The Relationship between Neurobiological Function and Inflammation in Depressed Children and Adolescents: A Scoping Review
Bibliographic record
Abstract
INTRODUCTION: Neurobiological dysfunction is associated with depression in children and adolescents. While research in adult depression suggests that inflammation may underlie the association between depression and brain alterations, it is unclear if altered levels of inflammatory markers provoke neurobiological dysfunction in early-onset depression. The aim of this scoping review was to provide an overview of existing literature investigating the potential interaction between neurobiological function and inflammation in depressed children and adolescents. METHODS: Systematic searches were conducted in six databases. Primary research studies that included measures of both neurobiological functioning and inflammation among children (≤18 years) with a diagnosis of depression were included. RESULTS: Four studies (240 participants; mean age 16.0 ± 0.6 years, 62% female) meeting inclusion criteria were identified. Studies primarily examined the inflammatory markers interleukin 6, tumor necrosis factor alpha, C-reactive protein, and interleukin 1 beta. Exploratory whole brain imaging and analysis as well as region of interest approaches focused on the anterior cingulate cortex, basal ganglia, and white matter tracts were conducted. Most studies found correlations between neurobiological function and inflammatory markers; however, depressive symptoms were not observed to moderate these effects. CONCLUSIONS: A small number of highly heterogeneous studies indicate that depression may not modulate the association between altered inflammation and neurobiological dysfunction in children and adolescents. Replication in larger samples using consistent methodological approaches (focus on specific inflammatory markers, examine certain brain areas) is needed to advance the knowledge of potential neuro-immune interactions early in the course of depression.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".