1. Joint Sparse Representation of Brain Activity Patterns in Multi-Task fMRI Data 2015/03/21

2. Concepts  fMRI(functional magnetic resonance imaging) BOLD(blood oxygenation-level dependent) Noninvasive Spatial resolution: mm Time resolution: about 1 second

3. Concepts  Data of fMRI Field of view: 64*64 Slices: 32 acquisition time: 2s images: hundreds  Type Rest Task

4. Concepts  Experimental design Block-design Event-related design

5. Concepts  fMRI data analysis Data-driven PCA 、 ICA 、 CA Model-driven GLM (SPM)

6. GLM  Model i.e. where Y is observations, X is the design matrix.

7. GLM

8. Steps for SPM 1.Slice timing 2.Realignment 3.coregister 4.Segment 5.normalise 6.Smooth 7.Specify 1st-level

9. ICA  Suppose the signal has the model The question is to find a matrix to estimate

10. Sparse representation

11. Problem  Multi-task  Capitalize on the joint information that may exist among tasks.  The joint information is not usually directly examined. Data fusion

12. Multivariate methods in fusion

13. Idea This would result in a set of dictionaries and sparse coefficients. To obtain the joint relation of the results we would need to combine the sparse coefficients.

14. Framework Feature: an activation map for each task and each individual. use SPM

15. Model  JSRA

16. Algorithm OMP(orthogonal matching pursuit) SVD(singular value decomposition)

17. Simulation A total of 20 simulated datasets that represent two groups of subjects, each with 10 datasets.

18. Simulation  FP, FN, TP, TN

19. Simulation

20. Experiment  Conditions

21. Experiment  K=4

22. Experiment  K=8

23. Experiment  K=12

24. Thank you!