SlideShare una empresa de Scribd logo

Towards psychoinformatics with machine learning and brain imaging

•
•
Gael Varoquaux
Gael Varoquaux

Informatics in the psychological sciences brings fascinating challenges as mental processes or pathologies have fuzzy definition and are hard to quantify. Brain imaging brings rich data on the neural substrate of these concepts, yet it is a non trivial link. The goal of this presentation is to put forward basic ideas of "psychoinformatics", using advanced processing on brain images to quantify better the elements of psychology. It discusses how machine learning can bridge brain images to behavior: to describe better mental processes involved in brain activity, or to extract biomarkers of pathologies, individual traits, or cognition.

Ciencias
1 de 72
Descargar para leer sin conexión
Towards psychoinformatics with machine learning and brain imaging Gaël Varoquaux
Towards psychoinformatics with machine learning and brain imaging Gaël Varoquaux Propose and illustrate a research program
Accumulation of functional brain data G Varoquaux 2
Theories of the mind Laws of the mind for perception, decision, action, emotion... G Varoquaux 3
Studying mental processes 1 Craft an experimental condition that recruits it G Varoquaux 4
Studying mental processes 1 Craft an experimental condition that recruits it 2 Do an elementary psychological manipulation G Varoquaux 4
Studying mental processes - -Results in a contrast Study by oppositions G Varoquaux 4
Studying mental processes - -Results in a contrast Study by oppositions Results tied to a simple psychological manipulation bound to a paradigm G Varoquaux 4
Proposing Generalization to build broader theories bridging paradigms Paradigm 1: Seen G Varoquaux 5
Proposing Generalization to build broader theories bridging paradigms Paradigm 1: Seen Paradigm 2: Imagined G Varoquaux 5
Generalization for broader theories bridging paradigms Seen objects G Varoquaux 6
Brain imaging ⇒ brain-mind associations Difficulty: modeling behavior & cognition G Varoquaux 7
Predictive models can broaden theories by generalizing brain-mind associations to arbitrary new tasks and stimuli [Varoquaux and Poldrack 2018] 1 Beyond oppositions: encoding 2 Generalizing across tasks 3 Universal cognitive representations 4 Across subjects: biomarkers G Varoquaux 8
1 Beyond oppositions: encoding [Eickenberg... 2017] G Varoquaux 9
1 Decomposing psychological process To study vision: Breaking down stimuli G Varoquaux 10
1 Decomposing psychological process To study vision: Breaking down stimuli [Hubel and Wiesel 1962] Neurons receptive to Gabors (edges) G Varoquaux 10
1 Decomposing psychological process To study vision: Breaking down stimuli [Hubel and Wiesel 1962] Neurons receptive to Gabors (edges) [Logothetis... 1995] Shapes in inferior temporal cortex G Varoquaux 10
1 Decomposing psychological process To study vision: Breaking down stimuli Image V1 cortex V2 cortex Inferior temporal cortex Fusiform face area Jack? Is there a “face” region? A “foot” region? A “left big toe” region? G Varoquaux 10
1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs G Varoquaux 11
1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs G Varoquaux 11
1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - G Varoquaux 11
1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - Representing only one aspect of the stimuli: too much reductionism G Varoquaux 11
1 Not crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - Representing only one aspect of the stimuli: too much reductionism Encoding Building complex representations of stimuli Predicting brain response from them G Varoquaux 11
1 Decomposing visual stimuli Low-level visual cortex is tuned to natural image statistics [Olshausen et al. 1996] What drives high-level representations? The concept of “left big toe”? G Varoquaux 12
1 Decomposing visual stimuli Low-level visual cortex is tuned to natural image statistics [Olshausen et al. 1996] What drives high-level representations? Convolutional Net G Varoquaux 12
1 Brain mapping: encoding with conv nets StimulirepresentationBrainactivity Input Layer 1 Layer 5 Linear predictive models Convolutional Net G Varoquaux 13
1 Brain mapping: encoding with conv nets StimulirepresentationBrainactivity Input Layer 1 Layer 5 Linear predictive models Convolutional Net Explains the brain activity from the stimuli much better than hand-crafted features On data from [Kay... 2008] natural images [Huth... 2012] movies [Eickenberg... 2017] G Varoquaux 13
1 Brain mapping: encoding with conv nets [Eickenberg... 2017] High-level conv-net layer map to high-level visual areas G Varoquaux 14
1 Brain mapping with conv nets: retinotopy Mapping response to exentricity: artifical retinotopy G Varoquaux 15
1 Brain mapping with conv nets: high-level concepts Opposing face versus place in the [Haxby... 2001] stimuli G Varoquaux 16
[Eickenberg... 2017] Beyond oppositions: encoding Recovers the hierarchy of visual modules Generalizes from natural images to retinotopy or face vs place G Varoquaux 17
2 Generalizing across tasks Characterizing the function of brain structures How much is observed activity a consequence of specificities of the paradigm? [Schwartz... 2013, Varoquaux... 2018] G Varoquaux 18
2 Large-scale decoding for reverse inference Cat Dog Ouaf Miaou [Poldrack... 2009] G Varoquaux 19
2 Large-scale decoding for reverse inference Cat Dog Ouaf Miaou [Poldrack... 2009] Variability: an opportunity and a challenge Technical heterogeneity (scanner, stimulus modality...) Paradigmatic isolation (cognition) [Newell 1973] G Varoquaux 19
2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? Describe tasks by their cognitive components Multi-label prediction: presence or absence of each label Visual Auditory Foot Hand Calculation Reading Checkboard Face Place Object Digit Saccade ... ... ... ... ... ... G Varoquaux 20
2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? Describe tasks by their cognitive components Multi-label prediction: presence or absence of each label Visual Auditory Checkboard Face ... ... ... ... Prediction across studies Describe a task never seen from the brain activity it evokes G Varoquaux 20
2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? + + Regions specific to that predict facets of cognition G Varoquaux 20
2 Decoding mega-analysis [Schwartz... 2013, Varoquaux... 2018] 30 studies 837 subjects 196 experimental conditions 6919 activation maps Different labs Dehaene Poldrack Wager ... Various cognitive domains Language Vision Decision making Mathematics ... All manually preprocessed, labeled, and curated G Varoquaux 21
2 Decoding mega-analysis [Schwartz... 2013, Varoquaux... 2018] 30 studies 837 subjects 196 experimental conditions 6919 activation maps Different labs Dehaene Poldrack Wager ... Various cognitive domains Language Vision Decision making Mathematics ... All manually preprocessed, labeled, and curated Decoding arbitrary new paradigms visualauditoryVcheckerboard Hcheckerboardobjectsscramblefacesplacesdigits wordsrighthandlefthandrightfootleftfootlanguagehumansound non-humansound 0.2 0.4 0.6 0.8 1.0 Predictionscore (ROCAUC) ontologydecoder logisticregression naiveBayes neurosynth ­0.3 ­0.2 ­0.1 0.0 +0.1 +0.2 +0.3 Relative score G Varoquaux 21
2 Mapping: regions specific to facets of cognition [Schwartz... 2013, Varoquaux... 2018] Contrasts: good rejection of confounds Decoding: reverse inference ⇒ Consensus to define regions The “place” concept Forward term effect Brain response to tasks with the “place” concept Forward ontology contrast Contrasting responses to “place” and related concepts Reverse decoder map Regions that predict a place- recognition task Consensus forward & reverse Regions selected by forward and reverse inference Replaces the careful crafting of control conditions G Varoquaux 22
2 A functional atlas: central gyrus, motor networks [Schwartz... 2013, Varoquaux... 2018] Neurosynth Reverse inference (Logistic regression) Decoding in an ontology z = -15 Reverse inference -15 nference DecodDecoding in an visual faces places objects scrambled words digits horizontal - vertical checkerboard Visual regions Contrasts -based analysis Neurosynth reverse inference Decoding and ontology + Language and auditory mapping in temporal cortex + Calculation and spatial attenation in IPS + Mapping the motor system G Varoquaux 23
[Varoquaux... 2018] Generalizing across tasks Cat Dog Ouaf Miaou Multi-label prediction Prediction (discriminative models) more robust to heterogeneity Decoding ⇒ evidence beyond ⇒ a paradigm Atlasing cognition G Varoquaux 24
Need an ontology of cognition Episodic memory working memory lexical memory Syntax comprehension Reading Mental calculation Shape recognition Face recognition Memory Language Vision G Varoquaux 25
3 Universal cognitive representations [Mensch... 2017] Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 26
3 Universal cognitive representations [Mensch... 2017] Face Shape Reading Shape Language Music Saccades CalculationMulti-task learning Solve many related but different problems Learn commonalities intermediate representations G Varoquaux 26
3 Deep architecture [Bengio 2009] ... Great for multiple output (tasks) G Varoquaux 27
3 Deep architecture [Bengio 2009] ... Great for multiple output (tasks) Millions of parameters, thousands of data points G Varoquaux 27
3 Deep architecture 2 2 Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify G Varoquaux 27
3 Shallow architecture 2 linear model Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify simplify more G Varoquaux 27
3 Shallow architecture 2 2 Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify simplify more G Varoquaux 27
3 Deep linear model – multi-task – Bayesian 4TB resting-state data HCP900 OpenfMRI HCP Camcan Brainomics 42000 task fMRI contrast maps for one model ... Many rest & task fMRI studies ... x Information flow x Learned network combination 1st layer assignment Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Joint training Bayesian deep learning Resting-state functional loadings 512 resting-state networks Task functional loadings 128 task-optimized networks Contrast maps 200,000 voxels Contrast name Probability vector FACE BODY HOUSE TOOL 512 components [Mensch... 2018] G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Improves decoding in each study 0% 20% 40% 60% 80% Decoding accuracy on test set Stop-signal w/ spoken & manual resp. The Human Connectome Project UCLA LA5C Simon task Plain or mirror-reversed text Stop-signal CamCan audio-visual Localizer Cross-language repetition priming Twin localizer High-level math Spatio-temporal judgement (retake) Constit. struct. of sent. & music Sentence/music complexity Word & object processing Emotion regulation Arithmetic & saccades Brainomics localizer Incidental encoding Spatio-temporal judgement False belief Visual object recognition Face recognition Stop-signal & classification Classification learning Compression Motor task & word/verb generation BART, stop-signal, emotion Rhyme judgment Auditory & Visual Oddball Weather prediction Stop-signal & classification (retake) Balloon Analog Risk-taking Mixed-gambles Classif. learning & reversal Task fMRI study Decoding from voxels Decoding from multi-study networks Chance level -5% 0% 5%10%15% Multi-study acc. gain G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Improves decoding in each study 4 16 32 100 400 Number of train subjects 0% 5% 10% 15% 20% Multistudyacc.gain Study Small studies benefit from large ones G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Enables smaller sample sizes 7 15 23 31 39 # training subjects in target study 70% 75% 80% 85% Testaccuracy Pinel et al. '07 9 18 28 37 47 70% 80% 90% Papadopoulos- Orfanos '12 60 12 60% 65% C (Sh 37 47 oulos- '12 60 121 181 242 302 60% 65% CamCan AV (Shafto et al. '14) Standard decoding Multi-study decoder 2 4 7 9 12 20% 40% 60% Cohen '09 G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
[Mensch... 2017] Universal cognitive representations Multi-task decoding: - decoding the original task labels Deep linear models: - intermediate representations across studies - statistical power from big to small studies Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 29
[Mensch... 2017] Universal cognitive representations Multi-task decoding: - decoding the original task labels Deep linear models: - intermediate representations across studies - statistical power from big to small studies Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 29
4 Across subjects: biomarkers G Varoquaux 30
4 Beyond heterogeneity: predicting autism across sites Accuracy Fraction of subjects used More data is better (up to 1000 subjects) [Abraham... 2017] G Varoquaux 31
4 Brain aging: a surrogate biomarker [Liem... 2017] Predicting brain aging = chronological age Multi-modal: brain connectivity and morphology Age prediction: mean absolute error of 4.3 years G Varoquaux 32
4 Brain aging: a surrogate biomarker [Liem... 2017] Predicting brain aging = chronological age Multi-modal: brain connectivity and morphology Age prediction: 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 G Varoquaux 32
@GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Cat Dog Ouaf Miaou
@GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions
@GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions Useful with imperfect labels Extracting common representations Surrogate biomarkers
@GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions Useful with imperfect labels Extracting common representations Surrogate biomarkers Software: nilearn http://nilearn.github.io ni
References I A. Abraham, M. P. 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, 147:736–745, 2017. Y. Bengio. Learning deep architectures for ai. Foundations and trends in Machine Learning, 2:1–127, 2009. M. Eickenberg, A. Gramfort, G. Varoquaux, and B. Thirion. Seeing it all: Convolutional network layers map the function of the human visual system. NeuroImage, 152:184–194, 2017. J. V. Haxby, I. M. Gobbini, M. L. Furey, ... Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293:2425, 2001. D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology, 160:106, 1962.
References II A. G. Huth, S. Nishimoto, A. T. Vu, and J. L. Gallant. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76:1210, 2012. K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant. Identifying natural images from human brain activity. Nature, 452:352, 2008. 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, 2017. N. K. Logothetis, J. Pauls, and T. Poggio. Shape representation in the inferior temporal cortex of monkeys. Current Biology, 5:552, 1995.
References III A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux. Learning neural representations of human cognition across many fMRI studies. In NIPS, 2017. A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux. Extracting universal representations of cognition across brain-imaging studies. In prep, 2018. A. Newell. You can’t play 20 questions with nature and win: Projective comments on the papers of this symposium. 1973. B. Olshausen ... Emergence of simple-cell remainsceptive field properties by learning a sparse code for natural images. Nature, 381:607, 1996. R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20:1364, 2009. Y. Schwartz, B. Thirion, and G. Varoquaux. Mapping cognitive ontologies to and from the brain. In NIPS, 2013.
References IV G. Varoquaux and R. A. Poldrack. Predictive models can overcome reductionism in cognitive neuroimaging. In rev, 2018. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014. G. Varoquaux, Y. Schwartz, R. A. Poldrack, B. Gauthier, D. Bzdok, J. Poline, and B. Thirion. Atlases of cognition with large-scale brain mapping. In rev, 2018.

Recomendados

Functional-connectome biomarkers to meet clinical needs?
Functional-connectome biomarkers to meet clinical needs?Functional-connectome biomarkers to meet clinical needs?
Functional-connectome biomarkers to meet clinical needs?Gael Varoquaux
 
Machine learning for functional connectomes
Machine learning for functional connectomesMachine learning for functional connectomes
Machine learning for functional connectomesGael Varoquaux
 
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...Joint causal inference on observational and experimental data - NIPS 2016 "Wh...
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...Sara Magliacane
 
Talk: Joint causal inference on observational and experimental data - NIPS 20...
Talk: Joint causal inference on observational and experimental data - NIPS 20...Talk: Joint causal inference on observational and experimental data - NIPS 20...
Talk: Joint causal inference on observational and experimental data - NIPS 20...Sara Magliacane
 
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ..."intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...Kevin McGrew
 
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...Kevin McGrew
 
Probability Forecasting - a Machine Learning Perspective
Probability Forecasting - a Machine Learning PerspectiveProbability Forecasting - a Machine Learning Perspective
Probability Forecasting - a Machine Learning Perspectivebutest
 
Selective inference and single-cell differential analysis
Selective inference and single-cell differential analysisSelective inference and single-cell differential analysis
Selective inference and single-cell differential analysistuxette
 

Recomendados

Functional-connectome biomarkers to meet clinical needs?
Functional-connectome biomarkers to meet clinical needs?Functional-connectome biomarkers to meet clinical needs?
Functional-connectome biomarkers to meet clinical needs?Gael Varoquaux
 
Machine learning for functional connectomes
Machine learning for functional connectomesMachine learning for functional connectomes
Machine learning for functional connectomesGael Varoquaux
 
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...Joint causal inference on observational and experimental data - NIPS 2016 "Wh...
Joint causal inference on observational and experimental data - NIPS 2016 "Wh...Sara Magliacane
 
Talk: Joint causal inference on observational and experimental data - NIPS 20...
Talk: Joint causal inference on observational and experimental data - NIPS 20...Talk: Joint causal inference on observational and experimental data - NIPS 20...
Talk: Joint causal inference on observational and experimental data - NIPS 20...Sara Magliacane
 
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ..."intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...
"intelligent" intelligence testing: Evaluating wihtin CHC domain test score ...Kevin McGrew
 
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...
How to evaulate the unusualness (base rate) of WJ IV cluster or test score di...Kevin McGrew
 
Probability Forecasting - a Machine Learning Perspective
Probability Forecasting - a Machine Learning PerspectiveProbability Forecasting - a Machine Learning Perspective
Probability Forecasting - a Machine Learning Perspectivebutest
 
Selective inference and single-cell differential analysis
Selective inference and single-cell differential analysisSelective inference and single-cell differential analysis
Selective inference and single-cell differential analysistuxette
 
Machine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questionsMachine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questionsGael Varoquaux
 
Atlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mappingAtlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mappingGael Varoquaux
 
Brain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizationsBrain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizationsGael Varoquaux
 
Brain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in PythonBrain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in PythonGael Varoquaux
 
Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...Gael Varoquaux
 
Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...Gael Varoquaux
 
Coding for science and innovation
Coding for science and innovationCoding for science and innovation
Coding for science and innovationGael Varoquaux
 
Succeeding in academia despite doing good_software
Succeeding in academia despite doing good_softwareSucceeding in academia despite doing good_software
Succeeding in academia despite doing good_softwareGael Varoquaux
 
Social-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsitySocial-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsityGael Varoquaux
 
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
 
Simple big data, in Python
Simple big data, in PythonSimple big data, in Python
Simple big data, in PythonGael Varoquaux
 
Evaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic valueEvaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic valueGael Varoquaux
 
Measuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imagingMeasuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imagingGael Varoquaux
 
Machine learning with missing values
Machine learning with missing valuesMachine learning with missing values
Machine learning with missing valuesGael Varoquaux
 
Dirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated dataDirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated dataGael Varoquaux
 
Representation learning in limited-data settings
Representation learning in limited-data settingsRepresentation learning in limited-data settings
Representation learning in limited-data settingsGael Varoquaux
 
Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...Gael Varoquaux
 
Similarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variablesSimilarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variablesGael Varoquaux
 
Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities Gael Varoquaux
 
A tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imagingA tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imagingGael Varoquaux
 
Scikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imagingScikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imagingGael Varoquaux
 
Computational practices for reproducible science
Computational practices for reproducible scienceComputational practices for reproducible science
Computational practices for reproducible scienceGael Varoquaux
 

Más contenido relacionado

Similar a Towards psychoinformatics with machine learning and brain imaging

Machine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questionsMachine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questionsGael Varoquaux
 
Atlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mappingAtlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mappingGael Varoquaux
 
Brain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizationsBrain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizationsGael Varoquaux
 
Brain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in PythonBrain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in PythonGael Varoquaux
 
Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...Gael Varoquaux
 
Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...Gael Varoquaux
 
Coding for science and innovation
Coding for science and innovationCoding for science and innovation
Coding for science and innovationGael Varoquaux
 
Succeeding in academia despite doing good_software
Succeeding in academia despite doing good_softwareSucceeding in academia despite doing good_software
Succeeding in academia despite doing good_softwareGael Varoquaux
 
Social-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsitySocial-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsityGael Varoquaux
 
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Ana Luísa Pinho
 
Simple big data, in Python
Simple big data, in PythonSimple big data, in Python
Simple big data, in PythonGael Varoquaux
 

Similar a Towards psychoinformatics with machine learning and brain imaging (11)

Machine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questionsMachine learning and cognitive neuroimaging: new tools can answer new questions
Machine learning and cognitive neuroimaging: new tools can answer new questions
 
Atlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mappingAtlases of cognition with large-scale human brain mapping
Atlases of cognition with large-scale human brain mapping
 
Brain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizationsBrain maps from machine learning? Spatial regularizations
Brain maps from machine learning? Spatial regularizations
 
Brain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in PythonBrain reading, compressive sensing, fMRI and statistical learning in Python
Brain reading, compressive sensing, fMRI and statistical learning in Python
 
Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...Python for brain mining: (neuro)science with state of the art machine learnin...
Python for brain mining: (neuro)science with state of the art machine learnin...
 
Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...Can we recover meaning full spatial information from multivariate pattern ana...
Can we recover meaning full spatial information from multivariate pattern ana...
 
Coding for science and innovation
Coding for science and innovationCoding for science and innovation
Coding for science and innovation
 
Succeeding in academia despite doing good_software
Succeeding in academia despite doing good_softwareSucceeding in academia despite doing good_software
Succeeding in academia despite doing good_software
 
Social-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsitySocial-sparsity brain decoders: faster spatial sparsity
Social-sparsity brain decoders: faster spatial sparsity
 
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
Individual Brain Charting, a high-resolution fMRI dataset for cognitive mappi...
 
Simple big data, in Python
Simple big data, in PythonSimple big data, in Python
Simple big data, in Python
 

Más de Gael Varoquaux

Evaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic valueEvaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic valueGael Varoquaux
 
Measuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imagingMeasuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imagingGael Varoquaux
 
Machine learning with missing values
Machine learning with missing valuesMachine learning with missing values
Machine learning with missing valuesGael Varoquaux
 
Dirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated dataDirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated dataGael Varoquaux
 
Representation learning in limited-data settings
Representation learning in limited-data settingsRepresentation learning in limited-data settings
Representation learning in limited-data settingsGael Varoquaux
 
Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...Gael Varoquaux
 
Similarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variablesSimilarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variablesGael Varoquaux
 
Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities Gael Varoquaux
 
A tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imagingA tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imagingGael Varoquaux
 
Scikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imagingScikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imagingGael Varoquaux
 
Computational practices for reproducible science
Computational practices for reproducible scienceComputational practices for reproducible science
Computational practices for reproducible scienceGael Varoquaux
 
Estimating Functional Connectomes: Sparsity’s Strength and Limitations
Estimating Functional Connectomes: Sparsity’s Strength and LimitationsEstimating Functional Connectomes: Sparsity’s Strength and Limitations
Estimating Functional Connectomes: Sparsity’s Strength and LimitationsGael Varoquaux
 
On the code of data science
On the code of data scienceOn the code of data science
On the code of data scienceGael Varoquaux
 
Scientist meets web dev: how Python became the language of data
Scientist meets web dev: how Python became the language of dataScientist meets web dev: how Python became the language of data
Scientist meets web dev: how Python became the language of dataGael Varoquaux
 
Scikit-learn: the state of the union 2016
Scikit-learn: the state of the union 2016Scikit-learn: the state of the union 2016
Scikit-learn: the state of the union 2016Gael Varoquaux
 
Inter-site autism biomarkers from resting state fMRI
Inter-site autism biomarkers from resting state fMRIInter-site autism biomarkers from resting state fMRI
Inter-site autism biomarkers from resting state fMRIGael Varoquaux
 
Scikit-learn for easy machine learning: the vision, the tool, and the project
Scikit-learn for easy machine learning: the vision, the tool, and the projectScikit-learn for easy machine learning: the vision, the tool, and the project
Scikit-learn for easy machine learning: the vision, the tool, and the projectGael Varoquaux
 
Building a cutting-edge data processing environment on a budget
Building a cutting-edge data processing environment on a budgetBuilding a cutting-edge data processing environment on a budget
Building a cutting-edge data processing environment on a budgetGael Varoquaux
 
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...Gael Varoquaux
 
Open Source Scientific Software
Open Source Scientific SoftwareOpen Source Scientific Software
Open Source Scientific SoftwareGael Varoquaux
 

Más de Gael Varoquaux (20)

Evaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic valueEvaluating machine learning models and their diagnostic value
Evaluating machine learning models and their diagnostic value
 
Measuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imagingMeasuring mental health with machine learning and brain imaging
Measuring mental health with machine learning and brain imaging
 
Machine learning with missing values
Machine learning with missing valuesMachine learning with missing values
Machine learning with missing values
 
Dirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated dataDirty data science machine learning on non-curated data
Dirty data science machine learning on non-curated data
 
Representation learning in limited-data settings
Representation learning in limited-data settingsRepresentation learning in limited-data settings
Representation learning in limited-data settings
 
Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...Better neuroimaging data processing: driven by evidence, open communities, an...
Better neuroimaging data processing: driven by evidence, open communities, an...
 
Similarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variablesSimilarity encoding for learning on dirty categorical variables
Similarity encoding for learning on dirty categorical variables
 
Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities Simple representations for learning: factorizations and similarities
Simple representations for learning: factorizations and similarities
 
A tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imagingA tutorial on Machine Learning, with illustrations for MR imaging
A tutorial on Machine Learning, with illustrations for MR imaging
 
Scikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imagingScikit-learn and nilearn: Democratisation of machine learning for brain imaging
Scikit-learn and nilearn: Democratisation of machine learning for brain imaging
 
Computational practices for reproducible science
Computational practices for reproducible scienceComputational practices for reproducible science
Computational practices for reproducible science
 
Estimating Functional Connectomes: Sparsity’s Strength and Limitations
Estimating Functional Connectomes: Sparsity’s Strength and LimitationsEstimating Functional Connectomes: Sparsity’s Strength and Limitations
Estimating Functional Connectomes: Sparsity’s Strength and Limitations
 
On the code of data science
On the code of data scienceOn the code of data science
On the code of data science
 
Scientist meets web dev: how Python became the language of data
Scientist meets web dev: how Python became the language of dataScientist meets web dev: how Python became the language of data
Scientist meets web dev: how Python became the language of data
 
Scikit-learn: the state of the union 2016
Scikit-learn: the state of the union 2016Scikit-learn: the state of the union 2016
Scikit-learn: the state of the union 2016
 
Inter-site autism biomarkers from resting state fMRI
Inter-site autism biomarkers from resting state fMRIInter-site autism biomarkers from resting state fMRI
Inter-site autism biomarkers from resting state fMRI
 
Scikit-learn for easy machine learning: the vision, the tool, and the project
Scikit-learn for easy machine learning: the vision, the tool, and the projectScikit-learn for easy machine learning: the vision, the tool, and the project
Scikit-learn for easy machine learning: the vision, the tool, and the project
 
Building a cutting-edge data processing environment on a budget
Building a cutting-edge data processing environment on a budgetBuilding a cutting-edge data processing environment on a budget
Building a cutting-edge data processing environment on a budget
 
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...
Scikit-learn: apprentissage statistique en Python. Créer des machines intelli...
 
Open Source Scientific Software
Open Source Scientific SoftwareOpen Source Scientific Software
Open Source Scientific Software
 

Último

Speech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxSpeech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxpriyankatabhane
 
Observational constraints on mergers creating magnetism in massive stars
Observational constraints on mergers creating magnetism in massive starsObservational constraints on mergers creating magnetism in massive stars
Observational constraints on mergers creating magnetism in massive starsSérgio Sacani
 
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...D. B. S. College Kanpur
 
User Guide: Magellan MX™ Weather Station
User Guide: Magellan MX™ Weather StationUser Guide: Magellan MX™ Weather Station
User Guide: Magellan MX™ Weather StationColumbia Weather Systems
 
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxMicrophone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxpriyankatabhane
 
PROJECTILE MOTION-Horizontal and Vertical
PROJECTILE MOTION-Horizontal and VerticalPROJECTILE MOTION-Horizontal and Vertical
PROJECTILE MOTION-Horizontal and VerticalMAESTRELLAMesa2
 
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptx
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptxGENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptx
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptxRitchAndruAgustin
 
Biological classification of plants with detail
Biological classification of plants with detailBiological classification of plants with detail
Biological classification of plants with detailhaiderbaloch3
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024AyushiRastogi48
 
Harmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms PresentationHarmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms Presentationtahreemzahra82
 
Pests of jatropha_Bionomics_identification_Dr.UPR.pdf
Pests of jatropha_Bionomics_identification_Dr.UPR.pdfPests of jatropha_Bionomics_identification_Dr.UPR.pdf
Pests of jatropha_Bionomics_identification_Dr.UPR.pdfPirithiRaju
 
trihybrid cross , test cross chi squares
trihybrid cross , test cross chi squarestrihybrid cross , test cross chi squares
trihybrid cross , test cross chi squaresusmanzain586
 
Quarter 4_Grade 8_Digestive System Structure and Functions
Quarter 4_Grade 8_Digestive System Structure and FunctionsQuarter 4_Grade 8_Digestive System Structure and Functions
Quarter 4_Grade 8_Digestive System Structure and FunctionsCharlene Llagas
 
bonjourmadame.tumblr.com bhaskar's girls
bonjourmadame.tumblr.com bhaskar's girlsbonjourmadame.tumblr.com bhaskar's girls
bonjourmadame.tumblr.com bhaskar's girlshansessene
 
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubai
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In DubaiDubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubai
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubaikojalkojal131
 
Thermodynamics ,types of system,formulae ,gibbs free energy .pptx
Thermodynamics ,types of system,formulae ,gibbs free energy .pptxThermodynamics ,types of system,formulae ,gibbs free energy .pptx
Thermodynamics ,types of system,formulae ,gibbs free energy .pptxuniversity
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPirithiRaju
 
《Queensland毕业文凭-昆士兰大学毕业证成绩单》
《Queensland毕业文凭-昆士兰大学毕业证成绩单》《Queensland毕业文凭-昆士兰大学毕业证成绩单》
《Queensland毕业文凭-昆士兰大学毕业证成绩单》rnrncn29
 
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxSTOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxMurugaveni B
 

Último (20)

Speech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptxSpeech, hearing, noise, intelligibility.pptx
Speech, hearing, noise, intelligibility.pptx
 
Observational constraints on mergers creating magnetism in massive stars
Observational constraints on mergers creating magnetism in massive starsObservational constraints on mergers creating magnetism in massive stars
Observational constraints on mergers creating magnetism in massive stars
 
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...
Fertilization: Sperm and the egg—collectively called the gametes—fuse togethe...
 
User Guide: Magellan MX™ Weather Station
User Guide: Magellan MX™ Weather StationUser Guide: Magellan MX™ Weather Station
User Guide: Magellan MX™ Weather Station
 
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptxMicrophone- characteristics,carbon microphone, dynamic microphone.pptx
Microphone- characteristics,carbon microphone, dynamic microphone.pptx
 
PROJECTILE MOTION-Horizontal and Vertical
PROJECTILE MOTION-Horizontal and VerticalPROJECTILE MOTION-Horizontal and Vertical
PROJECTILE MOTION-Horizontal and Vertical
 
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptx
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptxGENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptx
GENERAL PHYSICS 2 REFRACTION OF LIGHT SENIOR HIGH SCHOOL GENPHYS2.pptx
 
Biological classification of plants with detail
Biological classification of plants with detailBiological classification of plants with detail
Biological classification of plants with detail
 
Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024Vision and reflection on Mining Software Repositories research in 2024
Vision and reflection on Mining Software Repositories research in 2024
 
Harmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms PresentationHarmful and Useful Microorganisms Presentation
Harmful and Useful Microorganisms Presentation
 
Pests of jatropha_Bionomics_identification_Dr.UPR.pdf
Pests of jatropha_Bionomics_identification_Dr.UPR.pdfPests of jatropha_Bionomics_identification_Dr.UPR.pdf
Pests of jatropha_Bionomics_identification_Dr.UPR.pdf
 
trihybrid cross , test cross chi squares
trihybrid cross , test cross chi squarestrihybrid cross , test cross chi squares
trihybrid cross , test cross chi squares
 
Quarter 4_Grade 8_Digestive System Structure and Functions
Quarter 4_Grade 8_Digestive System Structure and FunctionsQuarter 4_Grade 8_Digestive System Structure and Functions
Quarter 4_Grade 8_Digestive System Structure and Functions
 
AZOTOBACTER AS BIOFERILIZER.PPTX
AZOTOBACTER AS BIOFERILIZER.PPTXAZOTOBACTER AS BIOFERILIZER.PPTX
AZOTOBACTER AS BIOFERILIZER.PPTX
 
bonjourmadame.tumblr.com bhaskar's girls
bonjourmadame.tumblr.com bhaskar's girlsbonjourmadame.tumblr.com bhaskar's girls
bonjourmadame.tumblr.com bhaskar's girls
 
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubai
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In DubaiDubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubai
Dubai Calls Girl Lisa O525547819 Lexi Call Girls In Dubai
 
Thermodynamics ,types of system,formulae ,gibbs free energy .pptx
Thermodynamics ,types of system,formulae ,gibbs free energy .pptxThermodynamics ,types of system,formulae ,gibbs free energy .pptx
Thermodynamics ,types of system,formulae ,gibbs free energy .pptx
 
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdfPests of soyabean_Binomics_IdentificationDr.UPR.pdf
Pests of soyabean_Binomics_IdentificationDr.UPR.pdf
 
《Queensland毕业文凭-昆士兰大学毕业证成绩单》
《Queensland毕业文凭-昆士兰大学毕业证成绩单》《Queensland毕业文凭-昆士兰大学毕业证成绩单》
《Queensland毕业文凭-昆士兰大学毕业证成绩单》
 
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptxSTOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
STOPPED FLOW METHOD & APPLICATION MURUGAVENI B.pptx
 

Towards psychoinformatics with machine learning and brain imaging

  • 1. Towards psychoinformatics with machine learning and brain imaging Gaël Varoquaux
  • 2. Towards psychoinformatics with machine learning and brain imaging Gaël Varoquaux Propose and illustrate a research program
  • 3. Accumulation of functional brain data G Varoquaux 2
  • 4. Theories of the mind Laws of the mind for perception, decision, action, emotion... G Varoquaux 3
  • 5. Studying mental processes 1 Craft an experimental condition that recruits it G Varoquaux 4
  • 6. Studying mental processes 1 Craft an experimental condition that recruits it 2 Do an elementary psychological manipulation G Varoquaux 4
  • 7. Studying mental processes - -Results in a contrast Study by oppositions G Varoquaux 4
  • 8. Studying mental processes - -Results in a contrast Study by oppositions Results tied to a simple psychological manipulation bound to a paradigm G Varoquaux 4
  • 9. Proposing Generalization to build broader theories bridging paradigms Paradigm 1: Seen G Varoquaux 5
  • 10. Proposing Generalization to build broader theories bridging paradigms Paradigm 1: Seen Paradigm 2: Imagined G Varoquaux 5
  • 11. Generalization for broader theories bridging paradigms Seen objects G Varoquaux 6
  • 12. Brain imaging ⇒ brain-mind associations Difficulty: modeling behavior & cognition G Varoquaux 7
  • 13. Predictive models can broaden theories by generalizing brain-mind associations to arbitrary new tasks and stimuli [Varoquaux and Poldrack 2018] 1 Beyond oppositions: encoding 2 Generalizing across tasks 3 Universal cognitive representations 4 Across subjects: biomarkers G Varoquaux 8
  • 14. 1 Beyond oppositions: encoding [Eickenberg... 2017] G Varoquaux 9
  • 15. 1 Decomposing psychological process To study vision: Breaking down stimuli G Varoquaux 10
  • 16. 1 Decomposing psychological process To study vision: Breaking down stimuli [Hubel and Wiesel 1962] Neurons receptive to Gabors (edges) G Varoquaux 10
  • 17. 1 Decomposing psychological process To study vision: Breaking down stimuli [Hubel and Wiesel 1962] Neurons receptive to Gabors (edges) [Logothetis... 1995] Shapes in inferior temporal cortex G Varoquaux 10
  • 18. 1 Decomposing psychological process To study vision: Breaking down stimuli Image V1 cortex V2 cortex Inferior temporal cortex Fusiform face area Jack? Is there a “face” region? A “foot” region? A “left big toe” region? G Varoquaux 10
  • 19. 1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs G Varoquaux 11
  • 20. 1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs G Varoquaux 11
  • 21. 1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - G Varoquaux 11
  • 22. 1 Crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - Representing only one aspect of the stimuli: too much reductionism G Varoquaux 11
  • 23. 1 Not crafting stimuli for cognitive oppositions Is there a “face” region? A “foot” region? A “left big toe” region? vs - Representing only one aspect of the stimuli: too much reductionism Encoding Building complex representations of stimuli Predicting brain response from them G Varoquaux 11
  • 24. 1 Decomposing visual stimuli Low-level visual cortex is tuned to natural image statistics [Olshausen et al. 1996] What drives high-level representations? The concept of “left big toe”? G Varoquaux 12
  • 25. 1 Decomposing visual stimuli Low-level visual cortex is tuned to natural image statistics [Olshausen et al. 1996] What drives high-level representations? Convolutional Net G Varoquaux 12
  • 26. 1 Brain mapping: encoding with conv nets StimulirepresentationBrainactivity Input Layer 1 Layer 5 Linear predictive models Convolutional Net G Varoquaux 13
  • 27. 1 Brain mapping: encoding with conv nets StimulirepresentationBrainactivity Input Layer 1 Layer 5 Linear predictive models Convolutional Net Explains the brain activity from the stimuli much better than hand-crafted features On data from [Kay... 2008] natural images [Huth... 2012] movies [Eickenberg... 2017] G Varoquaux 13
  • 28. 1 Brain mapping: encoding with conv nets [Eickenberg... 2017] High-level conv-net layer map to high-level visual areas G Varoquaux 14
  • 29. 1 Brain mapping with conv nets: retinotopy Mapping response to exentricity: artifical retinotopy G Varoquaux 15
  • 30. 1 Brain mapping with conv nets: high-level concepts Opposing face versus place in the [Haxby... 2001] stimuli G Varoquaux 16
  • 31. [Eickenberg... 2017] Beyond oppositions: encoding Recovers the hierarchy of visual modules Generalizes from natural images to retinotopy or face vs place G Varoquaux 17
  • 32. 2 Generalizing across tasks Characterizing the function of brain structures How much is observed activity a consequence of specificities of the paradigm? [Schwartz... 2013, Varoquaux... 2018] G Varoquaux 18
  • 33. 2 Large-scale decoding for reverse inference Cat Dog Ouaf Miaou [Poldrack... 2009] G Varoquaux 19
  • 34. 2 Large-scale decoding for reverse inference Cat Dog Ouaf Miaou [Poldrack... 2009] Variability: an opportunity and a challenge Technical heterogeneity (scanner, stimulus modality...) Paradigmatic isolation (cognition) [Newell 1973] G Varoquaux 19
  • 35. 2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? Describe tasks by their cognitive components Multi-label prediction: presence or absence of each label Visual Auditory Foot Hand Calculation Reading Checkboard Face Place Object Digit Saccade ... ... ... ... ... ... G Varoquaux 20
  • 36. 2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? Describe tasks by their cognitive components Multi-label prediction: presence or absence of each label Visual Auditory Checkboard Face ... ... ... ... Prediction across studies Describe a task never seen from the brain activity it evokes G Varoquaux 20
  • 37. 2 Generalizing to arbitrary paradigms [Schwartz... 2013, Varoquaux... 2018] What is this brain doing? + + Regions specific to that predict facets of cognition G Varoquaux 20
  • 38. 2 Decoding mega-analysis [Schwartz... 2013, Varoquaux... 2018] 30 studies 837 subjects 196 experimental conditions 6919 activation maps Different labs Dehaene Poldrack Wager ... Various cognitive domains Language Vision Decision making Mathematics ... All manually preprocessed, labeled, and curated G Varoquaux 21
  • 39. 2 Decoding mega-analysis [Schwartz... 2013, Varoquaux... 2018] 30 studies 837 subjects 196 experimental conditions 6919 activation maps Different labs Dehaene Poldrack Wager ... Various cognitive domains Language Vision Decision making Mathematics ... All manually preprocessed, labeled, and curated Decoding arbitrary new paradigms visualauditoryVcheckerboard Hcheckerboardobjectsscramblefacesplacesdigits wordsrighthandlefthandrightfootleftfootlanguagehumansound non-humansound 0.2 0.4 0.6 0.8 1.0 Predictionscore (ROCAUC) ontologydecoder logisticregression naiveBayes neurosynth ­0.3 ­0.2 ­0.1 0.0 +0.1 +0.2 +0.3 Relative score G Varoquaux 21
  • 40. 2 Mapping: regions specific to facets of cognition [Schwartz... 2013, Varoquaux... 2018] Contrasts: good rejection of confounds Decoding: reverse inference ⇒ Consensus to define regions The “place” concept Forward term effect Brain response to tasks with the “place” concept Forward ontology contrast Contrasting responses to “place” and related concepts Reverse decoder map Regions that predict a place- recognition task Consensus forward & reverse Regions selected by forward and reverse inference Replaces the careful crafting of control conditions G Varoquaux 22
  • 41. 2 A functional atlas: central gyrus, motor networks [Schwartz... 2013, Varoquaux... 2018] Neurosynth Reverse inference (Logistic regression) Decoding in an ontology z = -15 Reverse inference -15 nference DecodDecoding in an visual faces places objects scrambled words digits horizontal - vertical checkerboard Visual regions Contrasts -based analysis Neurosynth reverse inference Decoding and ontology + Language and auditory mapping in temporal cortex + Calculation and spatial attenation in IPS + Mapping the motor system G Varoquaux 23
  • 42. [Varoquaux... 2018] Generalizing across tasks Cat Dog Ouaf Miaou Multi-label prediction Prediction (discriminative models) more robust to heterogeneity Decoding ⇒ evidence beyond ⇒ a paradigm Atlasing cognition G Varoquaux 24
  • 43. Need an ontology of cognition Episodic memory working memory lexical memory Syntax comprehension Reading Mental calculation Shape recognition Face recognition Memory Language Vision G Varoquaux 25
  • 44. 3 Universal cognitive representations [Mensch... 2017] Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 26
  • 45. 3 Universal cognitive representations [Mensch... 2017] Face Shape Reading Shape Language Music Saccades CalculationMulti-task learning Solve many related but different problems Learn commonalities intermediate representations G Varoquaux 26
  • 46. 3 Deep architecture [Bengio 2009] ... Great for multiple output (tasks) G Varoquaux 27
  • 47. 3 Deep architecture [Bengio 2009] ... Great for multiple output (tasks) Millions of parameters, thousands of data points G Varoquaux 27
  • 48. 3 Deep architecture 2 2 Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify G Varoquaux 27
  • 49. 3 Shallow architecture 2 linear model Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify simplify more G Varoquaux 27
  • 50. 3 Shallow architecture 2 2 Great for multiple output (tasks) Millions of parameters, thousands of data points Simplify simplify more G Varoquaux 27
  • 51. 3 Deep linear model – multi-task – Bayesian 4TB resting-state data HCP900 OpenfMRI HCP Camcan Brainomics 42000 task fMRI contrast maps for one model ... Many rest & task fMRI studies ... x Information flow x Learned network combination 1st layer assignment Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Joint training Bayesian deep learning Resting-state functional loadings 512 resting-state networks Task functional loadings 128 task-optimized networks Contrast maps 200,000 voxels Contrast name Probability vector FACE BODY HOUSE TOOL 512 components [Mensch... 2018] G Varoquaux 28
  • 52. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
  • 53. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Improves decoding in each study 0% 20% 40% 60% 80% Decoding accuracy on test set Stop-signal w/ spoken & manual resp. The Human Connectome Project UCLA LA5C Simon task Plain or mirror-reversed text Stop-signal CamCan audio-visual Localizer Cross-language repetition priming Twin localizer High-level math Spatio-temporal judgement (retake) Constit. struct. of sent. & music Sentence/music complexity Word & object processing Emotion regulation Arithmetic & saccades Brainomics localizer Incidental encoding Spatio-temporal judgement False belief Visual object recognition Face recognition Stop-signal & classification Classification learning Compression Motor task & word/verb generation BART, stop-signal, emotion Rhyme judgment Auditory & Visual Oddball Weather prediction Stop-signal & classification (retake) Balloon Analog Risk-taking Mixed-gambles Classif. learning & reversal Task fMRI study Decoding from voxels Decoding from multi-study networks Chance level -5% 0% 5%10%15% Multi-study acc. gain G Varoquaux 28
  • 54. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Improves decoding in each study 4 16 32 100 400 Number of train subjects 0% 5% 10% 15% 20% Multistudyacc.gain Study Small studies benefit from large ones G Varoquaux 28
  • 55. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks Enables smaller sample sizes 7 15 23 31 39 # training subjects in target study 70% 75% 80% 85% Testaccuracy Pinel et al. '07 9 18 28 37 47 70% 80% 90% Papadopoulos- Orfanos '12 60 12 60% 65% C (Sh 37 47 oulos- '12 60 121 181 242 302 60% 65% CamCan AV (Shafto et al. '14) Standard decoding Multi-study decoder 2 4 7 9 12 20% 40% 60% Cohen '09 G Varoquaux 28
  • 56. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
  • 57. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
  • 58. 3 Deep linear model – multi-task – Bayesian OpenfMRI HCP Camcan Brainomics ... ... Information flow Non-negative matrix factorization SUCCESSFUL GO HOUSE HORIZONTAL CHECKERBOARD Study-wise decoding REWARD BALLOON EXPLODE ............ Deep linear model Task functional loadings 128 task-optimized networks [Mensch... 2018] Intermediate representation: Task-optimized networks G Varoquaux 28
  • 59. [Mensch... 2017] Universal cognitive representations Multi-task decoding: - decoding the original task labels Deep linear models: - intermediate representations across studies - statistical power from big to small studies Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 29
  • 60. [Mensch... 2017] Universal cognitive representations Multi-task decoding: - decoding the original task labels Deep linear models: - intermediate representations across studies - statistical power from big to small studies Face Shape Reading Shape Language Music Saccades Calculation G Varoquaux 29
  • 61. 4 Across subjects: biomarkers G Varoquaux 30
  • 62. 4 Beyond heterogeneity: predicting autism across sites Accuracy Fraction of subjects used More data is better (up to 1000 subjects) [Abraham... 2017] G Varoquaux 31
  • 63. 4 Brain aging: a surrogate biomarker [Liem... 2017] Predicting brain aging = chronological age Multi-modal: brain connectivity and morphology Age prediction: mean absolute error of 4.3 years G Varoquaux 32
  • 64. 4 Brain aging: a surrogate biomarker [Liem... 2017] Predicting brain aging = chronological age Multi-modal: brain connectivity and morphology Age prediction: 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 G Varoquaux 32
  • 65. @GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Cat Dog Ouaf Miaou
  • 66. @GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions
  • 67. @GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions Useful with imperfect labels Extracting common representations Surrogate biomarkers
  • 68. @GaelVaroquaux Psychoinformatics with machine learning Prediction for broader theories AI to model stimuli / the world Explicit generalization across paradigms Beyond oppositions Encoding complete descriptions of tasks Decoding multiple facets of cognitions Useful with imperfect labels Extracting common representations Surrogate biomarkers Software: nilearn http://nilearn.github.io ni
  • 69. References I A. Abraham, M. P. 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, 147:736–745, 2017. Y. Bengio. Learning deep architectures for ai. Foundations and trends in Machine Learning, 2:1–127, 2009. M. Eickenberg, A. Gramfort, G. Varoquaux, and B. Thirion. Seeing it all: Convolutional network layers map the function of the human visual system. NeuroImage, 152:184–194, 2017. J. V. Haxby, I. M. Gobbini, M. L. Furey, ... Distributed and overlapping representations of faces and objects in ventral temporal cortex. Science, 293:2425, 2001. D. H. Hubel and T. N. Wiesel. Receptive fields, binocular interaction and functional architecture in the cat’s visual cortex. The Journal of physiology, 160:106, 1962.
  • 70. References II A. G. Huth, S. Nishimoto, A. T. Vu, and J. L. Gallant. A continuous semantic space describes the representation of thousands of object and action categories across the human brain. Neuron, 76:1210, 2012. K. N. Kay, T. Naselaris, R. J. Prenger, and J. L. Gallant. Identifying natural images from human brain activity. Nature, 452:352, 2008. 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, 2017. N. K. Logothetis, J. Pauls, and T. Poggio. Shape representation in the inferior temporal cortex of monkeys. Current Biology, 5:552, 1995.
  • 71. References III A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux. Learning neural representations of human cognition across many fMRI studies. In NIPS, 2017. A. Mensch, J. Mairal, B. Thirion, and G. Varoquaux. Extracting universal representations of cognition across brain-imaging studies. In prep, 2018. A. Newell. You can’t play 20 questions with nature and win: Projective comments on the papers of this symposium. 1973. B. Olshausen ... Emergence of simple-cell remainsceptive field properties by learning a sparse code for natural images. Nature, 381:607, 1996. R. A. Poldrack, Y. O. Halchenko, and S. J. Hanson. Decoding the large-scale structure of brain function by classifying mental states across individuals. Psychological Science, 20:1364, 2009. Y. Schwartz, B. Thirion, and G. Varoquaux. Mapping cognitive ontologies to and from the brain. In NIPS, 2013.
  • 72. References IV G. Varoquaux and R. A. Poldrack. Predictive models can overcome reductionism in cognitive neuroimaging. In rev, 2018. G. Varoquaux and B. Thirion. How machine learning is shaping cognitive neuroimaging. GigaScience, 3:28, 2014. G. Varoquaux, Y. Schwartz, R. A. Poldrack, B. Gauthier, D. Bzdok, J. Poline, and B. Thirion. Atlases of cognition with large-scale brain mapping. In rev, 2018.