A Reduced-Dimension fmri! Shared Response Model
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1 A Reduced-Dimension fmri! Shared Response Model Po-Hsuan! (Cameron)! Chen 1! Janice! Chen 2! Yaara! Yeshurun 2! Uri! Hasson 2! James! Haxby 3! Peter! Ramadge 1! 1 Department of Electrical Engineering, Princeton University! 2 Princeton Neuroscience Institute and Department of Psychology, Princeton University! 3 Department of Psychological and Brain Sciences! and Center for Cognitive Neuroscience, Dartmouth College!
2 Functional magnetic resonance! imaging (fmri) data! TR = Time of repetition! Volume! Voxel!
3 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!
4 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?!
5 Learn subject specific functional topographies! Subject 1! Subject 2! voxel space! shared feature space!
6 Data collected while subjects receiving stimuli! 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!
7 Factor Model! voxels! time! voxels! features!!! features! time! fmri data!
8 fmri response as linear combination of functional topographies! voxels! time! voxels! features! time!!!! features! fmri data! functional! topographies! features!
9 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!
10 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!
11 A generative model! feature space! S shared features!
12 A generative model! feature space! W 1 $ S W m subject specific! functional! topographies! shared features!
13 A generative model! voxel space! feature space! X 1 W 1 $ $ S X m W m synthesized! shared response! subject specific! functional! topographies! shared features!
14 A generative model! voxel space! feature space! min! X 1 W 1 $ $ $ S min! X m W m fmri data! synthesized! shared response! subject specific! functional! topographies! shared features!
15 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!
![=$ +$ X 1 W 1 $ $ $ $ 0 E s x [S] =$ +$ X m & distribution! W m fmri! data!](/docs-images/89/99040749/images/16-2.jpg "subject! specific! response! shared! response! subject specific! functional!")
16 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!
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 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! Dataset$ sherlock! forrest! raider!
19 1 st $half$ movie! movie$ data! data$ learn bases! image 2 nd $half$$ data/! movie$data$ 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! Dataset$ sherlock! forrest! raider!
20 Generalization to new subject! with time segment matching!
21 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!
22 Generalization to new subject and distinct stimulus with image classification! Outperforms within-subject classification!
23 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!
24 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!
25 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!
26 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!
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! 0.54±0.06! 0.70±0.04! 0.82±0.04!
28 ! Conclusion! SRM achieves state-of-the-art performance using multi-subject data, demonstrates higher sensitivity! SRM outperforms within subject classification! Low dimensional representation of brain response! SRM decouples shared and individual responses, allowing detection of group specific responses! Recent Extensions:! Kernel version of SRM to unlock information in higher order statistics from fmri data! Information theoretic based SRM!
29 A Reduced-Dimension fmri! Shared Response Model Poster 23$ Po-Hsuan! (Cameron)! Chen! Janice! Chen! Yaara! Yeshurun! Uri! Hasson! James! Haxby! Peter! Ramadge!
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