Automated extraction and variability analysis of sulcal neuroanatomy

Systematic mapping of the variability in cortical sulcal anatomy is an area of increasing interest which presents numerous methodological challenges, To address these issues, we have implemented sulcal extraction and assisted labeling (SEAL) to automatically extract the two-dimensional (2-D) surface ribbons that represent the median axis of cerebral sulci and to neuroanato mically label these entities, To encode the extracted three-dimensional (3-D) cortical sulcal schematic t opography (CSST) we define a relational graph structure composed of two mai n features: vertices (representing sulci) and arcs (representing the relati onships between sulci), Vertices contain a parametric representation of the surface ribbon buried within the sulcus, Points on this surface are expres sed in stereotaxic coordinates (i.e., with respect to a standardized brain coordinate system). For each of these vertices, we store length, depth, and orientation as well as anatomical attributes (e.g., hemisphere, lobe, sulc us type, etc.). Each are stores the 3-D location of the junction between su lci as well as a list of its connecting sulci, Sulcal labeling is performed semiautomatically by selecting a sulcal entity in the CSST and selecting from a menu of candidate sulcus names. In order to help the user in the labeling task, the menu is restricted to the most l ikely candidates by using priors for the expected sulcal spatial distributi on, These priors, i.e., sulcal probabilistic maps, were created from the sp atial distribution of 34 sulci traced manually on 36 different subjects, Gi ven these spatial probability maps, the user is provided with the likelihoo d that the selected entity belongs to a particular sulcus, The cortical structure representation obtained by SEAL is suitable to extra ct statistical information about both the spatial and the structural compos ition of the cerebral cortical topography, This methodology allows for the iterative construction of a successively more complete statistical models o f the cerebral topography containing spatial distributions of the most impo rtant structures, their morphometrics, and their structural components.