Independent component analysis on Lie groups for multi-object analysis of first episode depression
Bibliographic record
Abstract
We propose a method for the analysis of brain structural data to simultaneously identify differences in position, orientation and size (i.e. pose), and in shape of multiple brain regions between young people with, and without, a depressive disorder. Different structures in both hemispheres of the brain of depressed and control participants were segmented and corresponding points on the surface of each structure were extracted. Coordinates of these surface points were used to obtain shape variations, and parameters of similarity transformations between brain structures across subjects were used to generate pose variations. Since these surface points and similarity transformations form Lie groups, a logarithmic mapping of members of the Lie groups was performed to transform them to a linear tangent space. Then, Independent Component Analysis (ICA) was used to obtain the independent sources of pose and shape variations on Lie group members, and their corresponding modulation profiles. A method for ordering the independent sources is proposed. The top ordered sources were used to detect pose and shape differences between the two groups, and confirm that even in their first depressive episode, the brains of depressed adolescents differ structurally from the brains of their nondepressed age- and sex-matched peers.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".