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Sholl analysis: A quantitative comparison of semi-automated methods

Binley, Kate E., Ng, Wai Siene, Tribble, James R., Song, Bing ORCID: and Morgan, James Edwards ORCID: 2014. Sholl analysis: A quantitative comparison of semi-automated methods. Journal of Neuroscience Methods 225 , pp. 65-70. 10.1016/j.jneumeth.2014.01.017

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Background: Sholl analysis remains one of the most commonly used methods to quantify neuronal dendritic complexity and is therefore a key analysis tool in neurobiology. While initially proposed when the quantification of neuronal structure was undertaken manually, the advent of software packages allowing automated analysis has resulted in the introduction of several semi and fully automated methods to quantify dendritic complexity. Unfortunately results from these methods have not in all cases been consistent. We therefore compared the results of five commonly used methods (Simple Neurite Tracer, manual, Fast Sholl, Bitmap, and Ghosh lab) using manual analysis as a ground truth. New method: Comparison of four semi-automated methods to the manual method using diolistically labelled mouse retinal ganglion cells. Results: We report consistency across a range of published techniques. While the majority perform well (Simple Neurite Tracer and Fast Sholl profiles have areas under the curve within 4.5% of the profile derived using the manual method), we highlight two areas in two of the methods (Bitmap and Ghosh lab methods) where errors may occur, namely undercounting (>20% relative to the manual profile) and a second peak. Comparison with existing methods: Our results support published validation of the Fast Sholl method. Conclusions: Our study highlights the importance of manual calibration of automated analysis software.

Item Type: Article
Date Type: Publication
Status: Published
Schools: Optometry and Vision Sciences
Systems Immunity Research Institute (SIURI)
Subjects: Q Science > QA Mathematics > QA76 Computer software
R Medicine > R Medicine (General)
Uncontrolled Keywords: Sholl analysis; Dendritic pruning; Automated analysis
Publisher: Elsevier
ISSN: 0165-0270
Funders: BBSRC
Date of First Compliant Deposit: 30 March 2016
Date of Acceptance: 17 January 2014
Last Modified: 10 Nov 2023 08:45

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