Increased low frequency fluctuation in the brain after acupuncture treatment in CSVDCI patients: A randomized control trial study
Bibliographic record
Abstract
Background Cerebral small vessel disease (CSVD) is one of two cognition-impairing diseases. Acupuncture (Acu) is a flexible treatment with few adverse effects and is thus widely used to treat neurological problems. Methods We recruited a total of 60 patients and assigned them to two groups ( n = 30 each group). During the study, some participants were excluded by quality control, and a total of 44 subjects (25 Acu and 19 controls) were completed to investigate the therapeutic efficacy of acupuncture on CSVD cognitive impairment (CSVDCI). The following demographic and clinical variables were compared between the two groups: gender, age, education, smoking, alcohol, Montreal cognitive assessment (MoCA), symbol digit modalities test (SDMT), verbal fluency test (VFT), digit span task (DST), Boston naming test (BNT) scores, and amplitude of low-frequency fluctuation (ALFF) under the typical band (0.01–0.08 Hz). Mixed effect analysis was utilized to test for differences between the two groups before and after the treatment. Results Following acupuncture treatment, the Acu group scored higher on MoCA, SDMT, VFT, DST, and BNT compared to controls ( P < 0.05). The brain regions showing substantially greater ALFF values in the Acu group were the right inferior temporal gyrus, left middle occipital gyrus, left superior occipital gyrus, left insula, bilateral postcentral gyrus, right superior parietal gyrus, right cerebellum, right precuneus, and right precentral gyrus ( P < 0.005, no correction). The ALFF values in the right inferior temporal gyrus ( P = 0.027), left middle occipital gyrus ( P = 0.005), left superior occipital gyrus ( P = 0.011), and right superior parietal gyrus ( P = 0.043) were positively associated with MoCA. Conclusion We found that acupuncture modulates the functional activity of temporal, occipital, and parietal regions of the brain in CSVDCI patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.000 | 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 teacher head, 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".