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Generalized Sparse Classifiers for Decoding Cognitive States in fMRI

Generalized Sparse Classifiers for Decoding Cognitive States in fMRI. 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

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Generalized Sparse Classifiers for Decoding Cognitive States in fMRI

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  1. Generalized Sparse Classifiers for Decoding Cognitive States in fMRI Bernard Ng1, Arash Vahdat2, Ghassan Hamarneh3, Rafeef Abugharbieh1 Contact email: bernardn@ece.ubc.ca 1Biomedical Signal and Image Computing Lab, The University of British Columbia, Canada 2Vision and Media Lab, Simon Fraser University, Canada 3Medical Image Analysis Lab, Simon Fraser University, Canada

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

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

  4. fMRI as Pattern Classification Training Set Test Set … … A B ? time (s) … … … … … Classifier Patt. Classif’n

  5. Pro’s and Con’s Training Set Test Set … … … Sample SVM Weights Patt. Classif’n

  6. Generalized Sparse Classifiers (GSC) I. Graph Embedding (GE)(Yan et. al, 2007) • Subspace Learning • LDA • PCA • Isomap • Laplacian eigenmap • Locally linear embedding • … II. Spectral Regression(Cai et. al., 2007) • Find y • Find y = XTa e.g. LASSO GSC

  7. Spatially Smooth Sparse LDA Elastic Nets (Zou et al., 2005) GSC SSLDA Recall GE GSC

  8. Star Plus Data Trial • 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 Stim 1, 4s Blank, 4s Stim 2, 4s Rest, 15s It is true that the staris below the plus. + * Results

  9. Quantitative Results Results

  10. Qualitative: LDA vs. SVM LDA Classifier Weights Results SVM Classifier Weights

  11. Qualitative: LASSO vs. Elastic Nets SLDA Classifier Weights Results EN-LDA Classifier Weights

  12. Qualitative: Elastic Nets vs. Proposed SSLDA EN-LDA Classifier Weights Results SSLDA Classifier Weights

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

  14. Conclusions • 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 Conclusions

  15. Questions

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