Human and animal studies suggest that acupuncture produces many beneficial effects through the central nervous system. However, the neural substrates of acupuncture actions are not completely clear to date. fMRI studies at Hegu (LI4) and Zusanli (ST36) indicated that the limbic system may play an important role for acupuncture effects. To test if this finding applies to other major classical acupoints, fMRI was performed on 10 healthy adults during manual acupuncture at Taichong (LV3), Xingjian (LV2), Neiting (ST44), and a sham point on the dorsum of the left foot. Although certain differences could be observed between real and sham points, the hemodynamic response (BOLD signal changes) and psychophysical response (sensory experience) to acupuncture were generally similar for all four points. Acupuncture produced extensive deactivation of the limbic-paralimbic-neocortical system. Clusters of deactivated regions were seen in the medial prefrontal cortex (frontal pole, pregenual cingulate), the temporal lobe (amygdala, hippocampus, and parahippocampus) and the posterior medial cortex (precuneus, posterior cingulate). The sensorimotor cortices (somatosensory cortices, supplementary motor cortex), thalamus and occasional paralimbic structures such as the insula and anterior middle cingulate cortex showed activation. Our results provide additional evidence in support of previous reports that acupuncture modulates the limbic-paralimbic-neocortical network. We hypothesize that acupuncture may mediate its antipain, antianxiety, and other therapeutic effects via this intrinsic neural circuit that plays a central role in the affective and cognitive dimensions of pain as well as in the regulation and integration of emotion, memory processing, autonomic, endocrine, immunological, and sensorimotor functions.

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Study of functional brain network (FBN) based on functional magnetic resonance imaging (fMRI) has proved successful in depression disorder classification. One popular approach to construct FBN is Pearson correlation. However, it only captures pairwise relationship between brain regions, while it ignores the influence of other brain regions. Another common issue existing in many depression disorder classification methods is applying only single local feature extracted from constructed FBN. To address these issues, we develop a new method to classify fMRI data of patients with depression and healthy controls. First, we construct the FBN using a sparse low-rank model, which considers the relationship between two brain regions given all the other brain regions. Moreover, it can automatically remove weak relationship and retain the modular structure of FBN. Secondly, FBN are effectively measured by eight graph-based features from different aspects. Tested on fMRI data of 31 patients with depression and 29 healthy controls, our method achieves 95% accuracy, 96.77% sensitivity, and 93.10% specificity, which outperforms the Pearson correlation FBN and sparse FBN. In addition, the combination of graph-based features in our method further improves classification performance. Moreover, we explore the discriminative brain regions that contribute to depression disorder classification, which can help understand the pathogenesis of depression disorder.