Weeks

Week 1 will introduce participants to a reproducible computational toolkit for neural data science, as well as a basic grounding in supervised and unsupervised machine learning methods. Short pre-recorded lectures and hands-on tutorials throughout the five days will provide participants with familiarity applying these methods to real data. Each participant will be required to complete 6 tutorials out of the following list:

• ⭐ Installation of software for brainhack school
• ⭐ Introduction to the terminal
• ⭐ Introduction to git and github
• Python for data analysis
• Machine learning - basics
• Project management
• High performance computing
• Open data
• Writing scripts in python

Note that the tutorials marked with a ⭐ are mandatory. As a participant, at the end of Week 1 you should be able to answer questions such as:

• How can I open a terminal, and use it to perform operations such as moving or creating files?
• What is version control, and how can I use it to improve my workflow?
• What is python, and examples of data analyses what I can do with it.
• How should I visualize and define features for machine learning in neuroimaging?

Short exercises need to be completed at the end of each tutorial.

Week 2 will be mostly focused on defining and piloting the project. As a participant, you will need to decide:

• What general topic do you want to work on? e.g. group comparison using fMRI, software for analysis of MEG data
• What skills do you want to learn, working on this project? e.g. preprocess fMRI data and run a classifier with sklearn, how to use git, etc.
• What resources do you want to work on? e.g. the CORR dataset, the nipype library, the Glasser parcellation paper, etc.
• What objectives do you want to achieve with the project? e.g. find differences in connectivity between two groups, replicate a multimodal brain parcellation, etc.
• What will be the outcome(s) of your project? a short proceedings paper, a new public dataset, a new feature in a toolbox, etc.

Each project will be presented orally and in writing, with rounds of feedback, and revised by the end of week 2.

There will still be time to work on training modules as well. You will be required to complete 3 out of the following modules:

• Introduction to deep learning
• Deep learning for neuroimaging
• Machine learning for neuroimaging
• Functional connectivity in fMRI
• Functional parcellations in fMRI
• The Brain Imaging Data Structure (BIDS) ecosystem
• DataLad for reproducible research data management
• Working with MNE-Python and EEG-BIDS
• Neuroimaging data and file structures in Python
• Introduction to dMRI
• Introduction to spinal cord MRI analysis

During week 3, participants will work on their project. The content of a typical day will include:

• Work on projects. Most of the time will be reserved to actually doing the work.
• Project clinics. Get daily feedback and support from instructors and residents.
• Presentation from instructors on their career paths.
• Collaborate. Take time each day to help someone else with their project.

Time to work on training modules is minimal. You will be required to complete 1 out of the following modules:

• Data visualization
• Python packaging
• Testing and continuous integration
• Containers