NONLINEAR PREDICTION OF HUMAN RESTING-STATE FUNCTIONAL CONNECTIVITY BASED ON NETWORK COMMUNICATION MEASURES

Fuliang Wang, Xue Chen, Yanjiang Wang

Abstract: Mounting evidence demonstrated that neuronal activity derived from functional magnetic resonance imaging (fMRI) relates to the underlying anatomical circuitry measured by diffusion tensor/spectrum imaging (DTI/DSI). However, exploring the relationship between functional connectivity (FC) and structural connectivity (SC) remains challengeable and thus has motivated a number of computational models to investigate the extent to which the dynamics depend on the topology. Nevertheless, most of the models are complex and difficult to treat analytically. In this paper, for simplicity, we utilize four network communication measures extracted from SC as well as polynomial curves fitting method to predict FC. Our results indicate that all of these measures predict FC via the nonlinear fitting method. Besides, compared with the linear method, the fitting value between predicted FC and empirical FC attains higher after applying nonlinear process on communication measures which may help to shed light on the function-structure relationship.

Keywords: brain connectivity; fMRI; DTI/DSI; network communication measure; nonlinear fitting

DOI: https://doi.org/10.15623/ijret.2015.0412014