Brain mapping 2.0: Putting together fmri datasets to map cognitive ontologies to the brain. Bertrand Thirion

Transcription

1 Brain mapping 2.0: Putting together fmri datasets to map cognitive ontologies to the brain Bertrand Thirion 1

2 Outline Introduction: statistical inference in neuroimaging and big data Part I: Mapping cognitive ontologies to brain function Part II: Statistical summaries for multi-contrasts studies Part III: Application Resting-state-based diagnosis 2

3 Statistical inference and sample size OASIS dataset, effect of sex on gray matter density n=20 n=30 n=50 Reproducibility by bootstrap: <1% 2% 5% n=70 8% n=100 n=200 17% 40% 3

4 Neuroimaging in the low power regime Low power unreliable findings Low positive predictive value: low likelihood that a statistically significant finding is actually true Exaggerated estimate of the magnitude of discovered effects winner's curse 4

5 Should n=16 be the reference? See [Ten ironic rules for non-statistical reviewers, Friston, NIMG 2012] One may be interested in medium-to-small size effects (d<1), especially in population studies Survive multiple comparisons requires more power Accuracy: Confidence intervals about estimated effects adequately estimated only from large n 5

6 Big data in neuroimaging Number of samples is not very large: A few hundreds in most cases images in enigma HCP: 4800 resting-state scans * 500 subjects But each sample is complex Large images: 105 to 106 voxels Low SNR, structured noise, between-subject variability 6

7 HCP ML, 29/01/2015 Has anyone on the list run group-wise analysis on the HCP resting state data, and if so what tools did you use? I am having memory issues when running more than 10 subjects and I was wondering if anyone has a way of getting around the large memory requirements when concatenating in time. 7

8 Part 1: Large-scale analysis of brain function Use predictive modeling to build an accurate model of brain function 8

9 Mapping cognitive ontologies to brain activity 9

10 fmri me(t)(g)a-analyses Coordinate based meta-analyses Activation peaks coordinates summarizing studies Fair functional specificity, but little spatial information per study brainmap.org [Laird et al., 2005] neurosynth.org [Yarkoni et al., 2011] 10

11 fmri me(t)(g)a-analyses Image based meta-analyses Use the actual statistical images More spatial information per study [Salimi-Khorshidi et al., 2009] Less datasets Less functional specificity

12 An image database Datasets OpenfMRI (18 studies) Neurospin (10 studies) Functional localizers, Language & music structure Arithmetic & saccades Language temporal bottleneck HCP k studies subjects images 12

13 Some Pitfalls with mega-analyses [Poldrack Psychol Sci 2009] Classify tasks across individuals 8 studies, 130 subjects One contrast/task per study Multiclass problem Leave-one subject out cross validation loop Cannot conclude that the classifier predicts the cognitive processes 13

14 Forward inference? What is the brain response common to these stimuli? Which regions are recruited by tasks containing a given term? General Linear Model (GLM) for terms effects x=y β+ε x Y Conditions images β ε Terms effect Design matrix Error 14

15 Forward inference Correlation of the design matrix: difficulties from the heavily correlated terms (database bias) 15

16 Results 16

17 Results 17

18 Reverse inference? What is this brain doing? Which regions are predictive of tasks containing a given term? Multilabel classification problem more than one class may be associated with each sample Predict the CogPO terms Data: experimental condition images Target 18

19 Hierarchical decoder Cross validation Problems Leave-one study out, leave-one lab out Predict unseen conditions Class distribution (imbalance & covariate shift) Long tailed distribution of terms [Schwartz et al. NIPS 2013] 19

20 Precision: does not label as positive a sample that is negative Recall: finds all the positive samples [Schwartz et al. NIPS 2013] 20

21 Precision: does not label as positive a sample that is negative Recall: finds all the positive samples 21

22 Forward and reverse inference Place 22

23 Cognitive brain atlases [Schwartz et al. in prep] 23

24 Discussion Nothing new? Make inference invariant to lab choices (scanning & stimulation parameters, baseline definition, description of protocols etc.) Question the relevance of cognitive terms in view of large-scale analyses Lots of room for improvement Need more open data! 24

25 Part 2: Statistical summaries for multi-contrasts studies How to build a picture of brain cognitive architecture from fmri datasets? 25

26 Atlasing brain activation patterns Goal: learn a brain atlas, i.e. extract a collection of cognitive atoms from brain images Technical issues: Inter-subject variability: both in spatial definition and functional characteristics How to measure model quality? Perform model selection? Tractability 26

27 Dataset and variability [Pinel et al. BMC neurosci 2007] 27

28 Functional segregation = sparse coding [Olshausen Nature 1996] Similar to learning contrasts + maps closely related to ICA or clustering 28

29 Related prior work [Smith PNAS 2009] ICA on the brainmap data [Lashkari Neuroimage 2012] Clustering of voxels 29

30 Extension to multiple subjects 30

31 Extension to multiple subjects Use a combination of the two: spatial correspondences + constraints on dictionary loadings x 31

32 Propositions and discussion Facilitate reuse of data Better archiving of protocols, non-image-data Give data a second life Workshop at the end of 2014 on meta-analysis Data sharing Localizer dataset [Varoquaux et al. IPMI 2013] 32

33 Discussion Variations on the theme: Resting-state fmri Allow variability across subject [Varoquaux et al. 2011] More complex priors than sparsity [Abraham et al. 2011] The computational challenge Fast online method: 104s on 100K images Memory-efficiency is the key! 33

34 Learning brain atlases with the TV-l1 prior More complex priors segment cleaner structures from restingstate datasets [Abraham et al MICCAI 2013] 34

35 Application to resting-state-based diagnosis Good statistical summaries for large-scale resting-state experiments 35

36 Large scale functional connectome mapping 871 subjects, GB of data 36

37 Large scale functional connectome mapping Classification of ASD patients Public ABIDE dataset [Di Martino et al. Mol. Psych. 2014] Large heterogeneity: multi-centric study Functional connectivity: weakly sensitive marker 68% correct classification (65% when generalizing across sites) 37

38 Large scale functional connectome mapping What are the most important factors of the pipeline? Response: the choice of the atlas. [Abraham et al. Subm to Mol. Psych.] 38

39 Large scale functional connectome mapping More subjects = more accuracy Impact of the number of regions Asymptote not yet reached at n=871 Number of subjects 39

40 Using rest data to better model task data Blend representation modeling and task classification into a unified statistical learning problem. Model = multinomial logistic regression with factored coefficients, coupled with an autoencoder [Bzdok et al. NIPS 2015] 40

41 Using rest data to better model task data Result: more accurate and interpretable neural models of psychological tasks in a reference dataset, as well as better generalization to other datasets. [Bzdok et al. NIPS 2015] 41

42 Scaling up functional imaging Obtain results less dependent on particular lab choices + generalization to new labs Reliable model selection Still limited by the amount of public data available Take advantage of unlabeled data 42

43 From great ideas to great software Machine learning for neuroimaging Scikit-learn-like API BSD, Python, OSS Classification of neuroimaging data (decoding) Functional connectivity analysis 43

44 Data... right handleft hand Button press reading Graspingorientation judgment Sentence checkerboards listen-read Hand side judgement false belief mechanistic (auditory) False belief mechanistic (visual) Speech-non speech expression - intension Computation instructions saccades rest gender assessment Intention - Face trustworthiness random 44

45 Individual brain charting in a nutshell Subproject of HBP Scan 12 subjects on 50 different acqs / 7 years / 38 of fmri : cover various cognitive domains High resolution (1.5 mm) All the data will be made public 45

46 Acknowledgement Parietal G. Varoquaux, P. Ciuciu A. Gramfort, O. Grisel, L. Estève, Y. Schwartz, F. Pedregosa, E. Dohmatob, A. H. Idrobo, V. Fritsch, M. Eickenberg, R. Bricquet A. Abraham D. Bzdok A. Frau N. Chauffert Other collaborators (thanks for the data) S. Dehaene E. Eger, R. Poldrack, K. Jimura, J. Haxby C. F. Gorgolevski JB. Poline Brainpedia project 46