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Automatic Identification of Dendritic Branches and their Orientation

Automatic Identification of Dendritic Branches and their Orientation

Published on: September 17, 2021

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

Partha Mitra¹, Samik Banerjee², Stephen Savoia³

¹Cold Spring Harbor Laboratory, Cold Spring Harbor, 11724, New York, USA.

Research Square

|June 30, 2025

Summary

We developed a novel computational neuroanatomy method to map vertebrate brain neuronal networks. This approach skeletonizes axon fragments and estimates density, improving biological relevance for neural circuit mapping.

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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, lacking biological meaning.
Difficulty in tracing individual axons within dense tracer labeling hinders detailed network analysis.

Purpose of the Study:

To introduce a new computational approach for more biologically meaningful quantification of neural circuitry.
To improve the analysis of tracer-injected data in vertebrate brains.
To bridge the gap between single-axon tracing and bulk tracer injection data.

Main Methods:

Skeletonization of labeled axon fragments.
Estimation of volumetric length density using deep nets and Discrete Morse (DM) theory.
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.
Successfully applied DM technique for the first time in computational neuroanatomy.

Conclusions:

The proposed method offers a more biologically relevant quantification of neural projections compared to traditional intensity-based methods.
This approach enhances the analysis of neural connectivity by integrating information from axon skeletons and tracer data.
The study represents a significant advancement in mapping vertebrate neural networks, paving the way for deeper insights into brain function.