Freesurfer cortical normative data for adults using Desikan-Killiany-Tourville and ex vivo protocols
Bibliographic record
Abstract
We recently built normative data for FreeSurfer morphometric estimates of cortical regions using its default atlas parcellation (Desikan-Killiany or DK) according to individual and scanner characteristics. We aimed to produced similar normative values for Desikan-Killianny-Tourville (DKT) and ex vivo-based labeling protocols, as well as examine the differences between these three atlases. Surfaces, thicknesses, and volumes of cortical regions were produced using cross-sectional magnetic resonance scans from the same 2713 healthy individuals aged 18–94 years as used in the reported DK norms. Models predicting regional cortical estimates of each hemisphere were produced using age, sex, estimated intracranial volume (eTIV), scanner manufacturer and magnetic field strength (MFS) as predictors. The DKT and DK models generally included the same predictors and produced similar R 2 . Comparison between DK, DKT, ex vivo atlases normative cortical measures showed that the three protocols generally produced similar normative values.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".