Excited to be a part of this work with Bharat Singhal!
Is your data good enough to reveal true cell-cell connectivity?
We developed a statistical method to find out — tested on simulations and real-world #networks.
shorturl.at/I3KfB
#NetworkScience #ComplexSystems #IEEEXplore

VIP hubs drive SCN circadian synchrony: We found a topologically unique group of VIP hub neurons that generate and broadcast signals with their extensive and long-range connections. SCN networks did not synchronize on ablating the VIP hubs but not AVP cells. (10/n)

To identify key hub cells driving SCN network synchrony, we simulated the experimentally inferred SCN maps in 6 mice and tested the impact of ablating various cell types on networks' ability to synchronize. (9/n)

We identified four functional cell types that mediate circadian signaling across the SCN neural network, acting as circadian signal 'generators,' 'broadcasters,' 'bridges,' or 'sinks.' (8/n)

Two asymmetrically coupled cellular networks drive SCN synchrony: Within each explant, we found dorsal and ventral cellular modules resembling the anatomical shell-core regions. Notably, these were absent in explants that failed to synchronize during our experiments. (6/n)

While SCN cells connect as a #smallworld network with most cells signaling proximally; intriguingly, we found a few long-range connections from ventral cells that project halfway > across the SCN, short-circuiting the network to enhance signal transmission. (5/n)

Mammalian clock is efficiently wired: SCN connectivity patterns were highly conserved, both bilaterally and across mice. SCN is sparsely connected compared to other brain areas (~720 connections/cell), yet signals reached any other SCN cell via just 3–4 intermediaries! (4/n)

We developed an #informationtheory method, MITE, to identify functional cell-cell connections by observing the coevolution of circadian gene expression as the cells synchronized. This framework can be used for inferring connectivity in any cellular network. (3/n)