Motion-corrected eye tracking improves gaze accuracy during visual fMRI experiments

Area of Science:

• Neuroscience
• Cognitive Science
• Biomedical Engineering

Background:

• High-precision eye tracking is crucial for understanding cognition.
• Functional Magnetic Resonance Imaging (fMRI) presents challenges for accurate eye tracking due to head motion.
• Head shifts cause data drift and gaze inaccuracies in fMRI eye tracking.

Purpose of the Study:

• To introduce a novel method for correcting eye tracking drift in fMRI.
• To enhance the accuracy and precision of gaze estimation during fMRI scans.
• To enable more robust investigations of naturalistic vision and cognition using fMRI.

Main Methods:

• Developed Motion-Corrected Eye Tracking (MoCET) technique.
• Utilized head motion parameters from fMRI preprocessing to correct eye tracking drift.
• Applied MoCET retrospectively to existing fMRI datasets without additional hardware.

Main Results:

• MoCET significantly improves the accuracy of gaze estimation compared to conventional detrending.
• The method offers superior spatial and temporal precision over existing magnetic resonance-based eye trackers.
• Demonstrated effective drift correction using fMRI head motion data.

Conclusions:

• MoCET overcomes a critical limitation in integrating eye tracking with fMRI.
• This technique enables precise investigation of naturalistic vision and cognition in fMRI.
• MoCET facilitates more reliable and accurate eye movement analysis in neuroimaging research.