Abstract #0953

Generalized Recurrent Neural Network accommodating Dynamic Causal Modelling for functional MRI analysis

Yuan Wang¹, Yao Wang², and Yvonne W Lui³

¹Tandon School of Engineering, New York University, Brooklyn, NY, United States, ²Tandon School of Engineering, New York University, ³School of Medicine, New York University

We propose DCM-RNN, a new model for effective connectivity estimation from fMRI signal that links the strengths of traditional Dynamic Casual Modelling (DCM) and deep learning. It casts DCM as a generalized Recurrent Neural Network (RNN) and estimates the effective connectivity using backpropagation. It extends DCM with a more flexible framework, unique estimation methods, and neural network compatibility. In simulated experiments, we demonstrate that DCM-RNN is feasible and can be used to estimate the effective connectivity.

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