Unifying community whole-brain imaging datasets enables robust neuron identification and reveals determinants of neuron position in C. elegans

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.

Keywords: C. elegans; CP: neuroscience; CP: systems biology; DANDI; NWB; calcium imaging; data corpus; machine learning; neuroanatomy; neurodevelopment; neuron identification; whole-brain imaging.