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Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing

By Jake Son, Lei Ai, Ryan Lim, Ting Xu, Stan Colcombe, Alexandre Franco, Jessica Cloud, Stephen LaConte, Jonathan Lisinski, Arno Klein, R Cameron Craddock, Michael P Milham

Posted 18 Jun 2018
bioRxiv DOI: 10.1101/347765

The collection of eye gaze information during functional magnetic resonance imaging (fMRI) is important for monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). However, the complexity and setup requirements of current in-scanner eye-tracking solutions can preclude many researchers from accessing such information. Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from the fMRI signal in the eye's orbit using a 1.5-minute calibration scan. Here, we provide confirmatory validation of the PEER method's ability to infer eye-gaze on a TR-by-TR basis during movie viewing, using simultaneously acquired eye tracking data in five individuals (median angular deviation < 2 degrees). Then, we examine variations in the predictive validity of PEER models across individuals in a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank, identifying head motion as a primary determinant. Finally, we accurately predict which of two movies is being watched based on the predicted eye gaze patterns (area under the curve = .90 +/- .02) and map the neural correlates of eye movements derived from PEER. PEER is a freely available and easy-to-use tool for determining eye fixations during naturalistic viewing.

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