Anatomical standardization of the human brain in euclidean 3-space and on the cortical 2-manifold
Bibliographic record
Abstract
Anatomical standardization (also called spatial normalization) is a key process in cross-sectional studies of brain structure and function using MRI, fMRI, PET and other imaging techniques. This process has two components: (i) specification of a 3D template brain, which defines a common coordinate space for analysis of any subsequent datasets; and (ii) a method to align the template with an individual 3D brain image, thereby associating each point of the standard template to a point on the individual. The association should be able to consistently match a particular template location to an anatomically corresponding location on each individual of a population. Standardization methods in widespread use employ a 3D affine spatial transformation to map from the individual to the template, which matches only overall size and gross shape of the input brain. A wide range of more flexible image deformation algorithms have been developed in order to better match fine detail. All such algorithms involve design choices that are subject to debate, and most have numerical parameters whose value must be specified by the user. In order to provide guidance for such choices, the first part of this thesis develops two measures of alignment consistency that are used to evaluate performance of a standardization method. The performance of different choices for algorithm design, numerical parameters, and template selection strategy for 3D normalization are compared. Since the processing of brain function occurs on a thin, highly convoluted sheet of cortex along the surface of the brain, there has been much recent interest in studying the structure and function along the brain cortex only, modelled as a 2D manifold. The second part of this thesis proposes an algorithm for highly-flexible deformation in 2D of a template cortex to an individual. The alignment consistency measures developed for 3D are reformulated for the 2D manifold and used to evaluate the algorithm design and numerical parameters. Finally, the question of whether it is better to standardize the 3D images or the 2D cortical manifold is addressed, identifying the problem classes which are best suited to each type of normalization.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".