Ungrouped design 96 images Each image presented 1x/run 3 comparisons Inanimate vs. animate Face vs. body Natural vs. artificial

12. Betas or t values? Misaki et al., NeuroImage, 2010

13. Pattern Normalization Misaki et al., NeuroImage, 2010

14. Pattern Normalization Misaki et al., NeuroImage, 2010

15. Data transformation so far… Mur et al., Soc Cog Affective Neurosci, 2009

16. Voxel Selection Typically, performance decreases as the # of voxels increases Data must be independent of classifier – Anatomically-defined region – Functional localizer – Training set from your experimental data E.g., ANOVA for all conditions at each voxel  select top N voxels

17. The Classifier Misaki et al., NeuroImage, 2010

18. Which classifier should you use? Misaki et al., NeuroImage, 2010

19. Data transformation complete! Mur et al., Soc Cog Affective Neurosci, 2009

20. How to implement the classifier AFNI 3dsvm Princeton MVPA toolbox PyMVPA toolbox LIBSVM toolbox

21. General Conclusions Design your experiment to yield as many independent patterns as possible Estimate your patterns using t values (or z scores) Use a linear classifier