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Better neuroimaging data processing:
driven by evidence,
open communities,
and careful engineering
Ga¨el Varoquaux, ,
Please allow me to introduce myself
I’m a man of wealth and taste
I’ve been around for a long, long year
PhD in Quantum Ph...
Methods instantiate the scientific method,
they are how we live and breath experimental
science
G Varoquaux 3
Methods instantiate the scientific method,
they are how we live and breath experimental
science
What fraction of the time o...
1 Tales from rest fMRI
2 Applicable methods: software
3 The truth of a method?
G Varoquaux 4
1 Tales from rest fMRI
G Varoquaux 5
The functional connectome
Study intrinsic brain activity and compare
individuals at scale
G Varoquaux 6
The functional connectome
No salient features in rest fMRI
G Varoquaux 6
The functional connectome
Define functional regions
G Varoquaux 6
The functional connectome
Define functional regions
Learn interactions
G Varoquaux 6
The functional connectome
Define functional regions
Learn interactions
Detect differences
[Varoquaux and Craddock 2013]
G Va...
1 What underlying connectivity structure?
Dependencies in the generative process?
My 2010 moment of glory
[Varoquaux... 20...
1 Inverse covariance My 2010 success
Observations
Covariance
0
1
2
3
4
Direct connections
Inverse covariance
0
1
2
3
4
Par...
1 Independence structure: sparsity My 2010 success
Zeros in partial correlations
give conditional independence
Reflects the...
1 Independence structure: sparsity My 2010 success
Zeros in partial correlations
give conditional independence
Ill-posed p...
1 Independence structure: sparsity My 2010 success
Zeros in partial correlations
give conditional independence
Ill-posed p...
1 Independence structure of brain activity
Good sparse estimate
[Varoquaux... 2010]
G Varoquaux 10
1 Choice of nodes?
G Varoquaux 11
1 Defining functional regions or networks
Clustering [Thirion... 2014]
Hierarchical
[Bellec... 2010]
KMeans
[Yeo... 2011]
N...
1 Defining functional regions or networks
Clustering [Thirion... 2014]
Hierarchical
[Bellec... 2010]
KMeans
[Yeo... 2011]
N...
1 Defining functional regions or networks
Clustering [Thirion... 2014]
Hierarchical
[Bellec... 2010]
KMeans
[Yeo... 2011]
N...
1 Which regions to use?
Endless discussions...
G Varoquaux 13
1 Taking a step back
RS-fMRI
Functional
connectivity
Time series
2
4
3
1
Diagnosis
ROIs
The full pipeline
Can it lead to b...
1 Evaluating biomarkers
[Varoquaux... 2017, Poldrack... 2019]
Evidence for prediction: testing on new data
Test setTrain s...
1 In connectome prediction settings
RS-fMRI
Functional
connectivity
Time series
2
4
3
1
Diagnosis
ROIs
Choice of regions f...
1 In connectome prediction settings
RS-fMRI
Functional
connectivity
Time series
2
4
3
1
Diagnosis
ROIs
Choice of regions f...
1 Connectivity matrix for predictive models
Time series
2
RS-fMRI
41
Diagnosis
ROIs Functional
connectivity
3
G Varoquaux ...
1 Connectivity matrix for predictive models
Time series
2
RS-fMRI
41
Diagnosis
ROIs Functional
connectivity
3
-0.1 0.0 +0....
1 Connectivity matrix for predictive models
Time series
2
RS-fMRI
41
Diagnosis
ROIs Functional
connectivity
3
Sparse inver...
1 Favorite controversy: Global Signal Regression
Time series
2
RS-fMRI
Functional
connectivity
4
3
1
Diagnosis
ROIs
Should...
1 Favorite controversy: Global Signal Regression
Time series
2
RS-fMRI
Functional
connectivity
4
3
1
Diagnosis
ROIs
-0.1 -...
1 Favorite controversy: Global Signal Regression
Time series
2
RS-fMRI
Functional
connectivity
4
3
1
Diagnosis
ROIs
Sparse...
2 Applicable methods: software
Putting tools
in the hands of users
c Theodore W. Gray
G Varoquaux 19
2 Applicable methods: software
Putting tools
in the hands of users
c Theodore W. Gray
Every model is wrong, but
some are a...
2 Scikit-learn: machine-learning in Python
scikit-learn
We needed machine learning
for our research
so we built something
...
2 Scikit-learn: machine-learning in Python
scikit-learn
Now an industry standard
Number of monthly users
2010 2012 2014 20...
2 Nilearn: statistical learning for neuroimaging
http://nilearn.github.io
ni
Extracting signal in brain images
Simple visu...
2 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
2 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
2 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
2 Reusable science
scikit-learn is the new machine-learning textbook
nilearn is the new neuroimaging review article
Experi...
2 Community-driven development
Our DNA: distributed development & decision making
Gave man power
2010 2014 2018
0
25
50
# ...
2 Community-driven development
Our DNA: distributed development & decision making
Gave man power
2010 2014 2018
0
25
50
# ...
2 Simplicity, a software-engineering factor of success
Complexity increase superlinearly
[An Experiment on Unit Increase i...
2 Simplicity, a software-engineering factor of success
Complexity increase superlinearly
[An Experiment on Unit Increase i...
2 Simplicity, a software-engineering factor of success
Complexity increase superlinearly
[An Experiment on Unit Increase i...
2 Technical debt
Once an asset, now a liability
G Varoquaux 25
3 The truth of a method?
Methods validation is an epistemological bottleneck
G Varoquaux 26
Science
The process of discovering
knowledge and mechanisms
Science helps shaping society
Autism and vaccines:
forged stud...
3 Our evidence comes from massaging data
In NeuroImaging,
nothing is ”clearly visible” from the data
Methods validity matt...
3 Generalization: evidence from external validity
Generalization
builds broader theories
[Varoquaux and Poldrack 2019]
bri...
3 Generalization: evidence from external validity
Generalization
builds broader theories
[Varoquaux and Poldrack 2019]
bri...
“A theory is a good theory if it satisfies two requirements:
It must accurately describe a large class of observa-
tions on...
3 Evidence for generalization
[Varoquaux... 2017, Poldrack... 2019]
Quantitative tests of model predictions on new data
Te...
3 Noisy evidence for generalization
[Varoquaux 2017]
30
100
200
300
umber of available samples    ­19% +15%
­20% +18%
­10%...
3 Noisy evidence for generalization
[Varoquaux 2017]
30
100
200
300
1000
Number of available samples   
­19% +15%
­20% +18...
3 Noisy evidence for generalization
[Varoquaux 2017]
Trivial analytic variations on a permuted data:
smoothing, SVM vs log...
3 Noisy evidence for generalization
[Varoquaux 2017]
Trivial analytic variations on a permuted data:
smoothing, SVM vs log...
3 Noisy evidence for generalization
[Varoquaux 2017]
In the literature, effect sizes decrease with sample sizes
50%
75%
100...
3 Noisy evidence for generalization
[Varoquaux 2017]
The variability of methods on small samples
undermines our evidence
G...
3 What can we do about it?
Increase sample sizes
Analysis across cohorts
Reduce analytical variability [Carp 2012]
Methods...
@GaelVaroquaux
Methods development and validity
Methods need to be validated strongly
- Multiple datasets
- Quantitative e...
References I
C. F. Beckmann and S. M. Smith. Probabilistic independent
component analysis for functional magnetic resonanc...
References II
K. Dadi, M. Rahim, A. Abraham, D. Chyzhyk, M. Milham,
B. Thirion, G. Varoquaux, and A. D. N. Initiative.
Ben...
References III
B. Thirion, G. Varoquaux, E. Dohmatob, and J. Poline. Which
fMRI clustering gives good brain parcellations?...
References IV
G. Varoquaux, A. Gramfort, F. Pedregosa, V. Michel, and
B. Thirion. Multi-subject dictionary learning to seg...
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Better neuroimaging data processing: driven by evidence, open communities, and careful engineering

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My current thoughts about methods validity and design in brain imaging.

Data processing is a significant part of a neuroimaging study. The choice of corresponding methods and tools is crucial. I will give an opinionated view how on a path to building better data processing for neuroimaging. I will take examples on endeavors that I contributed to: defining standards for functional-connectivity analysis, the nilearn neuroimaging tool, the scikit-learn machine-learning toolbox -an industry standard with a million regular users. I will cover not only the technical process -statistics, signal processing, software engineering- but also the epistemology of methods development. Methods govern our results, they are more than a technical detail.

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Better neuroimaging data processing: driven by evidence, open communities, and careful engineering

  1. 1. Better neuroimaging data processing: driven by evidence, open communities, and careful engineering Ga¨el Varoquaux, ,
  2. 2. Please allow me to introduce myself I’m a man of wealth and taste I’ve been around for a long, long year PhD in Quantum Physics Atom optics, zero gravity Worked in a software startup Enthought: scientific computing consulting in Python Open-source coder Core Python packages Research between AI and Neuroimaging PI at Inria G Varoquaux 2
  3. 3. Methods instantiate the scientific method, they are how we live and breath experimental science G Varoquaux 3
  4. 4. Methods instantiate the scientific method, they are how we live and breath experimental science What fraction of the time of a neuroimag- ing study is spent processing data? G Varoquaux 3
  5. 5. 1 Tales from rest fMRI 2 Applicable methods: software 3 The truth of a method? G Varoquaux 4
  6. 6. 1 Tales from rest fMRI G Varoquaux 5
  7. 7. The functional connectome Study intrinsic brain activity and compare individuals at scale G Varoquaux 6
  8. 8. The functional connectome No salient features in rest fMRI G Varoquaux 6
  9. 9. The functional connectome Define functional regions G Varoquaux 6
  10. 10. The functional connectome Define functional regions Learn interactions G Varoquaux 6
  11. 11. The functional connectome Define functional regions Learn interactions Detect differences [Varoquaux and Craddock 2013] G Varoquaux 6
  12. 12. 1 What underlying connectivity structure? Dependencies in the generative process? My 2010 moment of glory [Varoquaux... 2010] Before [Smith... 2011] G Varoquaux 7
  13. 13. 1 Inverse covariance My 2010 success Observations Covariance 0 1 2 3 4 Direct connections Inverse covariance 0 1 2 3 4 Partial correlations G Varoquaux 8
  14. 14. 1 Independence structure: sparsity My 2010 success Zeros in partial correlations give conditional independence Reflects the large-scale brain interaction structure G Varoquaux 9
  15. 15. 1 Independence structure: sparsity My 2010 success Zeros in partial correlations give conditional independence Ill-posed problem: multi-collinearity ⇒ noisy partial correlations Independence between nodes makes estimation of partial correlations well-conditionned. Chicken and egg problem G Varoquaux 9
  16. 16. 1 Independence structure: sparsity My 2010 success Zeros in partial correlations give conditional independence Ill-posed problem: multi-collinearity ⇒ noisy partial correlations Independence between nodes makes estimation of partial correlations well-conditionned. 0 1 2 3 4 0 1 2 3 4 + Joint estimation: Sparse inverse covariance [Varoquaux... 2010] G Varoquaux 9
  17. 17. 1 Independence structure of brain activity Good sparse estimate [Varoquaux... 2010] G Varoquaux 10
  18. 18. 1 Choice of nodes? G Varoquaux 11
  19. 19. 1 Defining functional regions or networks Clustering [Thirion... 2014] Hierarchical [Bellec... 2010] KMeans [Yeo... 2011] Normalized cuts [Craddock... 2012] G Varoquaux 12
  20. 20. 1 Defining functional regions or networks Clustering [Thirion... 2014] Hierarchical [Bellec... 2010] KMeans [Yeo... 2011] Normalized cuts [Craddock... 2012] Network extraction ICA [Beckmann and Smith 2004] Sparse methods [Varoquaux... 2011] G Varoquaux 12
  21. 21. 1 Defining functional regions or networks Clustering [Thirion... 2014] Hierarchical [Bellec... 2010] KMeans [Yeo... 2011] Normalized cuts [Craddock... 2012] Network extraction Multimodal ICA [Beckmann and Smith 2004] Sparse methods [Varoquaux... 2011] [Glasser... 2016] G Varoquaux 12
  22. 22. 1 Which regions to use? Endless discussions... G Varoquaux 13
  23. 23. 1 Taking a step back RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs The full pipeline Can it lead to biomarkers? G Varoquaux 14
  24. 24. 1 Evaluating biomarkers [Varoquaux... 2017, Poldrack... 2019] Evidence for prediction: testing on new data Test setTrain set Full data G Varoquaux 15
  25. 25. 1 In connectome prediction settings RS-fMRI Functional connectivity Time series 2 4 3 1 Diagnosis ROIs Choice of regions for best prediction? G Varoquaux 16
  26. 26. 1 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 16
  27. 27. 1 Connectivity matrix for predictive models Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 G Varoquaux 17
  28. 28. 1 Connectivity matrix for predictive models Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 -0.1 0.0 +0.1 e prediction scores (AUC) h o t f Partial correlation -0.1 0.0 +0.1 Graph Lasso Ledoit -Wolf Tangent [Dadi... 2019]G Varoquaux 17
  29. 29. 1 Connectivity matrix for predictive models Time series 2 RS-fMRI 41 Diagnosis ROIs Functional connectivity 3 Sparse inverse covariance not important [Varoquaux... 2010] was not that useful, after all G Varoquaux 17
  30. 30. 1 Favorite controversy: Global Signal Regression Time series 2 RS-fMRI Functional connectivity 4 3 1 Diagnosis ROIs Should we regress out the global signal mean? G Varoquaux 18
  31. 31. 1 Favorite controversy: Global Signal Regression Time series 2 RS-fMRI Functional connectivity 4 3 1 Diagnosis ROIs -0.1 -0.05 0.0 +0.05 +0.1 Relative prediction scores (AUC) Low-pass filtering and no global signal mean regression Low-pass filtering and global signal mean regression No low-pass filtering and no global signal mean regression COBRE ADNI ADNIDOD ACPI ABIDE [Dadi... 2019] G Varoquaux 18
  32. 32. 1 Favorite controversy: Global Signal Regression Time series 2 RS-fMRI Functional connectivity 4 3 1 Diagnosis ROIs Sparse inverse covariance not important [Varoquaux... 2010] was not that useful, afterall G Varoquaux 18
  33. 33. 2 Applicable methods: software Putting tools in the hands of users c Theodore W. Gray G Varoquaux 19
  34. 34. 2 Applicable methods: software Putting tools in the hands of users c Theodore W. Gray Every model is wrong, but some are available in easily- usable and open toolboxes. Alexandre Gramfort G Varoquaux 19
  35. 35. 2 Scikit-learn: machine-learning in Python scikit-learn We needed machine learning for our research so we built something and invited a few friends over G Varoquaux 20
  36. 36. 2 Scikit-learn: machine-learning in Python scikit-learn Now an industry standard Number of monthly users 2010 2012 2014 2016 2018 200000 400000 600000 800000 Cited 21384 times G Varoquaux 20
  37. 37. 2 Nilearn: statistical learning for neuroimaging http://nilearn.github.io ni Extracting signal in brain images Simple visualizations Functional connectivity pipeline Prediction of behavior & phenotype from brain images Making it easy to experiment and teach Open source, Python, Light-weight G Varoquaux 21
  38. 38. 2 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html G Varoquaux 22
  39. 39. 2 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Real examples are resource intensive: Data ⇒ Fight for good open data Computation ⇒ Find good algorithms and tradeoffs Forces us to distill the literature (as a review) G Varoquaux 22
  40. 40. 2 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Package development consolidates science and moves it outside the lab G Varoquaux 22
  41. 41. 2 Reusable science scikit-learn is the new machine-learning textbook nilearn is the new neuroimaging review article Experiments reproduced at each commit eg: brain reading nilearn.github.io/auto examples/02 decoding/plot miyawaki reconstruction.html Package development consolidates science and moves it outside the lab scipy-lectures: living book for Python in science G Varoquaux 22
  42. 42. 2 Community-driven development Our DNA: distributed development & decision making Gave man power 2010 2014 2018 0 25 50 # monthly contributors and the right focus People fix & improve what’s important to them G Varoquaux 23
  43. 43. 2 Community-driven development Our DNA: distributed development & decision making Gave man power 2010 2014 2018 0 25 50 # monthly contributors and the right focus People fix & improve what’s important to them Open communities for the win But software still needs sustainability and investment G Varoquaux 23
  44. 44. 2 Simplicity, a software-engineering factor of success Complexity increase superlinearly [An Experiment on Unit Increase in Problem Complexity, Woodfield 1979] 25% increase in problem complexity ⇒ 100% increase in code complexity G Varoquaux 24
  45. 45. 2 Simplicity, a software-engineering factor of success Complexity increase superlinearly [An Experiment on Unit Increase in Problem Complexity, Woodfield 1979] 25% increase in problem complexity ⇒ 100% increase in code complexity The 80/20 rule 80% of the usecases can be solved with 20% of the lines of code Avoid feature creep G Varoquaux 24
  46. 46. 2 Simplicity, a software-engineering factor of success Complexity increase superlinearly [An Experiment on Unit Increase in Problem Complexity, Woodfield 1979] 25% increase in problem complexity ⇒ 100% increase in code complexity The 80/20 rule 80% of the usecases can be solved with 20% of the lines of code Avoid feature creep Tensorflow https://github.com/tensorflow/tensorflow/pull/33460 ”Every [code addition] takes around 16 CPU/GPU hours of [quality control]. As such, we cannot just run every [code addition] through the [quality control] infrastructure.” G Varoquaux 24
  47. 47. 2 Technical debt Once an asset, now a liability G Varoquaux 25
  48. 48. 3 The truth of a method? Methods validation is an epistemological bottleneck G Varoquaux 26
  49. 49. Science The process of discovering knowledge and mechanisms Science helps shaping society Autism and vaccines: forged study: [Wakefield et al, Lancet 1998] ⇒ Drop in vaccination, measles outbreak Loss of trust in science is very costly We need solid evidence G Varoquaux 27
  50. 50. 3 Our evidence comes from massaging data In NeuroImaging, nothing is ”clearly visible” from the data Methods validity matter G Varoquaux 28
  51. 51. 3 Generalization: evidence from external validity Generalization builds broader theories [Varoquaux and Poldrack 2019] brings external validity to methods Paradigm 1: Seen G Varoquaux 29
  52. 52. 3 Generalization: evidence from external validity Generalization builds broader theories [Varoquaux and Poldrack 2019] brings external validity to methods Paradigm 1: Seen Paradigm 2: Imagined G Varoquaux 29
  53. 53. “A theory is a good theory if it satisfies two requirements: It must accurately describe a large class of observa- tions on the basis of a model that contains only a few arbitrary elements, and it must make definite predic- tions about the results of future observations.” Stephen Hawking, A Brief History of Time. G Varoquaux 30
  54. 54. 3 Evidence for generalization [Varoquaux... 2017, Poldrack... 2019] Quantitative tests of model predictions on new data Test setTrain set Full data G Varoquaux 31
  55. 55. 3 Noisy evidence for generalization [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 32
  56. 56. 3 Noisy evidence for generalization [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 32
  57. 57. 3 Noisy evidence for generalization [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 32
  58. 58. 3 Noisy evidence for generalization [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 32
  59. 59. 3 Noisy evidence for generalization [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 32
  60. 60. 3 Noisy evidence for generalization [Varoquaux 2017] The variability of methods on small samples undermines our evidence G Varoquaux 32
  61. 61. 3 What can we do about it? Increase sample sizes Analysis across cohorts Reduce analytical variability [Carp 2012] Methods researchers Define “canonical processing pipelines” Methods validation on many datasets Cut the dead branches e.g. [Varoquaux... 2010] Neuroscience endeavors Preregistration G Varoquaux 33
  62. 62. @GaelVaroquaux Methods development and validity Methods need to be validated strongly - Multiple datasets - Quantitative evidence, that generalize Technical debt must be controlled And analytical variability squashed
  63. 63. References I C. F. Beckmann and S. M. Smith. Probabilistic independent component analysis for functional magnetic resonance imaging. Trans Med Im, 23:137, 2004. P. Bellec, P. Rosa-Neto, O. Lyttelton, H. Benali, and A. Evans. Multi-level bootstrap analysis of stable clusters in resting-state fMRI. NeuroImage, 51:1126, 2010. J. Carp. On the plurality of (methodological) worlds: Estimating the analytic flexibility of fMRI experiments. Frontiers in neuroscience, 6, 2012. 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, 33:1914, 2012.
  64. 64. References II 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. M. F. Glasser, T. S. Coalson, E. C. Robinson, C. D. Hacker, J. Harwell, E. Yacoub, K. Ugurbil, J. Andersson, C. F. Beckmann, M. Jenkinson, ... A multi-modal parcellation of human cerebral cortex. Nature, 536(7615):171, 2016. R. Poldrack, G. Huckins, and G. Varoquaux. Establishment of best practices for evidence for prediction: a review. In press, 2019. S. Smith, K. Miller, G. Salimi-Khorshidi, M. Webster, C. Beckmann, T. Nichols, J. Ramsey, and M. Woolrich. Network modelling methods for fMRI. Neuroimage, 54:875, 2011.
  65. 65. References III 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 and R. C. Craddock. Learning and comparing functional connectomes across subjects. NeuroImage, 80: 405, 2013. G. Varoquaux and R. A. Poldrack. Predictive models avoid excessive reductionism in cognitive neuroimaging. Current Opinion in Neurobiology, 2019. G. Varoquaux, A. Gramfort, J. B. Poline, and B. Thirion. Brain covariance selection: better individual functional connectivity models using population prior. In NIPS. 2010.
  66. 66. References IV 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, page 562, 2011. 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. 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.

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