1. 1 Bernard Ng 1, Arash Vahdat 2, Ghassan Hamarneh 3, Rafeef Abugharbieh 1 Contact email: bernardn@ece.ubc.ca 1 Biomedical Signal and Image Computing Lab, The University of British Columbia, Canada 2 Vision and Media Lab, Simon Fraser University, Canada 3 Medical Image Analysis Lab, Simon Fraser University, Canada

2. 2  Introduction  fMRI Analysis as Pattern Classification  Generalized Sparse Classifiers  Graph Embedding  Spectral Regression  Spatially-Smooth Sparse LDA  Results  Conclusions

3. 3 Introduction … … Rest Stim time (s) ≈ Expected Response Activation Statistics Maps Voxel Time Course BOLD Volumes Pre-processing

4. 4 Patt. Classif’n … AB ? time (s) … Training SetTest Set …… … Classifier ……

5. 5 Patt. Classif’n Sample SVM Weights Pro’s Multivariate ↑ Temporal Resolution Con’s #Features >> #Samples => Overfitting Difficult to interpret …… … Training Set Test Set

6. 6 GSC 1.Find y 2.Find y = X T a I. Graph Embedding (GE) (Yan et. al, 2007) II. Spectral Regression (Cai et. al., 2007) Subspace Learning LDA PCA Isomap Laplacian eigenmap Locally linear embedding … Pro’s Sparse LARS Con’s #Selected Features ≤ #Samples Can’t handle correlated features e.g. LASSO

7. 7 Elastic Nets (Zou et al., 2005) GSCSSLDA Pro’s Sparse Can potentially select all features Jointly select correlated features LARS Con’s Does not model other properties GSC Recall GE

8. 8 Results 6 subjects available online, 25 brain regions 40 trials => 320 samples per class Distinguish pictures from sentences Comparisons: LDA, SVM, SLDA, EN-LDA, SSLDA Five-fold cross validation + * It is true that the star is below the plus. Trial Stim 1, 4sBlank, 4sStim 2, 4sRest, 15s

9. 9 Results

10. 10 Results LDA Classifier Weights SVM Classifier Weights

11. 11 Results EN-LDA Classifier Weights SLDA Classifier Weights

12. 12 Results SSLDA Classifier Weights EN-LDA Classifier Weights

13. 13 Results Spatial Distribution Metric (Carroll et al., 2009)

14. Proposed using GSC for fMRI classification – Simultaneous sparse feature selection and classification – Greater flexibility in choice of penalties Explicitly modeling spatial correlations – ↑Predictive accuracy – Neurologically plausible classifier weight patterns Future Work – Explore other applications, e.g. spatiotemporal smoothness 14 Conclusions

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