A Scale-adaptive Vision Model Links C. elegans Neuronal Morphology to Behavior for Neurotoxicity Assessment

2026-07-25

AI Summary

Current neurotoxicity assessments rely on subjective manual scoring, which struggles to efficiently quantify multiscale degenerative lesions from C. elegans neural imagery or predict associated behavioral phenotypes. This work proposes a scale-adaptive masked image modeling approach to build a self-supervised visual foundation model tailored for dopaminergic neurons in C. elegans, enabling joint learning of multiresolution features under fixed computational budgets while overcoming conventional grid constraints. For the first time, this framework achieves end-to-end extraction of structural semantics from sparse, multiscale images. Evaluated on CeNeuMorph—a newly established multimodal confocal imaging benchmark—the model outperforms general-purpose and biomedical foundation models across classification, segmentation, and detection tasks. By integrating morphological and visual features, it effectively predicts dopamine-related behavioral deficits (R² = 0.498) and successfully screens 180 agrochemicals, identifying benzimidazole as a novel dopaminergic neurotoxicity determinant.

Abstract

Neurological disorders are a leading cause of global disability and are increasingly linked to environmental chemical exposures. Yet neurotoxicity assessment still relies on hand-scored morphological readouts that are subjective and poorly predictive of behavioral outcomes. Caenorhabditis elegans provides a genetically tractable, 3R-compliant alternative, but quantifying neuronal phenotypes from confocal microscopy at scale remains computationally challenging: existing vision foundation models, trained on natural or radiological images, cannot resolve the sparse signals and multi-scale lesions of neuronal imaging. Here, we introduce a dedicated self-supervised vision model for C. elegans dopaminergic neurons, together with CeNeuMorph, a multi-grained confocal benchmark of 27,117 annotated images. Specifically, moving beyond standard Masked Autoencoders, we propose a scale-adaptive masked image modeling strategy that jointly learns representations across resolutions and patch sizes under a fixed token budget. By decoupling structural semantic learning from rigid grid constraints, the model effectively resolves the full spectrum of neurodegenerative lesions - ranging from fine dendritic beading to gross soma shrinkage - within a tractable computational framework. Finally, our model surpasses both generalist and biomedical foundation models across classification, segmentation and detection tasks. Fusing visual features with morphological descriptors enables prediction of dopamine-dependent behavioral deficits ($R^2=0.498$). Screening 180 agrochemicals, we identify the benzimidazole moiety as a previously unrecognized determinant of dopaminergic neurotoxicity. Together, the work demonstrates how scale-adaptive self-supervised learning can connect morphology to function for a scalable alternative to mammalian in vivo models for neurotoxicity assessment and drug discovery.

Problem

• neurotoxicity assessment
• C. elegans
• neuronal morphology
• behavioral prediction
• multi-scale lesions

Innovation

• scale-adaptive masked image modeling
• self-supervised vision model
• multi-scale neuronal lesion detection
• C. elegans neurotoxicity assessment
• morphology-to-behavior prediction