We started this project because two data sets in our lab, SPINS (about schizophrenia) and SPASD (about autism), have strong motion effects that cannot be separated from group effects (SSD vs ASD vs Controls). This could be due to differences in the clinical populations given their symptoms. To alleviate the effect of motion in the analysis, Power et al. (2014) suggested ways to quality control for motion and introduced scrubbing as an additional step before cleaning the data. Scrubbing is a procedure that removes the TRs that have a big motion (as indicated by FD values that exceed a certain threshold) and the TRs between two motion spikes that are too close to each other (the TR section in between two spikes is called the island of which the length can be specified).
With SPINS and SPASD in mind, we would like to test if scrubbing is a possible solution to remove the motion effects that confound the group effects. However, schizophrenia and autism patients all tend to move more compared to healthy controls, so it might be worth checking different scrubbing arguments to leverage the quality of the data and the amount of usable data that go into the final analysis.
How to participate:
We have the scripts to 1) run scrubbing and cleaning and 2) plot the figures for quality control (QC). In this project, you can participate in three ways:
Help us improve the documentation and join our discussion of deciding the best scrubbing parameters.
Add other features to the procedure (e.g., add options to perform different scrubbing techniques)
Bring your own data to perform scrubbing or to QC for motion effects
Title
Scrubbing with clinical samples
Leaders
Ju-Chi Yu (Twitter: @juchiyu / Mattermost: @juchiyu) and Jerrold Jeyachandra (Mattermost: @jerdra)
Collaborators
No response
Brainhack Global 2022 Event
BrainHack Toronto
Project Description
Objectives:
We started this project because two data sets in our lab, SPINS (about schizophrenia) and SPASD (about autism), have strong motion effects that cannot be separated from group effects (SSD vs ASD vs Controls). This could be due to differences in the clinical populations given their symptoms. To alleviate the effect of motion in the analysis, Power et al. (2014) suggested ways to quality control for motion and introduced scrubbing as an additional step before cleaning the data. Scrubbing is a procedure that removes the TRs that have a big motion (as indicated by FD values that exceed a certain threshold) and the TRs between two motion spikes that are too close to each other (the TR section in between two spikes is called the island of which the length can be specified).
With SPINS and SPASD in mind, we would like to test if scrubbing is a possible solution to remove the motion effects that confound the group effects. However, schizophrenia and autism patients all tend to move more compared to healthy controls, so it might be worth checking different scrubbing arguments to leverage the quality of the data and the amount of usable data that go into the final analysis.
How to participate:
We have the scripts to 1) run scrubbing and cleaning and 2) plot the figures for quality control (QC). In this project, you can participate in three ways:
References:
Power's Paper: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3849338/
Lindquist Paper: https://onlinelibrary.wiley.com/doi/epdf/10.1002/hbm.24528
Benchmark Paper: https://www.sciencedirect.com/science/article/pii/S1053811917302288
PR w/Nilearn Code: nilearn/nilearn#3385
Link to project repository/sources
https://github.com/TIGRLab/brainhack-2022-scrubbing
Goals for Brainhack Global
Good first issues
issue one: show the length of scans after scrubbing
issue two: add options to scrub by removing a certain number of TRs after each motion spike
Communication channels
Join us on Discord
Skills
Onboarding documentation
https://github.com/TIGRLab/brainhack-2022-scrubbing#readme
What will participants learn?
Data to use
The project uses private data sets, but you can bring your own data too!
Number of collaborators
2
Credit to collaborators
Project contributors are listed on the README.md
Image
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Type
documentation, pipeline_development, visualization
Development status
1_basic structure
Topic
other
Tools
BIDS, fMRIPrep, Jupyter, other
Programming language
documentation, Python,
R
, shell_scriptingModalities
fMRI
Git skills
1_commit_push
Anything else?
Topic: pipeline development Tools: RStudio
Things to do after the project is submitted and ready to review.
Hi @brainhackorg/project-monitors my project is ready!