V Congreso Latinoamericano de Ingeniería Biomédica (CLAIB2011)

Complex mouse brain anatomical network attributes estimated via diffusion-MRI data and graph theory
Iturria Medina Yasser, Ontivero Ortega Marlis, Canales Rodríguez Erick Jorge, Melie García Lester, Valdés Hernández Pedro, Pérez Fernández Alejandro

##manager.scheduler.building##: Palacio de Convenciones de La Habana
##manager.scheduler.room##: Sala 7
Fecha: 2011-05-20 01:45  – 02:00
Última modificación: 2011-05-05 08:59


Evidence about high global and local parallel information processing between brain gray matter regions has being previously reported for different mammalian species (e.g. cat, monkey and humans). Here our goal is to study these characteristics but this time in mouse looking for other different mammalian species that shares this trait, which should be indicating similar brain structural growing organizational strategies shaped conveniently trough the evolutionary processes. Specifically, anatomical connections between 150 anatomic regions covering all the gray matter of healthy (C3HeB.FeJ, n = 5) mice were estimated by means of fiber tractography techniques based on high resolution Diffusion Weighted MRI data, and from the resultant information individual brain structural networks were created considering each brain region as a node that can be connected to any other node (region) depending of the obtained evidence supporting white matter fiber connections amount them. Then the created individual structural brain networks were analyzed attending to five different topological measures: clustering, mean path length, local efficiency, global efficiency and small worldness index. The results reveals the small-word attributes of the structural network of the mouse brain, at the same time that confirms a smaller global efficiency and bigger local efficiency in comparison with the equivalent random networks, which in conjunction is in agreement with the previous studies on mammalian species. Considering that mouse animal models are of especial interest for biomedical research, in conjunction with the fact that current trends in human brain networks analyses are focused to detect topological network alterations associated to specific states of pathology, our methodology/findings could be of significant utility for the scientific community that employ the mouse animal model in the study of specific brain pathologies as well as their responds to different therapies.