Sponsor: Prof. Sidney Spector Computational anatomy to assess growth pattern of early brain development in healthy and disease populations Guido Gerig 1, 2 ; John H. Gilmore 1 ; Matthieu Jomier 1 ; Sarang Joshi 3, 2 ; Joseph Piven 1 Departments of 1 Psychiatry, 2 Computer Science, 3 Radiation Oncology University of North Carolina, Chapel Hill,NC 27614, USA, Background Methods Imaging studies of early brain development get increasing attention as improved modeling of the pattern of normal development might lead to a better understanding of origin, timing and nature of morphologic differences in neurodevelopmental disorders. Quantitative MR imaging studies face the challenge that cross-sectional inter- individual variability is very large in relation to longitudinal change, which underscores the critical importance of a longitudinal design of such studies. It is our goal to model the trajectory of early brain development, primarily focusing on the most challenging group of very young children in the age range from birth to 6 years, as a 4- dimensional atlas that is represented by a time series of 3-D images and quantitative description of local growth. In addition, the same technique is applied to generate representative atlases for various groups, e.g. group-specific atlases for female/male populations and for healthy controls and patients. Adult Open Issues: Modeling growth trajectory of early brain growth, normative data of average brain and variability Quantitative description of local brain changes throughout the whole volume Assessment of group differences and hypothesis testing directly on volumetric image data Trajectory of Brain Growth Head growth illustrated by axial MRI and 3D head models obtained from individual subjects. The images show a neonate, its 1yr follow-up and a 2yrs old subject. All the 2D and 3D images are displayed at the correct scale. Please note the significant growth between neonate, 1year and up to 2yrs. Brain growth in longitudinal study (Aut/DD/Typ) of children at 2yrs and 4yrs. Notice the large cross-sectional variability. Computational Anatomy Tool: Unbiased atlas building by simultaneous diffeomorphic high-dimensional deformation of a population of images Group difference and longitudinal change analysis by describing volumetric deformation between atlases Differentiation of deformation field (Jacobian) describes local volume changes New tool is not limited to the cortex but extends to a fully volumetric description of changes/differences, including white matter, cortical gray, subcortical structures and csf. Pediatric neuroimaging studies at UNC contributing to this research: Longitudinal study of neonatal brain development in high risk children and controls (N tot =134), follow-up at 1yr (PI John H. Gilmore). Autism study with baseline scans at 2yrs and follow-up at 4yrs (51 AUT, 25 TYP/DD) Sarang Joshi, Brad Davis, Matthieu Jomier, Guido Gerig, Unbiased Diffeomorphic Atlas Construction for Computational Anatomy, vol. 23, NeuroImage 2004 ATLAS 2yrs ATLAS Neonates