Bibliometric and Visual Analysis of Research on the Links between the Microbiome-Gut-Brain axis and Depression
Bibliographic record
Abstract
Abstract Background The pathways and mechanisms underlying the associations between the gut microbiome and the brain are collectively known as the microbiome-gut-brain axis (MGBA). Depression is a common and frequent psychological and psychiatric disease. The interactions comprising the MGBA may play a role in the pathogenesis of depression. Nevertheless, the general aspects of the links between the MGBA and depression have not been systematically investigated through bibliometric analysis. Methods Publications from 1994 to 2022, focusing on the links between the MGBA and depression, were downloaded from the Web of Science Core Collection. HistCite, VOSviewer, CiteSpace, and the Bibliometrix Package and were used for bibliometric analysis and visualization. The main analyses we performed included collaboration network analysis, co-citation analysis, co-occurrence analysis, and citation burst detection. Results A total of 829 publications related to the relationship between the MGBA and depression were identified. The number of such publications has been rapidly growing since 2014. The People's Republic of China, University College Cork, and John F. Cryan were the most influential country, institute, and scholar, respectively. BRAIN BEHAV IMMUN and NUTRIENTS were the most productive and co-cited journals. Five hot topics in research linking the MGBA with depression were depression, gut microbiota, gut-brain axis, microbiota, and anxiety. Five frontier topics in the field were cytokine, maternal separation, neuroinflammation, probiotics, and vagus. The most representative and symbolic reference with the highest co-citation number was an article by Bravo J. A. et al. (2011). Conclusions These results provide an instructive perspective for research on the relationship between the MGBA and depression, and a timely review and analysis of research hotspots and research trends, which will promote the development of this field. Future research will focus on understanding the underlying mechanism of action of the MGBA on depression, and on elucidating microbial-based interventions and treatment strategies for depression.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.065 | 0.138 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.000 | 0.006 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".