Multimetric structural covariance in first-episode major depressive disorder: a graph theoretical analysis
Bibliographic record
Abstract
BACKGROUND: Abnormalities of cortical morphology have been consistently reported in major depressive disorder (MDD), with widespread focal alterations in cortical thickness, surface area and gyrification. However, it is unclear whether these distributed focal changes disrupt the system-level architecture (topology) of brain morphology in MDD. If present, such a topological disruption might explain the mechanisms that underlie altered cortical morphology in MDD. METHODS: Seventy-six patients with first-episode MDD (33 male, 43 female) and 66 healthy controls (32 male, 34 female) underwent structural MRI scans. We calculated cortical indices, including cortical thickness, surface area and local gyrification index, using FreeSurfer. We constructed morphological covariance networks using the 3 cortical indices separately, and we analyzed the topological properties of these group-level morphological covariance networks using graph theoretical approaches. RESULTS: Topological differences between patients with first-episode MDD and healthy controls were restricted to the thickness-based network. We found a significant decrease in global efficiency but an increase in local efficiency of the left superior frontal gyrus and the right paracentral lobule in patients with first-episode MDD. When we simulated targeted lesions affecting the most highly connected nodes, the thickness-based networks in patients with first-episode MDD disintegrated more rapidly than those in healthy controls. LIMITATIONS: Our sample of patients with first-episode MDD has limited generalizability to patients with chronic and recurrent MDD. CONCLUSION: A systems-level disruption in cortical thickness (but not surface area or gyrification) occurs in patients with first-episode MDD.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".