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Functional-connectome biomarkers to meet clinical needs?

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Extracting Functional-Connectome Biomarkers with Machine Learning: a talk in the symposium on how do current predictive connectivity models meet clinician’s needs?

This talk is a bit provocative and first sets visions, before bringing a few technical suggestions

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Functional-connectome biomarkers to meet clinical needs?

  1. 1. Extracting Functional-Connectome Biomarkers with Machine Learning Ga¨el Varoquaux
  2. 2. Extracting Functional-Connectome Biomarkers with Machine Learning Ga¨el Varoquaux How Do Current Predictive Connectivity Models Meet Clinician’s Needs? This house believes that predictive biomarkers are, today, the most useful endeavor for clinical application of func- tional connectivity
  3. 3. Extracting Functional-Connectome Biomarkers with Machine Learning Ga¨el Varoquaux How Do Current Predictive Connectivity Models Meet Clinician’s Needs? This house believes that predictive biomarkers are, today, the most useful endeavor for clinical application of func- tional connectivity They are just not reliable enough
  4. 4. 1 Prediction matters 2 Extracting biomarkers from rest fMRI G Varoquaux 2
  5. 5. 1 Prediction matters Machine learning is useful, and must be done right G Varoquaux 3
  6. 6. Prediction? Nah...Prediction? Nah... We want neurobiological insightWe want neurobiological insight G Varoquaux 4
  7. 7. What if I told youWhat if I told you Brain imaging predicts the risk that a 2 year-oldBrain imaging predicts the risk that a 2 year-old develops on the autism spectrumdevelops on the autism spectrum Brain imaging predicts long-term cognitive deficitBrain imaging predicts long-term cognitive deficit after strokeafter stroke G Varoquaux 5
  8. 8. 1 Heterogeneity is a roadblock? [Abraham... 2017] Autism: ill-defined diagnostic criteria sensitive to parents’ social-economic status ABIDE: post-hoc aggregation of data across many cities and countries Can autism biomarkers carry over to new sites? Training set Testing set G Varoquaux 6
  9. 9. 1 Heterogeneity is a roadblock? [Abraham... 2017] Autism: ill-defined diagnostic criteria sensitive to parents’ social-economic status ABIDE: post-hoc aggregation of data across many cities and countries Can autism biomarkers carry over to new sites? Training set Testing set Accuracy Fraction of subjects used Prediction to new sites works as well G Varoquaux 6
  10. 10. Yes we can extract biomarkersextract biomarkers despite heterogeneitydespite heterogeneity Multi-variate predictive models, unlike classical statistics, can learn to reject confounds, given examples of confound- ing heterogeneity G Varoquaux 7
  11. 11. 1 Proxy clinical outcomes [Liem... 2016] Predicting brain aging = chronological age Predicts age with a mean absolute error of 4.3 years G Varoquaux 8
  12. 12. 1 Proxy clinical outcomes [Liem... 2016] Predicting brain aging = chronological age Predicts age with a mean absolute error of 4.3 years Discrepency with chronological age correlates with cognitive impairment 0 2 4 Brain aging discrepancy (years) -0.38 0.74 1.72 Objective Cognitive Impairment group Normal Mild Major Biomarker surrogate, but useful G Varoquaux 8
  13. 13. 1 Better descriptions of subjects? [Rahim... 2017] An individual should not be reduced to a single diagnostic or behavioral quantity G Varoquaux 9
  14. 14. 1 Better descriptions of subjects? [Rahim... 2017] Multi-output prediction Predict jointly multiple individual phenotypes behavioral scores diagnostic status They improve eachother’s prediction Adding MMSE as a target improves AD prediction Functional connectivity (fMRI) Protein biomarkers (CSF) Hippocampus volumetry (MRI) 50% 60% 70% 80% 90% Cross-validation accuracy Stacked predictions of fMRI, MRI, CSF mono- modal multi- modal Classification: AD vs. MCI Single-output Multi-output G Varoquaux 9
  15. 15. 1 Trustworthy biomarkers [Woo... 2017] Good biomarkers generalize to new subjects to new sites G Varoquaux 10
  16. 16. 1 Trustworthy biomarkers [Woo... 2017] Good biomarkers generalize to new subjects to new sites Bad biomarkers overly adapt to a few subjects to site observation noise Predictive modeling: machine learning Prediction rather than association out-of-sample statistics G Varoquaux 10
  17. 17. One does not simplyOne does not simply claim predictionclaim prediction G Varoquaux 11
  18. 18. 1 Prediction requires more than association [R. Poldrack, G. Huckins, G. Varoquaux, submitted] 2 1 0 1 2 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 order = 1 0 0 20 40 60 80 100 Meansquarederror G Varoquaux 12
  19. 19. 1 Prediction requires more than association [R. Poldrack, G. Huckins, G. Varoquaux, submitted] 2 1 0 1 2 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 order = 1 order = 2 0 0 20 40 60 80 100 Meansquarederror G Varoquaux 12
  20. 20. 1 Prediction requires more than association [R. Poldrack, G. Huckins, G. Varoquaux, submitted] 2 1 0 1 2 5.0 7.5 10.0 12.5 15.0 17.5 20.0 22.5 25.0 order = 1 order = 2 order = 15 0 0 20 40 60 80 100 Meansquarederror Quality of fit on data used to fit is not meaningful Only new (test) data, can measure prediction G Varoquaux 12
  21. 21. 1 Evidence for prediction [Varoquaux... 2017] Established by cross-validation Test setTrain set Full data G Varoquaux 13
  22. 22. [R. Poldrack, G. Huckins, G. Varoquaux, submitted] One does not simplyOne does not simply claim predictionclaim prediction 100 last publications on “fMRI prediction” 0 20 40 None K-fold Leave-one-out Leave-X-out Other G Varoquaux 14
  23. 23. 1 Cross-validation is solid evidence? [Varoquaux 2017] In the literature, effect sizes decrease with sample sizes 50% 75% 100% p=.05 Wolfer2015: Psychiatric diagnostic p=.05 Arbabshirani2017: Alzheimer's p=.05 Woo2017: Alzheimer's p=.05 Woo2017: Depression 30 100 3001000 50% 75% 100% p=.05 Brown2017: Connectome learning 30 100 3001000 p=.05 Arbabshirani2017: Schizophrenia 30 100 3001000 p=.05 Woo2017: Psychosis 30 100 3001000 p=.05 Reportedaccuracy Study sample size Woo2017: Autism G Varoquaux 15
  24. 24. 1 Cross-validation is solid evidence? [Varoquaux 2017] Trivial analytic variations on a permuted data: smoothing, SVM vs log-reg, feature selection 30% 40% 50% 60% 70% Cross­validation scores for different decoders             4 first 4 last 6 first 6 last all 12 Sessions used  25% 39% 40% 71% 38% 57% 47% 57% 44% 52% n~72 n~72 n~108 n~108 n~216 G Varoquaux 15
  25. 25. 1 Cross-validation is solid evidence? [Varoquaux 2017] Trivial analytic variations on a permuted data: smoothing, SVM vs log-reg, feature selection 30% 40% 50% 60% 70% Cross­validation scores for different decoders             4 first 4 last 6 first 6 last all 12 Sessions used  25% 39% 40% 71% 38% 57% 47% 57% 44% 52% n~72 n~72 n~108 n~108 n~216 With small n, by chance, some analytic choices give seemingly good predictions G Varoquaux 15
  26. 26. 1 Cross-validation is solid evidence? [Varoquaux 2017] 30 100 200 300 umber of available samples    ­19% +15% ­20% +18% ­10% +8% ­10% +10% ­7% +5% ­7% +7% ­5% +4% ­6% +6% LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test Sampling distribution of test error for n = 30 G Varoquaux 15
  27. 27. 1 Cross-validation is solid evidence? [Varoquaux 2017] 30 100 200 300 1000 Number of available samples    ­19% +15% ­20% +18% ­10% +8% ­10% +10% ­7% +5% ­7% +7% ­5% +4% ­6% +6% ­3% +2% ­3% +3% LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test LOO 50 splits, 20% test G Varoquaux 15
  28. 28. 1 Cross-validation is solid evidence? [Varoquaux 2017] ­45% ­30% ­15%  0% +15% +30% Difference between public and private scores        ­15% +14% Kaggle competition on r-fMRI for Schizophrenia 2 different test sets: size 30 and 28 G Varoquaux 15
  29. 29. One does not simplyOne does not simply claim predictionclaim prediction We need Clean cross-validation strong-generalization = testing on data never seen Several 100s subjects G Varoquaux 16
  30. 30. Yes we can Reliable prediction of clinical end- points would be game changing But we need larger sizes, reduced analytical variability, and clean validation G Varoquaux 17
  31. 31. 2 Extracting biomarkers from rest fMRI Addressing the perils of analytical variabality Systematic study: 6 different cohorts More than 2000 individuals [Dadi... 2019] G Varoquaux 18
  32. 32. From rest-fMRI to biomarkers No salient features in rest fMRI G Varoquaux 19
  33. 33. From rest-fMRI to biomarkers Define functional regions G Varoquaux 19
  34. 34. From rest-fMRI to biomarkers Define functional regions Learn interactions G Varoquaux 19
  35. 35. From rest-fMRI to biomarkers Define functional regions Learn interactions Detect differences G Varoquaux 19
  36. 36. From rest-fMRI to biomarkers Functional connectivity matrix Time series extraction Region definition Supervised learning RS-fMRI G Varoquaux 20
  37. 37. 2 Defining regions Anatomical atlases Clustering k-means ward [Thirion... 2014] ... ... ... ... ... G Varoquaux 21
  38. 38. 2 Defining regions Anatomical atlases Clustering k-means ward [Thirion... 2014] Decomposition models time voxels time voxels time voxels Y +E · S= 25 N G Varoquaux 21
  39. 39. 2 Defining regions Anatomical atlases Clustering k-means ward [Thirion... 2014] Decomposition models ICA: seek independence of maps Sparse dictionary learning: seek sparse maps G Varoquaux 21
  40. 40. 2 In connectome prediction settings RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs Choice of regions for best prediction? G Varoquaux 22
  41. 41. 2 In connectome prediction settings RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs Choice of regions for best prediction? [Dadi... 2019] G Varoquaux 22
  42. 42. 2 Connectome: building a connectivity matrix How to capture and represent interactions? G Varoquaux 23
  43. 43. 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 24
  44. 44. 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 24
  45. 45. 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 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 25
  46. 46. 2 Connectivity matrix for predictive models Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 [Dadi... 2019] G Varoquaux 26
  47. 47. 2 Machine learning for connectome prediction Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs Supervised learning step Linear models Random forests Sparse or non sparse? G Varoquaux 27
  48. 48. 2 Machine learning for connectome prediction Functional connectivity Time series 3 4 Diagnosis 2 RS-fMRI 1 ROIs Supervised learning step Linear models Random forests Sparse or non sparse? [Dadi... 2019] G Varoquaux 27
  49. 49. @GaelVaroquaux Functionnal-connectome biomarkers Biomarkers Early assessment Pronostic Proxy clinical endpoints Reliable biomarkers Larger sample sizes Clean evidence of generalization Higher standards [Woo... 2017]
  50. 50. @GaelVaroquaux Functionnal-connectome biomarkers Biomarkers game-changing if trustworthy Rest-fMRI biomarkers extraction Functional regions (extracted by dictionary learning) Tangent space to compare connectomes Linear model for supervised learning RS-fMRI Diagnosis Connectivity Parameterization Supervised Learning Defining Brain ROIs 1 2 3 Software: nilearn ni
  51. 51. References I A. Abraham, M. Milham, A. Di Martino, R. C. Craddock, D. Samaras, B. Thirion, and G. Varoquaux. Deriving reproducible biomarkers from multi-site resting-state data: An autism-based example. NeuroImage, 2017. K. Dadi, M. Rahim, A. Abraham, D. Chyzhyk, M. Milham, B. Thirion, G. Varoquaux, and A. D. N. Initiative. Benchmarking functional connectome-based predictive models for resting-state fmri. NeuroImage, 2019. F. Liem, G. Varoquaux, J. Kynast, F. Beyer, S. K. Masouleh, J. M. Huntenburg, L. Lampe, M. Rahim, A. Abraham, R. C. Craddock, ... Predicting brain-age from multimodal imaging data captures cognitive impairment. NeuroImage, 2016. M. Rahim, B. Thirion, D. Bzdok, I. Buvat, and G. Varoquaux. Joint prediction of multiple scores captures better individual traits from brain images. Neuroimage, in rev, 2017.
  52. 52. References II B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline. Which fMRI clustering gives good brain parcellations? Name: Frontiers in Neuroscience, 8:167, 2014. G. Varoquaux. Cross-validation failure: small sample sizes lead to large error bars. NeuroImage, 2017. 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, 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. C.-W. Woo, L. J. Chang, M. A. Lindquist, and T. D. Wager. Building better biomarkers: brain models in translational neuroimaging. Nature neuroscience, 20(3):365, 2017.

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