Deriving a multi-subject functional-connectivity atlas to inform connectome estimation

Ronald Phlypo 1, 2, * Bertrand Thirion 1, 2 Gaël Varoquaux 1, 2
* Corresponding author
1 PARIETAL - Modelling brain structure, function and variability based on high-field MRI data
Inria Saclay - Ile de France, NEUROSPIN - Service NEUROSPIN
Abstract : The estimation of functional connectivity structure from functional neuroimaging data is an important step toward understanding the mechanisms of various brain diseases and building relevant biomarkers. Yet, such inferences have to deal with the low signal-to-noise ratio and the paucity of the data. With at our disposal a steadily growing volume of publicly available neuroimaging data, it is however possible to improve the estimation procedures involved in connectome mapping. In this work, we propose a novel learning scheme for functional connectivity based on sparse Gaussian graphical models that aims at minimizing the bias induced by the regularization used in the estimation, by carefully separating the estimation of the model support from the coefficients. Moreover, our strategy makes it possible to include new data with a limited computational cost. We illustrate the physiological relevance of the learned prior, that can be identified as a functional connectivity atlas, based on an experiment on 46 subjects of the Human Connectome Dataset.
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https://hal.inria.fr/hal-00991124
Contributor : Ronald Phlypo <>
Submitted on : Friday, September 19, 2014 - 2:29:33 PM
Last modification on : Thursday, March 7, 2019 - 3:34:14 PM
Long-term archiving on : Friday, April 14, 2017 - 3:15:32 PM

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Ronald Phlypo, Bertrand Thirion, Gaël Varoquaux. Deriving a multi-subject functional-connectivity atlas to inform connectome estimation. Medical Image Computing and Computer-Assisted Intervention, Sep 2014, Boston, United States. pp.185-192, ⟨10.1007/978-3-319-10443-0_24⟩. ⟨hal-00991124v4⟩

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