Scientific Computing Working Group Workshop on performing analysis of neuroimaging data in Python

Developed by

• Jerry Jeyachandra (https://github.com/jerdra)
• Michael Joseph (https://github.com/josephmje)
• Olivia Stanley (https://github.com/ostanley)
• Jason Kai (https://github.com/kaitj)

Getting workshop material for SciNet workshops

---

If you're using SciNet's Jupyter System

Instructions with pictures

Open up a terminal and enter the following:

  ssh <user>@teach.scinet.utoronto.ca
  module load anaconda3
  source /scinet/course/ss2019/3/6_mripython/setup_workshop
  python -m ipykernel install --user --name mripython_conda

Open a new terminal and enter the following:

  ssh -L 8888:jupyterhub<X>:8000 <user>@teach.scinet.utoronto.ca -N

Where <X> is a number between 1-6.

If nothing happens that's great! Now open up your favourite browser and enter the following in your address bar:

  https://localhost:8888

You're ready to go!

---

If you're using Binder

Click the following button:

This will open up a jupyter terminal for you. Then just hit:

• New --> Terminal

This will open up a terminal. Once you're in here type the following:

./setup_workshop

Then leave it running in the background and switch tabs over back to the previous tab (says "Home" on Chrome)

---

---

---

Getting workshop material for CAMH Workshops

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 git. 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 git on your PC:

Installing Git

Once you've installed git, open up your terminal and do the following:

git clone https://github.com/jerdra/scwg2018_python_neuroimaging.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 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 scwg2018_python_neuroimaging
5. In the scwg2018_python_neuroimaging 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 scwg2018_python_neuroimaging folder you downloaded earlier.
9. Done!

Linux and MacOS

After installing Anaconda, open terminal and type:

cd scwg2018_python_neuroimaging
conda create -p ./scwg2018_nilearn
source activate $(pwd)/scwg2018_nilearn
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 (my favourite) [Linux, MacOS]

An alternative method uses pyenv with 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 scwg2018_python_neuroimaging
pyenv virtualenv 3.6.0 scwg2018_nilearn
echo scwg2018_nilearn > .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 acquire the data we use Amazon AWS S3. You can set up an account using the link. Then you'll need to set up the awscli python tool using your AWS account credentials (more info: Amazon AWS CLI)

aws configure
AWS Access Key ID [None]: AKIAIOSFODNN7EXAMPLE
AWS Secret Access Key [None]: wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY
Default region name [None]: ca-central-1
Default output format [None]: ENTER

To download (warning: large download size!) the subset of the data used for the tutorial:

cd scwg2018_python_neuroimaging

# 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 scwg2018_python_neuroimaging

#Include below line if using anaconda environment
source activate $(pwd)/scwg2018_nilearn

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)