Gamma coherence mediates interhemispheric integration during multiple object tracking
Nicholas Simon BlandiD, Jason B. MattingleyiD, Martin V. SaleiD
Abstract
Our ability to track the paths of multiple visual objects moving between the hemifields requires effective integration of information between the two cerebral hemispheres. Coherent neural oscillations in the gamma band (35-70 Hz) are hypothesised to drive this information transfer. Here we manipulated the need for interhemispheric integration using a novel multiple object tracking (MOT) task in which stimuli either moved between the two visual hemifields--requiring interhemispheric integration--or moved within separate visual hemifields. We used electroencephalography (EEG) to measure interhemispheric coherence during the task. Human observers (21 female; 20 male) were poorer at tracking objects between-versus within-hemifields, reflecting a cost of interhemispheric integration. Critically, gamma coherence was greater in trials requiring interhemispheric integration, particularly between sensors over parieto-occipital areas. In approximately half of the participants, the observed cost of integration was associated with a failure of the cerebral hemispheres to become coherent in the gamma band. Moreover, individual differences in this integration cost correlated with endogenous gamma coherence at these same sensors, though with generally opposing relationships for the real and imaginary part of coherence. The real part (capturing synchronisation with a near-zero phase-lag) benefited between-hemifield tracking; imaginary coherence was detrimental. Finally, instantaneous phase-coherence over the tracking period uniquely predicted between-hemifield tracking performance, suggesting that effective integration benefits from sustained interhemispheric synchronisation. Our results show that gamma coherence mediates interhemispheric integration during MOT, and add to a growing body of work demonstrating that coherence drives communication across cortically distributed neural networks.
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