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Spatio-Temporal Clustering

This work discusses a generative model for analyzing fMRI data that incorporates spatio-temporal clustering. Active components represent localized clusters of brain activity with temporal signatures correlated to the activation paradigm, while null components account for background activity that is temporally uncorrelated. The method applies an Expectation-Maximization (EM) algorithm for parameter estimation and features how clusters' properties can be inferred at each voxel over time. Results from auditory and face data demonstrate the model's efficacy in distinguishing brain activity patterns.

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Spatio-Temporal Clustering

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  1. Spatio-TemporalClustering Will Penny Karl Friston Acknowledgments: Stefan Kiebel and John Ashburner The Wellcome Department of Imaging Neuroscience, UCL http//:www.fil.ion.ucl.ac.uk/~wpenny

  2. Generative Model • We have ACTIVE components which describe spatially localised clusters of activity with a temporal signature correlated with the activation paradigm. • We have NULL components which describe spatially distributed background activity temporally uncorrelated with the paradigm. • At each voxel and time point fMRI data is a mixture of ACTIVE and NULL components.

  3. Generative Model S1 r0 m1 S2 r1 m2 r2 The fundamental quantities of interest are the properties of spatial clusters of activation

  4. Generative Model At each voxel i and time point t 1. Select component k with probability Spatial Prior 2. Draw a sample from component k’s temporal model General Linear Model

  5. Generative Model Scan 9

  6. Parameter Estimation Expectation-Maximisation (EM) algorithm Temporal E-Step Spatial Posterior Normalizer

  7. Parameter Estimation Expectation-Maximisation (EM) algorithm M-Step Prototype time series for component k Variant of Iteratively Reweighted Least Squares mk and Sk updated using line search

  8. Auditory Data SPM MGLM (K=4)

  9. Face Data SPM MGLM (K=2)

  10. Face Data Prototype time series for cluster (dotted line) GLM Estimate (solid line) 60 secs

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