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https://github.com/remi-gau/bids_workshop


https://github.com/remi-gau/bids_workshop

bids mri neuroimaging workshop

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README 

          
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7110398.svg)](https://doi.org/10.5281/zenodo.7110398)

[![License: CC BY 4.0](https://img.shields.io/badge/License-CC_BY_4.0-lightgrey.svg)](https://creativecommons.org/licenses/by/4.0/)



 Table of content 



- [Content](#content)

  - ["Theory": The BIDS data management principles](#theory-the-bids-data-management-principles)

  - [BIDS conversion by hand](#bids-conversion-by-hand)

  - [Automated BIDS conversion](#automated-bids-conversion)

  - [Using BIDS apps](#using-bids-apps)

- [Notes](#notes)

- [Not covered](#not-covered)

- [Pre-requisites](#pre-requisites)

- [Softwares to install](#softwares-to-install)

  - [Visual studio code](#visual-studio-code)

  - [Python](#python)

  - [Dcm2niix](#dcm2niix)

  - [BIDScoin](#bidscoin)

  - [Docker](#docker)

  - [Optional: VSCode extensions](#optional-vscode-extensions)







The

[brain imaging data structure (BIDS)](https://bids-specification.readthedocs.io/en/latest/)

is fast becoming the main standard to organize raw neuroimaging data.



Having your data in BIDS offers many downstream advantages to users: use of

standardized pipelines, facilitate data sharing and reuse…



But getting your data to be BIDS compliant and using can also be source of much

frustration. Especially when it is the first time you are dealing with

neuroimaging data.



This workshop aims to lessen the frustration level you will experience when

curating and using your data.



## Content



This workshop is divided in 4 parts.



### "Theory": The BIDS data management principles



Remembering and following a rule is always easier when you understand why the

rule was put in place: this first part explains some of the reasons why BIDS is

structured the way it is.



Slides are available [here](https://osf.io/zxp9e).



### BIDS conversion by hand



Using automated BIDS converters is recommended, but using them too early can

turn BIDS into a black box. So doing the conversion of one small dataset by hand

helps better understand "what data/information goes where".



To do this part you can follow along the ["cookbook" page](./cookbook.md).



### Automated BIDS conversion



Converting large numbers files quickly becomes impractical and error prone when

by hand. So learning to use a bids converters should be the next logical step.



Here are a few [intro slides](https://osf.io/zcfne).



To do this part you can follow along the

["bids_conversion" page](./bids_conversion.md).



### Using BIDS apps



To wrap up it is good use BIDS applications on the dataset we have created.



To do this part you can follow along the ["bids_app" page](./bids_apps.md).



## Notes



This workshop is mostly geared for the MRI part of BIDS but many principles of

BIDS apply to all imaging modalities.



We will finish the workshop with a "ask me anything about BIDS" session where

you can come with more specific questions as they relate to your use case.



## Not covered



The follow issues are be covered in this workshop:



- Where to store data

- Ethical issues around data sharing (notably GDPR)

- Data version control (for example with Datalad)



## Pre-requisites



- Coding experience: `none`

- Prior experience with neuroimaging data: `none`

- Data: feel free to bring your own dataset to work on but if you do not have

  data, some data is provided.

- Data type

  - fMRI and resting state

  - Anat

  - DTI

  - Physio / eyetracking



## Softwares to install



Below is a list of softwares, you will need to install to follow the workshop.



If you have troubles when installing any of these, please contact me before the

workshop and come to the "open-office" that will happen before the workshop`

starts.



For more details and trouble shooting eventual installation problems, check the

page dedicated to your operating system:



- [Windows](./troubleshooting_install/win.md)

- [MacOs](./troubleshooting_install/macos.md)

- [Linux](./troubleshooting_install/linux.md)



### Visual studio code



Install [Visual studio code](https://code.visualstudio.com/) as your code

editor.



### Python



If you have never used Python before, install it with the installer adapted to

your system (32 vs 64 bit, Apple M1 vs Intel...)

[miniconda](https://docs.conda.io/en/latest/miniconda.html#latest-miniconda-installer-links).



- For Windows, you should only have to run the `.exe` installer.

- For MacOs, you can try to simply use the `.pkg` installer adapted for your

  computer. If that does not work try the method described

  [here](./troubleshooting_install/macos.md#python).

- For Linux, you should use the bash installer as described

  [here](./troubleshooting_install/linux.md#python).



### Dcm2niix



Install the DICOM to Nifti converter:

[Dcm2niix](https://github.com/rordenlab/dcm2niix#install)



If installing with

[the correct installer for your operating system](https://github.com/rordenlab/dcm2niix/releases)

does not work, try installing

[MRIcroGL](https://www.nitrc.org/frs/?group_id=889) that should ship with

`Dcm2niix`.



### BIDScoin



Install the [BIDScoin converter](https://bidscoin.readthedocs.io/en/stable/)



If you have installed Miniconda properly, you should only need to open a

Terminal (for Linux and MacOS) or an Anaconda Powershell Prompt (for Windows)

and type:



```bash

pip install bidscoin

```



BIDScoin will need to be able to find the `dcm2niix` executable that you

installed on your computer.



To test this you can run the following command in a terminal:



```bash

bidseditor .

```



This will ask you to open a `bidsmap.yml` file, you can click `cancel` to load

the default values. You can then go to the `Options` tab where there is a

`dcm2niix2bids - plugin` section.



bidscoin_dcm2nii



The `command` part of this section refers to what command bidscoin must run to

use `dcm2niix`.



This should be the path to the `dcm2niix` executable on your computer.



- For example on Windows: `"C:\Program Files\dcm2niix\bin\dcm2niix.exe"`

- For Linux or Mac: `/usr/local/bin/dcm2niix`



On Mac or Linux, you can check the path to `dcm2niix` by running the following:



```bash

which dcm2niix

```



More details on the

[bidscoin documentation](https://bidscoin.readthedocs.io/en/stable/options.html#dcm2niix2bids-plugin).



### Docker



Install [Docker Desktop](https://www.docker.com/)



To check that things are properly installed, open a Terminal (for Linux and

MacOS) or an Anaconda Powershell Prompt (for Windows) and type the following to

make sure everything works:



```bash

docker run hello-world

```



A brief introductory message should then be printed to the screen.



Once you have done this, you can download the "images" of 2 bids app: be careful

that they may take quite a bit of space on your hard drive (about 12 Go each),

so make sure you have enough free space.



- download the latest version of the

  [MRIQC](https://mriqc.readthedocs.io/en/latest/) docker image for quality

  control.



```bash

docker pull nipreps/mriqc:latest

```



- download the latest version of the [fmriprep](https://fmriprep.org/en/stable/)

  docker image for preprocessing.



```bash

docker pull nipreps/fmriprep:latest

```



Note that to run fmriprep, you will also need a license for Freesurfer that you

can get for free here: https://surfer.nmr.mgh.harvard.edu/registration.html.



### Optional: VSCode extensions



Those extensions are not needed but may help you when dealing with python code

or json files in general.



> ars. (The author listed for all of these extensions should be `Microsoft`.)



- `Python` (n.b., you will need to reload VSCode after installing this)

- `json`





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