4 research outputs found
Tracing Network Evolution Using the PARAFAC2 Model
Characterizing time-evolving networks is a challenging task, but it is
crucial for understanding the dynamic behavior of complex systems such as the
brain. For instance, how spatial networks of functional connectivity in the
brain evolve during a task is not well-understood. A traditional approach in
neuroimaging data analysis is to make simplifications through the assumption of
static spatial networks. In this paper, without assuming static networks in
time and/or space, we arrange the temporal data as a higher-order tensor and
use a tensor factorization model called PARAFAC2 to capture underlying patterns
(spatial networks) in time-evolving data and their evolution. Numerical
experiments on simulated data demonstrate that PARAFAC2 can successfully reveal
the underlying networks and their dynamics. We also show the promising
performance of the model in terms of tracing the evolution of task-related
functional connectivity in the brain through the analysis of functional
magnetic resonance imaging data.Comment: 5 pages, 5 figures, conferenc