The role of the amygdala in depression: a bibliometric analysis (2015–2024)
Bibliographic record
Abstract
Background: Depression is a highly heterogeneous disorder with complex mechanisms. Given converging evidence implicating the amygdala in its pathophysiology, a systematic and quantitative synthesis is warranted to map the research landscape, hotspots, and emerging trends. Objective: To systematically characterize the research landscape of depression-amygdala studies from 2015 to 2024 using bibliometric and visualization analyses, identify core hotspots and emerging themes, and track their evolution to inform subsequent mechanistic research and precision interventions. Methods: . Publication trends, country and institutional contributions, highly cited papers, and keyword co-occurrence/clustering were analyzed to delineate the field's structure and evolution. Results: A total of 5,999 publications were included. Annual output increased steadily from 399 in 2015 to a peak of 831 in 2024. The United States (1,813, 30.2%), China (1,122, 18.7%), and Germany (357, 6.0%) were the top contributors. The ratio of multi-country publications (MCP) was highest for Germany (28.3%), followed by the United Kingdom (28.1%) and Canada (24.6%). At the institutional and journal levels, the University of California system, Harvard University, and Harvard Medical School ranked among the leading contributors. The international collaboration network exhibited a U.S.-centered structure, with frequent partnerships between the United States and China (n = 113), the United States and the United Kingdom (n = 86), and the United States and Germany (n = 81). Keyword co-occurrence and clustering analyses revealed four major thematic clusters: emotion regulation networks, biological signaling and regulation, developmental risk factors, and animal models. Overall, the field has evolved from region-specific studies toward network- and system-level integration, highlighting the amygdala's pivotal role in the neurobiological mechanisms of depression and its potential as a candidate neuromarker. Conclusion: Bibliometric evidence indicates that research on depression-amygdala relationships is moving toward multi-dimensional and cross-disciplinary integration. The amygdala's involvement in emotion regulation and early-life stress is increasingly recognized; alterations in its functional connectivity show promise as a neuromarker of depression, though clinical translation requires multi-center validation and standardized analytic workflows.
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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 | 0.008 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.122 | 0.168 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".