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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Communication dans un congrès
MICCAI - 16th International Conference on Medical Image Computing and Computer Assisted Intervention - 2013, Sep 2013, Nagoya, Japan. Springer, 2013
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https://hal.inria.fr/hal-00853242
Contributeur : Alexandre Abraham <>
Soumis le : jeudi 11 septembre 2014 - 15:54:55
Dernière modification le : mardi 20 février 2018 - 09:40:21
Document(s) archivé(s) le : vendredi 12 décembre 2014 - 10:45:53

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  • HAL Id : hal-00853242, version 1

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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. Springer, 2013. 〈hal-00853242〉

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