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[DOC] some edits of the docs #187

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Jan 10, 2025
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[DOC] some edits of the docs #187

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8a38385
rephrase the intro
jdkent Dec 3, 2024
7f53a2b
add instructions on adding ssh key
jdkent Dec 3, 2024
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2 changes: 1 addition & 1 deletion docs/source/contributing.md
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## Setting up your environment for development

1. Fork the repository from github and clone your fork locally
1. Fork the repository from github and clone your fork locally (see [here](https://docs.github.com/en/authentication/connecting-to-github-with-ssh/adding-a-new-ssh-key-to-your-github-account) to setup your ssh key):

```bash
git clone [email protected]:<your_username>/giga_connectome.git
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16 changes: 9 additions & 7 deletions docs/source/index.rst
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Welcome to giga_connectome's documentation!
===========================================

Functional connectivity is a common approach in analysing resting state fMRI data. Python tool Nilearn
provides utilities to extract, denoise time-series on a parcellation and compute functional connectivity.
Currently there's no standalone one stop solution to generate connectomes from fMRIPrep outputs.
This BIDS-app combines Nilearn, TemplateFlow to denoise the data and generate timeseries and functional
connectomes directly from fMRIPrep outputs.
The workflow comes with several built in denoising strategies and several choices of atlases
(MIST, Schaefer 7 networks, DiFuMo, Harvard-Oxford).
Functional connectivity is a common approach in analysing resting state fMRI data.
The Python tool Nilearn provides utilities to extract and denoise time-series on a parcellation.
Nilearn also has methods to compute functional connectivity.
While Nilearn provides useful methods to generate connectomes,
there is no standalone one stop solution to generate connectomes from fMRIPrep outputs.
Giga_connectome (a BIDS-app!) combines Nilearn and TemplateFlow to denoise the data, generate timeseries,
and most critically giga_connectome generates functional connectomes directly from fMRIPrep outputs.
The workflow comes with several built-in denoising strategies and
there are several choices of atlases (MIST, Schaefer 7 networks, DiFuMo, Harvard-Oxford).
Users can customise their own strategies and atlases using the configuration json files.

.. toctree::
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