Bibliographic record
Abstract
The cortical surface in humans is comprised of several folds which are juxtaposed together to form a biologically meaningful pattern. For many biological reasons, the geometry of this pattern changes in time: folds get longer, wider and deeper. The relationship between the shape of cortical folds and biological factors such as gender and aging can be studied using statistical shape analysis techniques.An essential step in this process is the matching of the folds on different cortical surfaces. Fold matching can be done using a scalar field describing the relative depth of folds on a surface. The match between folds is established by comparing the scalar field of each surface. The first contribution of this thesis is to define a function as a solution to a sparse linear system, which characterizes the relative depth of the folds on a surface. This thesis shows that the accuracy of surface registration is improved by 11% using the proposed scalar field as a shape descriptor to register surfaces. The second contribution of this thesis is to propose a statistical method to detect directional differences in the shape of folds on the cerebral cortex. A directional difference can be, for example, a difference in its length in the direction parallel to it. Previous statistical tests for shape analysis only determine if two folds are different locally, and they typically average across multiple local directions. The method proposed in this thesis provides directional information to better understand the factors that relate to differences in fold shape.The third contribution of this thesis is the development of anisotropic diffusion kernels on surfaces to highlight shape differences that affect fold shape. Diffusion of scalar and tensor fields is used in statistical shape analysis to increase the detection power of statistical tests. However, diffusion also decreases the capacity of statistical tests to localize significant shape differences. Prior to this thesis, diffusion kernels used on surfaces were isotropic in shape and blurred information over multiple folds. Anisotropic diffusion kernels, on the other hand, can increase statistical power by concentrating diffusion along fold orientation and highlighting the variability in shape that is localized to specific folds.In summary, this thesis provides tools that increase the amount of information that can be gathered about the morphometry of the cerebral cortex using statistical shape analysis. The accuracy of surface registration is increased, the analysis of the underlying deformation field allows us to determine if a difference in shape affects fold length or width and diffusion kernels produce statistical results that highlight the variability in shape that is localized to specific folds.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".