At the Developmental Neural Network Lab, we study structure-function relationships in the developing brain. Our research is dedicated to a comprehensive exploration of brain structure and connectivity, and it serves as a bridge between neuroimaging and neurobehavioral data. Our work encompasses a spectrum of conditions from autism to traumatic brain injury. We aim to optimize childhood brain health by harnessing high-dimensional feature sets to pinpoint and ameliorate large-scale brain network dysfunction, structural anomalies, and decline.
Structural Connectivity Networks: Dr. Zielinski’s research explores the structural connectivity of brain networks in autism and other related developmental diseases, particularly in adolescents and children. He investigates differences in network structure within large-scale intrinsic connectivity networks such as the socioemotional salience, executive control, and default mode networks.
Longitudinal Development: Uniquely, our lab conducts longitudinal studies to explore brain development differences in autism. By analyzing changes in network connectivity and cortical thickness over time, we aim to clarify age-related trajectories of neural development and identify specific regional differences based on developmental stages.
AI and Machine Learning: To handle the complex datasets generated from extensive studies, we leverage advances in artificial intelligence and machine learning. Dr. Zielinski, recruited as part of the University’s AI Initiative, plans to build AI frameworks using UF’s HiPerGator, enhancing the processing and analysis of both current and previously collected data.
