Circuit motifs and graph properties of connectome development in <i>C. elegans</i>

Matelsky, Jordan; Norman-Tenazas, Raphael; Davenport, Franca; Reilly, Elizabeth P.; Gray-Roncal, William

doi:10.1101/2021.07.11.451911

Cited by 3 publications

(4 citation statements)

References 14 publications

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“…Finally, the worm connectome contains a small number of highly connected hubs, which are interconnected in a core or rich club and facilitate communication between modules 55 ; similar rich club topology has been observed in bigger brains, including the human cortex 56, 57 . Shared structural features are also apparent at the microcircuit level; for example, feed-forward motifs are significantly overrepresented in the nematode connectome, just as in the mammalian cortex 58–60 . Thus, insights gained from analysis of neuropeptide signaling networks in C. elegans may also reveal organizational principles that are conserved in larger brains.…”

Section: Introductionmentioning

confidence: 99%

The neuropeptidergic connectome ofC. elegans

Sanchez

Watteyne

Sun

et al. 2022

Preprint

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Efforts are currently ongoing to map synaptic wiring diagrams or connectomes in order to understand the neural basis of brain function. However, chemical synapses represent only one type of functionally important neuronal connection; in particular, extrasynaptic, "wireless" signaling by neuropeptides is widespread and plays essential roles in all nervous systems. By integrating single-cell anatomical and gene expression datasets with comprehensive biochemical analysis of receptor-ligand interactions, we have generated a draft connectome of neuropeptide signaling in the C. elegans nervous system. This neuropeptide connectome is characterized by a high connection density, extended signaling cascades, autocrine foci, and a decentralized topology, with a large, highly interconnected core containing three constituent communities sharing similar patterns of input connectivity. Intriguingly, several of the most important nodes in this connectome are little-studied neurons that are specialized for peptidergic neuromodulation. We anticipate that the C. elegans neuropeptidergic connectome will serve as a prototype to understand basic organizational principles of neuroendocrine signaling networks.

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Section: Introductionmentioning

confidence: 99%

The neuropeptidergic connectome ofC. elegans

Sanchez

Watteyne

Sun

et al. 2022

Preprint

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“…A number of whole-animal or brain region connectomes are becoming available in numerous species that will enable more direct comparisons with brain organoids. Moreover, the development of analytic tools based on graph theory are providing new methods for quantifying the functional connectomes of biological neural networks [ 81 , 82 ]. Finally, the identification of specific neural circuit motifs that contribute to each computational architecture will be a necessary first step toward determining the epistemological criteria that might be appropriate for moral considerations of next generation brain organoids, and other non-conventional beings.…”

Section: Discussionmentioning

confidence: 99%

Moral considerability of brain organoids from the perspective of computational architecture

Boyd

2024

Oxford Open Neuroscience

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Human brain organoids equipped with complex cytoarchitecture and closed-loop feedback from virtual environments could provide insights into neural mechanisms underlying cognition. Yet organoids with certain cognitive capacities might also merit moral consideration. A precautionary approach has been proposed to address these ethical concerns by focusing on the epistemological question of whether organoids possess neural structures for morally-relevant capacities that bear resemblance to those found in human brains. Critics challenge this similarity approach on philosophical, scientific, and practical grounds but do so without a suitable alternative. Here, I introduce an architectural approach that infers the potential for cognitive-like processing in brain organoids based on the pattern of information flow through the system. The kind of computational architecture acquired by an organoid then informs the kind of cognitive capacities that could, theoretically, be supported and empirically investigated. The implications of this approach for the moral considerability of brain organoids are discussed.

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“…For larger host graphs and for our parameter sweeping operations, we used an on-premise SLURM compute cluster, for which our job engineering code is available. The much more computationally expensive ellipsoid body graph has a density of 0.389, roughly an order of magnitude higher than C. elegans , 0.044 [10, 16]. The ellipsoid body graph took a total of 1,125 CPU-days to run on the compute cluster (around 36,000 searches at µ = 45 minutes each).…”

Section: Methodsmentioning

confidence: 99%

“…Furthermore, connectomes are often one-of-a-kind, meaning that traditional statistical models are challenging or impossible to implement. Finally, exhaustive subgraph matching or counting can be computationally intractable for even relatively small connectomes [15, 16].…”

Section: Introductionmentioning

confidence: 99%

Data-driven motif discovery in biological neural networks

Matelsky,

Robinette,

Wester

et al. 2023

Preprint

Self Cite

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Data from a variety of domains are represented as graphs, including social networks, transportation networks, computer networks, and biological networks. A key question spans these domains: are there meaningful repeated subgraphs, or motifs, within the structure of these larger networks? This is a particularly relevant problem when searching for repeated neural circuits in networks of biological neurons, as the field now regularly produces large brain connectivity maps of neurons and synapses, or connectomes. Given acquisition costs, however, these neuron-synapse connectivity maps are mostly one-of-a-kind. With current graph analysis techniques, it is very challenging to discover new "interesting" subgraphs a priori given small sample sizes of host graphs. Another challenge is that for even relatively modest graph sizes, an exhaustive search of all possible subgraphs is computationally intractable. For these reasons, motif discovery in biological graphs remains an unsolved challenge in the field. In this work, we present a motif discovery approach that can derive a list of undirected or directed motifs, with occurrence counts which are statistically significant compared to randomized graphs, from a single graph example. We first address common pitfalls in the current most common approaches when testing for motif statistical significance, and outline a strategy to ameliorate this problem with improved graph randomization techniques. We then propose a progressive-refinement approach for motif discovery, which addresses issues of computational cost. We demonstrate that our sampling correction technique allows for significance testing of target motifs while highlighting misleading conclusions from standard random graph models. Finally, we share our reference implementation, which is available as an open-source Python package, and demonstrate real-world preliminary results on the C. elegans connectome and the ellipsoid body of the Drosophila melanogaster fruit fly connectome.

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Circuit motifs and graph properties of connectome development in C. elegans

Cited by 3 publications

References 14 publications

The neuropeptidergic connectome ofC. elegans

The neuropeptidergic connectome ofC. elegans

Moral considerability of brain organoids from the perspective of computational architecture

Data-driven motif discovery in biological neural networks

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