Multivariate Pattern Analysis

1. Multivariate Pattern Analysis John Clithero Duke University 01.13.10

2. Overview • MVPA and fMRI • Examples in the Literature • PyMVPA Example

3. Motivation for MVPA in fMRI • Complements univariate approaches that investigate the involvement of regions in a specific mental activity. • Investigates the representational content of regions. • Distributed responses and representations. • Model-free vs model-based analysis. • Machine learning is concerned with developing algorithms that automatically learn with experience. • For each example, learn to predict the value of its label (can be one of two classes, yes/no).

4. MVPA and fMRI data Norman et al. (2006)

5. Orientation Selectivity in Visual Cortex Kamitani and Tong (2005)

6. Unconscious Determinants of Free Decisions Soon et al. (2008)

7. Grey Matter Density and Psychotic Illness Pattern classification analysis achieved 86.1% accuracy in discriminating patients from controls using leave-one-out cross-validation. Sun et al. (2009)

8. Performance Evaluation • Main goal is accurate prediction. • How to estimate true error rate? • Use the entire training data to select classifier and estimate the error rate • Final model will normally overfit the training data. • Use N-fold Cross-Validation • use N-1 folds for training and the remaining one for testing. • Estimate of true error rate is then an average of N error rates.

9. What exactly goes into the classifier? Features for the classifier will be M voxels. • Time-compressed preprocessed BOLD signal (average of 2 or 3 consecutive TRs). • Single-trial beta estimates from a GLM. • Same voxels but at different timepoints. • Structural information (e.g., GMD).

10. What exactly goes into the classifier? Feature selection can be done in many ways. • Mean activation level. • Activation differences between classes. • Consistent behavior. • Filter: rank all voxels, pick the best M. • Recursive: Rank, pick best P%. Repeat. • Searchlights.

11. Processes of Economic Valuation Shown is the increase (red) or decrease (blue) in CV performance when local information from one ROI combined with local information from another. Clithero et al. (2009)

12. What does it mean to find common neural patterns within individuals versus across individuals? Clithero et al. (Submitted)

13. Comparing Within- and Cross-Participant Models Clithero et al. (Submitted)

14. Predictive Spatial Patterns and Individual Behavior Clithero et al. (Submitted)

15. Dissimilarity Analysis Is there an elegant way to compare activity patterns of experimental conditions? Kriegeskorte et al. (2008)

16. Connecting Research Branches Kriegeskorte et al. (2008)

17. Odor quality coding and categorization Howard et al. (2009)

18. Common steps in MVPA studies • Preprocessing • Picking a feature space • Making examples • Feature selection • Training/testing • Correlation/similarity • Reporting results

19. PyMVPA Terminology Hanke et al. (2009)

20. An (Almost) Complete Analysis in PyMVPA You don’t need to know a lot of Python… …to do use PyMVPA. • PyMVPA is freely available and (sometimes) easy to install. • MVPA is generally very computationally intensive, but PyMVPA has optimized many of its scripts. • Analyses can take hours or even days using a single processor. • Computing clusters make life better if you want to do large-scale or whole-brain MVPA. • What you need from Python that is probably not already installed on your linux box:

21. Getting your data into PyMVPA

22. Classifier (Lots of Options) • k-Nearest Neighbor • Ridge Regression • Penalized Logistic Regression • Sparse Multinomial Logistic Regression • Support Vector Machines

23. Feature Spaces and Selection • PyMVPA has built-in functions for • Searchlight feature selection. • Recursive feature selection. • To run searchlight analyses for all voxels in your mask:

24. Sensitivity Analysis

25. Confusion Matrices and Generalization LaConte (2009)

26. Statistics • Multiple Comparisons (e.g., # of searchlights) • Binomial Tests • Bootstrap estimates of accuracy variance • How dependent is accuracy/model on the presence of specific examples? • Permutation tests • How likely is the process to bias results optimistically in the presence of no information in the example labels?

27. PyMVPA is very flexible… Hanke et al. (2009)

28. Relevant Packages • SciPy (http://www.scipy.org/) • Pynifti (http://niftilib.sourceforge.net/pynifti/) • PyMVPA (http://www.pymvpa.org/) • LIBSVM (http://www.csie.ntu.edu.tw/~cjlin/libsvm/) • MVPA Toolbox (http://www.csbmb.princeton.edu/mvpa/) • 3DSVM (http://afni.nimh.nih.gov/pub/dist/doc/program_help/3dsvm.html)

29. (Possibly) Helpful Readings • Norman et al. (2006). Beyond mind-reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 10: 424-430. • Haynes and Rees (2006). Decoding mental states from brain activity in humans. Nature Reviews Neuroscience, 7: 523-34. • Mitchell et al. (2004). Learning to decode cognitive states from brain images. Machine Learning, 57:145-175. • Mur et al. (2009). Representational content with pattern-information fMRI: an introductory guide. Social Cognitive and Affective Neuroscience, 4: 101-109. • Kriegeskorte et al. (2009). Representational similarity analysis – connecting the branches of systems neuroscience. Frontiers in Systems Neuroscience, 2: 1-28. • Kriegeskorte et al. (2009). Circular analysis in systems neuroscience: the dangers of double dipping. Nature Neuroscience, 12: 535-540. • Hanke et al. (2009). PyMVPA: A Python toolbox for multivariate pattern analysis of fMRI data. Neuroinformatics, 7: 37-53.

30. Acknowledgments • Scott Huettel • McKell Carter • David Smith • Chris Coutlee • Anne Harsch • Ed McLaurin • O’Dhaniel Mullette-Gillman • Brandi Newell • Allison Scott • Adrienne Taren • Vinod Venkatraman • Amy Winecoff • Richard Yaxley