new and functional link (with some HR related fixes) here:
www.ucl.ac.uk/work-at-ucl/...

By providing a robust framework for examining the interplay between eye movements and neural processes, our method opens new avenues for both research and clinical applications, potentially advancing early detection and intervention strategies for neurological and psychiatric disorders. (8/8)

Finally, resting-state ORFs reliably decoded most ocular behaviours across eye features and regularity conditions, even from task-related neural activity during passive listening, highlighting their robustness and versatility. (7/8)

We then applied resting-state ORFs to a passive listening task. 🎶🧠 This "proof-of-principle" revealed overlaps in midbrain, temporal, and parietal regions, suggesting shared mechanisms between auditory and saccade-related activity, with saccades potentially influencing auditory responses. (6/8)

...and vice versa! We could also reliably decode eye features from neural data, either using whole-brain information or PCA-based methods 🧠👁️ (5/8)

Our approach allows for the temporally and spatially precise prediction of neural activity based on ocular action... 🌀👁️ (4/8)

We propose a solution: "Ocular Response Functions". We used simultaneous magnetoencephalographic (MEG) and eye-tracking recordings during the resting-state combined with temporal response functions to precisely map the relationship between oculomotion and neural activity. (3/8)

Eye movements are usually treated as “artefacts” in neuroimaging - something to remove or correct. This means we lose opportunities to study how oculomotion might actually shape neural responses or contribute to the mapping between cognitive tasks and neural activity. 🤔 So how do we fix this? (2/8)

Oculomotor activity (eye movements) offers critical insights into cognition and health. 🧠👁️ It's tied to attention, memory, sensory processing - and even to conditions like schizophrenia, dementia, depression, and tinnitus. Yet, it’s often underexplored in neuroimaging studies. Why? (1/8)

By providing a robust framework for examining the interplay between eye movements and neural processes, our method opens new avenues for both research and clinical applications, potentially advancing early detection and intervention strategies for neurological and psychiatric disorders. (8/8)

Finally, resting-state ORFs reliably decoded most ocular behaviours across eye features and regularity conditions, even from task-related neural activity during passive listening, highlighting their robustness and versatility. (7/8)

We then applied resting-state ORFs to a passive listening task. 🎶🧠 This "proof-of-principle" revealed overlaps in midbrain, temporal, and parietal regions, suggesting shared mechanisms between auditory and saccade-related activity, with saccades potentially influencing auditory responses. (6/8)

...and vice versa! We could also reliably decode eye features from neural data, either using whole-brain information or PCA-based methods 🧠👁️ (5/8)

Our approach allows for the temporally and spatially precise prediction of neural activity based on ocular action... 🌀👁️ (4/8)

We propose a solution: "Ocular Response Functions". We used simultaneous magnetoencephalographic (MEG) and eye-tracking recordings during the resting-state combined with temporal response functions to precisely map the relationship between oculomotion and neural activity. (3/8)

Eye movements are usually treated as “artefacts” in neuroimaging - something to remove or correct. This means we lose opportunities to study how oculomotion might actually shape neural responses or contribute to the mapping between cognitive tasks and neural activity. 🤔 So how do we fix this? (2/8)

Oculomotor activity (eye movements) offers critical insights into cognition and health. 🧠👁️ It's tied to attention, memory, sensory processing—and even to conditions like schizophrenia, dementia, depression, and tinnitus. Yet, it’s often underexplored in neuroimaging studies. Why? (1/8)