NIH R01 · 2024
Dynamic embedding time series models in functional brain imaging
Project Summary We will develop new large-scale dynamic embedding models of network data with a focus on dynamic connec- tivity matrices from non-stationary multivariate time series obtained from human functional magnetic resonance images (fMRI). We propose to model brain networks as 2D curved surfaces, where the surface geodesics give connectivity information. Our approach will bypass the use of parcellations and more accurately evaluate the evolutionary dynamics of functional brain networks at the voxel level. We propose to build dynamically changing functional brain networks from a dataset with 1206 subjects from the Human Connectome Project (HCP) database containing T1-weighted magnetic…
From the public funding record at NIH RePORTER. Describes the funded project, not the reviews below.