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Using Data to Understand the Brain
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Using Data to Understand the Brain

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

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

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Using Data to Understand the Brain Using Data to Understand the Brain Presentation Transcript

  • Using Data to Understand the Brain John Myles White March 26, 2012 1 / 57
  • We want to learn how the brain works 2 / 57
  • 3 / 57
  • 4 / 57
  • How can learn about the mind and brain using data and models? 5 / 57
  • 6 / 57
  • 7 / 57
  • 8 / 57
  • Brain damage victims taught us about functional localization 9 / 57
  • 10 / 57
  • How can we learn more without shooting people? 11 / 57
  • 12 / 57
  • MRI can give us: High-resolution anatomical images Real-time measurements of blood flow (fMRI) 13 / 57
  • 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 14 / 57
  • 15 / 57
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  • 17 / 57
  • How can we use blood flow measurements to learn about function? 18 / 57
  • 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 19 / 57
  • 20 / 57
  • Search blood flow data for brain regions that respond to tasks 21 / 57
  • How can we analyze blood flow data to perform this search? 22 / 57
  • 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 23 / 57
  • A newer approach, called MVPA, works in the reverse direction 24 / 57
  • In MVPA, we try to predict tasks using blood flow 25 / 57
  • 26 / 57
  • We have fewer task-tagged brain images than voxels, so n < p 27 / 57
  • Need to use regularization to perform any analysis 28 / 57
  • Consider solving y = Xβ 29 / 57
  • When n = p, there may be a unique solutionWhen n > p, we must choose an approximationWhen n < p, there are infinitely many exact solutionsTo find “correct” solution, we have to introduce constraints 30 / 57
  • In linear regression, we minimize (y − Xβ)2 31 / 57
  • In L2 regularization, we minimize (y − Xβ)2 + λβ 2 32 / 57
  • L2 constraint lets us solve n < p case 33 / 57
  • But L2 often does not help us find the few voxels that matter most 34 / 57
  • We can try another regularization system called L1 regularization 35 / 57
  • In L1 regularization, we minimize (y − Xβ)2 + λ|β| 36 / 57
  • L1 regularization is very modernObjective function is not differentiableBut is convex and can be minimized computationallySolution, β ∗ , to minimization problem is typically sparse 37 / 57
  • A sparse solution is one in which most features have a weight of 0 38 / 57
  • For MVPA, L1 sparse solutions are, sadly, too sparse 39 / 57
  • 40 / 57
  • From neurology, we know we should expect large clusters of voxels 41 / 57
  • 42 / 57
  • Best current approach: mix L1 and L2 43 / 57
  • In the Elastic Net, we minimize (y − Xβ)2 + λ1 |β| + λ2 β 2 44 / 57
  • 45 / 57
  • Improving localization using MVPA is an ongoing problem 46 / 57
  • But MVPA has already changed our understanding of the brain 47 / 57
  • Forget localization. Focus on prediction 48 / 57
  • Imagine we have built a classifier that identifies tasks correctly 49 / 57
  • We can use the classifier to test how people think about other tasks 50 / 57
  • 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 51 / 57
  • 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 52 / 57
  • Our approach: We train classifier to identify type of list being memorized We use classifier to assess mental state during free-recall 53 / 57
  • 54 / 57
  • In short, we’re already able to read minds 55 / 57
  • The real questions for the field are: How can we do it better? How can we learn more about the brain from fMRI data? 56 / 57
  • Any questions? 57 / 57