Guest lecture by John Myles White on learning from fMRI data, for Data-driven Modeling 2012

1. Using Data to Understand the Brain
John Myles White
March 26, 2012
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2. We want to learn how the brain works
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5. How can learn about the mind and brain using data and models?
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9. Brain damage victims taught us about functional localization
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11. How can we learn more without shooting people?
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13. MRI can give us:
High-resolution anatomical images
Real-time measurements of blood flow (fMRI)
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14. A typical MRI experimental data set contains:
10 MB of anatomical data
∼ 1 GB of fMRI data
Time series of blood flow sampled every 2s
Sampled at 60 x 60 x 60 voxels
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18. How can we use blood flow measurements to learn about function?
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19. Design an experiment that randomly switches between tasks:
Tapping one finger vs. sitting motionless
Looking at faces vs. looking at places
Thinking about people vs. thinking about objects
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21. Search blood flow data for brain regions that respond to tasks
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22. How can we analyze blood flow data to perform this search?
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23. The typical approach looks at each voxel separately:
Try to predict blood flow using task events
Uses standard linear regression
Many connections with signal processing
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24. A newer approach, called MVPA, works in the reverse direction
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25. In MVPA, we try to predict tasks using blood flow
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27. We have fewer task-tagged brain images than voxels, so n < p
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28. Need to use regularization to perform any analysis
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29. Consider solving y = Xβ
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30. When n = p, there may be a unique solution
When n > p, we must choose an approximation
When n < p, there are infinitely many exact solutions
To find “correct” solution, we have to introduce constraints
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31. In linear regression, we minimize (y − Xβ)2
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32. In L2 regularization, we minimize (y − Xβ)2 + λβ 2
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33. L2 constraint lets us solve n < p case
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34. But L2 often does not help us find the few voxels that matter most
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35. We can try another regularization system called L1 regularization
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36. In L1 regularization, we minimize (y − Xβ)2 + λ|β|
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37. L1 regularization is very modern
Objective function is not differentiable
But is convex and can be minimized computationally
Solution, β ∗ , to minimization problem is typically sparse
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38. A sparse solution is one in which most features have a weight of 0
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39. For MVPA, L1 sparse solutions are, sadly, too sparse
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41. From neurology, we know we should expect large clusters of voxels
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43. Best current approach: mix L1 and L2
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44. In the Elastic Net, we minimize (y − Xβ)2 + λ1 |β| + λ2 β 2
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46. Improving localization using MVPA is an ongoing problem
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47. But MVPA has already changed our understanding of the brain
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48. Forget localization. Focus on prediction
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49. Imagine we have built a classifier that identifies tasks correctly
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50. We can use the classifier to test how people think about other tasks
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51. The free-recall task:
Experimental subjects memorize items from 3 lists:
1. Locations
2. Faces
3. Objects
Subjects then try to recall as many items as they can
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52. Our theory:
To remember items, you return to the mental state you were
in when you memorized the lists
Before you name any specific item, you return to the state
concerned with that item’s category
Only then can you name any specific items
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53. Our approach:
We train classifier to identify type of list being memorized
We use classifier to assess mental state during free-recall
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55. In short, we’re already able to read minds
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56. The real questions for the field are:
How can we do it better?
How can we learn more about the brain from fMRI data?
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57. Any questions?
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