Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open Workflows (Lightning talk): Conquering confounds and covariates in neuroscientific analyses with an open, high quality library #13

Open
jsheunis opened this issue Apr 24, 2020 · 2 comments

Comments

@jsheunis
Copy link
Contributor

Conquering confounds and covariates in neuroscientific analyses with an open, high quality library

By Pradeep Reddy Raamana, Rotman Research Institute, Baycrest Health Sciences, Toronto, Canada

  • Theme: Open Workflows
  • Format: Lightning talk

Abstract

Given the ever increasing complexity and sample sizes of the open datasets , there is clear need for deconfounding methods in various facets of neuroscientific analyses including predictive modeling. Recently this important topic has been getting increasingly more attention. However, there are still many challenges, including but not limited to lack of consensus on 1) what really constitutes a confound?, 2) when should we try to defoncound it? and 2) how do we properly assess their impact? etc. This calls for bridging a clearly unfilled need for a well-tested high-quality software library implementing the deconfounding methods as well as related tools to answer the aforementioned questions and open challenges. Towards this end, I built a python library called confounds, that is extensible and built for development with a community-first attitude following the best practices of open science. I would like to present its features, roadmap and encourage contributions from the deconfounding enthusiasts of all levels.

By conquering confounds, I mean methods and tools to

  • visualize and establish the presence of confounds (e.g. quantifying confound-to-target relationships),
  • offer solutions to handle them appropriately via correction or removal etc, and
  • analyze the effect of the deconfounding methods in the processed data (e.g. ability to check if they worked at all, or if they introduced new or unwanted biases etc).

Useful Links

https://github.com/raamana/confounds
https://crossinvalidation.com/2020/03/04/conquering-confounds-and-covariates-in-machine-learning/

Tagging @raamana

@raamana
Copy link

raamana commented Jun 19, 2020

Yes, these details are correct.

@raamana
Copy link

raamana commented Jun 24, 2020

I am unable to edit the top issue, so can someone edit the above making a note that second link has slides? Thanks.

Sign up for free to join this conversation on GitHub. Already have an account? Sign in to comment
Projects
None yet
Development

No branches or pull requests

3 participants