Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

nAdder: A scale-space approach for the 3D analysis of neuronal traces

Abstract : Tridimensional microscopy and algorithms for automated segmentation and tracing are revolutionizing neuroscience through the generation of growing libraries of neuron reconstructions. Innovative computational methods are needed to analyze these neural traces. In particular, means to analyse the geometric properties of traced neurites along their trajectory have been lacking. Here, we propose a local tridimensional (3D) scale metric derived from differential geometry, which is the distance in micrometers along which a curve is fully 3D as opposed to being embedded in a 2D plane or 1D line. We apply this metric to various neuronal traces ranging from single neurons to whole brain data. By providing a local readout of the geometric complexity, it offers a new mean of describing and comparing axonal and dendritic arbors from individual neurons or the behavior of axonal projections in different brain regions. This broadly applicable approach termed nAdder is available through the GeNePy3D open-source Python quantitative geometry library.
Document type :
Preprints, Working Papers, ...
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-03083517
Contributor : Emmanuel Beaurepaire Connect in order to contact the contributor
Submitted on : Saturday, December 19, 2020 - 10:55:44 AM
Last modification on : Friday, December 3, 2021 - 11:42:54 AM

File

2020.06.01.127035v2.full.pdf
Files produced by the author(s)

Identifiers

Citation

Minh Son Phan, Katherine Matho, Emmanuel Beaurepaire, Jean Livet, Anatole Chessel. nAdder: A scale-space approach for the 3D analysis of neuronal traces. 2020. ⟨hal-03083517⟩

Share

Metrics

Record views

61

Files downloads

30