Back

Recurrent beta-gamma interactions between olfactory bulb and piriform cortex support cross-sniff perceptual continuity in humans

Norden, F.; Winter, A. L.; Kay, L. M.; Arshamian, A.; Lundqvist, M.; Lundström, J. N.

2026-09-04 neuroscience
10.64898/2026.08.31.747807 bioRxiv
Show abstract

Olfactory perception relies on active sampling, with successive inhalations providing discrete sensory inputs that in humans can be separated by several seconds. Yet odors are perceived as continuous and stable, raising the question of how the brain integrates sensory information across these temporal gaps. Here, we used electrobulbogram (EBG) recordings in 48 participants to demonstrate that successive sniffs are not processed independently but are linked through recurrent oscillatory dynamics between the olfactory bulb (OB) and piriform cortex (PC). Specifically, first-sniff alpha/beta activity in the OB and OB-PC functional connectivity predicted second-sniff gamma power, while transfer entropy indicated a directional temporal dependence from first-sniff alpha/beta to second-sniff gamma dynamics. In parallel, second-sniff gamma activity selectively tracked subjective odor valence prior to inhalation onset. At the network level, the PC exhibited stronger alpha/beta-band connectivity with orbitofrontal, insular, and prefrontal regions during the first sniff than during the second, suggesting that early evaluative processing provides a contextual signal that is carried forward to shape subsequent sensory representations. These results demonstrate that the human OB-PC circuit carries evaluative information across inhalations through directed alpha/beta-to-gamma interactions, providing a mechanism for maintaining perceptual continuity in a sensory system defined by temporally discrete sampling.

Matching journals

The top 5 journals account for 50% of the predicted probability mass.

50% of probability mass above

"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.