FISIA, an information-based imaging tool, uncovers new spatial distributional patterns in neural circuits beyond...

Area of Science:

• Neuroscience
• Bioimaging
• Computational Biology

Background:

• Conventional fluorescence imaging analysis for neural circuits relies on intensity, overlooking spatial distribution.
• This limitation hinders the accurate correlation of neural anatomy with its function.

Purpose of the Study:

• To introduce FISIA (Fluorescence Image Spatial Information Analyzer), an information-based framework.
• To overcome the limitations of intensity-based analysis by quantifying spatial distributional information variation.

Main Methods:

• FISIA automates subregional subdivision and Kullback-Leibler Divergence-based distributional variation quantification.
• It employs Andrews plots for latent factor differentiation and 2D local fluorescence comparisons.
• Quantitative benchmarking against established tools was performed.

Main Results:

• FISIA directly quantified spatial distributional variation, outperforming comparison tools in showing stronger anatomy-behavior associations.
• AAV and BDA tracers showed similar spatial variation in M1, despite intensity differences.
• FISIA metrics, unlike others, significantly correlated with locomotor recovery in spinal cord data.

Conclusions:

• FISIA offers a novel, information-based, and spatially aware quantitative framework for neural circuit analysis.
• It advances beyond intensity-based methods to reveal biologically organized and functionally informative structures.
• The tool enhances the understanding of neural architecture and its relation to function and recovery.