WormID-Benchmark: Extracting Whole-Brain Neural Dynamics of C. elegans At the Neuron Resolution

Abstract

The nematode C. elegans is a premier model organism for studying neural circuit function due to its fully mapped connectome and genetically identifiable neurons. Recent advances in 3D light microscopy and fluorescent protein tagging have enabled whole-brain imaging at single-neuron resolution. However, extracting meaningful neural dynamics from these high-resolution recordings requires addressing three fundamental challenges: (i) accurate detection of individual neurons in fluorescence images, (ii) precise identification of neuron classes based on anatomical and colorimetric cues, and (iii) robust tracking of neurons over time in calcium imaging videos. To systematically evaluate these challenges, we introduce WormID-Bench, a large-scale, multi-laboratory dataset comprising 118 worms from five distinct research groups, along with standardized evaluation metrics for detection, identification, and tracking. Our benchmark reveals that existing computational approaches show substantial room for improvement in sensitivity, specificity, and generalization across diverse experimental conditions. By providing an open and reproducible benchmarking framework1, WormID-Bench aims to accelerate the development of high-throughput and scalable computational tools for whole-brain neural dynamics extraction in C. elegans, setting the stage for broader advancements in functional connectomics.