Vollständiger Abstract
Worum geht es in dieser Arbeit?
This systematic review aims to synthesise evidence from neuroimaging studies to illustrate that acupuncture does have measurable multi-target central actions for major depressive disorder (MDD) treatment. The key mechanisms identified in studies over the past decade, using fMRI, PET and EEG, are: Acupuncture reduces hyperconnectivity of the default mode network (DMN) and increases its anti-correlation with the salience network. In task-based imaging, there is observed reduction in hyperactivity in the amygdala in conjunction with an increase in activity in the dorsolateral prefrontal cortex. Morphometric analyses showed that sustained EAc caused an increase in gray matter in the hippocampus and prefrontal cortex. PET studies have been reported to show alterations in serotonin transporter (SERT) binding and opioid receptor (OR) availability in the limbic system following treatment. So far, although there have been a lot of clinical evidence showing the antidepressant effects of acupuncture, the neurobiological understanding of acupuncture’s effects remained unclear. Existing studies suggest that there are potential neural modulation targets to consider for the pathophysiology of MDD, such as dysregulated neurotransmission, and corticolimbic connectivity; however, there have been some limitations in the studies, with small sample sizes, heterogeneous protocols, and relatively short follow-up. Further multicenter randomized controlled trials involving longitudinal imaging will be required to determine definite mechanistic and clinical standards.
Bibliografischer Nachweis
Publikationsdaten
- Autor:innen
- Jia Sun
- Quelle
- International Journal of Biology and Life Sciences
- Publikation
- 2026-01-01
- Band / Ausgabe
- Nicht angegeben
- Seiten
- Nicht angegeben
- ISSN / ISBN
- 2957-9511
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Zitierfähiger Nachweis
Jia Sun (2026). Neuroimaging Evidence of Acupuncture Treatment for Depression Over the Past Decade: A Systematic Review. International Journal of Biology and Life Sciences. https://doi.org/10.54097/660g1733
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