Benchmarking of tools for axon length measurement in individually-labeled projection neurons



Downloads per month over past year

Rubio Teves, Mario and Díez Hermano, Sergio and Porrero, César and Sánchez Jiménez, Abel and Prensa Sepúlveda, Lucía and Clascá, Francisco and García Amado, María and Villacorta Atienza, José Antonio (2021) Benchmarking of tools for axon length measurement in individually-labeled projection neurons. PLoS Computational Biology, 17 (12). pp. 1-22. ISSN 1553-734X

[thumbnail of Rubio-Treves, M. et al. 2021. Benchmarking of tools for axon length.....pdf]
Creative Commons Attribution.


Official URL:


Projection neurons are the commonest neuronal type in the mammalian forebrain and their individual characterization is a crucial step to understand how neural circuitry operates. These cells have an axon whose arborizations extend over long distances, branching in complex patterns and/or in multiple brain regions. Axon length is a principal estimate of the functional impact of the neuron, as it directly correlates with the number of synapses formed by the axon in its target regions; however, its measurement by direct 3D axonal tracing is a slow and labor-intensive method. On the contrary, axon length estimations have been recently proposed as an effective and accessible alternative, allowing a fast approach to the functional significance of the single neuron. Here, we analyze the accuracy and efficiency of the most used length estimation tools—design-based stereology by virtual planes or spheres, and mathematical correction of the 2D projected-axon length—in contrast with direct measurement, to quantify individual axon length. To this end, we computationally simulated each tool, applied them over a dataset of 951 3D-reconstructed axons (from, and compared the generated length values with their 3D reconstruction counterparts. The evaluated reliability of each axon length estimation method was then balanced with the required human effort, experience and know-how, and economic affordability. Subsequently, computational results were contrasted with measurements performed on actual brain tissue sections. We show that the plane-based stereological method balances acceptable errors (~5%) with robustness to biases, whereas the projection-based method, despite its accuracy, is prone to inherent biases when implemented in the laboratory. This work, therefore, aims to provide a constructive benchmark to help guide the selection of the most efficient method for measuring specific axonal morphologies according to the particular circumstances of the conducted research.

Item Type:Article
Uncontrolled Keywords:Axon; Neurons; Neural networks
Subjects:Sciences > Computer science > Bioinformatics
Medical sciences > Biology > Neurosciences
ID Code:72667
Deposited On:03 Jun 2022 11:27
Last Modified:03 Jun 2022 11:44

Origin of downloads

Repository Staff Only: item control page