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Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain George Chen, Evelina Fedorenko, Nancy Kanwisher,

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Presentation on theme: "Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain George Chen, Evelina Fedorenko, Nancy Kanwisher,"— Presentation transcript:

1 Deformation-Invariant Sparse Coding for Modeling Spatial Variability of Functional Patterns in the Brain George Chen, Evelina Fedorenko, Nancy Kanwisher, Polina Golland 12/16/2011NIPS MLINI Workshop 20111

2 Talk Outline 1.Finding correspondences between functional regions in the brain 2.A new generative model 3.Results for language fMRI study 12/16/2011NIPS MLINI Workshop 20112

3 Functional Region Correspondences 12/16/2011NIPS MLINI Workshop 20113 Given stimulus, get functional activation regions Subject 1 Subject 2 Align to common anatomical space Functional variability! Goal: Find correspondences between “parcels” contiguous region in brain group-level parcels Parcel : contiguous region in brain Biology : brain compartmentalized into functional modules  parcels represent these modules

4 Functional Variability Standard approach: just average in common anatomical space 12/16/2011NIPS MLINI Workshop 20114 Functional variability  less pronounced activation in group average space Subject 1 Subject 2 space Average space Aligned space

5 Previous Work Thirion et al. 2007: treat parcels as discrete objects and find parcel correspondences across subjects by matching Xu et al. 2009: generative, hierarchical model representing activation regions as Gaussian mixtures Sabuncu et al. 2010: groupwise functional registration 12/16/2011NIPS MLINI Workshop 20115

6 Previous Work Thirion et al. 2007: treat parcels as discrete objects and find parcel correspondences across subjects by matching Xu et al. 2009: generative, hierarchical model representing activation regions as Gaussian mixtures Sabuncu et al. 2010: groupwise functional registration 12/16/2011NIPS MLINI Workshop 20116

7 Our Generative Model 12/16/2011NIPS MLINI Workshop 20117 To generate image for a subject: 1.Choose weights for each group-level parcel 2.Form weighted sum of group-level parcels 3.Deform pre-image and add noise Pre-image e.g. (0.2, 1) Group-level parcels 1: 2: Goal: Estimate group-level parcels and deformations

8 Estimating Group-level Parcels and Deformations 12/16/2011NIPS MLINI Workshop 20118 sparsitysmoothnessparcelidentifiability

9 Language fMRI Study 12/16/2011NIPS MLINI Workshop 20119

10 Left frontal lobe Left temporal lobe Estimated Group-level Parcels Correspond to known language processing regions 12/16/2011NIPS MLINI Workshop 201110 Spatial support of group-level parcels Right temporal lobe Right cerebellum Example group-level parcels

11 Apply estimated deformation to fMRI data for each subject and redo standard fMRI group analysis on separate data 12/16/2011NIPS MLINI Workshop 201111 Modeling functional variability increases statistical significance in each group-level parcel Group-level Parcel Index Negative log p-value Improving fMRI Group Analysis with Estimated Deformations

12 12/16/2011NIPS MLINI Workshop 201112 space Subject 1 Subject 2 Average Aligned space Why is the variance so high for statistical significance values for our model?

13 Improving fMRI Group Analysis with Estimated Deformations 12/16/2011NIPS MLINI Workshop 201113 Average space Why is the variance so high for statistical significance values for our model? Group-level parcel support Variation using anatomical alignment only Variation using our model

14 Apply estimated deformation to fMRI data for each subject and redo standard fMRI group analysis 12/16/2011NIPS MLINI Workshop 201114 Modeling functional variability increases statistical significance in each group-level parcel Group-level Parcel Index Negative log p-value Improving fMRI Group Analysis with Estimated Deformations

15 Contributions 12/16/2011NIPS MLINI Workshop 201115

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