Acupuncture has been used to treat vascular dementia in
China for a very long time. However therapeutic effect of acupuncture on the
vascular dementia is inconsistent in part, due to the different selection of
acupoints used in the treatment. So finding the acupoints with better efficacy
remains challenging.
Recently a group of scientists and clinicians led by
Professor FR Liang in China carried out a study to find out the most frequently
appeared acupoints in treatment of vascular dementia. The data was published in
journal of Evidence-Based Complementary and Alternative Medicine.
Professor Liang and colleagues employed a technique
called data mining that is an analytic process designed to explore a large
amount of data in search of consistent patterns between variables, and then to
validate the findings by applying the detected patterns to a new subset of
data, ultimately predicts the more effective acupoints.
Two-hundred and thirty-eight acupuncture prescriptions
for vascular dementia were analysed by data mining technique. It was found that
Baihui (GV20), Sishencong (EX-HN 1), Fengchi (GB 20), Shuigou (GV 26), and
Shenting (GV 24) emerged as the most frequently in the analysis and were
potential acupoints for vascular dementia treatment.
Many studies reported that needling Baihui (GV20)
improved calculation ability and short-term memory and corrected the changes in
personality of patients with vascular dementia. Needling Shuigou (GV26)
improved naming ability and short-term memory. A SPECT study showed that
stimulation at Baihui (GV20) activated the inner temporal system,
thalaencephalon system and prefrontal cortical system, while needling Shuigou
(GV26) activated prefrontal cortical system.
Authors suggest that Baihui (GV 20), Sishencong (EX-HN
1), Fengchi (GB 20), Shuigou (GV 26), and Shenting (GV 24), emerging higher
frequencies in the analysis, may have better therapeutic effects on vascular
dementia.
Reference:
S Feng et al., Discovery
of Acupoints and Combinations with Potential to Treat Vascular Dementia: A Data
Mining Analysis. Evidence-Based Complementary and Alternative Medicine Volume
2015, Article ID 310591, 12 pages. http://www.hindawi.com/journals/ecam/2015/310591/
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