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=<path to surface subjects directory FreeSurfer/FastSurfer> # Subject to processSUBJECT=sub-01 # Output directoryOUT=<path to output directory> # 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/
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.028 | 0.281 |
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; both teacher heads agree on what is shown here.
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".