Detecting Changes of Functional Connectivity by Dynamic Graph Embedding Learning

Abstract

Our current understandings reach the unanimous consensus that the brain functions and cognitive states are dynamically changing even in the resting state rather than remaining at a single constant state. Due to the low signal-to-noise ratio and high vertex-time dependency in BOLD (blood oxygen level dependent) signals, however, it is challenging to detect the dynamic behavior in connectivity without requiring prior knowledge of the experimental design. Like the Fourier bases in signal processing, each brain network can be summarized by a set of harmonic bases (Eigensystem) which are derived from its latent Laplacian matrix. In this regard, we propose to establish a subject-specific spectrum domain, where the learned orthogonal harmonic-Fourier bases allow us to detect the changes of functional connectivity more accurately than using the BOLD signals in an arbitrary sliding window. To do so, we first present a novel dynamic graph learning method to simultaneously estimate the intrinsic BOLD signals and learn the joint harmonic-Fourier bases for the underlying functional connectivity network. Then, we project the BOLD signals to the spectrum domain spanned by learned network harmonic and Fourier bases, forming the new system-level fluctuation patterns, called dynamic graph embeddings. We employ the classic clustering approach to identify the changes of functional connectivity using the novel dynamic graph embedding vectors. Our method has been evaluated on working memory task-based fMRI dataset and comparisons with state-of-the-art methods, where our joint harmonic-Fourier bases achieves higher accuracy in detecting multiple cognitive states.