ConnectoFM: A Foundation Model for Learning the Language of the Connectome

Abstract

Accurate reconstruction of neural circuits from electron microscopy (EM) data is central to connectomics, yet modern datasets are now so large and heterogeneous that manual annotation and dataset-specific model retraining have become major challenges. While recent EM foundation models provide general visual representations, they are not specifically tailored to the connectomics domain, where preserving fine membrane boundaries and synaptic structures is essential to mitigate topological and connectivity errors. Here, we present ConnectoFM, the first foundation model for connectomics, pretrained on a diverse corpus of 1.7 million unlabeled EM images drawn from six species and 25 subdomains. ConnectoFM combines masked image modeling with contrastive alignment to learn robust visual representations directly from large-scale connectomics data. These representations organize EM images into biologically meaningful clusters across species, brain regions, developmental cohorts, and acquisition domains. Using frozen pretrained features with lightweight decoder heads, we transfer ConnectoFM to three important downstream tasks: binary segmentation, multiclass cell typing, and instance segmentation. Across 29 diverse datasets, including established benchmarks, ConnectoFM consistently outperforms existing EM foundation models and state-of-the-art methods that require task-specific training from scratch. With only 10% labeled data, ConnectoFM surpasses the baselines trained on 100% annotation budget, showing the superiority of ConnectoFM in low-data regimes. Improvements of ConnectoFM are especially pronounced for challenging and biologically important targets, including membranes, mitochondria, vesicles, post-synaptic densities and synapses, and remain strong in low-label settings. Extension to 3D volumetric segmentation and qualitative comparisons further show that ConnectoFM enables more accurate and biologically faithful performance across downstream tasks. These results establish ConnectoFM as a generalizable and data-efficient foundation model for connectomics and provide a scalable route towards more reliable neural circuit reconstruction.