Brain Function in Middle-Aged Hong Chuan Tai Chi Players
Bibliographic record
Abstract
Abstract Background: Tai Chi has been proved as an effective strategy to improve cognitive function while the mechanism remains unclear. This cross-sectional study aimed to compare the brain functional connection of prefrontal cortex (PFC), motor cortex (MC) and occipital cortex (OC) in middle-aged Tai Chi practitioners and Tai Chi-naïve controls. Methods: 18 Hong chuan Tai Chi practitioners (age:55.78±2.64y) and 22 demographically matched healthy Tai Chi-naïve controls (age:54.69±3.10y) were recruited in this study. Global cognition was measured by the Montreal Cognitive Assessment Scale (MoCA), and the functional connection between PFC, MC and OC in five frequency intervals (I, 0.6-2Hz; II, 0.145-0.6Hz; III, 0.052-0.145Hz; IV, 0.021-0.052Hz; V, 0.0095-0.021Hz) was analyzed by wavelet phase coherence (WPCO). The changes in cerebral oxygenation (Δ[HbO2]) were measured by functional near-infrared spectroscopy (fNIRS). Continuous recordings of NIRS signals were obtained from the left and right prefrontal cortex (PFC), motor cortex (MC) and occipital cortex (OC) in resting state. Results: Compared with age-matched Tai Chi-naïve controls, Hong chuan Tai Chi practitioners had better global cognition and showed higher functional connection levels between left and right PFC, MC, OC in intervals I, III, VI and V in resting state. Conclusion: This study showed that middle-aged Hong chuan Tai Chi practitioners had higher functional connection between PFC, MC and OC, as well as the coordination of left and right brain in resting state, which maybe the contributing factors to higher global cognition.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".