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Abstract #0954

A simple data driven predictive dynamical model of whole brain resting state fMRI signal behavior

Eric Wong1

1Radiology/Psychiatry, UC San Diego, La Jolla, CA, United States

In this work we present a simple dynamical model that learns from resting state fMRI data to predict future brain states using information from one or more past brain states. We use a fully connected neural network model, and data from the Human Connectome Project. Our group model explains over 45% of the variance across 472 subjects, is very consistent when trained on subsets of the subjects, predicts realistic dynamics, gravitates towards the default mode when started in nearly any simulated brain state, and is complementary to correlation analysis.

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