White matter free water and depressive symptoms in medication-free depressed adolescents: moderation by peripheral inflammation
Bibliographic record
Abstract
Major Depressive Disorder (MDD) often emerges during adolescence and significantly impacts psychological and social functioning. Increasing evidence links both peripheral inflammation and white matter abnormalities to the pathophysiology of MDD. Free-water (FW) imaging, sensitive to neuroinflammatory and microstructural changes, enables investigation of their interplay in depression. However, the role of FW imaging in adolescents with MDD, along with its clinical and inflammatory associations, remains underexplored. Here, we conducted a cross-sectional analysis and exploratory analysis of the relationship between white matter FW, peripheral inflammation, and depressive symptoms in adolescents. 3-T multi-shell diffusion-weighted magnetic resonance imaging data and peripheral cytokine were collected from 147 participants aged 12-18 years, including 63 medication-free adolescents with MDD and 84 healthy controls (HC). FW maps were generated using the DIPY toolbox, followed by voxel-wise analyses conducted with Tract-Based Spatial Statistics in FSL. Our findings reveal that adolescents with MDD exhibited lower levels of inflammatory cytokines, including IFN-γ, IL-2, TNF-α, and IL-4 (all p < 0.05), along with significantly reduced white matter FW (family-wise error-corrected p < 0.05). Importantly, IFN-γ levels significantly moderated the relationship between altered white matter FW and depressive symptoms (β = 0.46, p = 0.003). Specifically, in adolescents with MDD and higher IFN-γ levels, greater white matter FW was associated with more severe depressive symptoms, while in those with lower IFN-γ levels, higher FW was linked to less severe symptoms. These results suggest that peripheral inflammation, particularly IFN-γ, may be associated with the relationship between white matter FW and the severity of depressive symptoms. This highlights the importance of considering an individual's inflammatory status when interpreting the biological and psychological functioning of adolescents with 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".