Detecting dynamic and genetic effects on brain structure using high-dimensional cortical pattern matching
Bibliographic record
Abstract
We briefly describe a set of algorithms to detect and visualize effects of disease and genetic factors on the brain. Extreme variations in cortical anatomy, even among normal subjects, complicate the detection and mapping of systematic effects on brain structure in human populations. We tackle this problem in two stages. First, we develop a cortical pattern matching approach, based on metrically covariant partial differential equations (PDEs), to associate corresponding regions of cortex in an MRI brain image database (N=102 scans). Second, these high-dimensional deformation maps are used to transfer within-subject cortical signals, including measures of gray matter distribution, shape asymmetries, and degenerative rates, to a common anatomic template for statistical analysis. We illustrate these techniques in two applications: (1) mapping dynamic patterns of gray matter loss in longitudinally scanned Alzheimer's disease patients; and (2) mapping genetic influences on brain structure. We extend statistics used widely in behavioral genetics to cortical manifolds. Specifically, we introduce methods based on h-squared distributed random fields to map hereditary influences on brain structure in human populations.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".