Fetal neonatal neuroimaging combining MRI, MEG and NIRS to study brain development.

Dr. Grant's research career is focused on improving pediatric care through innovations in magnetic resonance imaging (MRI), near infrared spectroscopy (NIRS) and magnetoencephalography (MEG) targeted to the unique demands of the pediatric population, with a focus on the neonate and fetus. In MRI we have research activities focused on placental, fetal and neonatal brain imaging with efforts in technology development to improve detection of brain compromise/injury as well as the effects of environment on early brain development. Markers of plasticity and synaptogenesis are being explored. They are involved in the development and analysis of many novel MR imaging sequences, including higher order diffusion imaging, resting state fMRI, arterial spin labeling perfusion, arterial flow quantification with phase contrast MR, venous oxygen saturation with TRUST, MR spectroscopy (including GABA and glutamate) as well as T1 and T2 quantitation. With NIRS they novel Frequency Domain NIRS to quantify cerebral blood volume and oxygen saturation in combination with Diffuse Correlation Spectroscopy (DCS) to quantify cerebral blood flow, which when combined give us bedside measures of Cerebra Rate of Oxygen Consumption (CMRO2). The potential for FDNIRS-DCS to detect brain injury and monitor response to therapy at the bedside is currently being assessed through NIH funding. In addition, they are exploring their utility in monitoring cerebral CMRO2 during open-heart surgery for neonates with congenital heart disease. Their MEG team has built the first whole head high resolution MEG system tailored to the size of an infant’s head. This system is now FDA approved and in clinical use in infants with epilepsy. As they have the ability to map vision, auditory, language and sensory motor networks, many research projects leveraging these abilities are under way. Finally, they also have projects in big data analysis where we are mining clinical data bases of MR images to search for patterns that predict outcomes.