Multivariate analysis

1. Multivariate analysis Methods and Applications

2. Content • univariate analysis • multivariate analysis • basic steps in MV analysis • three examples • possible applications in neuroeconomical research Sabrina Strang

3. Univariate Analysis • Which regions of the brain are involved in a task? • measuring activity from thousands of voxels repeatedly • each voxel is compared with itself over time and conditions • voxels are treated as independent voxels x,y and z are more activated in condition a than in condition b Sabrina Strang

4. Univariate Analysis • disadvantage: • voxels might not be independent • data is often spatially smoothed • blurs out fine-grained spatial patterns that might discriminate between conditions loss of information Sabrina Strang

5. Spatial Smoothing • data is spatially smoothed by convolution with a 3D Gaussian kernel • each voxel is replaced by a weighted average value calculated across neighbouring voxels unsmoothed data smoothed data Sabrina Strang

6. Multivariate Analysis • considers relative activity changes in and across individual voxels • analysis of spatially distributed activity patterns across multiple voxels Sabrina Strang

7. Multivariate Analysis • advantages: • no spatial smoothing no loss of information • voxels that individually do not carry information about a cognitive state, might nonetheless do so when jointly analysed Sabrina Strang

8. Basic steps in MV analysis • uses known items to create algorithms to categorize new events define a subset of voxels classify new data based on training algorithm Feature selection Generalization testing create a multivariate pattern classification algorithm Classifier training Sabrina Strang

9. Basic steps in MV analysis 1. feature selection • deciding which voxels will be included in the classification analysis • based on ROI, univariat analysis, searchlight method… Norman, Polyn, Detre and Haxby (2006) Sabrina Strang

10. Basic steps in MV analysis 2. classifier training • brain patterns are labeled according to which experimental condition generated the pattern • labeled patterns are used to train a clasiffier function that maps between brain patterns and experimental condition Norman et al., (2006) Sabrina Strang

11. 3. generalization testing trained classifier function defines a decision boundary trained classifier is used to predict category membership green dot: correctly identified blue dot: misidentified Basic steps in MV analysis Norman et al., (2006) Sabrina Strang

12. Example 1 Overlapping representations • Anterior inferotemporal cortex (aIT) and fusiform face area (FFA) are involved in face idendification • However, it has not been directly demonstrated that either FFA or aIT respond with distinct activity patterns to different individual faces • Kriegeskorte, Formisano, Sorger and Goebel (2007): • investigated whether response patterns associated with different faces are statistically distinct Sabrina Strang

13. Example 1 Overlapping representations • methods: • two different faces • two different houses as control stimuli • subjects had to perform an anomaly–detection task, which required them to pay close attention to each repeated presentation of an image Sabrina Strang

14. results: univariate analysis: category effects: FFA and aIT were more activated by faces than by houses no effects for individual faces multivariate analysis: the right aIT responds with a distinct activity pattern to each of the faces Example 1 Overlapping representations Sabrina Strang

15. Example 1applications in neuroeconomical research • some areas are activated in a lot of task • ACC for example • there might be overlapping representations in this area • using MV analysis responses might be distinguishable Sabrina Strang

16. Example 2mind reading/lie detection • Davatzikos, Ruparel, Fan, Shen, Acharyya, Loughead, Gur and Langleben (2005) • used MV analysis to discriminate between the spatial patterns of brain activity associated with lie and truth Sabrina Strang

17. Example 2mind reading/lie detection • methods: • participants got an envelope containing 2 cards (5 clubs and 7 spades) and $20 • instructed to deny possession of one of the cards and acknowledge possession of the other during imaging phase • participants were told that they could keep the $20 only if successful in concealing identity of the lie card during the scan session Sabrina Strang

18. Example 2mind reading/lie detection • results: • devided data in training and test data sets • tested the trained classifiers on previously unseen data sets (test data) • prediction acccuracy was above 85% Sabrina Strang

19. results: green: regions relativiely more activated during truth telling red: regions relativiely more activated during lying blue: regions that were most informative in terms of classification Example 2mind reading/lie detection Sabrina Strang

20. Example 2applications in neuroeconomical research • most informative regions might be verry interesting • prediction of decisions • not “just“ correlation between response and magnitude of activation Sabrina Strang

21. Example 3cross generalization • Stokes, Thompson, Cusack and Duncan (2009) • investigating changes in brain activity while participants imagined or viewed the letter “X” and “O” Sabrina Strang

22. Example 3cross generalization • methods: • imagery task: • instructed via an auditory cue to imagine either an “X” or an “O” • perception task: • X and O were randomly presented Sabrina Strang

23. Example 3cross generalization • results: • classification was significantly above 50% in imagery task and in perception task Sabrina Strang

24. Example 3cross generalization • results: • cross generalization • classifier trained on data from perceptual task could accurately discriminate between imagery states neural code underlying visual imagery shares significant similarity to the corresponding code for visual perception Sabrina Strang

25. Example 3applications in neuroeconomical research • might be interesting to train a pattern classifier on one decision making task and test whether it can predict performance on a different decision making task Sabrina Strang

26. Questions Sabrina Strang

27. References • Davatzikos, C., Ruparel, K., Fan, Y., Shen, D.G., Acharyya, M., Loughead, J.W., Gur, R.C. and Langleben, D.D. (2005). Classifying spatial patterns of brain activity with machine learning methods: Application to lie detection. NeuroImage, 28, 663-668. • Kenneth, A.N., Polyn, S.M., Detre, G.J. And Haxby, J.V. (2006). Beyond mind reading: multi-voxel pattern analysis of fMRI data. Trends in Cognitive Sciences, 19 (9), 424-430. • Kriegeskorte, N., Formisano, E., Sorger, B. and Goebel, R. (2007). Individual faces elicit distinct response patterns in human anterior temporal cortex. Proceedings of the National Academy of Sciences, 104 (51), 20600- 20605. • Stokes, M., Thompson, R., Cusack, R. and Duncan, J. (2009). Top-Down activation of shape-specific population codes in visual cortex during mental imagery. The Journal of Neuroscience, 29 (5), 1565-1572. Sabrina Strang