Visual and Oculomotor Integration: Representations and Temporal Mechanisms

The visual system recruits the oculomotor system to enhance processing at a particular location of interest with the use of saccadic eye movements. This involves the transfer of visual information from the visual system to the oculomotor system so that the correct location or object may be fixated at the expense of all othersa process called target selection. However, the relative extent of visual processing between the visual and oculomotor systems to facilitate this process is disputed. Here, this question is examined by specifically investigating the extent of oculomotor processing prior to a saccade. First, the nature of object representations in the ventral stream of the visual system is examined to gain insight into how complex visual representations are encoded. Next, target selection was examined in a visual context requiring extremely complex visual computations in order to select the correct stimulus. Last, the temporal factors that affect oculomotor target selection were examined. This research demonstrated that objects of considerable complexity elicit similar perceptual behaviours as do simple visual features. This elucidates that there are very robust modes of encoding object representations, which generalize to objects of varying complexity and familiarity. Furthermore, when these same complex visual representations were utilized on a target selection task (visual search), there was evidence of oculomotor competition between them. Given the complexity of these stimuli and the limitations of oculomotor visual processing, it was reasoned that the visual system performed these computations, as observed in the previous experiment, and the results of this computation were output to the oculomotor system. Finally, an analysis of the target selection time course suggested that the oculomotor competition observed previously is likely due to cortical top-down input, further elucidating the role of the visual system in mediating oculomotor target selection

A statistical model for brain networks inferred from large-scale electrophysiological signals

Network science has been extensively developed to characterize structural properties of complex systems, including brain networks inferred from neuroimaging data. As a result of the inference process, networks estimated from experimentally obtained biological data, represent one instance of a larger number of realizations with similar intrinsic topology. A modeling approach is therefore needed to support statistical inference on the bottom-up local connectivity mechanisms influencing the formation of the estimated brain networks. We adopted a statistical model based on exponential random graphs (ERGM) to reproduce brain networks, or connectomes, estimated by spectral coherence between high-density electroencephalographic (EEG) signals. We validated this approach in a dataset of 108 healthy subjects during eyes-open (EO) and eyes-closed (EC) resting-state conditions. Results showed that the tendency to form triangles and stars, reflecting clustering and node centrality, better explained the global properties of the EEG connectomes as compared to other combinations of graph metrics. Synthetic networks generated by this model configuration replicated the characteristic differences found in brain networks, with EO eliciting significantly higher segregation in the alpha frequency band (8-13 Hz) as compared to EC. Furthermore, the fitted ERGM parameter values provided complementary information showing that clustering connections are significantly more represented from EC to EO in the alpha range, but also in the beta band (14-29 Hz), which is known to play a crucial role in cortical processing of visual input and externally oriented attention. These findings support the current view of the brain functional segregation and integration in terms of modules and hubs, and provide a statistical approach to extract new information on the (re)organizational mechanisms in healthy and diseased brains.Comment: Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract appearing here is slightly shorter than that in the PDF fil

Evidence of inverted-gravity driven variation in predictive sensorimotor function.

We move our eyes to place the fovea into the part of a viewed scene currently of interest. Recent evidence suggests that each human has signature patterns of eye movements like handwriting which depend on their sensitivity, allocation of attention and experience. Use of implicit knowledge of how earth's gravity influences object motion has been shown to aid dynamic perception. We used a projected ball tracking task with a plain background offering no context cues to probe the effect of acquired experience about physical laws of gravitation on performance differences of 44 participants under a simulated gravity and an atypical (upward) antigravity condition. Performance measured by the unsigned difference between instantaneous eye and stimulus positions (RMSE) was consistently worse in the antigravity condition. In the vertical RMSE, participants took about 200ms longer to improve to the best performance for antigravity compared to gravity trials. The antigravity condition produced a divergence of individual performance which was correlated with levels of questionnaire based quantified traits of schizotypy but not control traits. Grouping participants by high or low traits revealed a negative relationship between schizotypy traits level and both initiation and maintenance of tracking, a result consistent with trait related impoverished sensory prediction. The findings confirm for the first time that where cues enabling exact estimation of acceleration are unavailable, knowledge of gravity contributes to dynamic prediction improving motion processing. With acceleration expectations violated, we demonstrate that antigravity tracking could act as a multivariate diagnostic window into predictive brain function