Back

Brain-wide oscillatory network selectively encodes aggression

Grossman, Y. S.; Talbot, A.; Gallagher, N. M.; Thomas, G.; Fink, A. J.; Walder-Christensen, K. K.; Russo, S.; Carlson, D. E.; Dzirasa, K.

2022-12-07 neuroscience
10.1101/2022.12.07.519272 bioRxiv
Show abstract

Aggression is a psychological state characterized by intent to harm oneself or others, and often manifests as hostile, harmful, or violent action. Social aggression can aid an organism in securing access to resources1,2, or it can impair group function and survival in behavioral pathology3-5. Since many brain regions contribute to multiple social behaviors6-8, expanded knowledge of how the brain distinguishes between social states would enable the development of interventions that suppress violent action, while leaving other social behaviors intact. Here we showed that a murine aggressive internal state is encoded by a widespread network. This network is organized by prominent and synchronized theta (4-11 Hz) and beta (14-30 Hz) oscillations that relay through the medial prefrontal cortex, and couples to widespread cellular firing. Strikingly, network activity during social isolation was found to encode the trait aggressiveness of mice, and causal cellular manipulations known to impact aggression were found to modulate the networks activity. We next established that this network mediates aggression using closed-loop stimulation of medial prefrontal cortex and causal mediation analysis. Finally, we deployed Long-term integration of Circuits using connexins (LinCx) to selectively edit a key circuit within the network from the medial prefrontal cortex to nucleus accumbens. Editing this circuit chronically suppressed violent action while leaving non-aggressive social behavior intact.

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.