Inter-site autism biomarkers from resting-state fMRI
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This document summarizes research predicting autism from resting-state fMRI data using a connectome classification pipeline. The pipeline involves defining regions of interest, extracting time series, computing functional connectivity matrices, and using supervised learning. The authors explore different choices for each step and find that learning regions with MSDL, using tangent-space embedding for connectivity, and standard SVM learning work best across datasets with different heterogeneity levels. The findings suggest connectome structure is less important for prediction than choice of regions and preprocessing.
![Predictive biomarkers from rest
Suitable for diminished patients
Probing intrinsic brain structure
Predicting pathologies as a validation proxy
Multi-site large autism dataset: ABIDE
Autism Spectrum Disorder
[Di Martino... 2014]
⇒ Patient/Control classification
16 sites
∼ 1200 subjects (900 after QC)
G Varoquaux 2](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Inter-site autism biomarkers from resting-state fMRI
- 1. Inter-site autism biomarkers from resting-state fMRI Ga¨el Varoquaux Manuscript submitted A. Abraham
- 2. Predictive biomarkers from rest Suitable for diminished patients Probing intrinsic brain structure Predicting pathologies as a validation proxy Multi-site large autism dataset: ABIDE Autism Spectrum Disorder [Di Martino... 2014] ⇒ Patient/Control classification 16 sites ∼ 1200 subjects (900 after QC) G Varoquaux 2
- 3. Experiments: autism prediction Seen sites: predicting to new subjects Training set Testing set Unseen sites: predicting to new sites Training set Testing set Training set Test set G Varoquaux 3
- 4. A connectome classification pipeline RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 4
- 5. A connectome classification pipeline RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Prediction accuracy (%) Seen sites 67±3 Unseen sites 67±5 What is important to predict? G Varoquaux 4
- 6. A connectome classification pipeline RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Prediction accuracy (%) Seen sites 67±3 Unseen sites 67±5 What is important to predict? G Varoquaux 4
- 7. 1 ROI definition RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 5
- 8. 1 ROI definition: Learning functional regions Harvard Oxford Anatomical atlases do not resolve functional structures Where is the default mode network? G Varoquaux 6
- 9. 1 ROI definition: Clustering approaches K-Means Related to [Yeo... 2011] Normalized cuts [Craddock... 2012] Ward clustering [Thirion... 2014] G Varoquaux 7
- 10. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses G Varoquaux 8
- 11. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Language G Varoquaux 8
- 12. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Audio G Varoquaux 8
- 13. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Visual G Varoquaux 8
- 14. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Dorsal Att. G Varoquaux 8
- 15. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Motor G Varoquaux 8
- 16. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Salience G Varoquaux 8
- 17. 1 ROI definition: Linear decomposition approaches Model: Observing linear mixtures of networks at rest Time courses Observing a mixture How to unmix networks? ICA: minimize mutual information across S Sparse decompositions: sparse penalty on maps G Varoquaux 8
- 18. 1 ROI definition: MSDL linear decomposition [Varoquaux... 2011, Abraham... 2013] MSDL: multi-subject dictionary learning Sparse Multi-subject Subject maps + Population-level atlas Spatial penalty (total variation) G Varoquaux 9
- 19. 1 ROI definition: MSDL linear decomposition [Varoquaux... 2011, Abraham... 2013] MSDL: multi-subject dictionary learning Sparse Multi-subject Subject maps + Population-level atlas Spatial penalty (total variation) L R y=-23 x=0 L R z=18 L R y=-78 x=2 L R z=4 Ventricular system Visual Cortex L R y=-47 x=51 L R z=11 L R y=-17 x=-47 L R z=8 TPJ Auditory Network G Varoquaux 9
- 20. 1 ROI definition: different optionsAtlases Craddock 2011 Ncuts Harvard- Oxford Yeo 2011 Regionlearning K-Means Ward MSDL ICA G Varoquaux 10
- 21. 1 ROI definition: impact of choice RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 11
- 22. 2 Time-series extraction Time series 2 RS-fMRI Functional connectivity 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 12
- 23. 2 Time-series extraction Time series 2 RS-fMRI Functional connectivity 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Remove motion regressors Compcorr Global mean regression? Empirically: different ways work G Varoquaux 12
- 24. 3 Functional-connectivity matrix Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 13
- 25. 3 Functional-connectivity matrix Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Correlation matrix? Partial correlation matrix? Tangent-space embedding? [Varoquaux... 2010] G Varoquaux 13
- 26. 3 Functional-connectivity matrix: different options 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 14
- 27. 3 Functional-connectivity matrix: impact of choice Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 15
- 28. 4 Supervised learning method Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 16
- 29. 4 Supervised learning method Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Standard supervised learning Ridge classifier SVC 2 penalized SVC 1 penalized G Varoquaux 16
- 30. 4 Supervised learning method: impact of choice Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 17
- 31. Importance of pipeline steps RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 18
- 32. Importance of pipeline steps RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning G Varoquaux 18
- 33. How general are these conclusions? Different subsets: Set Card. Sites Selection criteria A 871 17 All subjects after QC B 736 11 Remove 6 smallest sites C 639 14 A without left handed and women D 420 14 C between 9 and 18 years old E 226 3 D from 3 biggest sites Exploring variability From most heterogeneous to most homogeneous G Varoquaux 19
- 34. Across different sets of subjects RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning MSDL outperform alternatives Intra-site Inter-site G Varoquaux 20
- 35. Across different sets of subjects RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning Tangent space outperform alternatives Intra-site Inter-site G Varoquaux 20
- 36. Across different sets of subjects RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs 1 ROI definition 2 Time-series extraction 3 Connectivity matrices 4 Supervised learning 2 classifiers outperform alternatives Intra-site Inter-site G Varoquaux 20
- 37. MSDL atlas L R y=-54 x=0 L R z=3 L R L R L R L R
- 38. More data is better Multivariate processing of a 1Tb of heterogeneous data is worth the trouble
- 39. @GaelVaroquaux Intersite biomarkers from rest [Abraham submitted] Prediction of autism Carries out across sites
- 40. @GaelVaroquaux Intersite biomarkers from rest [Abraham submitted] Prediction of autism Carries out across sites Recipe for a good pipeline Choice of regions critical (MSDL to learn them) Tangent-space embedding Standard SVM Connectome structure not central for prediction
- 41. @GaelVaroquaux Intersite biomarkers from rest [Abraham submitted] Prediction of autism Carries out across sites Recipe for a good pipeline Choice of regions critical (MSDL to learn them) Tangent-space embedding Standard SVM Definition of regions MSDL Linear decomposition + sparsity + total-variation ni
- 42. 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. R. C. Craddock, G. A. James, P. E. Holtzheimer, X. P. Hu, and H. S. Mayberg. A whole brain fMRI atlas generated via spatially constrained spectral clustering. Human brain mapping, 33(8): 1914–1928, 2012. A. Di Martino, C.-G. Yan, Q. Li, E. Denio, F. X. Castellanos, K. Alaerts, J. S. Anderson, M. Assaf, S. Y. Bookheimer, M. Dapretto, ... The autism brain imaging data exchange: towards a large-scale evaluation of the intrinsic brain architecture in autism. Molecular psychiatry, 19:659, 2014. B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline. Which fMRI clustering gives good brain parcellations? Name: Frontiers in Neuroscience, 8:167, 2014.
- 43. References II 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, pages 200–208. 2010. G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, and B. Thirion. Multi-subject dictionary learning to segment an atlas of brain spontaneous activity. In Inf Proc Med Imag, pages 562–573, 2011. B. Yeo, F. Krienen, J. Sepulcre, M. Sabuncu, ... The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysio, 106:1125, 2011.