Neuroimaging Series: Ying Guo

Ying Guo, Director, Emory Center for Biomedical Imaging Statistics (CBIS), Emory Statistical methods for helping improve reliability of brain network analysis Network-oriented analyses have become increasingly important in neuroimaging studies to advance understanding of neural circuitry among healthy brains and also to investigate disease-related network differences. These analyses often encounter challenges including low signal-to-noise ratio in neuroimaging data, high between-subject heterogeneity in brain networks and network variations across conditions. In this talk, we present new statistical methods to help improve the reliability in brain network analysis. The proposed methods tackle the aforementioned challenges by joint network learning using brain images obtained on the same subjects under different conditions, by multimodality integrative network modeling that incorporates anatomical structure in constructing brain functional networks, and by developing novel statistical tests to more accurately detect network differences between subpopulations. Webex information: https://gsumeetings.webex.com/gsumeetings/j.php?MTID=m85f5f0d535c2d0e514253f3aa4ec626b Meeting code: 739 520 259