Fusion analysis of first episode depression: Where brain shape deformations meet local composition of tissue
Bibliographic record
Abstract
Computational neuroanatomical techniques that are used to evaluate the structural correlates of disorders in the brain typically measure regional differences in gray matter or white matter, or measure regional differences in the deformation fields required to warp individual datasets to a standard space. Our aim in this study was to combine measurements of regional tissue composition and of deformations in order to characterize a particular brain disorder (here, major depressive disorder). We use structural Magnetic Resonance Imaging (MRI) data from young adults in a first episode of depression, and from an age- and sex-matched group of non-depressed individuals, and create population gray matter (GM) and white matter (WM) tissue average templates using DARTEL groupwise registration. We obtained GM and WM tissue maps in the template space, along with the deformation fields required to co-register the DARTEL template and the GM and WM maps in the population. These three features, reflecting tissue composition and shape of the brain, were used within a joint independent-components analysis (jICA) to extract spatially independent joint sources and their corresponding modulation profiles. Coefficients of the modulation profiles were used to capture differences between depressed and non-depressed groups. The combination of hippocampal shape deformations and local composition of tissue (but neither shape nor local composition of tissue alone) was shown to discriminate reliably between individuals in a first episode of depression and healthy controls, suggesting that brain structural differences between depressed and non-depressed individuals do not simply reflect chronicity of the disorder but are there from the very outset.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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 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".