Neuroscout: A platform for reproducible re-analysis of public naturalistic fMRI studies

[adelavega]

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

• Glasgow
• Asia / Pacific
• Europe / Middle East / Africa
• Americas

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

• Set up a brainhack friendly computing environment
• A introduction to Bash
• Python: Writing a script
• Python: Data analysis with Python
• Python: Visualisation
• Machine learning basics
• BIDS
• Machine learning for neuroimaging
• VCS: Using Git and Github
• VCS: Data management with Datalad
• Virtualization
• High Performance Computing: Oracle cloud resource

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

• I agree to follow the OHBM Code of Conduct during the hackathon

[stebo85]

Dear @adelavega,

Cool project!

It might be helpful for your project to have a look at our brainhack cloud project (#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]

That's an awesome idea, I'll look into it and asses if it's something I can achieve at the hackathon.

[djarecka]

Thank you for submitting the project! We have 35 projects right now, woohoo! But that means the projects pitches will have to be short. We will give you tomorrow 2 minutes to pitch your project, you can have one slide or no slides!
If you decide to use a slide, please include the link to the slide here.

And don't worry, you will still have more time to talk about your project during the BrainHack :-)

[rbroc]

Link to the project pitch slide: https://docs.google.com/presentation/d/1wAZ6qccLzzpYy4TEbvbqF1qHxuui9WzufBid7xPg_vs/edit#slide=id.p

[adelavega]

Additional goal: Talk to @soichih about adding FitLins / Neuroscout to Brainlife