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Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches

Abstract : Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuro-science. Recent evidence suggests there's a tightly connected network shared between humans. Obtaining this network will, among many advantages , allow us to focus cognitive and clinical analyses on common connections, thus increasing their statistical power. In turn, knowledge about the common network will facilitate novel analyses to understand the structure-function relationship in the brain. In this work, we present a new algorithm for computing the core structural connectivity network of a subject sample combining graph theory and statistics. Our algorithm works in accordance with novel evidence on brain topology. We analyze the problem theoretically and prove its complexity. Using 309 subjects, we show its advantages when used as a feature selection for connectivity analysis on populations, outperforming the current approaches.
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https://hal.inria.fr/hal-01426870
Contributor : Demian Wassermann <>
Submitted on : Saturday, January 7, 2017 - 2:51:03 PM
Last modification on : Tuesday, November 5, 2019 - 1:38:02 PM
Long-term archiving on: : Saturday, April 8, 2017 - 12:15:42 PM

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Distributed under a Creative Commons Attribution 4.0 International License

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Nahuel Lascano, Guillermo Gallardo, Rachid Deriche, Dorian Mazauric, Demian Wassermann. Extracting the Groupwise Core Structural Connectivity Network: Bridging Statistical and Graph-Theoretical Approaches. Information Processing in Medical Imaging, 2017, Boone, United States. ⟨10.1007/978-3-319-59050-9_30⟩. ⟨hal-01426870v3⟩

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