Surface shape analysis with an application to brain surface asymmetry in schizophrenia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Some methods for the statistical analysis of surface shapes and asymmetry are introduced. We focus on a case study where magnetic resonance images of the brain are available from groups of 30 schizophrenia patients and 38 controls, and we investigate large-scale brain surface shape differences. Key aspects of shape analysis are to remove nuisance transformations by registration and to identify which parts of one object correspond with the parts of another object. We introduce maximum likelihood and Bayesian methods for registering brain images and providing large-scale correspondences of the brain surfaces. Brain surface size-and-shape analysis is considered using random field theory, and also dimension reduction is carried out using principal and independent components analysis. Some small but significant differences are observed between the the patient and control groups. We then investigate a particular type of asymmetry called torque. Differences in asymmetry are observed between the control and patient groups, which add strength to other observations in the literature. Further investigations of the midline plane location in the 2 groups and the fitting of nonplanar curved midlines are also considered.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it