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Incremental Cluster-wise Regression Analysis of Functional fMRI data

Incremental Cluster-wise Regression Analysis of Functional fMRI data. Sennay Ghebreab. Informatics Institute, Faculty of Science University of Amsterdam, The Netherlands. Background. What and how to map?. Sensory Stimulation. Brain Activation. What (visual) features to address ?

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Incremental Cluster-wise Regression Analysis of Functional fMRI data

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  1. Incremental Cluster-wise Regression Analysis of Functional fMRI data Sennay Ghebreab Informatics Institute, Faculty of Science University of Amsterdam, The Netherlands

  2. Background What and how to map? Sensory Stimulation Brain Activation • What (visual) features to address ? • What fMRI analysis approach to use ?

  3. Background + no prior model required + spatial & temporal pattern - patterns may be meaningless - work on all voxels PCA ICA data-driven CVA PLS + soft prior model required + spatial & temporal pattern - work on all voxels data-driven + model-based multivariate + meaningful patterns - good model/design required - spatial pattern only - work on single voxels GLM Model-based univariate

  4. Background CSCA video experiment Brain reading competition experiment

  5. Motivation Exploit data characteristics: data is continuous and multivariate, not discrete Exploit activation characteristic: activations are localized in time and space, not voxel specific or volume specific Functional data analysis Incremental Cluster-wise Regression

  6. Standard data analysis Mapping to high-level vector space • disregard of spatial correlation • disregard of multi-feature nature

  7. Functional data analysis Mapping to high-level functional space feature 1 time feature 2 • continuous data representation • multivariate data representation

  8. Functional data analysis registration functional PCA functional GLM

  9. Incremental cluster-wise regression Find fMRI functional subspace that best explains stimulation FD of HRF convolved amusement feature rating by subject 1 over 20 minutes (stimulation) FD of fmri of subject1 from BRC (64x64x32 voxels/FDs) while watching 20 minute movie (tr 1.75)

  10. Incremental cluster-wise regression Functional PCA Data described by 3 functional pca’s (capturing 91% of variability )

  11. Incremental cluster-wise regression Selection of activations similar to stimulation

  12. Incremental cluster-wise regression Cluster-wise GLM regression Y = XB + E

  13. Incremental cluster-wise regression After a few increments you end up with • Spatial patterns (voxel clusters) • Temporal patterns (activation cluster, predictor model) • HRF patterns (hrf estimation model) Do this for all subjects separately, then repeat same procedure for all resulting activation clusters of all subjects to say something about a group of subjects

  14. Example result Brain Reading Competition Cluster wise GLM fit F-statistics (green line: p<0.05) Prediction of amusement rating in movie 2 by subject 2, based on amusement rating and fmri of subject1 (red=prediction, blue=true rating)

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