This proposal is designed to facilitate the career development and transition to independence of Brandon A. Zielinski, M.D., Ph.D. At the conclusion of the award term, Dr. Zielinski will be a fully independent investigator, poised to build a rigorous, broad-based research laboratory that will make substantial contributions to diagnosis, prognosis, treatment, and understanding of childhood neurological disorders by probing functional and structural connectivity abnormalities in large-scale brain networks using innovative neuroimaging methods. Although Dr. Zielinski has extensive mentored experience in functional and structural MRI research, he is in need of further training in specific methodologiesrelating to MRI data acquisition and processing (particularly DTI), technical aspects of structuraland functional MRI, integration of complex multimodal neuroimaging data, neuropsychological assessment of normal and autistic children, and multidimensional statistics. This proposal will provide intensive and multifaceted mentored training experience through which he will acquire in-depth understanding in all of these areas. Under this award, he will conduct comprehensive neuroimaging research requiring subject assessment, data acquisition, processing, and management, advanced statistical analysis, and integration of complex neuroimaging and neurobehavioral datasets. Dr. Zielinski will be supported by a mentorship team with extensive and complementary expertise in all of these areas. Through this proposal, he will obtain the depth as well as breadth of knowledge, skill, and experience required to become an exceptional independent researcher, and a leader in his field. Structure begets function in the human brain, yet the structure-function interrelationships of human brain networks remain unknown. This proposal seeks to establish that large-scale brain networks reflect conserved intrinsic patterns ofcorrelated functional activity, white matter fiber connectivity, and gray matter morphology shared by neural neighborhoods separated by long distances. Major goals are to identify and characterize functional and structural brain networks in children, and to determine how large-scale network patterns emerge in early human development. Accumulating evidence suggests that autism is a network-based disease, and that abnormalities in network structure may underlie the abnormal brain function at the core of the disease. Discovery of early network abnormalities in autism will provide a new framework for understanding this complex illness and lead to new pathways for diagnosis and treatment. We propose to combine an emerging neuroimaging technique (structural covariance MRI, scMRI) with established advanced neuroimaging techniques (functional connectivity MRI, fcMRI, and diffusion tensor imaging, DTI) in order to identify and characterize the brain's fundamental large-scale network architecture. This innovative approach will enable development of a structure-function model of network organization in the human brain synthesizing gray matter structure, white matter structure, and brain network function. Applying this approach in children will provide a foundation for understanding emergence of large-scale brain networks involved in specific domains of human cognition, greatly impacting our understanding of human growth and development. Furthermore, identifying normal neurodevelopmental patterns will advance our ability to detect perturbations of these systems in conditions of neurologic dysfunction and disease, enabling innovative approaches to optimization, preservation, and recovery of function in neurologically affected children. Discovering the unique bioimaging phenotype of autism could profoundly impact our ability to detect, diagnose, and treat this devastating condition. This work will form the basis ofa synergistic and fundamental model of human brain organization and development with broad application to human health and disease, fueling Dr. Zielinski for the duration of his career.]
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
