English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Journal Article

Magnetic resonance-based eye tracking using deep neural networks

MPS-Authors

Frey,  Markus
Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Kavli Institute, Norwegian University of Science and Technology, Trondheim, Norway;
Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

Nau,  Matthias
Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Kavli Institute, Norwegian University of Science and Technology, Trondheim, Norway;
Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society;

/persons/resource/persons221475

Doeller,  Christian F.
Egil and Pauline Braathen and Fred Kavli Centre for Cortical Microcircuits, Kavli Institute, Norwegian University of Science and Technology, Trondheim, Norway;
Department Psychology (Doeller), MPI for Human Cognitive and Brain Sciences, Max Planck Society;
Institute of Psychology, University of Leipzig, Germany;

External Resource
No external resources are shared
Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/21.11116/0000-0009-79CA-C
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.