Methods from Computational Topology and Geometry for Analysing Neuronal Tree and Graph Data

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PI: Mitra, Partha Pratim (contact); Wang, Yusu

Email: mitra@cshl.edu

Institution: Cold Spring Harbor Laboratory 

Title: Methods from Computational Topology and Geometry for Analysing Neuronal Tree and Graph Data

Software to compute Persistence Vectors from SWC files is available open source at GitHub https://github.com/Nevermore520/NeuronTools. The tools have been deployed on http://neuromorpho.org/  Details can be found in the associated publication, https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0182184

This tool uses topological methods to characterize and classify neuronal shape, i.e. the morphologies of the dendritic and axonal arbors of a given neuron. Previous methods rely on ad-hoc, handcrafted feature vectors to characterize this tree shape. We use the Persistent Homology of descriptor functions defined on the neurons, to provide a principled method that respects the intrinsic characteristics of neuronal shape without having to hand-craft feature vectors.

Grant #: EB022899 

Status: Completed

Deliverables:

 

2021 Brain PI Meeting

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Demo:

 

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