Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization

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PI: Luo, Xi


Institution: Brown University 

Title: Large-scale Network Modeling for Brain Dynamics: Statistical Learning and Optimization

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 underlying neuronal processes. However, ODE modeling is computationally expensive and remains to be a confirmatory or hypothesis-driven approach. The critical challenge was to infer, in a data-driven fashion, the underlying differential equation models from fMRI data. To address this challenge, we developed a causal dynamic network (CDN) framework to estimate brain activations and connections simultaneously without prespecified ODE models. Built on machine learning principals and optimization theory, we developed fast algorithms to fit large-scale ODE network models with up to hundreds of nodes. Compared with various effective connectivity methods, our method achieved higher estimation accuracy while improving the computational speed by from tens to thousands of times. Our method applies to both resting-state and task fMRI experiments. A Python implementation of our method is publicly available on PyPI at

Grant #: EB022911 

Status: Completed




2021 Brain PI Meeting


Link to Poster:



BRAIN Math Project - Luo.pptx




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