Please use this identifier to cite or link to this item: http://cimat.repositorioinstitucional.mx/jspui/handle/1008/1096
A novel robust network community identification method forstructural connectome analysis
Juan Luis Villarreal Haro
Acceso Abierto
Atribución-NoComercial
COMPUTACIÓN
MATEMÁTICAS INDUSTRIALES
In complex networks, the communities of a graph are clusters grouped by the connectivity attributes of nodes. Our case study is the brain structural connectomes derived from DW- MRI. Although communities have been widely studied, little has been done to analyze their estimation reproducibility. Here we propose a subject-specific robust method to estimate structural connectome communities. Our proposal successfully groups and separates intra- subject and inter-subject data, respectively. We show the robustness of the method to the variability introduced by: the acquisition noise, image pre-processing, the estimation of the Fiber Orientation Distributions, tractography parameters, and graph construction parameters.
01-09-2019
Trabajo de grado, maestría
OTRAS
Versión aceptada
acceptedVersion - Versión aceptada
Appears in Collections:Tesis del CIMAT

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