Evaluating fMRI-Based Estimation of Eye Movements during Naturalistic Viewing

The collection of eye movement information during functional magnetic resonance imaging (fMRI) is a valuable, though commonly overlooked component of monitoring variations in attention and task compliance, particularly for naturalistic viewing paradigms (e.g., movies). Predictive eye estimation regression (PEER) is a previously developed support vector regression-based method for retrospectively estimating eye gaze from fMRI data that simply adds a 1.5-minute calibration scan to any protocol. Here, we provide a large-scale assessment of PEER for inferring eye fixations on a TR-by-TR basis during movie viewing using a subset of data (n=448) from the Child Mind Institute Healthy Brain Network Biobank. Consistent with prior work, we demonstrate the ability of PEER to provide accurate estimates of fixation location throughout the course of fMRI scans and we establish head motion as the primary determinant of model accuracy. Minimum data requirement analyses suggest model estimation can be carried out with less than half the data obtained in the 1.5-minute calibration scan. We demonstrate the ability to predict the movie an individual is watching (i.e., Despicable Me, The Present) based on the PEER time series. Out-of-scanner eye tracker-based measurements obtained during a repeat viewing of the movie The Present was used to further validate the time series obtained using PEER. Consistent with prior findings in the eye tracking literature, the fixation sequences showed a high consistency across participants, reducing the ability to identify an individual based on their fixation sequence. Finally, examination of neural activations associated with the PEER time series replicated prior findings regarding the neural correlates of eye movements. In summary, we demonstrate that PEER is an inexpensive, easy-to-use tool for researchers to determine eye fixations from naturalistic viewing data that overcomes the cost and burdens of in-scanner eye tracking.