Detecting brain growth patterns in normal children using tensor-based morphometry

Previous magnetic resonance imaging (MRI)-based volumetric studies have shown age-related increases in the volume of total white matter and decreases in the volume of total gray matter of normal children. Recent adaptations of image analysis strategies enable the detection of human brain growth with improved spatial resolution. In this article, we further explore the spatio-temporal complexity of adolescent brain maturation with tensor-based morphometry. By utilizing a novel non-linear elastic intensity-based registration algorithm on the serial structural MRI scans of 13 healthy children, individual Jacobian growth maps are generated and then registered to a common anatomical space. Statistical analyses reveal significant tissue growth in cerebral white matter, contrasted with gray matter loss in parietal, temporal, and occipital lobe. In addition, a linear regression with age and gender suggests a slowing down of the growth rate in regions with the greatest white matter growth. We demonstrate that a tensor-based Jacobian map is a sensitive and reliable method to detect regional tissue changes during development.