Spatio-Temporal Dynamics of Intrinsic Networks in Functional Magnetic Imaging Data Using Recurrent Neural Networks

Frontiers in Neuroscience

R. Devon Hjelm¹  Kyunghyun Cho³  Helmut Laufs⁴  Sergey M. Plis⁵  Eswar Damaraju⁶  Vince D. Calhoun⁶ 

[1] Microsoft Research, Montreal, QC, Canada;Montréal Institute for Learning Algorithms, Montreal, QC, Canada;New York University, New York, NY, United States;Schleswig University Hospital, Kiel, Germany;The Mind Research Network, Albuquerque, NM, United States;The University of New Mexico, Albuquerque, NM, United States;

关键词: deep learning;    ICA;    RNN;    neuroimaging methods;    fMRI;    resting-state fMRI;   

DOI  :  10.3389/fnins.2018.00600

来源: DOAJ

【 摘 要 】

We introduce a novel recurrent neural network (RNN) approach to account for temporal dynamics and dependencies in brain networks observed via functional magnetic resonance imaging (fMRI). Our approach directly parameterizes temporal dynamics through recurrent connections, which can be used to formulate blind source separation with a conditional (rather than marginal) independence assumption, which we call RNN-ICA. This formulation enables us to visualize the temporal dynamics of both first order (activity) and second order (directed connectivity) information in brain networks that are widely studied in a static sense, but not well-characterized dynamically. RNN-ICA predicts dynamics directly from the recurrent states of the RNN in both task and resting state fMRI. Our results show both task-related and group-differentiating directed connectivity.

【 授权许可】

Unknown