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

R. Devon Hjelm, Eswar Damaraju, Kyunghyun Cho, Helmut Laufs, Sergey M. Plis, Vince D. Calhoun

Research output: Contribution to journalArticle


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.

Original languageEnglish (US)
Article number00600
JournalFrontiers in Neuroscience
Issue numberSEP
StatePublished - Sep 20 2018



  • Aod
  • Deep learning
  • Fmri
  • Ica
  • Neuroimaging methods
  • Resting-state fmri
  • Rnn

ASJC Scopus subject areas

  • Neuroscience(all)

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