Prototype workflow #4
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Beginnings of a first workflow for generating scientific hypotheses from ABCD image data and supporting tabular data
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It looks like I need permissions for this repository, to add assignees, reviewers, etc. |
permissions added! |
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Better not to commit the ipynb files, just commit the py files. THe ipynb you can always export to html output or similar and upload to google drive, if you just want to share output |
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This is a collection of Jupyter lab notebooks and corresponding plain Python files. The most recent / most interesting of them is
nilearn_prototype.ipynb. It has code with comments that give details for multiple steps of a workflow to predict voxel values in "md" and "fa" images from "tested variables" (these are KSADS variables in our case) and "confounding variables" (these are "interview_age" and similar in our case). Slices of the output are displayed to show the location of voxels that are good at being predicted by the tested and confounding variables.We use nilearn.mass_univariate.permuted_ols() to compute the needed linear regressions.
Yet to be included in this pull request:
permuted_ols().The
nilearn_prototype.ipynbJupyter lab also includesTODO:comments that should be considered for subsequent pull requests, or high-priority ones can be included in this pull request -- please add comments to this discussion.