Abstract #3818

An overcomplete and efficient ICA for BOLD-fMRI.

Michael Hütel^1,2, Andrew Melbourne¹, Jonathan Rohrer², and Sebastien Ourselin^1,2

¹Translational Imaging Group, University College London, London, United Kingdom, ²Dementia Research Centre, University College London, London, United Kingdom

Independent Component Analysis (ICA) has been proven to produce compact representations of recurrent patterns in BOLD-fMRI imaging data. Most ICA implementations used in BOLD-fMRI, however, optimize for spatial sparse decompositions rather than independent decompositions. We describe a neural-network ICA framework that optimizes directly for sparsity and also allows for overcomplete basis representation.

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