Multiplexed BOLD oscillations reveal the interplay of normalization and attention
Rafeh, R. W.; Ngo, G. N.; Muller, L. E.; Khan, A. R.; Menon, R. S.; Mur, M.; Schmitz, T. W.
Show abstract
Monkey electrophysiology has linked attention to divisive normalization, yet noninvasive evidence in humans remains limited. We use frequency-tagged fMRI to isolate visual cortical populations that simultaneously encode multiple competing inputs. We show that responses of these sites are suppressed during inattention and enhanced during attention - consistent with the normalization model, which predicts that attention selectively disinhibits competing inputs - offering a noninvasive translational bridge to study fine-grained computations underlying attentional selection.
Matching journals
The top 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Representations in human primary visual cortex drift over time 97%
- Rethinking simultaneous suppression in visual cortex via compressive spatiotemporal population receptive fields 97%
- Differential spatial computations in ventral and lateral face-selective regions are scaffolded by structural connections 96%
Similar papers in this journal
- THINGS-data: A multimodal collection of large-scale datasets for investigating object representations in human brain and behavior 96%
- Differential destinations, dynamics, and functions of high- and low-order features in the feedback signal during object processing 96%
- Neural dynamics of visual working memory representation during sensory distraction 96%
Similar papers in this journal
Similar papers in this journal
- Unraveling the mesoscale resting-state functional connectivity of ocular dominance columns in humans using high-resolution functional MRI. 97%
- Multimodal identification of the mouse brain using simultaneous Ca2+ imaging and fMRI 96%
- The spatial layout of antagonistic brain regions is explicable based on geometric principles 96%
"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.