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Automated Sholl Analysis of Digitized Neuronal Morphology at Multiple Scales
Published on: November 14, 2010
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Sholl analysis: a quantitative comparison of semi-automated methods
Kate E Binley1, Wai S Ng1, James R Tribble1
1School of Optometry and Vision Sciences, Cardiff University, Maindy Road, Cardiff CF24 4LU, Wales, United Kingdom.
Journal of Neuroscience Methods
|February 4, 2014
Summary
Automated Sholl analysis methods for neuronal complexity show general consistency, but manual calibration is crucial. Some methods like Bitmap and Ghosh lab may introduce errors in dendritic complexity quantification.
Area of Science:
- Neurobiology
- Computational Neuroscience
Background:
- Sholl analysis is a standard method for quantifying neuronal dendritic complexity.
- Automated methods have been developed to streamline this process, but consistency issues have arisen.
- Comparing different automated methods against manual analysis is essential for reliable results.
Purpose of the Study:
- To compare the accuracy and consistency of five commonly used Sholl analysis methods.
- To identify potential errors and limitations in semi-automated and automated neuronal complexity quantification techniques.
Main Methods:
- Compared four semi-automated methods (Simple Neurite Tracer, Fast Sholl, Bitmap, Ghosh lab) against manual Sholl analysis.
- Utilized diolistically labeled mouse retinal ganglion cells for the comparison.
Main Results:
- Most methods demonstrated good consistency with manual analysis, with Simple Neurite Tracer and Fast Sholl showing high accuracy.
- Identified undercounting and secondary peak artifacts in the Bitmap and Ghosh lab methods, potentially leading to significant errors.
- Results support the validated performance of the Fast Sholl method.
Conclusions:
- Manual calibration of automated Sholl analysis software is critical for accurate neuronal complexity assessment.
- Researchers should be aware of potential pitfalls in specific automated methods to ensure reliable neurobiological data.
