RT Journal Article
SR Electronic
T1 Evaluating fMRI-Based Estimation of Eye Gaze during Naturalistic Viewing
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 347765
DO 10.1101/347765
A1 Jake Son
A1 Lei Ai
A1 Ryan Lim
A1 Ting Xu
A1 Stanley Colcombe
A1 Alexandre Rosa Franco
A1 Jessica Cloud
A1 Stephen LaConte
A1 Jonathan Lisinski
A1 Arno Klein
A1 R. Cameron Craddock
A1 Michael Milham
YR 2019
UL http://biorxiv.org/content/early/2019/07/25/347765.abstract
AB 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°). 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 classify 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.