Skeletonization of neuronal processes using Discrete Morse techniques from computational topology.

Area of Science:

• Computational neuroanatomy
• Neuroscience
• Computational topology

Background:

• Mapping mesoscale neural circuitry in vertebrate brains is crucial for understanding biological intelligence.
• Current methods using tracer injections quantify regional projections by label intensity, which lacks biological meaning.
• Difficulty in tracing individual axons within densely labeled neural networks hinders detailed circuit analysis.

Purpose of the Study:

• To develop a biologically meaningful quantification of neural circuitry from tracer data.
• To bridge the gap between single-axon tracing and bulk tracer injection data.
• To introduce a novel computational approach for analyzing neural networks.

Main Methods:

• Skeletonization of labeled axon fragments.
• Estimation of volumetric length density using deep nets and Discrete Morse (DM) technique.
• Application of DM technique for noise-robustness by incorporating nonlocal connectivity information.

Main Results:

• Demonstrated utility and scalability on whole-brain tracer injected data.
• Developed an information-theoretic measure to quantify additional information from individual axon morphologies.
• The DM technique provides noise-robust quantification of neural projections.

Conclusions:

• The proposed method offers a biologically meaningful quantification of neural projections, improving upon traditional label intensity measures.
• This approach represents the first application of the DM technique in computational neuroanatomy.
• The method enhances the analysis of neural networks by integrating information from tracer injections and single-axon data.