AI Insight
Researchers developed Stars2Cells (S2C), a new computational tool that tracks individual neurons across multiple brain imaging sessions by using geometric patterns similar to how astronomers identify star positions. The method achieved 98.4% accuracy in matching neurons across sessions compared to 36.0% for existing methods, and revealed that while population-level neural activity in the dorsomedial striatum appeared stable during drug self-administration, individual neurons were almost completely replaced over time. This previously undetectable "representational drift" demonstrates how population stability can mask dramatic single-cell turnover.
Why it matters
This tool enables researchers to reliably track the same neurons over days or weeks, making it possible to study how individual brain cells change during learning, memory formation, and disease progression. The discovery that stable group behavior can hide complete individual neuron turnover challenges assumptions about how the brain maintains consistent function and may inform understanding of addiction, neuroplasticity, and neurological disorders.
Understand the Science
⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Chronic calcium imaging offers a window into how single neurons and ensemble activity change across days where identifying the same neurons from one session to the next is the prerequisite for answering questions regarding learning, drift, and plasticity over time. Yet only ~2-3% of imaging laboratories publish longitudinal cross-session work, because existing registration tools depend on spatial-footprint or temporal correlations that degrade under repeated recording sessions. Here, we introduce Stars2Cells (S2C), a tracking pipeline inspired by astrometric plate-solving that represents each neuron’s local geometry as a four-dimensional quad descriptor invariant to rotation, translation, and uniform scaling. S2C operates purely on centroid coordinates and combines descriptor-space matching, Random Sample Consensus (RANSAC) verification, and Hungarian assignment. Across a synthetic benchmark of 1,265 paired runs spanning 100-1,000 neurons and 8 perturbation conditions plus 1 identity sanity-floor, S2C reached pooled F1 = 98.4% compared to the standard ROI-based matching of 36.0%. To show what this enables, we applied the pipeline to dorsomedial striatum (DMS) imaging during oral fentanyl behavioral-economics self-administration. Here, we show that a conserved population-rewarded lever press response in DMS masks near-complete single-neuron turnover. This representational-drift signature we demonstrated is invisible to the bulk photometry, and resolving it requires the same-cell tracking S2C provides. S2C is distributed as a GUI-driven standalone application for both macOS and Windows, requiring no Python, command line, or virtual environment setup.
Source: Stars2Cells: Astrometric Tracking of Neurons Across Imaging Sessions