title: Bayesian construction of geometrically based cortical thickness metrics.
abstract: This paper describes the construction of cortical metrics quantifying the probabilistic occurrence of gray matter, white matter, and cerebrospinal fluid compartments in their correlation to the geometry of the neocortex as measured in 0.5-1.0 mm magnetic resonance imagery. These cortical profiles represent the density of the tissue types as a function of distance to the cortical surface. These metrics are consistent when generated across multiple brains indicating a fundamental property of the neocortex. Methods are proposed for incorporating such metrics into automated Bayes segmentation.
authors: Center for Imaging Science, The Johns Hopkins University, Charles and 34th Street, Baltimore, Maryland 21218-2686, USA.
authors: M I
authors: MI
authors: Miller
authors: A B
authors: AB
authors: Massie
authors: J T
authors: JT
authors: Ratnanather
authors: K N
authors: KN
authors: Botteron
authors: J G
authors: JG
authors: Csernansky
mesh_terms: D000886:Anthropometry; D016477:Artifacts; D001499:Bayes Theorem; D001931:Brain Mapping; D002540:Cerebral Cortex / Q000033:anatomy & histology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000706:statistics & numerical data*; D008279:Magnetic Resonance Imaging / Q000706:statistics & numerical data*; D015233:Models, Statistical; D019579:Neocortex / Q000033:anatomy & histology; D012016:Reference Values