Brain Networks in Visual Attention : Network Modeling of Multiple Object Tracking

by Genevieve Richard

Institution: University of Oslo
Year: 1000
Keywords: VDP::260
Record ID: 1283125
Full text PDF: https://www.duo.uio.no/handle/10852/37658



Visual attention is an important feature of adaptive behavior. Unfortunately, attentional dysfunctions are often observed in several clinical disorders. One of the reasons that pathologies in visual attention are commonly seen across a variety of brain disorders is possibly due to the fact that rather than relying on a single and unitary attentional center even low-level attentional processes rely on the integrated coordination of an ensemble of inter-connected networks responsible for sustaining visual attention in a dynamic world. The multiple object tracking (MOT) task is a powerful paradigm for studying the dynamics of visual attention both in health and disease. The utility of MOT in clinical populations with severe attentional dysfunctions assumes that the same attentional networks are engaged across various load conditions. However, previous studies employing the MOT paradigm have included high load conditions which are likely to be too demanding for patients suffering from e.g. visual neglect after unilateral stroke. Thus, the present study employed the MOT paradigm using low load conditions during functional magnetic resonance imaging (fMRI) in order to identify and characterize the extended brain network involved in low-level attention processing using a combination of conventional contrast based analysis and an explorative network modeling approach in healthy volunteers. In a first step, we demonstrated that low-level attentional load engage the same pattern of co-activation as previously shown with high-level attentional load using a blocked version of MOT. In a second step, we used an explorative approach to investigate the alterations in brain network connectivity during sustained attention using a continuous version of MOT. By utilizing the proposed protocol in combination with the novel network modeling approach, future planned studies will potentially gain a better understanding of attentional pathologies which is likely to benefit future treatment methods.