Abstract：
:Recently, a related morphometry-based connection concept has been introduced using local mean cortical thickness and volume to study the underlying complex architecture of the brain networks. In this article, the surface area is employed as a morphometric descriptor to study the concurrent changes between brain structures and to build binarized connectivity graphs. The statistical similarity in surface area between pair of regions was measured by computing the partial correlation coefficient across 186 normal subjects of the Cuban Human Brain Mapping Project. We demonstrated that connectivity matrices obtained follow a small-world behavior for two different parcellations of the brain gray matter. The properties of the connectivity matrices were compared to the matrices obtained using the mean cortical thickness for the same cortical parcellations. The topology of the cortical thickness and surface area networks were statistically different, demonstrating that both capture distinct properties of the interaction or different aspects of the same interaction (mechanical, anatomical, chemical, etc.) between brain structures. This finding could be explained by the fact that each descriptor is driven by distinct cellular mechanisms as result of a distinct genetic origin. To our knowledge, this is the first time that surface area is used to study the morphological connectivity of brain networks.
journal_name
Neuroimagejournal_title
NeuroImageauthors
Sanabria-Diaz G,Melie-García L,Iturria-Medina Y,Alemán-Gómez Y,Hernández-González G,Valdés-Urrutia L,Galán L,Valdés-Sosa Pdoi
10.1016/j.neuroimage.2010.01.028subject
Has Abstractpub_date
2010-05-01 00:00:00pages
1497-510issue
4eissn
1053-8119issn
1095-9572pii
S1053-8119(10)00048-0journal_volume
50pub_type
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