The “Ocular Response Function” for encoding and decoding oculomotor related neural activity

Abstract Oculomotor activity provides critical insights into cognition and health, with growing evidence demonstrating its involvement in various cognitive functions such as attention, memory, and sensory processing. Furthermore, eye movements are emerging as significant indicators of psychopathologies and neurological disorders, including schizophrenia, dementia, depression, and tinnitus. Despite its crucial importance across domains, the role of oculomotion has been underexplored in neuroimaging studies - largely due to methodological challenges. These challenges often involve treating eye movements as artefacts to be removed from the neural signal. While useful for data cleaning, this approach risks discarding valuable information about oculomotor control. An alternative is to directly model these signals. Using recently established time-resolved regression methods, we apply and extend this approach to present a unified framework we term ‘Ocular Response Functions’ (ORFs). Using simultaneous magnetoencephalography (MEG) and eye-tracking during the resting-state, we derive ORFs that characterise the neural signatures of distinct oculomotor events, specifically saccades, blinks, and pupil dilation. We demonstrate how this framework can be used to model the relationship between ocular action and neural activity (encoding) and, conversely, to reconstruct ocular events from brain activity. We further validate this approach by applying resting-state derived ORFs to a passive listening task, showing they can reveal oculomotor contributions to task-related neural processing. By providing an accessible framework for examining the interplay between eye movements and neural processes, we offer a powerful tool for research that has potential applications in clinical neuroscience. Competing Interest Statement The authors have declared no competing interest. Footnotes Short section on proof of principle