See Elegans: Simple-to-use, accurate, and automatic 3D detection of neural activity from densely packed neurons

Enrico Lanza, Valeria Lucente, Martina Nicoletti, Silvia Schwartz, Ilaria F Cavallo, Davide Caprini, Christopher W Connor, Mashel Fatema A Saifuddin, Julia M Miller, Noelle D L’Etoile and Viola Folli

PLOS ONE, 2024, vol. 19, issue 3, 1-21

Abstract: In the emerging field of whole-brain imaging at single-cell resolution, which represents one of the new frontiers to investigate the link between brain activity and behavior, the nematode Caenorhabditis elegans offers one of the most characterized models for systems neuroscience. Whole-brain recordings consist of 3D time series of volumes that need to be processed to obtain neuronal traces. Current solutions for this task are either computationally demanding or limited to specific acquisition setups. Here, we propose See Elegans, a direct programming algorithm that combines different techniques for automatic neuron segmentation and tracking without the need for the RFP channel, and we compare it with other available algorithms. While outperforming them in most cases, our solution offers a novel method to guide the identification of a subset of head neurons based on position and activity. The built-in interface allows the user to follow and manually curate each of the processing steps. See Elegans is thus a simple-to-use interface aimed at speeding up the post-processing of volumetric calcium imaging recordings while maintaining a high level of accuracy and low computational demands. (Contact: enrico.lanza@iit.it).

Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0300628

DOI: 10.1371/journal.pone.0300628

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