Slides of the HBM 2012 symposium on recovery of spatial information using machine learning and multivariate pattern analysis from fMRI brain images.

1. Can we recover meaningful spatial informa-
tion from multivariate pattern analysis?
Ga¨l Varoquaux
e INRIA/Parietal
Alexandre
Gramfort
Bertrand
Thirion

2. Can we recover meaningful spatial informa-
tion from multivariate pattern analysis?
Ga¨l Varoquaux
e INRIA/Parietal
Alexandre
Gramfort
Bertrand
Thirion
Yes we can!

3. Can we recover meaningful spatial informa-
tion from multivariate pattern analysis?
Ga¨l Varoquaux
e INRIA/Parietal
Alexandre
Gramfort
Bertrand
Thirion

4. 1 Prediction versus recovery
2 Random parcellations and sparsity
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5. 1 Prediction versus recovery
?
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6. 1 Standard analysis and MVPA
Standard analysis MVPA
Test whether the voxel is Overall predictive model
recruited by the task
Many voxels ⇒ problem Many voxels ⇒ curse of
of multiple comparisons dimensionality
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7. 1 Standard analysis and MVPA
Standard analysis MVPA
Test whether the voxel is Overall predictive model
recruited by the task
Many voxels ⇒ problem Many voxels ⇒ curse of
of multiple comparisons dimensionality
F-test Searchlight
Analyzes of regional-average activation and multi-
voxel pattern information tell complementary stories,
K. Jimura, R.A. Poldrack, Neuropsychologia 2011
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8. 1 Good prediction = good recovery
Simple simulations: y = w X + e
X: observed fMRI images: spatially smooth
e: noise
w: coefficients (brain regions)
Ground truth
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9. 1 Good prediction = good recovery
Sparse models (lasso):
Prediction: 0.78 explained variance
Amplitude of the weights:
max
0
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10. 1 Good prediction = good recovery
SVM:
Prediction: 0.71 explained variance
Amplitude of the weights:
max
0
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11. 1 Good prediction = good recovery
Standard univariate analysis (ANOVA):
F-score:
max
0
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12. 1 Good prediction = good recovery
Lasso
Prediction: 0.77
Recovery: 0.461
SVM
Prediction: 0.71
Recovery: 0.464
F-score
Prediction:
Recovery: 0.963
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13. 1 Multivariate analysis for recovery?
Considering each voxel separately is
suboptimal: they share information
Most often, we know that we are looking for
a small fraction of the cortex
A voxel is more likely to be activated
if its neighbor is
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14. 1 Multivariate analysis for recovery?
Considering each voxel separately is
suboptimal: they share information
Most often, we know that we are looking for
a small fraction of the cortex
Sparse models
A voxel is more likely to be activated
if its neighbor is
Spatial models
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15. 1 Sparse models
Compressive sensing:
detection of k signals out of p (voxels)
with only n observations ∝ k
Iterpretable
Selects random subsets in correlated signals
Face vs house
discrimination
Data from [Haxby 2001]
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16. 1 Sparse models
Compressive sensing:
detection of k signals out of p (voxels)
with only n observations ∝ k
Iterpretable
Selects random subsets in correlated signals
Stability selection:
Face vsrandom perturbations to the data
Apply house
discrimination that are selected often
Keep voxels
Data from [Haxby 2001] [Meinhausen 2010]
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17. 1 Spatial models
Brain parcellations:
Ward clustering to reduce voxel numbers
Supervised clustering [Michel 2011]
... ... ...
... ...
Clustering blind to experimental conditions
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18. 2 Random parcellations and
sparsity
Combining
Clustering
Sparsity
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19. 2 Random parcellations and
sparsity
+
Randomization
Stability scores
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20. 2 Algorithm
1 loop: perturb randomly data
2 Ward agglomeration to form n features
3 sparse linear model on reduced features
4 accumulate non-zero features
5 threshold map of apparition counts
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21. 2 Recovery performance
RandomizedClusteredLasso:
Selection scores
max
0
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22. 2 What is the best method for feature recovery?
For small brain regions: elastic net
For large brain regions: randomized-clustered sparsity
Large regions and very smooth images: F-tests
[Varoquaux 2012] ICML
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23. 2 fMRI: face vs house discrimination [Haxby 2001]
F-scores
L R
L R
y=-31 x=17
z=-17
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24. 2 fMRI: face vs house discrimination [Haxby 2001]
Randomized Clustered Sparsity
L R
L R
y=-31 x=17
z=-17
Less background noise
(source of false positive)
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25. 2 Predictive power of selected voxels
Object recognition [Haxby 2001]
Using recovered voxels improves prediction
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26. Can we recover meaningful spatial information
from multivariate pattern analysis?
SVM and sparse models less powerful then F-score
Sparsity + clustering + randomization:
excellent recovery
⇒ Multivariate brain mapping
Simultaneous prediction and recovery
Prediction
accuracy:
93%
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27. For more details
G. Varoquaux, A. Gramfort, and B. Thirion, Small-sample
brain mapping: sparse recovery on spatially correlated de-
signs with randomization and clustering, ICML 2012
Acknowledgments, for sharing data:
J. Haxby R. Poldrack K. Jimura
Software
scikit-learn: machine learning in Python
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