FISIA, an information-based imaging tool, uncovers new spatial distributional patterns in neural circuits beyond fluorescence intensity
- 1Department of Health Sciences and Technology, Anhui University, Hefei, China
- 2College of Computing, Georgia Institute of Technology, Atlanta, GA USA
Abstract
Fluorescence imaging is a cornerstone of neural circuit mapping, yet conventional intensity-based analysis methods overlook critical spatial distributional information variation, limiting the ability to relate anatomy to function. We developed FISIA (Fluorescence Image Spatial Information Analyzer), an information-based framework that separates signal magnitude from spatial organization and reveals structure that conventional pipelines cannot access. FISIA automates subregional subdivision, Kullback-Leibler Divergence-based distributional variation quantification, Andrews plots for latent factors differentiation, and 2D local fluorescence comparisons. We quantitatively benchmarked FISIA against established tools and showed that only FISIA directly quantified spatial distributional variation and showed stronger anatomy-behavior associations than the comparison tools. Moreover, despite adeno-associated virus (AAV) tracer’s higher fluorescence intensity at the injection epicenter, AAV and biotinylated dextran amine (BDA) tracers encoded similar spatial distributional variation in primary motor cortex (M1). While M1 to the secondary motor cortex’s projections were dense and spatially concentrated, M1 to reticular formation’s projections were dispersed and highly variable, suggesting fundamental differences in mesoscale architectures. In the spinal cord dataset, only FISIA-derived metrics, but not comparison measures, were significantly associated with locomotor recovery. Thus, FISIA provides an information-based, spatially aware, quantitative framework advancing beyond what is bright toward what is biologically organized and functionally informative.
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BibTeX
@article{xu2026fisia,
title = {{FISIA, an information-based imaging tool, uncovers new spatial distributional patterns in neural circuits beyond fluorescence intensity}},
author = {Zhiwei Xu and Wenbo Yang and Cui Zhang and Qiang Sun and Qi Yang},
journal = {Communications Biology},
year = {2026},
month = jul,
doi = {10.1038/s42003-026-10605-1},
url = {https://doi.org/10.1038/s42003-026-10605-1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - FISIA, an information-based imaging tool, uncovers new spatial distributional patterns in neural circuits beyond fluorescence intensity
AU - Zhiwei Xu
AU - Wenbo Yang
AU - Cui Zhang
AU - Qiang Sun
AU - Qi Yang
PY - 2026
DA - 2026/07/07
JO - Communications Biology
DO - 10.1038/s42003-026-10605-1
UR - https://doi.org/10.1038/s42003-026-10605-1
AB - Fluorescence imaging is a cornerstone of neural circuit mapping, yet conventional intensity-based analysis methods overlook critical spatial distributional information variation, limiting the ability to relate anatomy to function. We developed FISIA (Fluorescence Image Spatial Information Analyzer), an information-based framework that separates signal magnitude from spatial organization and reveals structure that conventional pipelines cannot access. FISIA automates subregional subdivision, Kullback-Leibler Divergence-based distributional variation quantification, Andrews plots for latent factors differentiation, and 2D local fluorescence comparisons. We quantitatively benchmarked FISIA against established tools and showed that only FISIA directly quantified spatial distributional variation and showed stronger anatomy-behavior associations than the comparison tools. Moreover, despite adeno-associated virus (AAV) tracer’s higher fluorescence intensity at the injection epicenter, AAV and biotinylated dextran amine (BDA) tracers encoded similar spatial distributional variation in primary motor cortex (M1). While M1 to the secondary motor cortex’s projections were dense and spatially concentrated, M1 to reticular formation’s projections were dispersed and highly variable, suggesting fundamental differences in mesoscale architectures. In the spinal cord dataset, only FISIA-derived metrics, but not comparison measures, were significantly associated with locomotor recovery. Thus, FISIA provides an information-based, spatially aware, quantitative framework advancing beyond what is bright toward what is biologically organized and functionally informative.
SN - 2399-3642
ER -