Can we recover meaning full spatial information from multivariate pattern analysis

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Slides of the HBM 2012 symposium on recovery of spatial information using machine learning and multivariate pattern analysis from fMRI brain images.

Slides of the HBM 2012 symposium on recovery of spatial information using machine learning and multivariate pattern analysis from fMRI brain images.

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  • 1. Can we recover meaningful spatial informa-tion from multivariate pattern analysis? Ga¨l Varoquaux e INRIA/Parietal Alexandre Gramfort Bertrand Thirion
  • 2. Can we recover meaningful spatial informa-tion from multivariate pattern analysis? Ga¨l Varoquaux e INRIA/Parietal Alexandre Gramfort Bertrand Thirion Yes we can!
  • 3. Can we recover meaningful spatial informa-tion from multivariate pattern analysis? Ga¨l Varoquaux e INRIA/Parietal Alexandre Gramfort Bertrand Thirion
  • 4. 1 Prediction versus recovery 2 Random parcellations and sparsityG Varoquaux 2
  • 5. 1 Prediction versus recovery ?G Varoquaux 3
  • 6. 1 Standard analysis and MVPA Standard analysis MVPA Test whether the voxel is Overall predictive model recruited by the task Many voxels ⇒ problem Many voxels ⇒ curse of of multiple comparisons dimensionalityG Varoquaux 4
  • 7. 1 Standard analysis and MVPA Standard analysis MVPA Test whether the voxel is Overall predictive model recruited by the task Many voxels ⇒ problem Many voxels ⇒ curse of of multiple comparisons dimensionalityF-test SearchlightAnalyzes of regional-average activation and multi-voxel pattern information tell complementary stories,K. Jimura, R.A. Poldrack, Neuropsychologia 2011G Varoquaux 4
  • 8. 1 Good prediction = good recovery Simple simulations: y = w X + e X: observed fMRI images: spatially smooth e: noise w: coefficients (brain regions) Ground truthG Varoquaux 5
  • 9. 1 Good prediction = good recovery Sparse models (lasso): Prediction: 0.78 explained variance Amplitude of the weights: max 0G Varoquaux 5
  • 10. 1 Good prediction = good recovery SVM: Prediction: 0.71 explained variance Amplitude of the weights: max 0G Varoquaux 5
  • 11. 1 Good prediction = good recovery Standard univariate analysis (ANOVA): F-score: max 0G Varoquaux 5
  • 12. 1 Good prediction = good recovery Lasso Prediction: 0.77 Recovery: 0.461 SVM Prediction: 0.71 Recovery: 0.464 F-score Prediction: Recovery: 0.963G Varoquaux 6
  • 13. 1 Multivariate analysis for recovery? Considering each voxel separately is suboptimal: they share information Most often, we know that we are looking for a small fraction of the cortex A voxel is more likely to be activated if its neighbor isG Varoquaux 7
  • 14. 1 Multivariate analysis for recovery? Considering each voxel separately is suboptimal: they share information Most often, we know that we are looking for a small fraction of the cortex Sparse models A voxel is more likely to be activated if its neighbor is Spatial modelsG Varoquaux 7
  • 15. 1 Sparse models Compressive sensing: detection of k signals out of p (voxels) with only n observations ∝ k Iterpretable Selects random subsets in correlated signals Face vs house discrimination Data from [Haxby 2001]G Varoquaux 8
  • 16. 1 Sparse models Compressive sensing: detection of k signals out of p (voxels) with only n observations ∝ k Iterpretable Selects random subsets in correlated signals Stability selection: Face vsrandom perturbations to the data Apply house discrimination that are selected often Keep voxels Data from [Haxby 2001] [Meinhausen 2010]G Varoquaux 8
  • 17. 1 Spatial models Brain parcellations: Ward clustering to reduce voxel numbers Supervised clustering [Michel 2011] ... ... ... ... ... Clustering blind to experimental conditionsG Varoquaux 9
  • 18. 2 Random parcellations and sparsity Combining Clustering SparsityG Varoquaux 10
  • 19. 2 Random parcellations and sparsity + Randomization Stability scoresG Varoquaux 10
  • 20. 2 Algorithm 1 loop: perturb randomly data 2 Ward agglomeration to form n features 3 sparse linear model on reduced features 4 accumulate non-zero features 5 threshold map of apparition countsG Varoquaux 11
  • 21. 2 Recovery performance RandomizedClusteredLasso: Selection scores max 0G Varoquaux 12
  • 22. 2 What is the best method for feature recovery? For small brain regions: elastic net For large brain regions: randomized-clustered sparsity Large regions and very smooth images: F-tests [Varoquaux 2012] ICMLG Varoquaux 13
  • 23. 2 fMRI: face vs house discrimination [Haxby 2001] F-scores L R L Ry=-31 x=17 z=-17 G Varoquaux 14
  • 24. 2 fMRI: face vs house discrimination [Haxby 2001] Randomized Clustered Sparsity L R L Ry=-31 x=17 z=-17 Less background noise (source of false positive) G Varoquaux 14
  • 25. 2 Predictive power of selected voxels Object recognition [Haxby 2001] Using recovered voxels improves predictionG Varoquaux 15
  • 26. Can we recover meaningful spatial information from multivariate pattern analysis? SVM and sparse models less powerful then F-score Sparsity + clustering + randomization: excellent recovery ⇒ Multivariate brain mapping Simultaneous prediction and recovery Prediction accuracy: 93%G Varoquaux 16
  • 27. For more details G. Varoquaux, A. Gramfort, and B. Thirion, Small-sample brain mapping: sparse recovery on spatially correlated de- signs with randomization and clustering, ICML 2012 Acknowledgments, for sharing data: J. Haxby R. Poldrack K. Jimura Softwarescikit-learn: machine learning in PythonG Varoquaux 17