ArticleCell reports methods2025
Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans.
Article in Cell reports methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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5 citing papers in PubMed.
- Deep neural networks to register and annotate cells in moving and deforming nervous systems.eLife · 2026Article
- Short-term memory by distributed neural network oscillators in a simple nervous system.Current biology : CB · 2025Article
- CeDNe: A multi-scale computational framework for modeling structure-function relationships in thebioRxiv : the preprint server for biology · 2025Article
- Ultrack: pushing the limits of cell tracking across biological scales.Nature methods · 2025Article
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Abstract
We develop a data harmonization approach for C. elegans volumetric microscopy data, consisting of a standardized format, pre-processing techniques, and human-in-the-loop machine-learning-based analysis tools. Using this approach, we unify a diverse collection of 118 whole-brain neural activity imaging datasets from five labs, storing these and accompanying tools in an online repository WormID (wormid.org). With this repository, we train three existing automated cell-identification algorithms, CPD, StatAtlas, and CRF_ID, to enable accuracy that generalizes across labs, recovering all human-labeled neurons in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. This growing resource of data, code, apps, and tutorials enables users to (1) study neuroanatomical organization and neural activity across diverse experimental paradigms, (2) develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and (3) share data with the community and comply with data-sharing policies.
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