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

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.

journal_name

Front Neurosci

authors

Hjelm RD,Damaraju E,Cho K,Laufs H,Plis SM,Calhoun VD

doi

10.3389/fnins.2018.00600

subject

Has Abstract

pub_date

2018-09-20 00:00:00

pages

600

eissn

1662-4548

issn

1662-453X

journal_volume

12

pub_type

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