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Brain maps from machine learning? Spatial regularizations

The document discusses machine learning applications in brain mapping and cognitive neuroimaging, emphasizing methods for decoding brain activity from fMRI data and the challenges in recovering predictive regions. It explores various techniques including support vector machines, spatial regularization, and penalties for enhancing prediction accuracy and interpretability. The conclusions highlight the importance of spatial models for better predictive maps and the limitations of standard decoders in this context.

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Brain maps from machine learning? Spatial regularizations Gaël Varoquaux
Brain mapping fMRI data > 50 000 voxels t stimuli Standard analysis Detect voxels that correlate to the stimuli G Varoquaux 2
Brain mapping fMRI data > 50 000 voxels t stimuli Standard analysis Detect voxels that correlate to the stimuli Mass-univariate analysis One statistical test per voxel Multiple comparisons G Varoquaux 2
Brain decoding Predicting stimulus / cognitive state [Haxby... 2001] Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex Supervised learning task Predictive modeling Find combinations of voxels to best predict G Varoquaux 3
Brain decoding Predicting stimulus / cognitive state [Haxby... 2001] Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex Supervised learning task Predictive modeling Find combinations of voxels to best predict Take home message: brain regions, not prediction G Varoquaux 3
Inference in cognitive neuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module?
Inference in cognitive neuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module? Brain mapping = task-evoked activity
Inference in cognitive neuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module? Reverse inference Brain mapping = task-evoked activity + crafting “contrasts” to isolate effects
Inference in cognitive neuroimaging [Kanwisher... 1997, Gauthier... 2000, Hanson and Halchenko 2008] What is the neural support of a function? What is function of a given brain module? Reverse inference Is there a face area?
Inference in cognitive neuroimaging [Poldrack... 2009] What is the neural support of a function? What is function of a given brain module? Reverse inference Find regions that predict observed cognition
1 Recovering predictive regions is hard 2 Opening the black box of decoders 3 Spatial regularization G Varoquaux 5
1 Recovering predictive regions is hard G Varoquaux 6
1 Brain decoding to recover predictive regions? Face vs house visual recognition [Haxby... 2001] SVM error: 26% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
1 Brain decoding to recover predictive regions? Face vs house visual recognition [Haxby... 2001] Sparse model error: 19% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
1 Brain decoding to recover predictive regions? Face vs house visual recognition [Haxby... 2001] Ridge error: 15% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
1 Brain decoding to recover predictive regions? Face vs house visual recognition [Haxby... 2001] Best predictor outlines the worst regions Best maps predict worst G Varoquaux 7
1 Brain decoding with local models Face vs cat visual recognition [Haxby... 2001] First 6 sessions Last 6 sessions SVM error: 24% G Varoquaux 8
1 Brain decoding with local models Face vs cat visual recognition full brain First 6 sessions Last 6 sessions SVM error: 43% G Varoquaux 8
1 Simple simulations y = w X + e X: observed fMRI images: spatially smooth e: noise w: true coefficients (brain regions) Ground truth G Varoquaux 9
1 Simple simulations SVM Prediction: 0.71 Recovery: 0.464 G Varoquaux 10
1 Simple simulations SVM Prediction: 0.71 Recovery: 0.464 Sparse models Prediction: 0.77 Recovery: 0.461 G Varoquaux 10
1 Simple simulations SVM Prediction: 0.71 Recovery: 0.464 Sparse models Prediction: 0.77 Recovery: 0.461 F-score Prediction: Recovery: 0.963 Good prediction === good recovery G Varoquaux 10
Recovering predictive regions is hard G Varoquaux 11
Recovering predictive regions is hard Not all informative features are need to predict G Varoquaux 12
Recovering predictive regions is hard Not all informative features are need to predict Weight on uninformative features may not harm prediction G Varoquaux 12
2 Opening the black box of decoders G Varoquaux 13
2 Brain decoding with linear models Design matrix × Coefficients = Coefficients are brain maps Target G Varoquaux 14
2 Estimation: an ill-posed problem Inverse problem Find brain maps w to minimize the prediction error Ill-posed: Many different w will give the same prediction error How to choose one? G Varoquaux 15
2 Estimation: an ill-posed problem Inverse problem Find brain maps w to minimize the prediction error Ill-posed: Many different w will give the same prediction error How to choose one? SVM Lasso Somewhat arbitrary choice Not informed by the data Different methods ⇒ different outputs G Varoquaux 15
2 SVM: exemplar-based classifier Find a separating hyperplane between images G Varoquaux 16
2 SVM: exemplar-based classifier Find a separating hyperplane between images SVM builds it by combining exemplars ⇒ hyperplane looks like train images G Varoquaux 16
2 SVM: exemplar-based classifier Find a separating hyperplane between images SVM builds it by combining exemplars ⇒ hyperplane looks like train images Often distributed by construction G Varoquaux 16
2 Risk minimization and penalization Inverse problem Minimize the error term: ˆw = argmin w l(y − X w) Ill-posed: Many different w will give the same prediction error To choose one: inject prior with a penalty ˆw = argmin w l(y − X w) + p(w) G Varoquaux 17
2 Sparse models: selecting predictive voxels? Lasso estimator minx y − Ax 2 2 + λ 1(x) Data fit Penalization x2 x1 G Varoquaux 18
2 Sparse models: selecting predictive voxels? Lasso estimator minx y − Ax 2 2 + λ 1(x) Data fit Penalization x2 x1 Between correlated features, selects a random subset [Wainwright 2009, Varoquaux... 2012] [Rish... 2012] Violates the restricted isometry property lasso: 23 non-zeros G Varoquaux 18
2 Tricks of the trade Spatial smoothing 12mm Face vs cat error rate: 43% 31% Giving up on resolution Feature selection 23% Giving up on multivariate G Varoquaux 19
From prediction to mapping? Inverse problem intractable in general Need suitable simplifying hypothesis Spatial structure / contiguity (as Random Field Theory) ⇒ Smoothed SVM work better Only a fraction of the brain useful to predict ⇒ Feature-selection + Sparsity Neighbooring voxel multi-colinear ⇒ Break multivariate estimators G Varoquaux 20
3 Spatial regularization G Varoquaux 21
[Varoquaux... 2012] Clustering to group similar voxels Hierarchical clustering: merge voxels with similar behavior ⇒ Good conditions for sparse models G Varoquaux 22
3 Brain parcellations + sparsity Sparse model on brain parcellation Mapping is much easier on a parcellation [Filippone... 2012] Parcellation / clustering is not always perfect G Varoquaux 23
3 Randomized parcellations + sparsity [Varoquaux... 2012] ... ... Cluster sub-sampled data Average the results G Varoquaux 24
3 Randomized parcellations + sparsity [Varoquaux... 2012] ... ... Cluster sub-sampled data Average the results Recovering predictive regions Good theoretical argument Extensive simulations G Varoquaux 24
3 fMRI: face vs house discrimination [Haxby... 2001] [Varoquaux... 2012] Sparse model L R y=-31 x=17 L R z=-17 G Varoquaux 25
3 fMRI: face vs house discrimination [Haxby... 2001] [Varoquaux... 2012] Randomized Clustered 1 Logistic L R y=-31 x=17 L R z=-17 G Varoquaux 25
3 better prediction scores [Hoyos-Idrobo... 2015] dataset sparse sparse + clustered ds105 (haxby) bottle/scramble 0.591 0.626 cat/chair 0.558 0.612 cat/house 0.698 0.963 chair/house 0.668 0.734 chair/scramble 0.700 0.743 face/house 0.766 0.742 tools/scramble 0.666 0.743 ds107 consonant/scramble 0.886 0.897 objects/consonant 0.855 0.901 objects/scramble 0.863 0.898 objects/words 0.689 0.708 words/scramble 0.782 0.841 ds108 negative cue / neutral cue 0.444 0.497 negative rating / neutral rating 0.520 0.537 negative stim / neutral stim 0.734 0.743 ds 109 false picture / false belief 0.664 0.675 oasis (vbm) gender discrimination 0.617 0.655
3 better prediction scores [Hoyos-Idrobo... 2015] dataset sparse sparse + clustered ds105 (haxby) bottle/scramble 0.591 0.626 cat/chair 0.558 0.612 cat/house 0.698 0.963 chair/house 0.668 0.734 chair/scramble 0.700 0.743 face/house 0.766 0.742 tools/scramble 0.666 0.743 ds107 consonant/scramble 0.886 0.897 objects/consonant 0.855 0.901 objects/scramble 0.863 0.898 objects/words 0.689 0.708 words/scramble 0.782 0.841 ds108 negative cue / neutral cue 0.444 0.497 negative rating / neutral rating 0.520 0.537 negative stim / neutral stim 0.734 0.743 ds 109 false picture / false belief 0.664 0.675 oasis (vbm) gender discrimination 0.617 0.655 Sparse-clustered: faster, better Faster: 10x speed up, due to smaller models More stable: bagging effect Averaging many estimates
Total variation: a penalty for regions Sparsity is useful Want to recover brain regions Total-variation penalization Impose sparsity on the gradient of the image: p(w) = 1( w) In fMRI: [Michel... 2011] Related to GraphNet [Grosenick... 2013] G Varoquaux 27
3 Penalty engineering: total variation Decoding prediction performance: SVM 0.77 Sparse regression 0.78 Total Variation 0.84 [Michel... 2011] (explained variance) Standard analysis G Varoquaux 28
3 Penalty engineering: TV- 1 Sparsity + regions [Baldassarre... 2012, Gramfort... 2013] ˆw = argmin w l(y − X w) + λ ρ 1(w) + (1 − ρ)TV (w) l: data-fit term Poster 3980, Thursday G Varoquaux 29
3 Penalty engineering: TV- 1 Sparsity + regions [Baldassarre... 2012, Gramfort... 2013] ˆw = argmin w l(y − X w) + λ ρ 1(w) + (1 − ρ)TV (w) l: data-fit term Poster 3980, Thursday Experiment results G Varoquaux 29
3 Simulations [Gramfort... 2013] Vary threshold, and count false positive/false negatives Precision ∼ FDR Recall ∼ # detections 0.0 0.2 0.4 0.6 0.8 1.0 Recall (sensivity, or TPR) 0.2 0.4 0.6 0.8 1.0 Precision(1-FDR) F test (area=0.847) SVR (area=0.604) SearchLight (area=0.503) Ridge (area=0.636) ElasticNet (area=0.750) TVL1 (area=0.912) G Varoquaux 30
3 Prediction on simulations [Gramfort... 2013] Prediction error SNR 2.5 5.0 7.5 10.0 SVR 28.4 22.6 18.1 14.5 Ridge 27.8 21.6 17.0 13.4 ElasticNet 26.6 20.7 16.4 13.1 TV- 1 25.7 20.1 15.8 12.5 Prediction is easy, region recovery is hard G Varoquaux 31
Wrapping up G Varoquaux 32
Wrapping up Software ni Very versatile but simple code Fast, memory efficient Many examples + docs http://nilearn.github.io G Varoquaux 32
@GaelVaroquaux Decoding for reverse-inference mapping Standard decoders do not retrieve good maps Infinite number of maps predict as well
@GaelVaroquaux Decoding for reverse-inference mapping Standard decoders do not retrieve good maps Spatial models to select predictive regions TV- 1 = Sparsity + regions Randomized clustering + sparsity
@GaelVaroquaux Decoding for reverse-inference mapping Standard decoders do not retrieve good maps Spatial models to select predictive regions Decoding + mega-analysis ⇒ reverse-inference atlas Multi-label prediction Predicting on new studies
@GaelVaroquaux Decoding for reverse-inference mapping Standard decoders do not retrieve good maps Spatial models to select predictive regions Decoding + mega-analysis ⇒ reverse-inference atlas Software: nilearn In Python http://nilearn.github.io ni[Varoquaux and Thirion 2014] How machine learning is shaping cognitive neuroimaging
References I L. Baldassarre, J. Mourao-Miranda, and M. Pontil. Structured sparsity models for brain decoding from fMRI data. In PRNI, page 5, 2012. M. Filippone, A. F. Marquand, C. R. Blain, S. C. Williams, J. Mourão-Miranda, and M. Girolami. Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. The annals of applied statistics, 6 (4):1883, 2012. I. Gauthier, M. J. Tarr, J. Moylan, P. Skudlarski, J. C. Gore, and A. W. Anderson. The fusiform “face area” is part of a network that processes faces at the individual level. J cognitive neuroscience, 12:495, 2000. A. Gramfort, B. Thirion, and G. Varoquaux. Identifying predictive regions from fMRI with TV-L1 prior. In PRNI, page 17, 2013.
References II L. Grosenick, B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor. Interpretable whole-brain prediction analysis with graphnet. NeuroImage, 72:304, 2013. S. J. Hanson and Y. O. Halchenko. Brain reading using full brain support vector machines for object recognition: there is no “face” identification area. Neural Computation, 20:486, 2008. J. V. Haxby, I. M. Gobbini, M. L. Furey, ... Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293:2425, 2001. R. Henson. Forward inference using functional neuroimaging: Dissociations versus associations. Trends in cognitive sciences, 10:64, 2006. A. Hoyos-Idrobo, Y. Schwartz, B. Thirion, and G. Varoquaux. Improving sparse recovery on structured images with bagged clustering. In PRNI. 2015.
References III N. Kanwisher, J. McDermott, and M. M. Chun. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neuroscience, 17:4302, 1997. V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion. Total variation regularization for fMRI-based prediction of behavior. Medical Imaging, IEEE Transactions on, 30:1328, 2011. R. Poldrack. Can cognitive processes be inferred from neuroimaging data? Trends in cognitive sciences, 10:59, 2006. R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20:1364, 2009.
References IV I. Rish, G. A. Cecchi, K. Heuton, M. N. Baliki, and A. V. Apkarian. Sparse regression analysis of task-relevant information distribution in the brain. In SPIE Medical Imaging, pages 831412–831412. International Society for Optics and Photonics, 2012. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014. G. Varoquaux, A. Gramfort, and B. Thirion. Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering. In ICML, page 1375, 2012. M. Wainwright. Sharp thresholds for high-dimensional and noisy sparsity recovery using 1-constrained quadratic programming. Trans Inf Theory, 55:2183, 2009.
Decoding object recognition 8 objects multi-class prediction Categories: face, chair, srambledpix, scissors, house, bottle, shoe, cat Difficult! Lasso 69.4% TV- 1 75.3% Distinguishing scissors in the dorsal stream, faces and cats in frontal areas ... [Eickenberg Submitted]

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Brain maps from machine learning? Spatial regularizations

  • 1.
    Brain maps frommachine learning? Spatial regularizations Gaël Varoquaux
  • 2.
    Brain mapping fMRI data> 50 000 voxels t stimuli Standard analysis Detect voxels that correlate to the stimuli G Varoquaux 2
  • 3.
    Brain mapping fMRI data> 50 000 voxels t stimuli Standard analysis Detect voxels that correlate to the stimuli Mass-univariate analysis One statistical test per voxel Multiple comparisons G Varoquaux 2
  • 4.
    Brain decoding Predicting stimulus/ cognitive state [Haxby... 2001] Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex Supervised learning task Predictive modeling Find combinations of voxels to best predict G Varoquaux 3
  • 5.
    Brain decoding Predicting stimulus/ cognitive state [Haxby... 2001] Distributed and Overlapping Representations of Faces and Objects in Ventral Temporal Cortex Supervised learning task Predictive modeling Find combinations of voxels to best predict Take home message: brain regions, not prediction G Varoquaux 3
  • 6.
    Inference in cognitiveneuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module?
  • 7.
    Inference in cognitiveneuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module? Brain mapping = task-evoked activity
  • 8.
    Inference in cognitiveneuroimaging [Poldrack 2006, Henson 2006] What is the neural support of a function? What is function of a given brain module? Reverse inference Brain mapping = task-evoked activity + crafting “contrasts” to isolate effects
  • 9.
    Inference in cognitiveneuroimaging [Kanwisher... 1997, Gauthier... 2000, Hanson and Halchenko 2008] What is the neural support of a function? What is function of a given brain module? Reverse inference Is there a face area?
  • 10.
    Inference in cognitiveneuroimaging [Poldrack... 2009] What is the neural support of a function? What is function of a given brain module? Reverse inference Find regions that predict observed cognition
  • 11.
    1 Recovering predictiveregions is hard 2 Opening the black box of decoders 3 Spatial regularization G Varoquaux 5
  • 12.
    1 Recovering predictiveregions is hard G Varoquaux 6
  • 13.
    1 Brain decodingto recover predictive regions? Face vs house visual recognition [Haxby... 2001] SVM error: 26% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
  • 14.
    1 Brain decodingto recover predictive regions? Face vs house visual recognition [Haxby... 2001] Sparse model error: 19% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
  • 15.
    1 Brain decodingto recover predictive regions? Face vs house visual recognition [Haxby... 2001] Ridge error: 15% http://nilearn.github.io/auto_examples/decoding/ plot_haxby_different_estimators.htmlG Varoquaux 7
  • 16.
    1 Brain decodingto recover predictive regions? Face vs house visual recognition [Haxby... 2001] Best predictor outlines the worst regions Best maps predict worst G Varoquaux 7
  • 17.
    1 Brain decodingwith local models Face vs cat visual recognition [Haxby... 2001] First 6 sessions Last 6 sessions SVM error: 24% G Varoquaux 8
  • 18.
    1 Brain decodingwith local models Face vs cat visual recognition full brain First 6 sessions Last 6 sessions SVM error: 43% G Varoquaux 8
  • 19.
    1 Simple simulations y= w X + e X: observed fMRI images: spatially smooth e: noise w: true coefficients (brain regions) Ground truth G Varoquaux 9
  • 20.
    1 Simple simulations SVM Prediction:0.71 Recovery: 0.464 G Varoquaux 10
  • 21.
    1 Simple simulations SVM Prediction:0.71 Recovery: 0.464 Sparse models Prediction: 0.77 Recovery: 0.461 G Varoquaux 10
  • 22.
    1 Simple simulations SVM Prediction:0.71 Recovery: 0.464 Sparse models Prediction: 0.77 Recovery: 0.461 F-score Prediction: Recovery: 0.963 Good prediction === good recovery G Varoquaux 10
  • 23.
    Recovering predictive regionsis hard G Varoquaux 11
  • 24.
    Recovering predictive regionsis hard Not all informative features are need to predict G Varoquaux 12
  • 25.
    Recovering predictive regionsis hard Not all informative features are need to predict Weight on uninformative features may not harm prediction G Varoquaux 12
  • 26.
    2 Opening theblack box of decoders G Varoquaux 13
  • 27.
    2 Brain decodingwith linear models Design matrix × Coefficients = Coefficients are brain maps Target G Varoquaux 14
  • 28.
    2 Estimation: anill-posed problem Inverse problem Find brain maps w to minimize the prediction error Ill-posed: Many different w will give the same prediction error How to choose one? G Varoquaux 15
  • 29.
    2 Estimation: anill-posed problem Inverse problem Find brain maps w to minimize the prediction error Ill-posed: Many different w will give the same prediction error How to choose one? SVM Lasso Somewhat arbitrary choice Not informed by the data Different methods ⇒ different outputs G Varoquaux 15
  • 30.
    2 SVM: exemplar-basedclassifier Find a separating hyperplane between images G Varoquaux 16
  • 31.
    2 SVM: exemplar-basedclassifier Find a separating hyperplane between images SVM builds it by combining exemplars ⇒ hyperplane looks like train images G Varoquaux 16
  • 32.
    2 SVM: exemplar-basedclassifier Find a separating hyperplane between images SVM builds it by combining exemplars ⇒ hyperplane looks like train images Often distributed by construction G Varoquaux 16
  • 33.
    2 Risk minimizationand penalization Inverse problem Minimize the error term: ˆw = argmin w l(y − X w) Ill-posed: Many different w will give the same prediction error To choose one: inject prior with a penalty ˆw = argmin w l(y − X w) + p(w) G Varoquaux 17
  • 34.
    2 Sparse models:selecting predictive voxels? Lasso estimator minx y − Ax 2 2 + λ 1(x) Data fit Penalization x2 x1 G Varoquaux 18
  • 35.
    2 Sparse models:selecting predictive voxels? Lasso estimator minx y − Ax 2 2 + λ 1(x) Data fit Penalization x2 x1 Between correlated features, selects a random subset [Wainwright 2009, Varoquaux... 2012] [Rish... 2012] Violates the restricted isometry property lasso: 23 non-zeros G Varoquaux 18
  • 36.
    2 Tricks ofthe trade Spatial smoothing 12mm Face vs cat error rate: 43% 31% Giving up on resolution Feature selection 23% Giving up on multivariate G Varoquaux 19
  • 37.
    From prediction tomapping? Inverse problem intractable in general Need suitable simplifying hypothesis Spatial structure / contiguity (as Random Field Theory) ⇒ Smoothed SVM work better Only a fraction of the brain useful to predict ⇒ Feature-selection + Sparsity Neighbooring voxel multi-colinear ⇒ Break multivariate estimators G Varoquaux 20
  • 38.
  • 39.
    [Varoquaux... 2012] Clustering togroup similar voxels Hierarchical clustering: merge voxels with similar behavior ⇒ Good conditions for sparse models G Varoquaux 22
  • 40.
    3 Brain parcellations+ sparsity Sparse model on brain parcellation Mapping is much easier on a parcellation [Filippone... 2012] Parcellation / clustering is not always perfect G Varoquaux 23
  • 41.
    3 Randomized parcellations+ sparsity [Varoquaux... 2012] ... ... Cluster sub-sampled data Average the results G Varoquaux 24
  • 42.
    3 Randomized parcellations+ sparsity [Varoquaux... 2012] ... ... Cluster sub-sampled data Average the results Recovering predictive regions Good theoretical argument Extensive simulations G Varoquaux 24
  • 43.
    3 fMRI: facevs house discrimination [Haxby... 2001] [Varoquaux... 2012] Sparse model L R y=-31 x=17 L R z=-17 G Varoquaux 25
  • 44.
    3 fMRI: facevs house discrimination [Haxby... 2001] [Varoquaux... 2012] Randomized Clustered 1 Logistic L R y=-31 x=17 L R z=-17 G Varoquaux 25
  • 45.
    3 better predictionscores [Hoyos-Idrobo... 2015] dataset sparse sparse + clustered ds105 (haxby) bottle/scramble 0.591 0.626 cat/chair 0.558 0.612 cat/house 0.698 0.963 chair/house 0.668 0.734 chair/scramble 0.700 0.743 face/house 0.766 0.742 tools/scramble 0.666 0.743 ds107 consonant/scramble 0.886 0.897 objects/consonant 0.855 0.901 objects/scramble 0.863 0.898 objects/words 0.689 0.708 words/scramble 0.782 0.841 ds108 negative cue / neutral cue 0.444 0.497 negative rating / neutral rating 0.520 0.537 negative stim / neutral stim 0.734 0.743 ds 109 false picture / false belief 0.664 0.675 oasis (vbm) gender discrimination 0.617 0.655
  • 46.
    3 better predictionscores [Hoyos-Idrobo... 2015] dataset sparse sparse + clustered ds105 (haxby) bottle/scramble 0.591 0.626 cat/chair 0.558 0.612 cat/house 0.698 0.963 chair/house 0.668 0.734 chair/scramble 0.700 0.743 face/house 0.766 0.742 tools/scramble 0.666 0.743 ds107 consonant/scramble 0.886 0.897 objects/consonant 0.855 0.901 objects/scramble 0.863 0.898 objects/words 0.689 0.708 words/scramble 0.782 0.841 ds108 negative cue / neutral cue 0.444 0.497 negative rating / neutral rating 0.520 0.537 negative stim / neutral stim 0.734 0.743 ds 109 false picture / false belief 0.664 0.675 oasis (vbm) gender discrimination 0.617 0.655 Sparse-clustered: faster, better Faster: 10x speed up, due to smaller models More stable: bagging effect Averaging many estimates
  • 47.
    Total variation: apenalty for regions Sparsity is useful Want to recover brain regions Total-variation penalization Impose sparsity on the gradient of the image: p(w) = 1( w) In fMRI: [Michel... 2011] Related to GraphNet [Grosenick... 2013] G Varoquaux 27
  • 48.
    3 Penalty engineering:total variation Decoding prediction performance: SVM 0.77 Sparse regression 0.78 Total Variation 0.84 [Michel... 2011] (explained variance) Standard analysis G Varoquaux 28
  • 49.
    3 Penalty engineering:TV- 1 Sparsity + regions [Baldassarre... 2012, Gramfort... 2013] ˆw = argmin w l(y − X w) + λ ρ 1(w) + (1 − ρ)TV (w) l: data-fit term Poster 3980, Thursday G Varoquaux 29
  • 50.
    3 Penalty engineering:TV- 1 Sparsity + regions [Baldassarre... 2012, Gramfort... 2013] ˆw = argmin w l(y − X w) + λ ρ 1(w) + (1 − ρ)TV (w) l: data-fit term Poster 3980, Thursday Experiment results G Varoquaux 29
  • 51.
    3 Simulations [Gramfort...2013] Vary threshold, and count false positive/false negatives Precision ∼ FDR Recall ∼ # detections 0.0 0.2 0.4 0.6 0.8 1.0 Recall (sensivity, or TPR) 0.2 0.4 0.6 0.8 1.0 Precision(1-FDR) F test (area=0.847) SVR (area=0.604) SearchLight (area=0.503) Ridge (area=0.636) ElasticNet (area=0.750) TVL1 (area=0.912) G Varoquaux 30
  • 52.
    3 Prediction onsimulations [Gramfort... 2013] Prediction error SNR 2.5 5.0 7.5 10.0 SVR 28.4 22.6 18.1 14.5 Ridge 27.8 21.6 17.0 13.4 ElasticNet 26.6 20.7 16.4 13.1 TV- 1 25.7 20.1 15.8 12.5 Prediction is easy, region recovery is hard G Varoquaux 31
  • 53.
  • 54.
    Wrapping up Software ni Very versatilebut simple code Fast, memory efficient Many examples + docs http://nilearn.github.io G Varoquaux 32
  • 55.
    @GaelVaroquaux Decoding for reverse-inferencemapping Standard decoders do not retrieve good maps Infinite number of maps predict as well
  • 56.
    @GaelVaroquaux Decoding for reverse-inferencemapping Standard decoders do not retrieve good maps Spatial models to select predictive regions TV- 1 = Sparsity + regions Randomized clustering + sparsity
  • 57.
    @GaelVaroquaux Decoding for reverse-inferencemapping Standard decoders do not retrieve good maps Spatial models to select predictive regions Decoding + mega-analysis ⇒ reverse-inference atlas Multi-label prediction Predicting on new studies
  • 58.
    @GaelVaroquaux Decoding for reverse-inferencemapping Standard decoders do not retrieve good maps Spatial models to select predictive regions Decoding + mega-analysis ⇒ reverse-inference atlas Software: nilearn In Python http://nilearn.github.io ni[Varoquaux and Thirion 2014] How machine learning is shaping cognitive neuroimaging
  • 59.
    References I L. Baldassarre,J. Mourao-Miranda, and M. Pontil. Structured sparsity models for brain decoding from fMRI data. In PRNI, page 5, 2012. M. Filippone, A. F. Marquand, C. R. Blain, S. C. Williams, J. Mourão-Miranda, and M. Girolami. Probabilistic prediction of neurological disorders with a statistical assessment of neuroimaging data modalities. The annals of applied statistics, 6 (4):1883, 2012. I. Gauthier, M. J. Tarr, J. Moylan, P. Skudlarski, J. C. Gore, and A. W. Anderson. The fusiform “face area” is part of a network that processes faces at the individual level. J cognitive neuroscience, 12:495, 2000. A. Gramfort, B. Thirion, and G. Varoquaux. Identifying predictive regions from fMRI with TV-L1 prior. In PRNI, page 17, 2013.
  • 60.
    References II L. Grosenick,B. Klingenberg, K. Katovich, B. Knutson, and J. E. Taylor. Interpretable whole-brain prediction analysis with graphnet. NeuroImage, 72:304, 2013. S. J. Hanson and Y. O. Halchenko. Brain reading using full brain support vector machines for object recognition: there is no “face” identification area. Neural Computation, 20:486, 2008. J. V. Haxby, I. M. Gobbini, M. L. Furey, ... Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293:2425, 2001. R. Henson. Forward inference using functional neuroimaging: Dissociations versus associations. Trends in cognitive sciences, 10:64, 2006. A. Hoyos-Idrobo, Y. Schwartz, B. Thirion, and G. Varoquaux. Improving sparse recovery on structured images with bagged clustering. In PRNI. 2015.
  • 61.
    References III N. Kanwisher,J. McDermott, and M. M. Chun. The fusiform face area: a module in human extrastriate cortex specialized for face perception. J Neuroscience, 17:4302, 1997. V. Michel, A. Gramfort, G. Varoquaux, E. Eger, and B. Thirion. Total variation regularization for fMRI-based prediction of behavior. Medical Imaging, IEEE Transactions on, 30:1328, 2011. R. Poldrack. Can cognitive processes be inferred from neuroimaging data? Trends in cognitive sciences, 10:59, 2006. R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20:1364, 2009.
  • 62.
    References IV I. Rish,G. A. Cecchi, K. Heuton, M. N. Baliki, and A. V. Apkarian. Sparse regression analysis of task-relevant information distribution in the brain. In SPIE Medical Imaging, pages 831412–831412. International Society for Optics and Photonics, 2012. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014. G. Varoquaux, A. Gramfort, and B. Thirion. Small-sample brain mapping: sparse recovery on spatially correlated designs with randomization and clustering. In ICML, page 1375, 2012. M. Wainwright. Sharp thresholds for high-dimensional and noisy sparsity recovery using 1-constrained quadratic programming. Trans Inf Theory, 55:2183, 2009.
  • 63.
    Decoding object recognition 8objects multi-class prediction Categories: face, chair, srambledpix, scissors, house, bottle, shoe, cat Difficult! Lasso 69.4% TV- 1 75.3% Distinguishing scissors in the dorsal stream, faces and cats in frontal areas ... [Eickenberg Submitted]