A Functional Data Method for Causal Dynamic Network Modeling of Task-Related fMRI

• Published in: Frontiers in neuroscience Vol. 13; p. 127
• Main Authors: Cao, Xuefei, Sandstede, Björn, Luo, Xi
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Research Foundation 27.02.2019; Frontiers Media S.A
• Subjects: Brain; brain connectivity; Brain mapping; Computational neuroscience; Data analysis; dynamic data analysis; Functional magnetic resonance imaging; Investigations; Methods; Neural networks; Neuroscience; Noise; optimization; ordinary differential equations; Parameter estimation; task-related fMRI; Time series; United States > US; brain connectivity; ordinary differential equations; optimization; task-related fMRI; dynamic data analysis
• ISSN: 1662-453X; 1662-4548; 1662-453X
• DOI: 10.3389/fnins.2019.00127

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, complex differential equation modeling methods (e.g., Dynamic Causal Modeling) are usually employed to infer the neuronal processes and to fit the resulting fMRI signals. However, this modeling strategy is computationally expensive and remains to be mostly a confirmatory or hypothesis-driven approach. One major statistical challenge here is to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. In this paper, we propose a causal dynamic network (CDN) method to estimate brain activations and connections simultaneously. Our method links the observed fMRI data with the latent neuronal states modeled by an ordinary differential equation (ODE) model. Using the basis function expansion approach in functional data analysis, we develop an optimization-based criterion that combines data-fitting errors and ODE fitting errors. We also develop and implement 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 two task-related fMRI experiments. Compared with various effective connectivity methods, our method achieves higher estimation accuracy while improving the computational speed by from tens to thousands of times. Though our method is developed for task-related fMRI, we also demonstrate the potential applicability of our method (with a simple modification) to resting-state fMRI, by analyzing both simulated and real data from medium-sized networks.