@article {68921, title = {Network statistics of the whole-brain connectome of Drosophila}, journal = {Nature}, volume = {634}, year = {2024}, month = {2024 Oct 02}, pages = {153{\textendash}165}, abstract = {

Brains comprise complex networks of neurons and connections, similar to the nodes and edges of artificial networks. Network analysis applied to the wiring diagrams of brains can offer insights into how they support computations and regulate the flow of\ information underlying perception and behaviour. The completion of the first whole-brain connectome of an adult fly, containing over 130,000 neurons and millions of synaptic connections, offers an opportunity to analyse the statistical properties and topological features of a complete brain. Here we computed the prevalence of two- and three-node motifs, examined their strengths, related this information to both neurotransmitter composition and cell type annotations, and compared these metrics with wiring diagrams of other animals. We found that the network of the fly brain displays rich-club organization, with a large population (30\% of the connectome) of highly connected neurons. We identified subsets of rich-club neurons that may serve as integrators or broadcasters of signals. Finally, we examined subnetworks based on 78 anatomically defined brain regions or neuropils. These data products are shared within the FlyWire Codex (https://codex.flywire.ai) and should serve as a foundation for models and experiments exploring the relationship between neural activity and anatomical structure.

}, doi = {10.1038/s41586-024-07968-y}, url = {https://doi.org/10.1038/s41586-024-07968-y}, author = {Albert Lin and Runzhe Yang and Sven Dorkenwald and Arie Matsliah and Amy R. Sterling and Philipp Schlegel and Szi-chieh Yu and Claire E. McKellar and Marta Costa and Katharina Eichler and Alexander Shakeel Bates and Nils Eckstein and Jan Funke and Gregory S.X.E. Jefferis and Mala Murthy} } @conference {67973, title = {Connectome-constrained Latent Variable Model of Whole-Brain Neural Activity}, booktitle = {International Conference on Learning Representations}, year = {2022}, month = {09/2021}, abstract = {

The availability of both anatomical connectivity and brain-wide neural activity measurements in C. elegans make the worm a promising system for learning detailed, mechanistic models of an entire nervous system in a data-driven way. However, one faces several challenges when constructing such a model. We often do not have direct experimental access to important modeling details such as single-neuron dynamics and the signs and strengths of the synaptic connectivity. Further, neural activity can only be measured in a subset of neurons, often indirectly via calcium imaging, and significant trial-to-trial variability has been observed. To address these challenges, we introduce a connectome-constrained latent variable model (CC-LVM) of the unobserved voltage dynamics of the entire C. elegans nervous system and the observed calcium signals. We used the framework of variational autoencoders to fit parameters of the mechanistic simulation constituting the generative model of the LVM to calcium imaging observations. A variational approximate posterior distribution over latent voltage traces for all neurons is efficiently inferred using an inference network, and constrained by a prior distribution given by the biophysical simulation of neural dynamics. We applied this model to an experimental whole-brain dataset, and found that connectomic constraints enable our LVM to predict the activity of neurons whose activity were withheld significantly better than models unconstrained by a connectome. We explored models with different degrees of biophysical detail, and found that models with realistic conductance-based synapses provide markedly better predictions than current-based synapses for this system.

}, url = {https://openreview.net/forum?id=CJzi3dRlJE-}, author = {Lu Mi and Richard Xu and Sridhama Prakhya and Albert Lin and Nir Shavit and Aravinthan Samuel and Srinivas C Turaga} }