tdaonabrainnetwork
Topological Data Analysis on a Brain Network
Science Score: 44.0%
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Low similarity (9.9%) to scientific vocabulary
Keywords
Repository
Topological Data Analysis on a Brain Network
Basic Info
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- Watchers: 1
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Metadata Files
README.md
Topological Data Analysis on a Brain Network
This is the repository containing the work done by Kieran Barber for his Masters Thesis at The Department of Mathematics, Uppsala University, Uppsala, Sweden, under the supervision of Raazesh Sainudiin.
The work resulted from discussions with several researchers, including, Kathryn Hess, Svante Janson, Wojciech Chachólski, Martina Scolamiero, and Michael Reimann.
Requirements
To run the code within, you will need the following:
* conslocpathway files: https://bbp.epfl.ch/nmc-portal/downloads.html (You want to download the average files which are located at the foot of this page under a file named average_full.tar, save the folder into data and untar there)
Models
Running of the scripts can be done by locating and running the script all_scripts.sh from the my_model folder. Options for which model are given upon calling the script.
To run some of the computations for the topological statistics there is a system requirement of RAM > 11gb.
A further note, to run any particular statistic, you are just required to uncomment out the relevant statistic in my_model/src/statistics.py.
Citation
@misc{Barber_Random_Graph_Models,
author = {Barber, Kieran and Sainudiin, Raazesh},
license = {Unlicense},
title = {{Random graph models of a neocortical column in a rat's brain and their topological statistical distributions}},
howpublished={\url{https://github.com/lamastex/TDAOnABrainNetwork}}
}
Acknowledgements
This research was partially supported by the Wallenberg AI, Autonomous Systems and Software Program funded by Knut and Alice Wallenberg Foundation. The distributed computing infrastructure for this pjoject was supported by Databricks University Alliance with AWS credits.
Owner
- Name: Raazesh Sainudiin
- Login: lamastex
- Kind: user
- Location: Uppsala, Sweden
- Company: lamastex.org
- Website: https://lamastex.github.io/
- Repositories: 18
- Profile: https://github.com/lamastex
I work at the interface of mathematics, computing and statistics. This inter-disciplinary research aims broadly to use computers to solve real-world problems.
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: >-
Random Graph Models of a neocortical column in a
rat's brain and their topological statistical
distributions
message: >-
If you use this software, please cite it using the
metadata from this file.
type: software
authors:
- given-names: Kieran
family-names: Barber
affiliation: 'Department of Mathematics, Uppsala University'
- affiliation: 'Department of Mathematics, Uppsala University'
given-names: Raazesh
family-names: Sainudiin
orcid: 'https://orcid.org/0000-0003-3265-5565'
identifiers:
- type: url
value: 'https://github.com/lamastex/TDAOnABrainNetwork'
description: Source Codes
repository: 'https://github.com/lamastex/TDAOnABrainNetwork/'
abstract: >-
There has been work done over the years to
understand what exactly are the driving forces
behind how the brain functions. Recently, with the
advent of cloud computing and being able to store
large volumes of data, reconstructing neocortical
columns from the brain has become not only
possible, but much easier. The Blue Brain Project
(BBP) created multiple digital reconstructions of a
rat's neocortical microcircuitry that closely
resembles the biological features that include the
numbers, types and densities of neurons and their
synaptic connectivity that followed anatomical and
physiological data obtained from experiments. What
followed were sets of microconnectomes that were
represented as directed graphs. After applying
stimuli to the microconnectomes, realisations of
connectivity were observed. One of these
observations has been identified as the Bio-M
microconnectome (MC). To determine the complexity
of the Bio-M MC, we use topological statistics on
the local level as well as the global level. We
study here, five models, each with an added level
of complexity ranging from the simplest random
graph of \ER to those that account for known
biological characteristics such as
distance-dependence and neocortical layers. From
our models, we observed that there was a lack of
complexity in the connectivity shown at the local
level, however we found surprisingly higher levels
of connectivity on the global level than first
expected. All codes needed to process the data and
implement the models are made available in this
repository under a permissive license.
keywords:
- brain
- rat
- random graph models
- topological data analysis
- statistics
license: Unlicense
GitHub Events
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- Watch event: 1
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Last synced: 9 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Kieran Barber | 3****r | 228 |
| Raazesh Sainudiin | r****n@g****m | 9 |
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- Joel-Dahne (2)
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Dependencies
- h5py *
- numpy *
- pandas *
- pyflagser *