Machine learning for functional connectomes
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The document discusses the application of machine learning techniques to analyze functional connectomes from resting-state fMRI data. It outlines concepts such as regional definition, time-series extraction, and the construction of functional connectivity matrices, emphasizing the role of supervised learning in predicting brain connectivity differences across subjects. Various clustering and decomposition methods, along with practical implementation points using tools like Nilearn, are also highlighted throughout the content.
![1 Testing prediction: generalization and cross-validation
[Varoquaux... 2017]
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![1 Testing prediction: generalization and cross-validation
[Varoquaux... 2017]
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![1 Testing prediction: generalization and cross-validation
[Varoquaux... 2017]
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![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-23-320.jpg)
![2 For connectome prediction [Dadi... 2018]
RS-fMRI
Functional
connectivity
Time series
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Diagnosis
ROIs
Choice of regions for best prediction?
G Varoquaux 11](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-28-320.jpg)
![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-29-320.jpg)
![2 Connectome: differences across subjects
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Partial correlation matrices
Spread-out variability in correlation matrices
Noise in partial-correlations
Strong dependence between coefficients
[Varoquaux... 2010]
G Varoquaux 15](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-36-320.jpg)
![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-37-320.jpg)
![2 Connectome: which parametrization maps differences?
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![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-39-320.jpg)
![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-40-320.jpg)
![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-41-320.jpg)
![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](https://image.slidesharecdn.com/slides-180929181122/85/Machine-learning-for-functional-connectomes-42-320.jpg)
Machine learning for functional connectomes
- 1. Machine learning for functional connectomes Gaël Varoquaux
- 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. 1 Intuitions on machine learning Adjusting models for prediction G Varoquaux 2
- 4. 1 Machine learning in a nutshell: an example Face recognition Andrew Bill Charles Dave G Varoquaux 3
- 5. 1 Machine learning in a nutshell: an example Face recognition Andrew Bill Charles Dave ?G Varoquaux 3
- 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. 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. 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. 1 Machine learning in a nutshell: intuitions A single descriptor: one dimension x y G Varoquaux 5
- 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. 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. 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. 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. 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. 1 Testing prediction: generalization and cross-validation [Varoquaux... 2017] x y x y G Varoquaux 6
- 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. 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. 2 Machine learning on rest fMRI for population imaging finding differences between subjects in functional connectomesG Varoquaux 7
- 19. From rest-fMRI to biomarkers No salient features in rest fMRI G Varoquaux 8
- 20. From rest-fMRI to biomarkers Define functional regions G Varoquaux 8
- 21. From rest-fMRI to biomarkers Define functional regions Learn interactions G Varoquaux 8
- 22. From rest-fMRI to biomarkers Define functional regions Learn interactions Find differences G Varoquaux 8
- 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. 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. 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. 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. 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. 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. 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. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
- 31. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
- 32. 2 Region definition: resulting parcellations Dictionary learning Group ICA Ward clustering K-Means clustering
- 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. 2 Connectome: building a connectivity matrix How to capture and represent interactions? G Varoquaux 14
- 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. 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. 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. 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. 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. 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. 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. 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. 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. 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. 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