Abstract
Acupuncture, an ancient therapeutic technique, is currently gaining popularity as an important modality of alternative and complementary medicine in the West world. Concurrently, scientific interests in exploring whether acupuncture is therapeutically effective are raised alongside those about the means by which it may operate. Modern neuroimaging techniques such as functional magnetic resonance imaging, positron emission tomography, electroencephalography, and magnetoencephalography provide a means to safely monitor brain activity in humans. In this review, we have summarized evidence derived from the neuroimaging studies and tried to elucidate the neurophysiological correlates of acupuncture. Previous investigations on the neural responses to acupuncture mainly focus on its acute effects and explore the correlation between the specific acupoints and cortical activations only in the spatial domain. However, abundant clinical reports and psychophysical analysis suggest the kinetics of acupuncture is longer acting as a function of time. Consequentially, an accurate interpretation of acupuncture actions depends on how effectively we can characterize the nature of temporal variations underlying neural activities, rather than simply detect the occurrence of such changes. This emerging picture indicates that both designing paradigms and statistical models involved in acupuncture studies should be applied with great care.
Keywords: Acupuncture, spatiotemporal response, brain correlates, neuroimaging studies
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
5