Author
MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust Quantitative Connectivity through Harmonized Preprocessing of Diffusion MRI
Sep. 3, 2024—Newlin, N. R., Schilling, K., Koudoro, S., Chandio, B. Q., Kanakaraj, P., Moyer, D., Kelly, C. E., Genc, S., Chen, J., Yang, J. Y.-M., Wu, Y., He, Y., Zhang, J., Zeng, Q., Zhang, F., Adluru, N., Nath, V., Pathak, S., Schneider, W., … Landman, B. A. (2024). MICCAI-CDMRI 2023 QuantConn Challenge Findings on Achieving Robust...
Learning site-invariant features of connectomes to harmonize complex network measures
Dec. 1, 2023—Figure 1. Previous research elucidated that connectomes suffer from confounding site effects. In this work we propose a data-driven model to learn disjoint site (𝑐 = {1,2}) and biological features (siteless z) for BIOCARD (orange) and VMAP (blue) (left). We then inject a prescribed site, c’, to the learned representations to compute harmonized connectome modularity,...
Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes
Sep. 6, 2023—Nancy R. Newlin, Francois Rheault, Kurt G. Schilling, Bennett A. Landman. “Characterizing Streamline Count Invariant Graph Measures of Structural Connectomes” Journal of Magnetic Resonance Imaging. January 2023. Full Text Background While graph measures are used increasingly to characterize human connectomes, uncertainty remains in how to use these metrics in a quantitative and reproducible manner. Specifically, there is...