Neuroimaging in Psychiatric Disorders: A Bibliometric Analysis of the 100 Most Highly Cited Articles
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Extensive research has been conducted to find neuroimaging biomarkers for psychiatric disorders. This study aimed at identifying trends of the 100 most highly cited articles on neuroimaging in primary psychiatric disorders. METHODS: The most highly cited original research articles were identified and analyzed, following searches of MEDLINE and Web of Science All Databases. RESULTS: The top 100 articles ranked by yearly citation (from 137.5 to 31.1) were published between 1989 and 2017. Depressive disorders (30 articles), schizophrenia spectrum and other psychotic disorders (27), autism spectrum disorder (17), substance-related and addictive disorders (7), and post-traumatic stress disorder (7) were among the most studied conditions. Functional magnetic resonance imaging (42), structural magnetic resonance imaging (30), and positron emission tomography (22) were the most utilized neuroimaging modalities. While 85 articles investigated the pathophysiology of psychiatric disorders (including 7 focusing on developmental changes and 1 on genetic susceptibility), 15 articles studied the impact of treatment, including antidepressants (6), deep brain stimulation (4), antipsychotics (3), behavior therapy (3), and exercise (1). The analysis also identified the most contributing authors, countries (the United States: 71 articles, the United Kingdom: 8, Canada: 5, and China: 5), and journals (JAMA Psychiatry: 20 articles and Biological Psychiatry: 17). Ninety-eight studies were prospective, and two were retrospective. The sample size ranged from 3 to 1,188 (median: 21). CONCLUSIONS: Our study identified intellectual milestones in the utility of neuroimaging in investigating primary psychiatric disorders. The historic trends could help guide future research in this field.
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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 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.179 | 0.181 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".