IC‐P‐130: Morphometric Cortical Normative Data Throughout Aging
Bibliographic record
Abstract
Structural neuroimaging data from cognitively healthy individuals across the lifespan are necessary if one is to assess deviation from normality. Unfortunately, such normative data are found lacking, especially in such a form as to be widely applicable, allowing cross-studies comparisons. Our objective was to produce normative values for regional cortical surfaces, thicknesses and volumes using a large sample of cognitively healthy adults and a free, widely available segmentation technique. We merged 3D T1-weighted MRI scans from 2,194 healthy adults (1,078 women; 49.1%) aged 18 to 94 years old (mean: 49.6; SD: 20.9). Data originated from fifteen datasets (Table 1). We used FreeSurfer (Version 5.3) to extract cortical surfaces, thicknesses, and volumes based on the “Desikan–Killiany–Tourville” (DKT) cortical atlas containing 31 bilateral subdivisions. We generated predictive models for each region with age, sex, intracranial volume (TIV), magnetic field strength (MFS), and manufacturer as predictors. Predictors’ selection was based on the predicted residual sum of squares statistic using a 10-fold cross-validation. The mean R of the 186 cortical measures revealed that the predictors explained a substantial amount of variance (.40). The mean R of each predictor showed that age (.17), sex (.08), and TIV (.12) explained most of the variance while MFS (.01), manufacturer (.02) and interactions between predictors (.01) explained only a limited amount. We report results for both hemisphere of the cortex as examples (formulas are included in Table 2). R for surface (L: .76, R: .77), thickness (L: .46, R: .44), and volumes (L: .80, R: .80) were considerably larger than that for cortical regional models. The strongest effects were TIV (L: .35, R: .35) and sex (R: L: .24, R: .25) for surface, age (R: L: .40, R: .39) and MFS (L: .02, R: 02) for thickness, and age (L: .38, R: .38) and TIV (L: .23, R: .23) for volume. Figure 1 displays age and sex effects on each measure.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".