Recursive Tensor Subspace Tracking for Dynamic Brain Network Analysis

Recent years have seen a rapid growth in computational methods for a better understanding of functional connectivity brain networks constructed from neuroimaging data. Most of the current work has been limited to static functional connectivity networks (FCNs), where the relationships between different brain regions is assumed to be stationary. Recent work indicates that functional connectivity is a dynamic process over multiple time scales and the dynamic formation and dissolution of connections plays a key role in cognition, memory, and learning. In the proposed work, we introduce a tensor-based approach for tracking dynamic functional connectivity networks. The proposed framework introduces a robust low-rank+sparse structure learning algorithm for tensors to separate the low-rank community structure of connectivity networks from sparse outliers. The proposed framework is used to both identify change points, where the low-rank community structure of the FCN changes significantly, and summarize this community structure within each time interval. The proposed framework is applied to the study of cognitive control from electroencephalogram data during a Flanker task.