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A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference

Abstract : Despite the clear potential benefits of combining fMRI and diffusion MRI in learning the neural pathways that underlie brain functions, little methodological progress has been made in this direction. In this paper, we propose a novel multimodal integration approach based on sparse Gaussian graphical model for estimating brain connectivity. Casting functional connectivity estimation as a sparse inverse covariance learning problem, we adapt the level of sparse penalization on each connection based on its anatomical capacity for functional interactions. Functional connections with little anatomical support are thus more heavily penalized. For validation, we showed on real data collected from a cohort of 60 subjects that additionally modeling anatomical capacity significantly increases subject consistency in the detected connection patterns. Moreover, we demonstrated that incorporating a connectivity prior learned with our multimodal connectivity estimation approach improves activation detection.
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Contributor : Bernard Ng Connect in order to contact the contributor
Submitted on : Monday, October 15, 2012 - 4:04:51 AM
Last modification on : Saturday, June 25, 2022 - 8:27:18 PM
Long-term archiving on: : Saturday, December 17, 2016 - 1:01:18 AM


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  • HAL Id : hal-00741631, version 1



Bernard Ng, Gaël Varoquaux, Jean-Baptiste Poline, Bertrand Thirion. A Novel Sparse Graphical Approach for Multimodal Brain Connectivity Inference. Medical Image Computing and Computer Assisted Intervention, Oct 2012, Nice, France. ⟨hal-00741631⟩



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