Evidence for an active handoff between cerebral hemispheres during target tracking
Broschard, M. B.; Roy, J. D.; Brincat, S. L.; Mahnke, M. K.; Miller, E. K.
Show abstract
The brain has somewhat separate cognitive resources for the left and right sides of our visual field. Despite this lateralization, we have a smooth and unified perception of our environment. This raises the question of how the cerebral hemispheres are coordinated to transfer information between them. We recorded neural activity in the lateral prefrontal cortex, bilaterally, as non-human primates covertly tracked a target that moved from one visual hemifield (i.e., from one hemisphere) to the other. Beta (15 to 30 Hz) power, gamma (30 to 80 Hz) power, and spiking information reflected sensory processing of the target. By contrast, alpha (10 to15 Hz) power, theta (4 to10 Hz) power, and spiking information seemed to reflect an active handoff of attention as target information was transferred between hemispheres. Specifically, alpha power and spiking information ramped up in anticipation of the hemifield cross. Theta power peaked after the cross, signaling its completion. Our results support an active hand-off of information between hemispheres. This handshaking operation may be critical for minimizing information loss, much like how mobile towers handshake when transferring calls between them.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- EEG signals index a global signature of arousal embedded in neuronal population recordings 96%
- Dissociation of attentional state and behavioral outcome using local field potentials 95%
- Neural representations of covert attention across saccades: comparing pattern similarity to shifting and holding attention during fixation 95%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.