Neurodevelopmental disorders such as autism spectrum disorder, attention deficit/hyperactivity disorder, fetal alcohol spectrum disorders, and complications associated with premature birth, impact the quality of life of affected individuals over the entire lifespan. Neuroanatomical anatomical differences between people affected by these conditions and “typically developing†individuals have been identified with magnetic resonance imaging (MRI), but the biological mechanisms leading to such differences, and their link to the disease processes, are incompletely understood. It is safe to perform MRI on pregnant women, and recent developments in the ability to account for fetal motion during image acquisition, have enabled high-resolution 3D imaging of the fetal brain. In order to better understand the developmental mechanisms that underlie the trajectory of anatomical changes observed by MRI in humans, longitudinal measurements are also collected in nonhuman primates. Human and nonhuman primates share many similarities in both brain structure and function that allow findings in one to be translated to the other. Advantages of nonhuman primate studies are that many factors that may vary between human pregnancies can be experimentally controlled, and much more detailed longitudinal imaging is possible. This research will develop computational approaches to more precisely link fetal growth between human and nonhuman brains. The work leverages unique human and nonhuman primate imaging datasets with new methods for systematically labeling the brain into corresponding sub-regions and establish closer links between developmental events. This increased precision will enhance knowledge gained from ongoing human observational studies and enable new clinical approaches to address neurodevelopmental diseases.
The ability to non-invasively monitor fetal brain growth in both human and nonhuman primates using magnetic resonance imaging (MRI) provides a new opportunity to characterize brain development with longitudinal experimental designs. However, given the increased frequency with which data can be acquired, and quality of high-resolution images, an important new limitation is the inability to translate developmental time points between species at the level of precision of the acquired data. Conventional approaches for studying postnatal brain images utilize processing steps such as spatial normalization to a common anatomical coordinate frame, segmentation into tissue classes, and parcellation into known neuroanatomical regions. Adaptation of these techniques to study the developing fetal brain requires age and species-specific definitions for quantities such as transient developmental zones, or emergence of cortical gyri and sulci. This project makes use of increasingly powerful machine learning techniques and leverages the increasingly rich fetal imaging data now being collected, to extract consistent cross-species measures of brain development. An additional objective is to develop fine scale anatomically and temporally consistent definitions across species. These neuroanatomically localized definitions will then be used to quantify regional morphometric growth during fetal brain development in both species. This work contributes to the computational science and the neuroscience that supports neuroimaging studies of fetal brain development. These developments will provide a new translational resource to link anatomically and temporally specific information about brain development, both in normal growth and in clinical and animal model experiments focused on neurodevelopmental disorders.
This award is being co-funded by the CISE Information and Intelligent Systems (IIS) through the CRCNA and BRAIN Programs, and the MPS Division of Mathematical Sciences (DMS) through the Mathematical Biology Program.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
