Section:
New Results
Identifying predictive regions from fMRI with TV-1 prior
Participants :
Gaël Varoquaux [Correspondant] , Bertrand Thirion, Alexandre Gramfort.
Decoding, i.e. predicting stimulus related quantities from functional
brain images, is a powerful tool to demonstrate differences between
brain activity across conditions. However, unlike standard brain
mapping, it offers no guaranties on the localization of this
information. Here, we consider decoding as a statistical estimation
problem and show that injecting a spatial segmentation prior leads to
unmatched performance in recovering predictive regions. Specifically,
we use 1 penalization to set voxels to zero and Total-Variation (TV)
penalization to segment regions. Our contribution is two-fold. On the
one hand, we show via extensive experiments that, amongst a large
selection of decoding and brain-mapping strategies, TV+1 leads to
best region recovery (see Fig. 8 ). On the other hand, we consider implementation
issues related to this estimator. To tackle efficiently this joint
prediction-segmentation problem we introduce a fast optimization
algorithm based on a primal-dual approach. We also tackle automatic
setting of hyper-parameters and fast computation of image operation on
the irregular masks that arise in brain imaging.
Figure
8. Results on fMRI data from (from left to right
F-test, ElasticNet and TV- ). The TV- regularized
model segments neuroscientificly meaningful predictive regions in
agreement with univariate statistics while the ElasticNet yields
sparse although very scattered non-zero weights.
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More details can be found in [59] .
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