Structure-function relationship in biophysically inspired small world networks
Fecha
2021
Autores
Profesor Guía
Formato del documento
Tesis
ORCID Autor
Título de la revista
ISSN de la revista
Título del volumen
Editor
Universidad de Valparaíso
Ubicación
ISBN
ISSN
item.page.issne
item.page.doiurl
Facultad
Facultad de Ciencias
Departamento o Escuela
Programa de Magister en Ciencias Biologicas Mencion Neurocienciasss
Determinador
Recolector
Especie
Nota general
Magíster en Ciencias
Resumen
ABSTRACT
How behavior arises from the neural activity observed in the brain is one of the main questions
in neuroscience. In the resting state, synchronized and highly stable activity has been found in
coordinated brain areas, forming what is known as Functional Connectivity. These brain areas
are neuronal structures that are interconnected in a very particular way, generating a physical
network known as Structural Connectivity. This connectivity has been studied in the light of
complex networks, as it has been observed that the particular properties of the brain structure
are related to small-world network topology. Interestingly, studies on Functional Connectivity
have also proposed similar topology. Functional connectivity is not static, instead, it transits or
“wanders” between several states of activity, which are constantly revisited. This dynamical wonder
has been framed as the Functional Connectivity Dynamics. However, the relationship between
this dynamic transit of Functional Connectivity and the structural basis on which it is supported
remains unknown. In this work, the relationship between Structural Connectivity and Functional
Connectivity Dynamics is explored by using three network topologies with a biophysically inspired
model of activity. Small world metrics, integration, and segregation metrics were used for
characterizing the structural connectivity, while multistability, metastability, and synchrony were
used for characterizing functional connectivity dynamics. Metastability or “wandering” between
activity patterns was higher in regular networks, and it diminished in small-world networks. The
relationship between the segregation properties of the network explains better this observed
behavior. Multistability, defined by the transit between multiples attractors, was also high in regular
networks, however, the relationship between structural and dynamic was better explained by small
world metrics. These analyses suggest that the dynamical richness may be originated due to the
segregation properties of the network, but the transit between those states is imposed by the ratio
between segregation and integration. Further analysis within the range of small-world, may help
to understand the importance of this ratio in the dynamical analysis of the brain activity.
Descripción
Lugar de Publicación
Auspiciador
Palabras clave
SIMULACION POR COMPUTADORES, ANALISIS FACTORIAL, REDES NEURONALES (COMPUTADORES)