Bibliographic record
Abstract
Generates surfaces at various white matter depths (default 1, 2, and 3 milimiters).The depths are calculated based on the real world image resolution voxel size and transformed to milimiters. MethodThis is done by first computing a Laplace field over white matter (cortex to subcortex+ventricles), and then shifting an exiting white matter surface along that gradient.Stopping conditions are set by geodesic distance travelled. Installation```git clone https://github.com/jordandekraker/superficial-white-matter.gitpip install superficial-white-matter/``` Usage with Freesurfer/Fastsurfer (example) > The code expects *standard* nifti orientation, in which the resolution is the diagonal of the header affine matrix. Running the inputs through `fslreorient2std` will ensure that everything is calculated correctly. ```bash# This is the freesurfer/fastSurfer Subject directorySUBJECTS_DIR= # Subject to processSUBJECT=sub-01 # Output directoryOUT= # Convert segmentation to NIFTIaparc_aseg=${OUT}/${SUBJECT}_aparc+aseg.nii.gzmri_convert ${SUBJECTS_DIR}/${SUBJECT}/mri/aparc+aseg.mgz ${aparc_aseg} # Reorient to standardfslreorient2std ${aparc_aseg} ${aparc_aseg} # 1. Calculate the Laplace fieldpython sWM/laplace_solver.py \ ${aparc_aseg} \ ${OUT}/${SUBJECT}_laplace-wm.nii.gz # 2. Generate the surfaces for each hemispherefor hemi in lh rh; do # White matter surface WM=${SUBJECTS_DIR}/${SUBJECT}/surf/${hemi}.white WM_gii=${OUT}/${SUBJECT}_hemi-${hemi}_label-white.surf.gii # Convert white matter to GIFTI mris_convert ${WM} ${WM_gii} # Calculate the SWM surfaces python sWM/surface_generator.py \ "${WM_gii}" \ ${OUT}/${SUBJECT}_laplace-wm.nii.gz \ ${OUT}/${SUBJECT}_hemi-${hemi}_label-sWF_depth-done ``` > If you ran `micapipe v0.2.0` or higher check the example script: [`example_usage.sh`](./example_usage.sh)> `SWM` is implemented in [`micapipe v0.2.3`](https://github.com/MICA-MNI/micapipe/releases/tag/v0.2.3) API `laplace_solver.py````pythonSolves Laplace equation over the domain of white matter. Using grey matter as the source and ventricles as the sink.Inputs are expected to be Free/FastSurfer aparc+aseg.mgz in .nii.gz format Parameters----------NIFTI : str Parcellation file generated by Freesurfer/fastsurfre in nii.gz format (from mri/aparc+aseg.mgz).NIFTI : str Output laplacian file path (nii.gz) Returns-------NIFTI Laplacian image (nii.gz) Usage-----laplace_solver.py aparc+aseg.nii.gz laplace-wm.nii.gz``` `surface_generator.py````pythonShifts a white matter surface inward along a Laplace field Parameters----------GIFTI : str White matter surface in GIFTI format (surf.gii)NIFTI : str laplacian image generated by laplace_solver.pyOUTPUT : str path and name to the output surfacesDEPTHS : list [int | float] (OPTIONAL) DEFAULT=[1,2,3] List of depths to sample (in voxels) Returns-------NIFTI a list of strings representing the header columns Usage-----surface_generator.py hemi-L_label-white.surf.gii laplace-wm.nii.gz hemi-L_label-sWF_depth- 1,2,3``` Superficial White Matter © 2023 by Jordan DeKraker is licensed under CC BY-NC-SA 4.0. To view a copy of this license, visit http://creativecommons.org/licenses/by-nc-sa/4.0/
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.184 | 0.105 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".