Framing major depressive disorder as a condition of network imbalance at the compartment level: a proof-of-concept study
Bibliographic record
Abstract
Major depressive disorder (MDD) is associated with hypoactivity in the frontoparietal (FP) system and hyperactivity in the limbic (LM) system. The widely accepted limbic-cortical dysregulation model has recently been extended by the concept of imbalanced reciprocal suppression between these 2 systems. This study investigates the refined theoretical framework. Neuroimaging datasets from 60 MDD and 60 healthy controls were obtained from the Canadian Biomarker Integration Network in Depression database, including structural magnetic resonance imaging (MRI) and resting-state functional MRI (rsfMRI). The cerebral cortex was parcellated using the modular analysis and similarity measurements (MOSI) technique. For each node, the average amplitude of low-frequency fluctuation (avgALFF) and nodal strength were calculated. Correlation analyses were conducted to establish an adjacency matrix and assess the relationship between nodal power and strength. The results indicated that the LM system in MDD displayed higher partition numbers and avgALFF (P < 0.005). A significant negative correlation between nodal strength and power was replicated (P < 1E-10), suggesting that greater functional input enhances regional neural suppression. Notably, MDD participants exhibited a higher negative correlation between FP nodal power and LM-FP connectivity (stronger suppression) but a lower negative correlation between LM nodal power and FP-LM connectivity (weaker suppression). These findings support the theory of abnormal cortical signal organization and reciprocal suppression in 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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".