ArticleNature communications2026
NTAC: Neuronal type assignment from connectivity.
Article in Nature communications, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Article
- Uncovering Sex Differences in thebioRxiv : the preprint server for biology · 2026Article
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Authors and funding
4 authors.
Funding
Abstract
Recent advances in electron microscopy and computer vision now allow the reconstruction of complete wiring diagrams, or connectomes, of animal brains. This creates an urgent need for methods that can automatically identify neuronal cell types directly from these large connectivity datasets. Here we show that synaptic connectivity alone can be used to assign neurons to cell types with high accuracy. We introduce NTAC (Neuronal Type Assignment from Connectivity), which groups neurons based only on connectivity. NTAC has two forms: a semi-supervised one that leverages a small fraction of labeled neurons to infer the types of all others, and an unsupervised one that requires no labels at all. Applied to multiple state-of-the-art fruit fly brain connectomes, NTAC achieves high accuracy within only minutes on a laptop, demonstrating that connectivity provides a powerful and scalable basis for classifying neuronal cell types across the brain.
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Registered trials
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