The Cerebellar Role in Emotions at a Turning Point: Bibliometric Analysis and Collaboration Networks
Bibliographic record
Abstract
The neural basis of emotional experience, both in neurotypical and clinical conditions, remains an open research topic. Historically, the cerebellum was considered a purely motor structure; however, studies since the mid-twentieth century and contributions like the cerebellar cognitive-affective syndrome, evidenced its role in emotion. This has led to an expansion of the paradigm, encouraging further research into the cerebellar role in emotion. Understanding this field's development is essential to assessing its current state, identifying knowledge gaps, and exploring emerging areas. This paper analyzes the evolution of scientific production, addressing how scientific interest has changed over time, factors driving growth, dominant topics, leading figures, and collaboration networks. This analysis identifies trends and opportunities, guiding strategies and advancing knowledge through a comprehensive view of the state-of-the-art in this research area. To achieve this, a systematic search was conducted in key databases, identifying 1,162 publications with which an exhaustive quantitative analysis was conducted using bibliometric techniques, network analysis, and visualization tools. The results show exponential growth in the field, evidenced by the increase in publications, researchers, funding sources, and the emergence of new topics. This interest, along with an interdisciplinary approach, has fostered collaboration, with large teams and multicenter projects emerging, although small, isolated teams still predominate. Research mainly focuses on neurological and affective disorders, with a predominance of studies in humans, followed by rodent models. Overall, the analysis reveals a highly interdisciplinary and expanding field. However, challenges remain, including unequal access to resources and limited exploration of some topics.
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.132 | 0.199 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".