Acupuncture Stimulation of Taichong (Liv3) and Hegu (LI4) Modulates the Default Mode Network Activity in Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVES: The acupuncture has been used in the therapy of Alzheimer disease (AD), however, its neural underpins are still unclear. The aim of this study is to examine the acupuncture effect on the default mode network (DMN) in AD by using resting state functional magnetic resonance imaging (RS-fMRI). METHODS: Twenty-eight subjects (14 AD and 14 normal controls (NC)) participated in this study. RS-fMRI data were acquired before and after acupuncture, while during the acupuncture, the procession of acupuncture stimulation on the acupoints of Tai chong (Liv3) and Hegu (LI4) lasted for 3 minutes. RESULTS: Region of interest analysis showed that the impaired DMN connectivity in AD (identified by comparing the pre-acupuncture RS-fMRI of AD and NC), specifically the left cingulate gyrus (CG) and right inferior parietal lobule (IPL), were significantly changed for the better. The whole-brain exploratory analysis further demonstrated these results and found some new regions respond to the acupuncture effect on AD, with a cluster in the left posterior cingulate cortex (PCC), the right middle temporal gyrus (MTG) together with right IPL showed increased within-DMN connectivity; and the bilateral CG and left PCu showed decreased within-DMN connectivity. Moreover, the acupuncture effect on the right MTG was significantly correlated with disease severity as measured by Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA) scores. CONCLUSION: It was found that the acupuncture stimulation could modulate the DMN activity in AD. The current findings suggest that the acupuncture treatment on the relative earlier AD patients might have a better therapy effect.
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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".