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  Magnetic resonance-based eye tracking using deep neural networks

Frey, M., Nau, M., & Doeller, C. F. (2021). Magnetic resonance-based eye tracking using deep neural networks. Nature Neuroscience, 24, 1772-1779. doi:10.1038/s41593-021-00947-w.

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 Creators:
Frey, Markus1, 2, Author
Nau, Matthias1, 2, Author
Doeller, Christian F.1, 2, 3, Author              
Affiliations:
1Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Kavli Institute, Norwegian University of Science and Technology, Trondheim, Norway, ou_persistent22              
2Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society, Leipzig, DE, ou_2591710              
3Institute of Psychology, University of Leipzig, Germany, ou_persistent22              

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Free keywords: Cognitive neuroscience; Computational neuroscience; Oculomotor system; Visual system
 Abstract: Viewing behavior provides a window into many central aspects of human cognition and health, and it is an important variable of interest or confound in many functional magnetic resonance imaging (fMRI) studies. To make eye tracking freely and widely available for MRI research, we developed DeepMReye, a convolutional neural network (CNN) that decodes gaze position from the magnetic resonance signal of the eyeballs. It performs cameraless eye tracking at subimaging temporal resolution in held-out participants with little training data and across a broad range of scanning protocols. Critically, it works even in existing datasets and when the eyes are closed. Decoded eye movements explain network-wide brain activity also in regions not associated with oculomotor function. This work emphasizes the importance of eye tracking for the interpretation of fMRI results and provides an open source software solution that is widely applicable in research and clinical settings.

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Language(s): eng - English
 Dates: 2020-10-222021-09-172021-11-08
 Publication Status: Published online
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: DOI: 10.1038/s41593-021-00947-w
Other: online ahead of print
PMID: 34750593
 Degree: -

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Title: Nature Neuroscience
  Other : Nat. Neurosci.
Source Genre: Journal
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Publ. Info: -
Pages: - Volume / Issue: 24 Sequence Number: - Start / End Page: 1772 - 1779 Identifier: ISSN: 1097-6256
CoNE: https://pure.mpg.de/cone/journals/resource/954925610931