MRI Neuroimaging provides a rich source of image content including structural (MRI, Diffusion DTI), functional (fMRI, Perfusion ASL), and metabolic (MRS) information. Today MRI capabilities allow to acquire these imaging techniques in one session in most cases. In order to be of diagnostic value, the immense and diverse data needs to be (i) automatically post-processed to extract the relevant information, e.g. 3D brain maps from 4D fMRI, and to be (ii) fused and visualized to correlate the voxel-based findings. The purpose of this study is to demonstrate the feasibility of automatic relevant information retrieval and fusion of MRI, fMRI, DTI, ASL, and MRS data of a pediatric population into a single semantic data representation. By using advanced imaging, we may able to detect a larger spectrum of abnormalities in the neonatal brain. Each imaging application, provides unique information about the physiology (fMRI, ASL), the anatomy (DTI), and the biochemistry (MRS) of the newborn brain in relation to normal development and brain injury. By being able to integrate this technology, we will be able to combine biochemical, physiologic and anatomic information which can provide unique insight about not only the normal development of the brain, but also injury of the neonatal brain.