Structural–functional calibration corrects single-neuron identity errors in volumetric calcium imaging
Year: 2026
Type: preprint
Abstract: Volumetric calcium imaging is increasingly used to capture larger neuronal populations at higher throughput, but high-speed axial sampling can compromise single-neuron identity. Here we identify cross-plane identity duplication as a structured error in volumetric imaging: anisotropic axial blurring and plane-wise functional segmentation can repeatedly detect the same neuron across adjacent planes, creating duplicate functional nodes that inflate neuronal counts and distort network phenotypes . We developed Comprehensive Label-Guided (CLG) volumetric imaging , a structural–functional calibration framework that uses nuclear labels as stable three-dimensional identity anchors for calcium signals . CLG combines nuclear labeling, deep-learning-based 3D segmentation, anatomical registration and identity-guided trace reassignment. In larval zebrafish whole-brain recordings, CLG resolved ∼30,000 redundant detections and reduced estimated neuronal counts by 37–46%. In mouse visual cortex, CLG consolidated ∼40% of putative duplicates and recovered over 2,000 active neurons missed by calcium-only analysis. Across baseline and perturbed conditions, calibration stabilized graph-derived measurements of hub organization, long-range correlations and network resilience. CLG therefore defines an anatomy-constrained identity-calibration layer for reliable single-neuron-resolved volumetric imaging.
Subfield: Biophysics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7204133226
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