Title: A causal dynamic network model for functional MRI
Authors: Xi Luo - Brown University (United States) [presenting]
Xuefei Cao - Brown University (United States)
Bjorn Sandstede - Brown University (United States)
Abstract: Functional MRI (fMRI) is a popular approach to investigate brain connections and activations when human subjects perform tasks. Because fMRI measures the indirect and convoluted signals of brain activities at a lower temporal resolution, differential equation modeling methods, for example dynamic causal modeling, are usually employed to model the neural processes and the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. A major statistical challenge is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. We propose a causal dynamic network (CDN) model to estimate brain activations and connections simultaneously. Our model links the observed fMRI data with the latent neural signals modeled by an ordinary differential equation (ODE) model. Using basis function expansions, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors, and we develop a block coordinate-descent algorithm to compute the ODE parameters efficiently. We illustrate the numerical advantages of our approach using data from realistic simulations and a task-related fMRI experiment.