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Meeting Abstract

Using GAN for learning joint task/response distribution in fMRI

MPS-Authors
/persons/resource/persons216049

Lee,  JY
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons84372

Loktyushin,  A
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons192717

Stelzer,  J
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

/persons/resource/persons133483

Lohmann,  G
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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https://openreview.net/pdf?id=rylMuVFoFN
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Citation

Lee, J., Loktyushin, A., Stelzer, J., & Lohmann, G. (submitted). Using GAN for learning joint task/response distribution in fMRI.


Cite as: https://hdl.handle.net/21.11116/0000-0007-80BE-2
Abstract
This is a proof-of-principle study on using generative adversarial network (GAN) to synthesize functional Magnetic Resonance Imaging (fMRI) data. We trained GAN to model the joint distribution of motor task functional magnetic resonance imaging (fMRI) data and the corresponding task labels. Synthesized images by the trained GAN successfully replicated the task relevant fMRI signal in the motor cortex. This result shows a potential for using GAN to augment fMRI data.