ArticleJournal of neuroscience methods2020
AutoSholl allows for automation of Sholl analysis independent of user tracing.
Article in Journal of neuroscience methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Neuropathological findings in Down syndrome, Alzheimer's disease and control patients with and without SARS-COV-2: preliminary findings.Acta neuropathologica · 2024Article
- Diethyl butylmalonate attenuates cognitive deficits and depression in 5×FAD mice.Frontiers in neuroscience · 2024Article
- Lentinan has a beneficial effect on cognitive deficits induced by chronic Toxoplasma gondii infection in mice.Parasites & vectors · 2023Article
- Motor neuron-specific RhoA knockout delays degeneration and promotes regeneration of dendrites in spinal ventral horn after brachial plexus injury.Neural regeneration research · 2023Article
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- β-Glucan alleviates goal-directed behavioral deficits in mice infected with Toxoplasma gondii.Parasites & vectors · 2023Article
- Ferulic Acid reduces amyloid beta mediated neuroinflammation through modulation of Nurr1 expression in microglial cells.PloS one · 2023Article
- Assaying Microglia Functions In Vitro.Cells · 2022Review
- Comparative Neurodevelopment Effects of Bisphenol A and Bisphenol F on Rat Fetal Neural Stem Cell Models.Cells · 2021Article
- PTEN in prefrontal cortex is essential in regulating depression-like behaviors in mice.Translational psychiatry · 2021Article
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3 authors.
Funding
Abstract
backgroundSholl analysis has been used to analyze neuronal morphometry and dendritic branching and complexity for many years. While the process has become semi-automated in recent years, existing software packages are still dependent on user tracing and hence are subject to observer bias, variability, and increased user times for analyses. Commercial software packages have the same issues as they also rely on user tracing. In addition, these packages are also expensive and require extensive user training. NEW
methodTo address these issues, we have developed a broadly applicable, no-cost ImageJ plugin, we call AutoSholl, to perform Sholl analysis on pre-processed and 'thresholded' images. This algorithm extends the already existing plugin in Fiji ImageJ for Sholl analysis by allowing for secondary analysis techniques, such as determining number and length of root, intermediate, and terminal dendrites; functions not currently supported in the existing Sholl Analysis plugin in Fiji ImageJ.
resultsThe algorithm allows for rapid Sholl analysis in both 2-dimensional and 3-dimensional data sets independent of user tracing. COMPARISON WITH EXISTING
methodsWe validated the performance of AutoSholl against pre-existing software packages using trained human observers and images of neurons. We found that our algorithm outputs similar results as available software (i.e., Bonfire), but allows for faster analysis times and unbiased quantification.
conclusionsAs such, AutoSholl allows inexperienced observers to output results like more trained observers efficiently, thereby increasing the consistency, speed, and reliability of Sholl analyses.
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