Summary and Setup
FIXME: Add information about fMRI analysis in Neuroimaging
Setting up the tutorial environment
Binder
Using Binder is the easiest and fastest way to get started with the workshop. Binder is a virtual environment containing the full computing environment required in order to go through the workshop. Note that Binder hosts the environment on a cloud server and therefore internet access is required to launch it.
Setting up Binder
Step 1:
Click the link here to spin up the workshop environment: Binder Workshop You will see an interface that looks like the following:
The left-hand pane shows a list of workshop notebooks that contain the content of the workshop itself. Before jumping into the workshop notebooks we need to perform a setup step to pull the neuroimaging data that will be used in the workshop…
Run the setup_workshop script
./setup_workshop
Hit enter once pasted and you should see the following
![](./fig/console_filled.png){alt='Console Filled' class="img-static"}
This will begin downloading the data required for the workshop onto your Binder instance so that it is usable for the workshop. Once started *do not close the tab by pressing the "x" button.* Instead, you may now open and begin working through the workshop notebooks.
## Getting workshop material locally
### Method 1: Downloading directly from the repository
On the GitHub repo (this page), click the green button that says "Clone or download", then click **Download ZIP**. Once downloaded, extract the ZIP file.
### Method 2: Using Git
Using this method requires a (very) useful piece of software called <code>git</code>. The process of installing git depends heavily on whether you're using MacOS, Windows or Linux. Follow the instructions in the link below to set up <code>git</code> on your PC:
[Installing Git](https://git-scm.com/book/en/v2/Getting-Started-Installing-Git)
Once you've installed <code>git</code>, open up your terminal and do the following:
git clone https://github.com/carpentries-incubator/SDC-BIDS-fMRI.git
This will download the repository directly into your current directory.
### Setting up Python environment
We use python version 3.6.0, but any newer version should also work (Python 2 versions haven't been tested). There are many methods to setting up a python environment but we'd recommend using some sort of virtual environment as to not break your system python install. Two methods (of many) are listed below:
### Method 1: Setting up conda environment (easiest) [Windows, Linux, MacOS]
For easy set-up we recommend [Anaconda](https://www.anaconda.com/download/) to manage python packages for scientific computing. Once installed, setting up the python environment can be done quite easily:
#### Windows
1. Install Anaconda Python version 3.7
2. Open **Anaconda Navigator**
3. Click on **Environments** on the left pane
4. Click **Create** then type in <code>sdc-bids-fmri</code>
5. In the <code>sdc-bids-fmri</code> entry click the play button then click **Open Terminal**
6. In terminal type:
conda install -y numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda conda install -y -c conda-forge awscli pip install nilearn nibabel
7. Close the terminal, click on the play button again and open **Jupyter Notebook**
8. Navigate to <code>sdc-bids-fmri</code> folder you downloaded earlier.
9. Done!
#### [Linux](Linux) and MacOS
After installing Anaconda, open terminal and type:
cd sdc-bids-fmri conda create -p ./sdc-fmri source activate $(pwd)/sdc-fmri conda install numpy pandas scipy scikit-learn matplotlib jupyter ipykernel nb_conda conda install -c conda-forge awscli pip install nilearn nibabel
##### Method 2: Using pyenv [Linux, MacOS]
An alternative method uses [pyenv](https://github.com/pyenv/pyenv) with [pyenv virtualenv](https://github.com/pyenv/pyenv-virtualenv). This is a favourite because it seamlessly integrates multiple python versions and environments into your system while maintaining use of pip (instead of conda).
cd sdc-bids-fmri pyenv virtualenv 3.6.0 sdc-fmri echo sdc-fmri > .python-version pip install –requirement requirements.txt
## Acquiring the data
This tutorial uses data derived from the **UCLA Consortium for Neuropsychiatric Phenomics LA5c Study [1]**.
To download (**warning: large download size!**) the subset of the data used for the tutorial:
cd sdc-bids-fmri
download T1w scans
cat download_list |
xargs -I ‘{}’ aws s3 sync –no-sign-request
s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/{}/anat
./data/ds000030/{}/anat
download resting state fMRI scans
cat download_list |
xargs -I ‘{}’ aws s3 sync –no-sign-request
s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/{}/func
./data/ds000030/{}/func
–exclude ’*’
–include ‘task-rest_bold’
download fmriprep preprocessed anat data
cat download_list |
xargs -I ‘{}’ aws s3 sync –no-sign-request
s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/fmriprep/{}/anat
./data/ds000030/derivatives/fmriprep/{}/anat
download fmriprep preprocessed func data
cat download_list |
xargs -I ‘{}’ aws s3 sync –no-sign-request
s3://openneuro/ds000030/ds000030_R1.0.5/uncompressed/derivatives/fmriprep/{}/func
./data/ds000030/derivatives/fmriprep/{}/func
–exclude ’*’
–include ‘task-rest_bold’
Finally open up the jupyter notebook to explore the tutorials:
cd sdc-bids-fmri
#Include below line if using anaconda environment source activate $(pwd)/sdc-fmri
jupyter notebook ```
Reference
[1] Gorgolewski KJ, Durnez J and Poldrack RA. Preprocessed Consortium for Neuropsychiatric Phenomics dataset [version 2; referees: 2 approved]. F1000Research 2017, 6:1262 (https://doi.org/10.12688/f1000research.11964.2)