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

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ID: 56008
2018
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Abstract
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.
Reference Key
hjelm2018spatiotemporalfrontiers Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Hjelm, R Devon;Damaraju, Eswar;Cho, Kyunghyun;Laufs, Helmut;Plis, Sergey M;Calhoun, Vince D;
Journal Frontiers in neuroscience
Year 2018
DOI
10.3389/fnins.2018.00600
URL
Keywords
Deep learning fmri aod resting-state fmri ica rnn neuroimaging methods

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