Effects of dance training on oxytocin secretion and neural activity in older adults with subjective cognitive decline
Bibliographic record
Abstract
Background and Objectives: Subjective cognitive decline (SCD) is a preclinical stage of mild cognitive impairment (MCI). Although dance training has been shown to be beneficial for mental health, cognitive function, and neural activity in older adults with MCI, its effect on SCD remains unclear. This study aimed to examine the effects of dance training on the aforementioned factors and on oxytocin secretion in older adults with SCD. Research Design and Methods: = 22). Apathy, depression, Montreal Cognitive Assessment scores, urinary oxytocin levels, and resting-state functional magnetic resonance imaging indices, including amplitude of low-frequency fluctuations (ALFF) and functional connectivity (FC), were evaluated pre- and post-intervention. Results: Compared to the control group, the intervention group exhibited significantly higher urinary oxytocin levels and significantly higher ALFF in the left medial orbitofrontal cortex post-intervention. Moreover, the intervention group showed more enhanced FC between the left medial orbitofrontal cortex and the left precuneus post-intervention than the control group. However, mental health or cognitive performance was not significantly different between the groups. Discussion and Implications: Our results are particularly important in light of previous findings that older adults with SCD show a reduced FC between the medial orbitofrontal cortex and the precuneus, and that oxytocin levels are positively associated with the prefrontal-amygdala oxytocinergic circuit in socioemotional processing. Thus, dance training may contribute to socioemotional resilience-related neural and molecular adaptations in SCD.
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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 | 0.001 |
| 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) | 0.002 | 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".