Multi-object statistical analysis of late adolescent depression
Bibliographic record
Abstract
Shape deformations and volumetric changes in the hippocampus and amygdala have previously been noted in Major Depressive Disorder (MDD). Unfortunately, these analyses are limited because relative shape and pose (rigid+scale transformation) information of multiple objects in brain are generally disregarded. We hypothesize that this information might complement studies of limbic structural deformation in MDD. We focus on changes in temporal (e.g., superior, middle and inferior temporal gyrus) and limbic (e.g., hippocampus and amygdala) lobes. Here, we use a multi-object statistical pose and shape model to analyze imaging data from young people with and without a depressive disorder. Nineteen individuals with a depressive disorder (mean age: 17.85) and twenty six healthy controls (age: 18) were enrolled in the study. A segmented atlas in MNI space has been used to segment hippocampus, amygdala, parahippocampal gyri, putamen, and the superior, inferior and middle temporal gyri in both hemispheres of the brain. Points on the surface of each structure were extracted and warped to each subjects’ structural MRI. These corresponding surface points were used within the analysis, to extract the pose and shape features. Pose and shape differences were detected between the two groups, such that second principal mode of pose variation (p = 0.022), and first principal mode of shape variation (p = 0.049) were found to differ significantly between the two groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".