[DOC] some edits of the docs (#187)

* rephrase the intro * add instructions on adding ssh key
bids-apps · Jan 10, 2025 · 96ed715 · 96ed715
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diff --git a/docs/source/contributing.md b/docs/source/contributing.md
@@ -2,7 +2,7 @@
 
 ## 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

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -6,13 +6,15 @@
 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::