Recording of Presentation
Speaker: Bo-yong Park
Bio: Bo-yong Park carried out undergraduate and PhD studies at Sungkyunkwan University in South Korea. He has been working as an invited researcher at the Max Planck Institute for Empirical Aesthetics in Germany in 2019 and joined the MICA Lab at the MNI for postdoctoral studies in fall 2019. He is interested in the development of computational methods for multimodal neuroimaging, connectomics, and machine learning and has worked in neurodevelopmental, neurological, and neuropsychiatric indications.
Talk Abstract: Recent advances in neuroimaging acquisition and analysis techniques provide novel ways to assess brain networks in vivo, which has put systems neuroscience in an unprecedented position to understand macroscale neural organization, development, and disease. Here, I present novel approaches to model structural connectome data based on cutting edge manifold learning techniques that can capture gradients of macroscale neural organization. I will present recent works that show (1) how manifolds learning techniques applied to whole-brain diffusion MRI connectomes can identify smooth gradients of structural connectivity variations and how they can predict time-varying changes in neural function. I will furthermore show (2) how they can be used for tracking neurodevelopment and for the prediction of cognitive maturation during adolescence, and finally (3) how they can index connectome-level perturbations in autism spectrum conditions, a prevalent and persistent condition of brain connectivity.