Neuroimaging Cookbook 🧠🍳📓

Project info

Title: Neuroimaging Cookbook 🧠🍳📓

Project lead: Shawn Rhoads GitHub: @shawnrhoads Twitter: @ShawnRhoads56 Mattermost: shawnrhoads

If you are reading this because of Brainhack MTL 2020ish, feel free to contact @SamGuay if you have any question! Twitter: @SamGuay_ Mattermost: SamGuay_

Project collaborators: TBD! Contributions of any kind are welcome!

Registered Brainhack Global 2020 Event: Brainhack DC

Brainhack MTL 2020ish

Project Description: Anyone ever Google how to accomplish a task for neuroimaging data more than once? For example, I’ve Googled “how to extract a mask from a parcellation scheme” or “how to output the time series from an ROI to a .csv file” hundreds of times because I can’t remember the exact command/function or flags to pass (and every time I am reminded just how many times I’ve visited the same webpages):

There are countless software packages with widely different commands and options to perform all sorts of different functions when working with neuroimaging data. But remembering them all is nearly impossible!

The Neuroimaging Cookbook will centralize “code snippets” (or “recipes”) for different, specific, and simple tasks in neuroimaging that people can incorporate in their own analysis pipelines. And most importantly, it would be scalable so that anyone can contribute their random, useful code snippet.

The idea is inspired by 30 Seconds of Code (specifically, 30 Seconds of Python), where people can easily reference code (across software) for different neuroimaging tasks like: how to convert across different image formats (e.g., BRIK/HEAD, img/hdr, nii, etc), flatten the lower triangle of a representational similarity matrix, extract a mask from a parcellation, output the time-series from an ROI to a .csv /.txt file, etc. Not exactly tutorials, but “code snippets” that people can adapt for their own scripts, apps, or Jupyter Notebooks.

Data to use: N/A

Link to project repository/sources: https://github.com/neuroimaging-cookbook

Goals for Brainhack Global 2020: Easy:

• Determine a list of simple, common tasks in neuroimaging (e.g., extracting a mask, converting BRIK/HEAD files to NIFTI), divide tasks among project members, and create various snippets of code to accomplish each task
• Organize each snippet by utility (e.g., data cleaning, data organization/processing, analysis), software package (e.g., AFNI, FSL), and programming language (e.g., Bash, Python, R)
• Test cookbook on desktop and mobile

Intermediate:

• Cookbook website design (should be a searchable webpage)
• Create documentation so that others can easily contribute

Good first issues:

1. Create first batch of recipes for use in the cookbook (Team 1)
2. Develop the cookbook webpage (Team 2)

Skills (at least one of these):

• Experience working with neuroimaging data
• Website development/deployment
• Basic Git/GitHub knowledge
• Documentation

Tools/Software/Methods to Use:

• Python (nipy, nipype, pyBIDS, nilearn, nibabel, pyMVPA, Brainiak, etc)
• R
• MATLAB
• AFNI
• Freesurfer
• FSL
• ANTs
• Bash shell
• Git/GitHub
• A text editor (e.g., Notepad, VSCode, Sublime, etc.)
• Google
• HTML/CSS

Communication channels: https://mattermost.brainhack.org/brainhack/channels/neuroimaging-cookbook

Project labels

• Type of project: #coding_methods, #data_management, #documentation,

• Project development status: #0_concept_no_content

• Topic of the project: #Bayesian_approaches, #causality, #connectome, #data_visualisation, #deep_learning, #diffusion, #EEG_EventRelatedResponseModelling, #Granger_causality, #ICA, #machine_learning, #neural_decoding, #neural_encoding, #neural_networks, #PCA, #reinforcement_learning, #reproducible_scientific_methods, #statistical_modelling, #tractography

• Tools used in the project: #AFNI, #ANTs, #BIDS, #Brainstorm, #CPAC, #Datalad, #DIPY, #FieldTrip, #fMRIPrep, #Freesurfer, #FSL, #Jupyter, #MNE, #MRtrix, #Nipype, #NWB, #SPM

• Tools skill level required to enter the project (more than one possible): #comfortable, #familiar, #no_skills_required

• Programming language used in the project: #no_programming_involved, #C++, #documentation, #Java, #Julia, #Matlab, #Python, #R, #shell_scripting, #Unix_command_line, #Web

• Modalities involved in the project (if any): #behavioral, #DWI, #ECOG, #EEG, #fMRI, #fNIRS, #MEG, #MRI, #PET

• Git skills reuired to enter the project (more than one possible): #0_no_git_skills, #1_commit_push, #2_branches_PRs, #3_continuous_integration

• I added all of the labels I want an associate to my project

Project Submission

Submission checklist

Once the issue is submitted, please check items in this list as you add under ‘Additional project info’

• Link to your project: could be a code repository, a shared document, etc.
• Goals for Brainhack Global 2020: describe what you want to achieve during this brainhack.
• Flesh out at least 2 “good first issues”: those are tasks that do not require any prior knowledge about your project, could be defined as issues in a GitHub repository, or in a shared document.
• Skills: list skills that would be particularly suitable for your project. We ask you to include at least one non-coding skill. Use the issue labels for this purpose.
• Chat channel: A link to a chat channel that will be used during the Brainhack Global 2020 event. This can be an existing channel or a new one. We recommend using the Brainhack space on Mattermost.

We would like to think about how you will credit and onboard new members to your project. If you’d like to share your thoughts with future project participants, you can include information about:

• Specify how you will acknowledge contributions (e.g. listing members on a contributing page).

Contributions: All contributions will be recognized in the cookbook and follow the all-contributors specification. Contributions of any kind are welcome!