https://github.com/bluebrain/diameter-synthesis

Synthesize diameters of neuronal morphologies

https://github.com/bluebrain/diameter-synthesis

Science Score: 75.0%

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  • CITATION.cff file
    Found CITATION.cff file
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  • DOI references
    Found 3 DOI reference(s) in README
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    Organization bluebrain has institutional domain (portal.bluebrain.epfl.ch)
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    Low similarity (16.2%) to scientific vocabulary

Keywords

morphology neuroscience

Keywords from Contributors

neurons luigi folder-diff folder-comparison folder-comparisation folder-compare file-differences file-diff file-comparison directory-diff
Last synced: 5 months ago · JSON representation ·

Repository

Synthesize diameters of neuronal morphologies

Basic Info
  • Host: GitHub
  • Owner: BlueBrain
  • License: apache-2.0
  • Language: Python
  • Default Branch: main
  • Homepage:
  • Size: 5.51 MB
Statistics
  • Stars: 3
  • Watchers: 7
  • Forks: 2
  • Open Issues: 1
  • Releases: 14
Archived
Topics
morphology neuroscience
Created over 4 years ago · Last pushed 12 months ago
Metadata Files
Readme Changelog Contributing License Citation Authors

README.md

[!WARNING] The Blue Brain Project concluded in December 2024, so development has ceased under the BlueBrain GitHub organization. Future development will take place at: https://github.com/openbraininstitute/diameter-synthesis

Version Build status Codecov.io License Documentation status DOI

Diameter synthesis

This code aims at generating synthetic diameters for neurons, with parameters learned from a set of biological neurons.

Installation

Use pip:

bash pip install diameter-synthesis

Main usage

Step 1: Building models

In folder example, you first have to modify create_jsons.py to suit your needs.

You have the following important parameters for the dict extract_models_params:

  • morph_path: path to morphology files
  • mtypes_sort: how to learn distributions: all to use all together, mtypes to use by mtypes , super_mtypes to use home made cells types (see diameter_types below)
  • models: to create several models (for now they are all the same, just different realisation of random numbers)
  • neurite_types: types of neurite to learn parameters for
  • extra_params: dict of additional model parameters

Step 2: Building diameters

Then simply run ./run_models.sh to create the models (saved in a json file).

In create_jsons.py, the dict generate_diameters_params needs to be updated, too, with entries matching the previous dict. The path in new_morph_path will be where the new morphologies will be saved.

Then run ./run_diamters.sh to generate diameters.

Additional scripts

Several additional scripts in folder scripts:

  • diameter-checks: run the diameter-check code (bluepymm) on the biological and sampled cells
  • diameter_types: cluster mtypes using distributions of surface areas (uses two privates repositories a the moment)
  • extract_morphometrics: from bio and sample cells, extracts and plot distribution of surface area and diameter as a function of branch order and path lengths
  • extract_morphologies: from a cell release, find the ones that can be run through diameter-check
  • plot_morphologies: plot all morphologies in mtype folders

Examples

The examples folder contains a simple example that will fetch morphologies from neuromorpho.org, learn a diameter model, rediametrize these morphologies, and perform some analysis of the results to compare original and diametrized morphologies. This example can simply be run using the following command: bash ./run.sh

Funding & Acknowledgment

The development of this software was supported by funding to the Blue Brain Project, a research center of the École polytechnique fédérale de Lausanne (EPFL), from the Swiss government’s ETH Board of the Swiss Federal Institutes of Technology.

For license and authors, see LICENSE.txt and AUTHORS.md respectively.

Copyright © 2021-2022 Blue Brain Project/EPFL

Owner

  • Name: The Blue Brain Project
  • Login: BlueBrain
  • Kind: organization
  • Email: bbp.opensource@epfl.ch
  • Location: Geneva, Switzerland

Open Source Software produced and used by the Blue Brain Project

Citation (CITATION.cff)

# This CITATION.cff file was generated with cffinit.
# Visit https://bit.ly/cffinit to generate yours today!

cff-version: 1.2.0
title: >-
  Computational synthesis of cortical dendritic
  morphologies.
message: >-
  If you use this software, please cite it using the
  metadata from this file.
type: software
authors:
  - family-names: Kanari
    given-names: Lida
    orcid: 'https://orcid.org/0000-0002-9539-5070'
  - family-names: Dictus
    given-names: Hugo
  - family-names: Chalimourda
    given-names: Athanassia
  - family-names: Van Geit
    given-names: Werner
    orcid: 'https://orcid.org/0000-0002-2915-720X'
  - family-names: Coste
    given-names: Benoit
  - family-names: Shillcock
    given-names: Julian
    orcid: 'https://orcid.org/0000-0002-7885-735X'
  - given-names: Kathryn
    family-names: Hess
    orcid: 'https://orcid.org/0000-0003-2788-9754'
  - family-names: Markram
    given-names: Henry
    orcid: 'https://orcid.org/0000-0001-6164-2590'
  - family-names: Arnaudon
    given-names: Alexis
    orcid: 'https://orcid.org/0000-0001-9486-1458'
identifiers:
  - type: doi
    value: 10.1016/j.celrep.2022.110586
    description: The DOI of the related article.
repository-code: 'https://github.com/BlueBrain/diameter-synthesis'
abstract: >-
  Neuronal morphologies provide the foundation for the electrical behavior of
  neurons, the connectomes they form, and the dynamical properties of the brain.
  Comprehensive neuron models are essential for defining cell types, discerning
  their functional roles, and investigating brain-disease-related dendritic
  alterations. However, a lack of understanding of the principles underlying
  neuron morphologies has hindered attempts to computationally synthesize
  morphologies for decades. We introduce a synthesis algorithm based on a
  topological descriptor of neurons, which enables the rapid digital
  reconstruction of entire brain regions from few reference cells. This
  topology-guided synthesis generates dendrites that are statistically similar to
  biological reconstructions in terms of morpho-electrical and connectivity
  properties and offers a significant opportunity to investigate the links between
  neuronal morphology and brain function across different spatiotemporal scales.
  Synthesized cortical networks based on structurally altered dendrites associated
  with diverse brain pathologies revealed principles linking branching properties
  to the structure of large-scale
  networks.
keywords:
  - Dendritic morphology
  - Topological synthesis
  - Artificial neuron
  - Topological Morphology Descriptor
  - Morphological synthesis
license: GPL-3.0

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aleksei sanin a****n@e****h 2
alexis arnaudon a****n@g****m 1
Dries Verachtert d****t@e****h 1
bbpgithubaudit 8****t@u****m 1
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Last synced: over 1 year ago

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  • Average comments per issue: 5.5
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