Capturing brain spatial topography reconfiguration using ultra-high-order independent component analysis (ICA)

The brain reorganizes its activity interactively at different temporal and spatial scales. These reconfigurations include variations within a region’s function, and changes in the spatial topography of functional organizations. Here, we present a novel approach utilizing the concept of functional hierarchy to capture between- and within-subject spatiotemporal variations. The approach uses ultra-high-order independent component analysis (ICA) (1000 components) to estimate functional units overcoming the limitation of current parcellation approaches and captures brain spatiotemporal reconfiguration at a finer spatial level. Our regularized cost function optimizes the selection of the best subsets of changes at each time-window and protects against spurious fluctuations.