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Machine learning for functional connectomes

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A tutorial on using machine-learning for functional-connectomes, for instance on resting-state fMRI. This is typically useful for population imaging: comparing traits or conditions across subjects.

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Machine learning for functional connectomes

  1. 1. Machine learning for functional connectomes Gaël Varoquaux
  2. 2. Machine learning for functional connectomes Gaël Varoquaux Outline: 1 Intuitions on machine learning 2 Machine learning on rest fMRI Pointers to code in nilearn & scikit-learn nilearn.github.io — scikit-learn.org Use the “API reference” to look up functions and scroll down for examples of usage
  3. 3. 1 Intuitions on machine learning Adjusting models for prediction G Varoquaux 2
  4. 4. 1 Machine learning in a nutshell: an example Face recognition Andrew Bill Charles Dave G Varoquaux 3
  5. 5. 1 Machine learning in a nutshell: an example Face recognition Andrew Bill Charles Dave ?G Varoquaux 3
  6. 6. 1 Machine learning in a nutshell A simple method: 1 Store all the known (noisy) images and the names that go with them. 2 From a new (noisy) images, find the image that is most similar. “Nearest neighbor” method G Varoquaux 4
  7. 7. 1 Machine learning in a nutshell A simple method: 1 Store all the known (noisy) images and the names that go with them. 2 From a new (noisy) images, find the image that is most similar. “Nearest neighbor” method How many errors on already-known images? ... 0: no errors Test data = Train data G Varoquaux 4
  8. 8. 1 Machine learning in a nutshell A simple method: 1 Store all the known (noisy) images and the names that go with them. 2 From a new (noisy) images, find the image that is most similar. “Nearest neighbor” method How many errors on already-known images? ... 0: no errors Test data = Train data G Varoquaux 4
  9. 9. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y G Varoquaux 5
  10. 10. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y x y Which model to prefer? G Varoquaux 5
  11. 11. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y x y Problem of “over-fitting” Minimizing error is not always the best strategy (learning noise) Test data = train data G Varoquaux 5
  12. 12. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y x y Prefer simple models = concept of “regularization” Balance the number of parameters to learn with the amount of data G Varoquaux 5
  13. 13. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y Two descriptors: 2 dimensions X_1 X_2 y The higher the number of descriptors the more the trouble G Varoquaux 5
  14. 14. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y Two descriptors: 2 dimensions X_1 X_2 y The higher the number of descriptors the more the trouble The higher the required number of subjects G Varoquaux 5
  15. 15. 1 Testing prediction: generalization and cross-validation [Varoquaux... 2017] x y x y G Varoquaux 6
  16. 16. 1 Testing prediction: generalization and cross-validation [Varoquaux... 2017] x y x y ⇒ Need test on independent, unseen data Train set Validation set Measures prediction accuracy sklearn.model_selection.train_test_split G Varoquaux 6
  17. 17. 1 Testing prediction: generalization and cross-validation [Varoquaux... 2017] x y x y ⇒ Need test on independent, unseen data Loop Test setTrain set Full data sklearn. model_selection. cross_val_score G Varoquaux 6
  18. 18. 2 Machine learning on rest fMRI for population imaging finding differences between subjects in functional connectomesG Varoquaux 7
  19. 19. From rest-fMRI to biomarkers No salient features in rest fMRI G Varoquaux 8
  20. 20. From rest-fMRI to biomarkers Define functional regions G Varoquaux 8
  21. 21. From rest-fMRI to biomarkers Define functional regions Learn interactions G Varoquaux 8
  22. 22. From rest-fMRI to biomarkers Define functional regions Learn interactions Find differences G Varoquaux 8
  23. 23. From rest-fMRI to biomarkers Functional connectivity matrix Time series extraction Region definition Supervised learning RS-fMRI Typical pipeline [Varoquaux and Craddock 2013] 1. Define regions 2. Extract times series 3. Build functional-connectivity matrix 4. Apply supervised machine learning G Varoquaux 9
  24. 24. 2 Defining regions from rest-fMRI Clustering nilearn.regions.Parcellations k-means Fast (in nilearn) No spatial model ⇒ smooth the data G Varoquaux 10
  25. 25. 2 Defining regions from rest-fMRI Clustering nilearn.regions.Parcellations k-means Fast (in nilearn) No spatial model ⇒ smooth the data Ward agglomerative clustering Recursive merges of clusters Spatial model constraints merges ⇒ fast ... ... ... ... ... G Varoquaux 10
  26. 26. 2 Defining regions from rest-fMRI Clustering nilearn.regions.Parcellations k-means Fast (in nilearn) No spatial model ⇒ smooth the data Ward agglomerative clustering Recursive merges of clusters Spatial model constraints merges ⇒ fast Decomposition models time voxels time voxels time voxels Y +E · S= 25 N G Varoquaux 10
  27. 27. 2 Defining regions from rest-fMRI Clustering nilearn.regions.Parcellations k-means Fast (in nilearn) No spatial model ⇒ smooth the data Ward agglomerative clustering Recursive merges of clusters Spatial model constraints merges ⇒ fast Decomposition models ICA: nilearn.decomposition.CanICA seek independence of maps Sparse dictionary learning: seek sparse maps nilearn.decomposition.DictLearning G Varoquaux 10
  28. 28. 2 For connectome prediction [Dadi... 2018] RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs Choice of regions for best prediction? G Varoquaux 11
  29. 29. 2 For connectome prediction [Dadi... 2018] RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs Choice of regions for best prediction? G Varoquaux 11
  30. 30. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
  31. 31. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
  32. 32. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
  33. 33. 2 Time-series extraction Extract ROI-average signal: Optional low-pass filter (≈ .1 Hz – .3 Hz) Regress out confounds (movement parameters, CSF & white matter signals, Compcorr, Global mean) Hard parcellations (eg from clustering) nilearn.input_data.NiftiLabelsMasker Soft parcellations (eg from ICA) nilearn.input_data.NiftiMapsMasker G Varoquaux 13
  34. 34. 2 Connectome: building a connectivity matrix How to capture and represent interactions? G Varoquaux 14
  35. 35. 2 Connectome: differences across subjects 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Correlation matrices 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 0 5 10 15 20 25 Partial correlation matrices 3 controls, 1 severe stroke patient Which is which? G Varoquaux 15
  36. 36. 2 Connectome: differences across subjects 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25Large lesion Correlation matrices 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25Large lesion Partial correlation matrices Spread-out variability in correlation matrices Noise in partial-correlations Strong dependence between coefficients [Varoquaux... 2010] G Varoquaux 15
  37. 37. 2 Information geometry: uniform-error parametrization Subject-specific noise in covariance form manifold Tangent space removes coupling in coefficients Controls Patient dΣ M anifold Tangent Tangent embedding[Varoquaux... 2010] G Varoquaux 16
  38. 38. 2 Connectome: which parametrization maps differences? 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25Large lesion Correlation matrices 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25Large lesion Partial correlation matrices 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25 Control 0 5 10 15 20 25 0 5 10 15 20 25Large lesion Tangent-space embedding [varoquaux 2010] G Varoquaux 17
  39. 39. 2 For connectome prediction [Dadi... 2018] Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 Connectivity matrix Correlation nilearn.connectome.ConnectivityMeasure Partial correlations Tangent space G Varoquaux 18
  40. 40. 2 For connectome prediction [Dadi... 2018] Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 Connectivity matrix Correlation nilearn.connectome.ConnectivityMeasure Partial correlations Tangent space G Varoquaux 18
  41. 41. 2 Supervised learning step [Dadi... 2018] Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs Supervised learning Stick with Linear models sklearn.linear_model.LogisticRegression G Varoquaux 19
  42. 42. 2 Supervised learning step [Dadi... 2018] Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs Supervised learning Stick with Linear models sklearn.linear_model.LogisticRegression G Varoquaux 19
  43. 43. Predicting from brain activity at rest RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1. Functional regions (eg clustering, decomposition, or BASC atlas) 2. Filtering and or confound removal 3. Tangent-space parametrization 4. Supervised linear models (eg SVMs) G Varoquaux 20
  44. 44. 3 References I A. Abraham, E. Dohmatob, B. Thirion, D. Samaras, and G. Varoquaux. Extracting brain regions from rest fMRI with total-variation constrained dictionary learning. In MICCAI, page 607. 2013. K. Dadi, M. Rahim, A. Abraham, D. Chyzhyk, M. Milham, B. Thirion, and G. Varoquaux. Benchmarking functional connectome-based predictive models for resting-state fmri. 2018. G. Varoquaux and R. C. Craddock. Learning and comparing functional connectomes across subjects. NeuroImage, 80:405, 2013. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014. G. Varoquaux, F. Baronnet, A. Kleinschmidt, P. Fillard, and B. Thirion. Detection of brain functional-connectivity difference in post-stroke patients using group-level covariance modeling. In MICCAI. 2010.G Varoquaux 21
  45. 45. 3 References II G. Varoquaux, P. R. Raamana, D. A. Engemann, A. Hoyos-Idrobo, Y. Schwartz, and B. Thirion. Assessing and tuning brain decoders: cross-validation, caveats, and guidelines. NeuroImage, 145:166–179, 2017. G Varoquaux 22

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