Nicolas Royackkers

Detection and statistical analysis of human cortical sulci.

Many studies dealing with the human brain use the spatial coordinate system of brain anatomy to localize functional regions. Unfortunately, brain anatomy, and especially cortical sulci, is characterized by a high interindividual variability. Specific tools called anatomical atlases must then be considered to make the interpretation of anatomical examinations easier. The work described here first aims at building a numerical atlas of the main cortical sulci. Our system is based on a database containing a collection of anatomical MRI of healthy volunteer brains. Their sulci have been manually drawn and labeled for both hemispheres. Sulci are represented as 3D superficial curves. After a nonlinear registration process, a statistical atlas of the cortical topography of a particular MRI is built from the database. It is an a priori model of cortical sulci, including three major components: an average curve represents the average shape and position of each sulcus; a search area accounts for its spatial variation domain; a set of quantitative parameters describes the variability of sulci geometry and topology. This atlas is completely individualized and adapted to the features of the brain under examination. The atlas is represented by a graph, the nodes of which represent sulci and the edges the relations between sulci. It can also be considered a statistical model that describes the cortical topography as well as its variability.

Sulcal variability in a computerized brain atlas

Computerized sulci atlas includes a mean representation of a standard brain in a standardized coordinate space, such as the Talairachs grid. In this reference system, differences between sulci at the surface of the brain are due to shape differences of the brain and to intrinsic anatomical differences of sulci. In this paper, four different methods are compared: non-proportional, global linear, local linear (Talairach) and nonlinear methods. From nine brains where sulci are labeled, a model of their mean location and its variability is computed. As expected, the nonlinear method is the most accurate one, with fewer variations than others.