Top-down signals such as attention not only modulate an individual neuron’s activity, but also orchestrate the synchrony in sensory areas. For example, spatial attention selectively increases the local gamma coherence for V4 neurons whose receptive fields (RF) overlap with the attended location, presumably enhancing the signal transmission efficacy of a local ensemble. In contrast, feature-based attention requires a larger-scale mechanism, as neurons within the same feature domain are usually distributed throughout the whole retinotopic map. Studies have found that feature-based attention works in such a parallel fashion by regulating neurons according to their feature selectivity, but not RFs. Two important questions emerge: (1) do neurons that share similar feature tuning but are physically far from each other still form a functional ensemble through synchrony, and (2) is this synchrony pattern modulated by feature-based attention? Driven by these questions, my thesis project investigated the network-level dynamics in macaque neocortical area V4 underlying visual processing and feature-based attention modulation. By simultaneously recording the V4 population activity during a feature match task, we found that (1) neural pairs with similar feature tuning properties showed stronger low-frequency synchrony without visual input yet higher gamma synchrony with visual input; (2) feature-based attention modulates the synchrony of neural pairs whose preferred feature matches the attended feature by decreasing the low-frequency synchrony and increasing the gamma synchrony during late visual period; (3) the feature-dependent and attention-modulated synchrony retains similar patterns regardless of the distance between their RF centers. Our results demonstrated a distributed and flexible network mechanism for feature-based attention. To further explore the behavioral effect of neural synchrony and the global brain network pattern underlying feature-based attention, we conducted a complementary project in human subjects using electroencephalogram (EEG) recordings. Our preliminary results indicated that human’s detection performance depends on the phase of alpha-oscillation immediately following stimulus onset, and interestingly, such phase dependency is suppressed by feature-based attention. Additionally, our functional connectivity analysis revealed distinct task-dependent global network patterns with and without consideration of the zero-lag phase synchrony. These findings provide a more global view and additional insights into the neural dynamics of feature-based attention.
Xia, Ruobing,
"The neural dynamics of feature-based attention"
(2019).
Neuroscience Theses and Dissertations.
Brown Digital Repository. Brown University Library.
https://doi.org/10.26300/7ge8-cy10
