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

• Published in: bioRxiv
• Main Authors: Sprague, Daniel Yutaka, Rusch, Kevin, Dunn, Raymond L, Borchardt, Jackson, Ban, Steven, Bubnis, Greg, Chiu, Grace, Wen, Chentao, Suzuki, Ryoga, Chaudhary, Shivesh, Lee, Hyun Jee, Yu, Zikai, Dichter, Benjamin, Ly, Ryan, Onami, Shuichi, Lu, Hang, Kimura, Koutarou, Yemini, Eviatar I, Kato, Saul
• Format: Journal Article
• Language: English
• Published: United States 29.06.2024
• ISSN: 2692-8205; 2692-8205
• DOI: 10.1101/2024.04.28.591397

We develop a data harmonization approach for volumetric microscopy data, still or video, consisting of a standardized format, data pre-processing techniques, and a set of human-in-the-loop machine learning based analysis software tools. We unify a diverse collection of 118 whole-brain neural activity imaging datasets from 5 labs, storing these and accompanying tools in an online repository called WormID (wormid.org). We use this repository to generate a statistical atlas that, for the first time, enables accurate automated cellular identification that generalizes across labs, approaching human performance in some cases. We mine this repository to identify factors that influence the developmental positioning of neurons. To facilitate communal use of this repository, we created open-source software, code, web-based tools, and tutorials to explore and curate datasets for contribution to the scientific community. This repository provides a growing resource for experimentalists, theorists, and toolmakers to investigate neuroanatomical organization and neural activity across diverse experimental paradigms, develop and benchmark algorithms for automated neuron detection, segmentation, cell identification, tracking, and activity extraction, and inform models of neurobiological development and function.