Brain Functional Connectivity Changes in Middle-Aged Hong Chuan Tai Chi Players
Bibliographic record
Abstract
Abstract Tai Chi has been shown an effective strategy to improve cognitive function in older populations while the underlying mechanism remains unclear. This cross-sectional study was performed to examine the brain functional connectivity changes in middle-aged Hong Chuan Tai Chi practitioners. The changes in cerebral oxygenation were measured by functional near-infrared spectroscopy. NIRS signals were obtained from left and right prefrontal cortex, motor cortex and occipital cortex in 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). Global cognition was measured by the Montreal Cognitive Assessment Scale, and spontaneous oscillations in cerebral oxygenation between three cortexes 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. Compared with age-matched Tai Chi-naïve controls, Hong Chuan Tai Chi practitioners had better global cognition, showed higher functional connectivity between left and right prefrontal cortex, motor cortex, occipital cortex in intervals I, III, VI and V. These findings showed that middle-aged Hong Chuan Tai Chi practitioners had higher functional connectivity between prefrontal cortex, motor cortex and occipital cortex, as well as the coordination of left and right brain, which maybe the contributing factors to higher global cognition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".