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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| Formato: | SER |
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_03029743_v10265LNCS_n_p373_Lascano |
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todo:paper_03029743_v10265LNCS_n_p373_Lascano2023-10-03T15:18:45Z Extracting the groupwise core structural connectivity network: Bridging statistical and graph-theoretical approaches Lascano, N. Gallardo-Diez, G. Deriche, R. Mazauric, D. Wassermann, D. Zhu H. Niethammer M. Styner M. Zhu H. Shen D. Yap P.-T. Aylward S. Oguz I. 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. SER info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar 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 Lascano, N. Gallardo-Diez, G. Deriche, R. Mazauric, D. Wassermann, D. Zhu H. Niethammer M. Styner M. Zhu H. Shen D. Yap P.-T. Aylward S. Oguz I. 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. |
| format |
SER |
| author |
Lascano, N. Gallardo-Diez, G. Deriche, R. Mazauric, D. Wassermann, D. Zhu H. Niethammer M. Styner M. Zhu H. Shen D. Yap P.-T. Aylward S. Oguz I. |
| author_facet |
Lascano, N. Gallardo-Diez, G. Deriche, R. Mazauric, D. Wassermann, D. Zhu H. Niethammer M. Styner M. Zhu H. Shen D. Yap P.-T. Aylward S. Oguz I. |
| author_sort |
Lascano, N. |
| 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 |
| url |
http://hdl.handle.net/20.500.12110/paper_03029743_v10265LNCS_n_p373_Lascano |
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1807322225442816000 |