Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches

Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuroscience. Recent evidence suggests there’s a tightly connected network shared between humans. Obtaining this network will, among many advantages, allow us to focus cognitive an...

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Detalles Bibliográficos
Autor principal:	Wassermann, Demián
Publicado:	2017
Materias:	Brain connectivity Core graph problem Diffusion MRI Group-wise connectome Image processing Magnetic resonance imaging Medical imaging Connectivity analysis Core graph Diffusion mris Graph theoretical approach Structural connectivity Structure-function relationship Graph theory
Acceso en línea:	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v10265LNCS_n_p373_Lascano http://hdl.handle.net/20.500.12110/paper_03029743_v10265LNCS_n_p373_Lascano
Aporte de:	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) de Universidad de Buenos Aires

id	paper:paper_03029743_v10265LNCS_n_p373_Lascano
record_format	dspace
spelling	paper:paper_03029743_v10265LNCS_n_p373_Lascano2023-06-08T15:28:14Z Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches Wassermann, Demián Brain connectivity Core graph problem Diffusion MRI Group-wise connectome Image processing Magnetic resonance imaging Medical imaging Brain connectivity Connectivity analysis Core graph Diffusion mris Graph theoretical approach Group-wise connectome Structural connectivity Structure-function relationship Graph theory Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuroscience. 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. © Springer International Publishing AG 2017. Fil:Wassermann, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. 2017 https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v10265LNCS_n_p373_Lascano http://hdl.handle.net/20.500.12110/paper_03029743_v10265LNCS_n_p373_Lascano
institution	Universidad de Buenos Aires
institution_str	I-28
repository_str	R-134
collection	Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA)
topic	Brain connectivity Core graph problem Diffusion MRI Group-wise connectome Image processing Magnetic resonance imaging Medical imaging Brain connectivity Connectivity analysis Core graph Diffusion mris Graph theoretical approach Group-wise connectome Structural connectivity Structure-function relationship Graph theory
spellingShingle	Brain connectivity Core graph problem Diffusion MRI Group-wise connectome Image processing Magnetic resonance imaging Medical imaging Brain connectivity Connectivity analysis Core graph Diffusion mris Graph theoretical approach Group-wise connectome Structural connectivity Structure-function relationship Graph theory Wassermann, Demián Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
topic_facet	Brain connectivity Core graph problem Diffusion MRI Group-wise connectome Image processing Magnetic resonance imaging Medical imaging Brain connectivity Connectivity analysis Core graph Diffusion mris Graph theoretical approach Group-wise connectome Structural connectivity Structure-function relationship Graph theory
description	Finding the common structural brain connectivity network for a given population is an open problem, crucial for current neuroscience. 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. © Springer International Publishing AG 2017.
author	Wassermann, Demián
author_facet	Wassermann, Demián
author_sort	Wassermann, Demián
title	Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
title_short	Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
title_full	Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
title_fullStr	Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
title_full_unstemmed	Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches
title_sort	extracting the groupwise core structural connectivity network: bridging statistical and graph-theoretical approaches
publishDate	2017
url	https://bibliotecadigital.exactas.uba.ar/collection/paper/document/paper_03029743_v10265LNCS_n_p373_Lascano http://hdl.handle.net/20.500.12110/paper_03029743_v10265LNCS_n_p373_Lascano
work_keys_str_mv	AT wassermanndemian extractingthegroupwisecorestructuralconnectivitynetworkbridgingstatisticalandgraphtheoreticalapproaches
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Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches

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