We formulate ICA as a sparse-recovery problem to give statistical control on the extracted brain maps based on a probabilistic model of the noise based on sole assumption that the interesting latent factors are sparsely-activated.
Patterns extracted by ICA from fMRI datasets display interpretable salient features, but also some background noise present to a varying degree in the different patterns.
We introduce a paradigm-free probabilistic model of the fMRI signal based on the assumption that the interesting latent factors are spatially sparse. From this model, we show that a simple algorithm using ICA can recover sparse activated regions in the fMRI signal with an exact statistical control on specificity and sensitivity.
We shown on real fMRI data that, unlike other existing methods, this algorithm finds the same consistent regions when ran on degraded data. Also, we show that uninterpretable patterns are rejected under the null hypothesis, due to the assumption of sparsity.
For more information, please see
- G. Varoquaux, M. Keller, J.-B. Poline, P. Ciuciu, B. Thirion.
ICA-based sparse feature recovery from fMRI datasets, Biomedical Imaging, IEEE International Symposium on, 2010.