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Generalized Sparse Classifiers for Decoding Cognitive States in fMRI

Learn about utilizing Generalized Sparse Classifiers for decoding cognitive states in fMRI through methods like Graph Embedding and Spectral Regression, with improved spatial correlations modeling.

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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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