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Magnetoencephalography (MEG) enables non-invasive recording of neuronal activity, with reconstruction methods providing estimates of underlying brain source locations and oscillatory dynamics from externally recorded neuromagnetic fields. The aim of our study was to use MEG to determine the effect of manual acupuncture on neuronal oscillatory dynamics. A major problem in MEG investigations of manual acupuncture is the absence of onset times for each needle manipulation. Given that beamforming (spatial filtering) analysis is not dependent upon stimulus-driven responses being phase-locked to stimulus onset, we postulated that beamforming could reveal source locations and induced changes in neuronal activity during manual acupuncture. In a beamformer analysis, a two-minute period of manual acupuncture needle manipulation delivered to the ipsilateral right LI-4 (Hegu) acupoint was contrasted with a two-minute baseline period. We considered oscillatory power changes in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-100 Hz) frequency bands. We found significant decreases in beta band power in the contralateral primary somatosensory cortex and superior frontal gyrus (SFG). In the ipsilateral cerebral hemisphere, we found significant power decreases in beta and gamma frequency bands in only the SFG. No significant power modulations were found in theta and alpha bands. Our results indicate that beamforming is a useful analytical tool to reconstruct underlying neuronal activity associated with manual acupuncture. Our main finding was of beta power decreases in primary somatosensory cortex and SFG, which opens up a line of future investigation regarding whether this contributes toward an underlying mechanism of acupuncture.
Pubmed ID: 23248594
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Probabilistic atlases covering 48 cortical and 21 subcortical structural areas, derived from structural data and segmentations kindly provided by the Harvard Center for Morphometric Analysis. T1-weighted images of 21 healthy male and 16 healthy female subjects (ages 18-50) were individually segmented by the CMA using semi-automated tools developed in-house. The T1-weighted images were affine-registered to MNI152 space using FLIRT (FSL), and the transforms then applied to the individual labels. Finally, these were combined across subjects to form population probability maps for each label. Segmentations used to create these atlases were provided by: David Kennedy and Christian Haselgrove, Centre for Morphometric Analysis, Harvard; Bruce Fischl, the Martinos Center for Biomedical Imaging, MGH; Janis Breeze and Jean Frazier from the Child and Adolescent Neuropsychiatric Research Program, Cambridge Health Alliance; Larry Seidman and Jill Goldstein from the Department of Psychiatry of Harvard Medical School.
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