This subproject is one of many research subprojects utilizing theresources provided by a Center grant funded by NIH/NCRR. The subproject andinvestigator (PI) may have received primary funding from another NIH source,and thus could be represented in other CRISP entries. The institution listed isfor the Center, which is not necessarily the institution for the investigator.The use of functional MRI has been applied to a number of brain systems such as language, sensory (motor, visual), pain, and higher level cognitive functions such as memory or spatial attention. Typically, there is a known model of the experiment (i.e. the subject moves their fingers at the appropriate time); however, in the case of acupuncture this may not hold true. There is known model of the presence of the needling but the brains response may differ. This response may be variable from subject to subject. We are proposing to use a combination of principal components analysis followed by independent component analysis to identify the primary brain responses to acupuncture of the visual system. These experiments have a large number of voxels (64x64x32) for a large number of time points (500) making this analysis difficult on standard computational hardware. Furthermore, we plan to investigate how the independent components interact across a population of subjects to identify the population-based time course of acupuncture. We seek the use of the super-computing resources (storage, processing and memory) to conduct our large scale analysis of within and across subject responses to acupuncture as measured by functional MRI collected using NIH funds from the National Center for Complementary and Alternative Medicine.
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