Back

Neuromodulation-induced burst firing in parvalbumin interneurons of the basolateral amygdala mediates transition between fear-associated network and behavioral states

Fu, X.; Teboul, E.; Maguire, J.; Tasker, J. G.

2021-06-21 neuroscience
10.1101/2021.04.19.440525 bioRxiv
Show abstract

Patterned coordination of network activity in the basolateral amygdala (BLA) is important for fear expression. Neuromodulatory systems play an essential role in regulating changes between behavioral states, however the mechanisms underlying the neuromodulatory control of BLA circuits that mediates transitions between brain and behavioral states remain largely unknown. We examined the role of neuromodulation of parvalbumin (PV)-expressing interneurons in the BLA in coordinating network and behavioral states using combined chemogenetics, ex vivo patch clamp recordings, and in vivo and ex vivo local field potential recordings. We show that Gq signaling, whether by the designer receptor, hM3D, 1A adrenoreceptors, or 5-HT2a serotonergic receptors, induces a previously undescribed, highly stereotyped bursting pattern of activity in BLA PV interneurons that generates synchronous bursts of inhibitory postsynaptic currents and phasic firing in the BLA principal neurons. The Gq activation in PV interneurons induced a transition from tonic to phasic firing in the BLA PV neurons and principal neurons and suppressed BLA gamma oscillations in slices and suppressed BLA gamma and potentiated theta power in vivo. Gq activation in BLA PV interneurons also facilitated fear memory recall, consistent with previous reports of BLA gamma suppression and theta potentiation during conditioned fear expression. Thus, our data reveal a BLA parvalbumin neuron-specific Gq neuromodulatory mechanism that mediates the transition to a fear-associated network and behavioral state.

Matching journals

The top 3 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.