Shared Response Model Tutorial! What works? How can it help you? Po-Hsuan (Cameron) Chen and Hejia Hasson Lab! Feb 15, 2017!
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1 Shared Response Model Tutorial! What works? How can it help you? Po-Hsuan (Cameron) Chen and Hejia Hasson Lab! Feb 15, 2017!
2 Outline! SRM Theory! SRM on fmri! Hands-on SRM with BrainIak!
3 SRM Theory!
4 Multi-view representation learning! What is multi-view learning and why is it important?! Exist an unknown underlying distribution, each view is a realization of it! Conventional ML models concatenate multiple views into one single view! Successful multi-view learning leads to better utilization of data with better performance!
5 Motivation! Modern fmri studies of human brain use data from multiple subjects! scientific reason! statistical reason! How can we aggregate fmri data from multiple subjects?! Challenge! Inter-subject variability in anatomical structure and functional topographies!
6 Given data from training subjects,!! Prediction:! can we predict the brain response of a test subject?!! Classification:! given brain response from a test subject, can we classify what s the stimulus?!
7 Shared latent temporal response +! subject specific functional topographies! Subject 1! voxels! time! subject specific functional! topographies is the key! Subject 2! voxels! time! fmri data matrix! voxel space! time trajectory! low dimensional! shared feature space!
8 Data collected while subjects receiving stimulus! Temporally synchronized naturalistic stimuli! 1. Sample a wide range of response from the subject! 2. Use time as anchor for learning shared response! sherlock! raider! forrest! audiobook! movie watching! movie and image! watching! auditory film! listening! audio book! listening!
9 Factor Model! voxels! time! voxels! features!!! features! time! fmri data!
10 fmri response as linear combination of functional topographies! voxels! time! voxels! features! time!!!! features! fmri data! functional! topographies! features!
11 Learning what is shared across subjects! subjects x voxels! time! voxels!! features!! features! time! One!s&mulus! Many!subjects! fmri data! subject specific! functional topographies! shared! features!
12 fmri data as linear combination of subject specific functional topographies! subjects x voxels! time! features! time! voxels!!! features! fmri data! subject specific! functional topographies! shared! features!
13 Shared Response Model in one figure! [J. Cohen et al., Nat. Neur., 2017]!
14 Shared Response Model (SRM) is a latent variable model! s t N (0, s ) s s t d x it s t N (W i s t + µ i, 2 i I) Wi T W i = I W i,µ i, i x it W i not square! m s t W i shared elicited response at time t! observations of subject i at time t! functional topographies for subject i! noise level for subject i s data! x it 2 i Feature identification with dimensionality reduction! Constrained EM algorithm! [P.-H. Chen et al., NIPS, 2015]!
![!!! 0 E s x [S] =! +! X m & distribution! W m fmri! data! subject!](/docs-images/89/97514543/images/15-2.jpg "specific! response! shared! response! subject specific! functional!")
15 Shared features, subject specific functional topographies, and subject specific response! voxel space! feature space! =! +! X 1 W 1!!!! 0 E s x [S] =! +! X m & distribution! W m fmri! data! subject! specific! response! shared! response! subject specific! functional! topographies! shared features!
16 SRM on fmri!
17 Evaluation with various datasets! Different MRI machines! Different institutes! Different subjects! Different preprocessing protocols! Different brain regions! Different data size! sherlock! raider! forrest! audiobook!
18 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
19 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
20 1 st!half! movie! movie! data! data! learn bases! image data/! movie data! project to! bases! projected! data! output! Generalization to new subject! with time segment matching! subject 1! subject 1! subject 1! Learning Subject Specific Bases!! shared response! (template)! Testing on Held-out Subject!!! train! classifier! subject m-1! subject m-1! subject m-1! subject m! W 1 W m 1 W m subject m! W 1 W m 1 W m subject m! test! Datasets! sherlock! forrest! raider!
21 1 st!half! movie! movie! data! data! learn bases! image 2 nd!half!! data/! movie!data! project to! bases! projected! data! output! Generalization to new subject! with time segment matching! subject 1! subject 1! subject 1! Learning Subject Specific Bases!! shared response! (template)! Testing on Held-out Subject!!! train! classifier! subject m-1! subject m-1! subject m-1! subject m! W 1 W m 1 W m subject m! W 1 W m 1 W m subject m! test! Datasets! sherlock! forrest! raider!
22 Generalization to new subject! with time segment matching!
23 Generalization to new subject and distinct stimulus with image classification! movie! data! subject 1! Learning Subject Specific Bases!! subject m-1! subject m! Dataset! raider! learn bases! image Image! data/! movie data! data! project to! bases! projected! data! W 1 W m 1 W m subject 1! subject 1! shared response! (template)! Testing on Held-out Subject!!! subject m-1! subject m-1! subject m! W 1 W m 1 W m subject m! output! train! classifier! test!
24 Generalization to new subject and distinct stimulus with image classification! Outperforms within-subject classification!
25 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
26 Classifying mental states! 40 subjects listening to narrated story! Separate 40 subjects into 2 groups! Two groups receive different prior contexts! Leading to different interpretations of the story! Predict prior context of a left-out subject! Dataset! audiobook!
27 Classifying mental states! shared! by all! Group 1! Group 2! within! group! individual! shared! by all! within! group! individual! Group! prediction! accuracy! with SRM! 0.72±0.06!
28 Classifying mental states! shared! by all! Group 1! Group 2! within! group! individual! shared! by all! within! group! individual! Group! prediction! accuracy! with SRM! 0.72±0.06! 0.54±0.06!
29 Classifying mental states! shared! by all! Group 1! Group 2! within! group! individual! shared! by all! within! group! individual! Group! prediction! accuracy! with SRM! 0.72±0.06! 0.54±0.06! 0.70±0.04!
30 Classifying mental states! shared! by all! Group 1! Group 2! within! group! individual! shared! by all! within! group! individual! Group! prediction! accuracy! with SRM! 0.72±0.06! 0.54±0.06! 0.70±0.04! 0.82±0.04!
31 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
32 [Slide from [Work Nicolas by Ivy Schuck]! Zhu]!
33 [Slide from Nicolas Schuck]!
34 [Slide from Nicolas Schuck]!
35 SRM with non-temporally synchronized dataset! Each observation is a noisy sample of the brain state! t! Subject 1! State!1! State!2! State!1! State!3! State!1! State!4! Subject 2! t! State!3! State!4! State!1! State!2!
36 SRM with non-temporally synchronized dataset! Step 1: reordering! t! Subject 1! State!1! State!2! State!3! State!4! Subject 2! t! State!1! State!2! State!3! State!4!
37 SRM with non-temporally synchronized dataset! Step 2: down sampling! t! Subject 1! State!1! State!2! State!3! State!4! Subject 2! t! State!1! State!2! State!3! State!4! Step 3: fit SRM with preprocessed data!
38 [Work by Ivy Zhu]!
39 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
40 Mapping Visual Field Maps: Retinotopy! [Work by Michael J. Arcaro]!
41 Original Phase Maps vs. SRM! Orig! SRM! Orig! SRM! Orig! SRM! Orig! SRM! [Work by Michael J. Arcaro]!
42 Transformation between subjects! [Work by Michael J. Arcaro]!
43 [Work by Michael J. Arcaro]!
44 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
45 Quantifying dimensionality of shared response! [J. Chen et al., Nat. Neur., 2017]! [P.-H. Chen et al. NIPS, 2015]!
46 Quantifying dimensionality of shared response! [H. Zhang et al. ArXiv, 2016]!
47 SRM on fmri! Generalize to new stimulus! Generalize to new subject! Decoupling shared and individual response! SRM with non-temporally synchronized stimulus! SRM with retinotopy! Quantifying dimensionality of shared response! Searchlight SRM!
48 Why searchlights?! Structured Sparsity! Searchlight!!!!! X i W i t-th column of! t-th column of! S t-th column of! W i X i t-th column of! S
49 Searchlight SRM! localized analysis across the whole brain! X 1 For a searchlight! X 2 SRM! W i,s Analysis! statistics! e.g. accuracy! Statistics map! X 3 Brain image! Learning phase! (training data)! Testing phase! (testing data)!
50 Time segment matching with searchlight SRM! Accuracy map from time segment matching experiment (Sherlock)!
51 How can SRM help?! What can SRM do?! Multi-subject data driven de-noising! Aggregation of multi-subject data! Generalizable to new subject and new stimulus! Outperform within subject classification! Decoupling of shared and individual response! Can I use SRM on my data?! Temporally synchronized stimuli! - No problem! Non-temporally synchronized stimuli! - Can also work with preprocessing!
52 List of extensions! SRM with word embedding! Semi-supervised SRM! Distributed SRM! Convolutional Autoencoder SRM! Spatial SRM! Kernelized SRM! Gaussian Process SRM! Information theoretic SRM! Matrix Normal SRM!
53 Key Takeaways! When should you consider using SRM?! 1. I want to figure out what s shared/not shared in my multiset data (multi-subject, multi-modality, multi-region, etc)! 2. I have multi-set data, I want better prediction accuracy!!
54 Hands-on SRM with BrainIak!
55 Code ready to use on your dataset! Simple setting, one line command to fit SRM on your data! Handles different numbers of voxels across subjects/views!
56 Jupyter notebook examples! Need jupyter notebook and brainiak properly installed with python 3!!! 1. git clone 2. cd SRM_tutorial! 3. chmod +x download-data.sh 4../download-data.sh 5. jupyter notebook!
57 ! Thank you! cameronphchen.github.io!
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