Computational Tools for Neuronal Morphometric Analysis: A Systematic Search and Review

Area of Science:

• Neuroscience
• Computational Biology
• Bioinformatics

Background:

• Neuronal morphometry is crucial for understanding brain function.
• Automated extraction of morphometric features (length, volume, branching) is enabled by increasing computational power.
• A comprehensive mapping of available neuronal morphometric analysis tools is currently lacking.

Purpose of the Study:

• To systematically identify and analyze open-source tools for neuronal morphometric analysis.
• To determine the specific morphometric characteristics extracted by these tools.
• To provide a standardized terminology for 150 unique morphometric features.

Main Methods:

• Systematic literature search and review of 1,586 papers.
• Analysis of tool documentation and testing of available open-source software.
• Categorization and standardization of extracted morphometric feature terminologies.

Main Results:

• 23 open-source neuronal morphometric analysis tools were identified and mapped.
• NeuroM, L-Measure, and NeuroMorphoVis were found to extract the most features.
• A novel, categorized, and standardized list of 150 unique morphometric features was presented.

Conclusions:

• This work provides the first comprehensive mapping of open-source neuronal morphometric tools.
• The standardized feature list and tool analysis facilitate more robust and comparable neuroscientific research.
• The findings contribute to a deeper understanding of the complex mechanisms underlying brain function.