Extracting brain regions from rest fMRI with Total-Variation constrained dictionary learning

Abstract : Spontaneous brain activity reveals mechanisms of brain function and dysfunction. Its population-level statistical analysis based on functional images often relies on the de nition of brain regions that must summarize e ciently the covariance structure between the multiple brain networks. In this paper, we extend a network-discovery approach, namely dictionary learning, to readily extract brain regions. To do so, we intro duce a new tool drawing from clustering and linear decomposition methods by carefully crafting a penalty. Our approach automatically extracts regions from rest fMRI that better explain the data and are more stable across subjects than reference decomposition or clustering methods.
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https://hal.inria.fr/hal-00853242
Contributor : Alexandre Abraham <>
Submitted on : Thursday, September 11, 2014 - 3:54:55 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Long-term archiving on : Friday, December 12, 2014 - 10:45:53 AM

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Alexandre Abraham, Elvis Dohmatob, Bertrand Thirion, Dimitris Samaras, Gaël Varoquaux. Extracting brain regions from rest fMRI with Total-Variation constrained dictionary learning. MICCAI - 16th International Conference on Medical Image Computing and Computer Assisted Intervention - 2013, Sep 2013, Nagoya, Japan. ⟨hal-00853242⟩

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