Following Forrest Gump: Smooth pursuit related brain activation during free movie viewing

Exploring oculomotor functions in a pilot study with healthy controls: Insights from eye-tracking and fMRI

Eye-tracking techniques have gained widespread application in various fields including research on the visual system, neurosciences, psychology, and human-computer interaction, with emerging clinical implications. In this preliminary phase of our study, we introduce a pilot test of innovative virtual reality technology designed for tracking head and eye movements among healthy individuals. This tool was developed to assess the presence of mild traumatic brain injury (mTBI), given the frequent association of oculomotor function deficits with such injuries. Alongside eye-tracking, we also integrated fMRI due to the complementary nature of these techniques, offering insights into both neural activation patterns and behavioural responses, thereby providing a comprehensive understanding of oculomotor function. We used fMRI with tasks evaluating oculomotor functions: Smooth Pursuit (SP), Saccades, Anti-Saccades, and Optokinetic Nystagmus (OKN). Prior to the scanning, the testing with a system of VR goggles with integrated eye and head tracking was used where subjects performed the same tasks as those used in fMRI. 31 healthy adult controls (HCs) were tested with the purpose of identifying brain regions associated with these tasks and collecting preliminary norms for later comparison with concussed subjects. HCs' fMRI results showed following peak activation regions: SP-cuneus, superior parietal lobule, paracentral lobule, inferior parietal lobule (IPL), cerebellartonsil (CT); Saccades-middle frontal gyrus (MFG), postcentral gyrus, medial frontal gyrus; Anti-saccades-precuneus, IPL, MFG; OKN-middle temporal gyrus, ACC, postcentral gyrus, MFG, CT. These results demonstrated brain regions associated with the performance on oculomotor tasks in healthy controls and most of the highlighted areas are corresponding with those affected in concussion. This suggests that the involvement of brain areas susceptible to mTBI in implementing oculomotor evaluation, taken together with commonly reported oculomotor difficulties post-concussion, may lead to finding objective biomarkers using eye-tracking tasks.

A large-scale fMRI dataset for the visual processing of naturalistic scenes

One ultimate goal of visual neuroscience is to understand how the brain processes visual stimuli encountered in the natural environment. Achieving this goal requires records of brain responses under massive amounts of naturalistic stimuli. Although the scientific community has put a lot of effort into collecting large-scale functional magnetic resonance imaging (fMRI) data under naturalistic stimuli, more naturalistic fMRI datasets are still urgently needed. We present here the Natural Object Dataset (NOD), a large-scale fMRI dataset containing responses to 57,120 naturalistic images from 30 participants. NOD strives for a balance between sampling variation between individuals and sampling variation between stimuli. This enables NOD to be utilized not only for determining whether an observation is generalizable across many individuals, but also for testing whether a response pattern is generalized to a variety of naturalistic stimuli. We anticipate that the NOD together with existing naturalistic neuroimaging datasets will serve as a new impetus for our understanding of the visual processing of naturalistic stimuli.

Neural mechanisms of background and velocity effects in smooth pursuit eye movements

Smooth pursuit eye movements (SPEM) are essential to guide behaviour in complex visual environments. SPEM accuracy is known to be degraded by the presence of a structured visual background and at higher target velocities. The aim of this preregistered study was to investigate the neural mechanisms of these robust behavioural effects. N = 33 participants performed a SPEM task with two background conditions (present and absent) at two target velocities (0.4 and 0.6 Hz). Eye movement and BOLD data were collected simultaneously. Both the presence of a structured background and faster target velocity decreased pursuit gain and increased catch-up saccade rate. Faster targets additionally increased position error. Higher BOLD response with background was found in extensive clusters in visual, parietal, and frontal areas (including the medial frontal eye fields; FEF) partially overlapping with the known SPEM network. Faster targets were associated with higher BOLD response in visual cortex and left lateral FEF. Task-based functional connectivity analyses (psychophysiological interactions; PPI) largely replicated previous results in the basic SPEM network but did not yield additional information regarding the neural underpinnings of the background and velocity effects. The results show that the presentation of visual background stimuli during SPEM induces activity in a widespread visuo-parieto-frontal network including areas contributing to cognitive aspects of oculomotor control such as medial FEF, whereas the response to higher target velocity involves visual and motor areas such as lateral FEF. Therefore, we were able to propose for the first time different functions of the medial and lateral FEF during SPEM.

Functional connectivity of cortical resting-state networks is differentially affected by rest conditions

Optimal conditions for resting-state functional magnetic resonance imaging (rs-fMRI) are still highly debated. Here, we comprehensively assessed the effects of various rest conditions on all cortical resting-state networks (RSNs) defined by an established atlas. Twenty-two healthy college students (22 ± 4 years old, 12 females) were scanned on a GE 3T MRI scanner. Rs-fMRI datasets were collected under four different conditions for each subject: (1) eyes open in dim light (Eyes-Open), (2) eyes closed and awake (Eyes-Closed), (3) eyes closed while remembering four numbers through the scan session (Eyes-Closed-Number) and (4) asked to watch a movie (Movie). We completed a thorough examination of the 17 functional RSNs defined by Yeo and colleagues. Importantly, the movie led to changes in global connectivity and should be avoided as a rest condition. Conversely, there were no significant connectivity differences between conditions within the frontoparietal control and limbic networks and the following subnetworks as defined by Yeo et al.: default-B, dorsal-attention-B and salience/ventral-attention-B. These were not even significant when compared to the highly stimulative Movie condition. A significant difference was not found between Eyes-Closed and Eyes-Closed-Number conditions in whole-brain, within-network and between-network comparisons. When considering other rest conditions, however, we observed connectivity changes in some subnetworks, including those of the default-mode network. Overall, we found conditions with more external stimulation led to more changes in functional connectivity during rs-fMRI. In conclusion, the comprehensive results of our study can aid in choosing rest conditions for the study of overall and specific functional networks.

Revealing Whole-Brain Causality Networks During Guided Visual Searching

In our daily lives, we use eye movements to actively sample visual information from our environment ("active vision"). However, little is known about how the underlying mechanisms are affected by goal-directed behavior. In a study of 31 participants, magnetoencephalography was combined with eye-tracking technology to investigate how interregional interactions in the brain change when engaged in two distinct forms of active vision: freely viewing natural images or performing a guided visual search. Regions of interest with significant fixation-related evoked activity (FRA) were identified with spatiotemporal cluster permutation testing. Using generalized partial directed coherence, we show that, in response to fixation onset, a bilateral cluster consisting of four regions (posterior insula, transverse temporal gyri, superior temporal gyrus, and supramarginal gyrus) formed a highly connected network during free viewing. A comparable network also emerged in the right hemisphere during the search task, with the right supramarginal gyrus acting as a central node for information exchange. The results suggest that all four regions are vital to visual processing and guiding attention. Furthermore, the right supramarginal gyrus was the only region where activity during fixations on the search target was significantly negatively correlated with search response times. Based on our findings, we hypothesize that, following a fixation, the right supramarginal gyrus supplies the right supplementary eye field (SEF) with new information to update the priority map guiding the eye movements during the search task.

A Case for Studying Naturalistic Eye and Head Movements in Virtual Environments

Using head mounted displays (HMDs) in conjunction with virtual reality (VR), vision researchers are able to capture more naturalistic vision in an experimentally controlled setting. Namely, eye movements can be accurately tracked as they occur in concert with head movements as subjects navigate virtual environments. A benefit of this approach is that, unlike other mobile eye tracking (ET) set-ups in unconstrained settings, the experimenter has precise control over the location and timing of stimulus presentation, making it easier to compare findings between HMD studies and those that use monitor displays, which account for the bulk of previous work in eye movement research and vision sciences more generally. Here, a visual discrimination paradigm is presented as a proof of concept to demonstrate the applicability of collecting eye and head tracking data from an HMD in VR for vision research. The current work’s contribution is 3-fold: firstly, results demonstrating both the strengths and the weaknesses of recording and classifying eye and head tracking data in VR, secondly, a highly flexible graphical user interface (GUI) used to generate the current experiment, is offered to lower the software development start-up cost of future researchers transitioning to a VR space, and finally, the dataset analyzed here of behavioral, eye and head tracking data synchronized with environmental variables from a task specifically designed to elicit a variety of eye and head movements could be an asset in testing future eye movement classification algorithms.

Replicability and reliability of the background and target velocity effects in smooth pursuit eye movements

When we follow a slowly moving target with our eyes, we perform smooth pursuit eye movements (SPEM). Previous investigations point to significantly and robustly reduced SPEM performance in the presence of a stationary background and at higher compared to lower target velocities. However, the reliability of these background and target velocity effects has not yet been investigated systematically. To address this issue, 45 healthy participants (17 m, 28 f) took part in two experimental sessions 7 days apart. In each session, participants were instructed to follow a horizontal SPEM target moving sinusoidally between ±7.89° at three different target velocities, corresponding to frequencies of 0.2, 0.4 and 0.6 Hz. Each target velocity was presented once with and once without a stationary background, resulting in six blocks. The blocks were presented twice per session in order to additionally explore potential task length effects. To assess SPEM performance, velocity gain was calculated as the ratio of eye to target velocity. In line with previous research, detrimental background and target velocity effects were replicated robustly in both sessions with large effect sizes. Good to excellent test-retest reliabilities were obtained at higher target velocities and in the presence of a stationary background, whereas lower reliabilities occurred with slower targets and in the absence of background stimuli. Target velocity and background effects resulted in largely good to excellent reliabilities. These findings not only replicated robust experimental effects of background and target velocity at group level, but also revealed that these effects can be translated into reliable individual difference measures.

Spontaneous eye movements during eyes-open rest reduce resting-state-network modularity by increasing visual-sensorimotor connectivity

During wakeful rest, individuals make small eye movements during fixation. We examined how these endogenously driven oculomotor patterns impact topography and topology of functional brain networks. We used a dataset consisting of eyes-open resting-state (RS) fMRI data with simultaneous eye tracking. The eye-tracking data indicated minor movements during rest, which correlated modestly with RS BOLD data. However, eye-tracking data correlated well with echo-planar imaging time series sampled from the area of the eye-orbit (EO-EPI), which is a signal previously used to identify eye movements during exogenous saccades and movie viewing. Further analyses showed that EO-EPI data were correlated with activity in an extensive motor and sensorimotor network, including components of the dorsal attention network and the frontal eye fields. Partialling out variance related to EO-EPI from RS data reduced connectivity, primarily between sensorimotor and visual areas. It also produced networks with higher modularity, lower mean connectivity strength, and lower mean clustering coefficient. Our results highlight new aspects of endogenous eye movement control during wakeful rest. They show that oculomotor-related contributions form an important component of RS network topology, and that those should be considered in interpreting differences in network structure between populations or as a function of different experimental conditions.

We studied how subtle eye movements made during fixation, in absence of any other task, are related to resting-state connectivity measured using fMRI. We used a dataset for which eye tracking and BOLD resting-state were acquired simultaneously. We correlated brain activity with both eye-tracking metrics as well as time series sampled from the area of the eye orbits (EO-EPI). Eye-tracking data correlated well with the EO-EPI data. Furthermore, EO-EPI correlated with BOLD signal in sensorimotor and visual brain systems. Removing variance related to EO-EPI reduced connectivity between sensorimotor and visual areas and resulted in more modular resting-state networks. Our findings show that oculomotor-related contributions are an important component of resting-state network topology, and that they can be studied using EPI data from the eye orbits.