Reading Group: Structured Sparsity Models for Brain Decoding from fMRI data

SpeakerLuca Baldassarre
DateThursday, 31 May 2012
Time16:00 - 17:00
LocationFoster Court 123
Event seriesMachine Learning for Neuroimaging Reading Group

Structured sparsity methods have been recently proposed that allow to incorporate additional spatial and temporal information for estimating models for decoding mental states from fMRI data.
These methods carry the promise of being more interpretable than simpler Lasso or Elastic Net methods.
However, despite sparsity has often been advocated as leading to more interpretable models, we show that by itself sparsity and also structured sparsity could lead to unstable models.
We assess several structured sparsity models on accuracy, sparsity and stability and our results indicate that structured sparsity can mitigate some of the instability inherent in simpler sparse methods, but more research is required to build methods that can reliably infer relevant activation patterns from fMRI data.

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