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Regional Approach to fMRI Data Analysis Using Hemodynamic Response Modeling

Regional Approach to fMRI Data Analysis Using Hemodynamic Response Modeling. Liang Liu 1 , Ashish Rao 1 , Thomas Talavage 1,2,3 1 School of Electrical and Computer Engineering, Purdue University 2 Weldon School of Biomedical Engineering, Purdue University

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Regional Approach to fMRI Data Analysis Using Hemodynamic Response Modeling

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  1. Regional Approach to fMRI Data Analysis Using Hemodynamic Response Modeling Liang Liu1, Ashish Rao1, Thomas Talavage1,2,3 1School of Electrical and Computer Engineering, Purdue University 2Weldon School of Biomedical Engineering, Purdue University 3Department of Radiology, Indiana University School of Medicine Electronic Imaging 2007, San Jose, CA

  2. Outline • Functional MRI and analysis methods • Proposed technique • Results • Summary L. Liu, A. Rao, T.M. Talavage

  3. Functional MRI (fMRI) • Use of MRI to measure brain activity • Relies on BOLD (blood-oxygenation-level dependent) contrast • Components • Diamagnetic oxygenated hemoglobin (Hb) • Paramagnetic deoxygenated hemoglobin (dHb) • At "Rest" • Ratio of [Hb] and [dHb] at equilibrium • During "Activity" • Ratio of [Hb] to [dHb] increases • Increased T2*-weighted MRI signal L. Liu, A. Rao, T.M. Talavage

  4. fMRI Experimental Designs Blocked-Design fMRI Large, slow fMRI signal modulations Task Rest OFF ON OFF ON OFF ON Event-Related fMRI Allows estimate of impulse response ISI L. Liu, A. Rao, T.M. Talavage

  5. Illustration of fMRI Data time n X time n voxels p Note: High dimensional data (p >> n) L. Liu, A. Rao, T.M. Talavage

  6. fMRI Data Analysis Procedures • Model-driven methods • t-test • Correlation Analysis • General Linear Models (GLMs) • Data-driven methods • Principal Component Analysis (PCA) • Independent Component Analysis (ICA) • Cluster Analysis L. Liu, A. Rao, T.M. Talavage

  7. GLM = "gold standard" (STD) Hypothesis Tests (multiple tests) H0: β=0, no activation HA: β≠0, activation Statistical inferences: t, F,… General Linear Model  H fMRI DataX = +  L. Liu, A. Rao, T.M. Talavage

  8. Hemodynamic Response (HDR) h 2t 4A e2 t  Approximate as Gamma-variate Model:* *Dale, AM, Buckner, RL, “Selective Averaging of Rapidly Presented Individual Trials Using fMRI,” Hum Brain Map 5:329-340, 1997 L. Liu, A. Rao, T.M. Talavage

  9. fMRI: Example of GLM Fit fMRI time series example 900 800 700 600 signal 500 400 300 200 0 50 100 150 200 250 300 time Active voxel Non-active voxel H (fit) L. Liu, A. Rao, T.M. Talavage

  10. Outline • Functional MRI and analysis methods • Proposed technique • Results • Summary L. Liu, A. Rao, T.M. Talavage

  11. Question: To which neighbor cluster does a given voxel best belong? Block Diagram Initial clustering Iterative segmentation Activation map generation L. Liu, A. Rao, T.M. Talavage

  12. Minimize Misclassification Risk • Minimize discriminant score: • Least square fit to get HDRs • Covariance is difficult to estimate • Presently assume  =s2I L. Liu, A. Rao, T.M. Talavage

  13. Outline • Functional MRI and analysis methods • Proposed technique • Results • Summary L. Liu, A. Rao, T.M. Talavage

  14. Synthetic Data Generation • 96x96 2D image • 9 functional blocks (8x8) with Cox* waveforms • Add noise by ARIMA process Active voxel Non-active voxel *Cox, RW, “http://afni.nimh.nih.gov/afni/doc/faq/17” L. Liu, A. Rao, T.M. Talavage

  15. Synthetic Data Analysis BL ER STD ICA CLU t-statisticmaps (CNRavg=0.5) L. Liu, A. Rao, T.M. Talavage

  16. Synthetic Data: ROC L. Liu, A. Rao, T.M. Talavage

  17. Human Data Analysis Left Hemifield Checkerboard Stimulation Subject 1 Most similar to gold standard Subject 2 Reference: STD (BL) STD (ER) ICA (ER) CLU (ER) L. Liu, A. Rao, T.M. Talavage

  18. Outline • Functional MRI and analysis methods • Proposed technique • Results • Summary L. Liu, A. Rao, T.M. Talavage

  19. Summary • Proposed method outperforms conventional fMRI analysis • Synthetic Data • Blocked (BL): large improvement • Event-Related (ER): small, but notable improvement • Human Data • Event-Related detection more resembles that of gold-standard GLM Blocked analysis L. Liu, A. Rao, T.M. Talavage

  20. Next Steps • Alternatives for noise model to improve covariance estimate • Independently distributed • Enables diagonal matrix • Autoregressive process • Adds additional diagonals • e.g. AR(2) → tridiagonal matrix • Nonstationary process • Requires regularized discriminant analysis L. Liu, A. Rao, T.M. Talavage

  21. Acknowledgments • Prof. Charles A. Bouman • Gregory G. Tamer, Jr. (data) • Olumide Olulade (data) • Partially supported by NIH R01 EB003990 L. Liu, A. Rao, T.M. Talavage

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