Structural-functional calibration corrects single-neuron identity errors in volumetric calcium imaging

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.