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DeepMReye enables cameraless eye tracking using functional magnetic resonance imaging (fMRI) signals. This novel method decodes gaze position, even with closed eyes, offering broad applicability in neuroscience research.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Cognitive Science

Background:

  • Viewing behavior is crucial for understanding cognition and health.
  • Eye tracking is vital for interpreting functional magnetic resonance imaging (fMRI) data.
  • Existing eye-tracking methods are often incompatible with MRI environments.

Purpose of the Study:

  • To develop a novel, cameraless eye-tracking method for fMRI research.
  • To decode gaze position directly from MRI signals.
  • To provide an open-source solution for widespread use in research and clinical settings.

Main Methods:

  • Development of DeepMReye, a convolutional neural network (CNN).
  • Decoding gaze position from the magnetic resonance signal of eyeballs.
  • Validation across diverse scanning protocols and with limited training data.

Main Results:

  • DeepMReye achieves subimaging temporal resolution for eye tracking.
  • The method performs accurately even with closed eyes and in existing datasets.
  • Decoded eye movements correlate with network-wide brain activity, including non-oculomotor regions.

Conclusions:

  • DeepMReye offers a versatile and accessible tool for eye tracking in fMRI studies.
  • This technology enhances the interpretation of fMRI results by incorporating viewing behavior.
  • The open-source nature of DeepMReye promotes its adoption in research and clinical practice.