Acupuncture effects on emotional reactivity of youth with self-reported depressive symptoms
Bibliographic record
Abstract
Background: Emotional symptomatology is a hallmark of depression. Antidepressant often fail to effectively target emotional blunting, while acupuncture, by contrast, has emerged as a promising alternative. However, the exact electrophysiologic mechanisms remain unclear. This study aimed to investigate how acupuncture influences emotional reactivity in youth with self-reported depressive symptoms. Methods: A modified oddball paradigm incorporating a negative emotional valence deviant, combined with event-related potential analysis, was used to measure emotional reactivity before and after intervention. Seventy individuals exhibiting depressive symptoms in the previous 2 weeks, were randomly assigned to either a verum or sham acupuncture group. Electroencephalogram data from 59 participants were analyzed following preprocessing and quality assessment. Occipital P1, N170, frontal N1, N2, and parietal P3 components were extracted. The Positive and Negative Affect Schedule (PANAS) was completed after each oddball session. The Massachusetts General Hospital Acupuncture Sensation Scale (MASS) was completed after each intervention session. Results: The MASS Index was significantly higher in the verum group. However, significant increases in occipital P1, N170, frontal N1, N2, and parietal P3 amplitudes for high-negative, mild-negative, and neutral pictures were observed after the intervention in both the verum and sham groups, with no significant difference between the groups. Additionally, both groups induced PANAS changes, and positive effect changes were significantly correlated with N170 and P1 (in response to high-negative pictures) changes in the sham group. Conclusion: Acupuncture altered emotional reactivity in youth with depressive symptoms, highlighting its potential role, albeit possibly non-specific, in depression prevention and treatment.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, 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".