How much can we trust our pipelines to give us meaningful answers? One way to answer this is to introduce tiny perturbations throughout our pipelines, and see how much our answers change. This project aims at making it easier to perform these analyses and answer these questions.
Our project is to develop a comprehensive course in Neuroimaging for students entering this field. At the Hackathon, our focus is on developing curriculum and training content for Neuro Data modules. Eventually, the course will be recorded and hosted on an online platform, for greater accessibility. Our team at the Neuro is working in coordination with other teams as part of this open source project, inspired by the Carpentries system.
There are great sources out there to search for open datasets and they work nicely if you have a specific question you are trying to answer. But what if you want to run some data-driven analyses on more than one single dataset? You might want to pull together several of them. To do so, you need to know which datasets can actually be combined based on some overlapping features. This new project aims at facilitating this process by creating a library of potentially mergeable aging datasets using Google Dataset Search.
Develop a pipeline where users can input MRI data with different resolutions and contrasts and have it be registered to a group average template. From here, the brain can be divided based on atlas based regions-of-interest, or move to a surface-based pipeline to investigate MR values at specified cortical depths.
Ever wonder if your application would benefit from writing to a different storage device or changing the amount of compute resources, and by how much? We will develop a tool that will help predict if your application can benefit from slight changes in execution mode that will result in significant speedups. Want to see how your pipeline could be improved? Want to help you with the project? Do not hesitate to come se us !
We will rely on a new Zenodo crawler to add datasets to the Canadian Open Neuroscience Platform (CONP). The data upload process should be as simple as uploading a zip file to Zenodo and adding a keyword, but the devil is in the details!
Add a feature to pycortex to support visualizing brain maps in VR. The idea is to use the existing architecture in pycortex to build a demo to get people interested in the data, similar to the demos in https://www.gallantlab.org/brain-viewers/. WebVR is a web technology that allows VR content to be distributed through the web browser; it's supported by Firefox on Windows and by Chrome on Android. pycortex is an ideal base for a WebVR brain visualizer because it already supports WebGL, so it should be a matter of building functionality of top of the existing one.
Improving the nilearn documentation and tutorials is crucial. There are many things to do, ranging from simple to more advanced. One of them would be to contribute back to nilearn some of the improvements made in the Main teaching materials.
BIDS-Apps can now be integrated into CBRAIN using Boutiques descriptor. Even if CBRAIN already have some BIDS-Apps integrated, it does not have documentation to do it. So let's add it.
dcm2bids needs more automation. Specifically, I hope to implement linting, write more tests, automate these tests with github actions
EEG, BIDS, API, open data updates on LORIS web-based open-source multimodal database from the MNI