stebo85
commented
2 years ago Dear @adelavega,
Cool project!
It might be helpful for your project to have a look at our brainhack cloud project (https://github.com/ohbm/hackathon2022/issues/50) as I think it could be very useful for your developments:
- you could provide large development VMs with docker installed (https://brainhack.org/brainhack_cloud/docs/docker/) to run your container
If you think this could be useful, make sure to sign up before the hackathon so we can create the accounts: https://brainhack.org/brainhack_cloud/docs/request/
All the best Steffen
adelavega
djarecka
rbroc
Title
Neuroscout: A platform for fast and flexible re-analysis of (naturalistic) fMRI studies
Short description and the goals for the OHBM BrainHack
Neuroscout has two primary goals: 1) to democratize reproducible fMRI analysis by making it trivially easy to specify and fit models to public fMRI datasets and 2) facilitate the analysis of naturalistic datasets by leveraging machine-learning algorithms for automated annotation. Combined, these two goals seek to increase the reproducibility and generalizability of neuroimaging analysis.
Neuroscout is already a stable platform. However, we are still working on growing our user base, and expanding the functionality. To that end we propose several goals for this hackathon:
1) Develop and get feedback on a end-to-end tutorial for Neuroscout. Currently, although Neuroscout features sufficient documentation, many users are not yet clear on how to best use the platform. By creating a complete end-to-end tutorial, and receiving feedback from the community, we aim to Neuroscout more accessible. This is a great issue for first time contributors!
2) Add more datasets. Neuroscout currently spans 40 distinct naturalistic stimuli across over a dozen independent datasets. However, many more datasets are made public yearly, and we will seek to further expand the number of datasets indexed. In particular, we hope to also include non-naturalistic datasets, to increase the scope of Neurocout.
3) Add more naturalistic features. Neuroscout uses machine-learning algorithms to annotate naturalistic stimuli such as movies. We developed a library (pliers) to provide a uniform API to diverse algorithms. However, there are many more algorithms that could be incorporated and tested. Many are available to extract but have not been actively worked on. A specific example would be to incorporate DeepGaze, to simulate eye tracking data in datasets without it. Validating new features by building models would be a relatively easy contribution for first timers.
4) Develop multivariate analysis pipelines. Neuroscout currently focuses on fitting multi-stage univariate GLM models using BIDS Stats Models (a formal specification). However, multivariate approaches are widely popular for analyzing naturalistic data. Although we don't aim to fully specify multivariate models in a standardized format, we aim to prototype Neuroscout-compatible multivariate workflows that can take advantage of the vast number of datasets and features made easily available by the Neuroscout API. In the future, this project could be refined to become a core component of Neuroscout, as an alternative to GLM models.
Link to the Project
https://neuroscout.org
Image for the OHBM brainhack website
https://user-images.githubusercontent.com/2774448/173400796-54ac74af-d913-425f-b251-2b8cd7421021.png
Project lead
Alejandro de la Vega (@neurozorro) Roberta Rocca
Main Hub
Glasgow
Other Hub covered by the leaders
Skills
Basic familiarity with neuroimaging analysis.
Plus: tutorial writing skills, experience with naturalistic data and multivariate modeling (specifically encoding and decoding models).
Recommended tutorials for new contributors
Good first issues
No response
Twitter summary
No response
Short name for the Discord chat channel (~15 chars)
neuroscout
Please read and follow the OHBM Code of Conduct