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The most accurate approaches involve using measurement equipment in many locations, which can be both expensive and difficult to manage due to immobile or complicated assets. Todays science collaborations depend on reliable, high performance networks, but monitoring the end-to-end performance of a network can be costly and difficult. Testing the Feasibility of a Low-Cost Network Performance Measurement InfrastructureĬhevalier, Scott Schopf, Jennifer M.
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This testbed was designed to incorporate many interoperable systems and services and was designed for measurement from the very beginning.more » The end results were key insights into how to use novel, high performance networking technologies and to accumulate measurements that will give insights into the networks of the future.« less This project was designed to use the unique opportunity presented at SC2000 to create a testbed network environment and then use that network to demonstrate and evaluate high performance computational and communication applications. At SC2000, large-scale and complex local and wide area networking connections were demonstrated, including large-scale distributed applications running on different architectures.
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Once a year, the SC conferences present a unique opportunity to create and build one of the most complex and highest performance networks in the world. Our finding that “significance” and “reliability” are dissociated properties of regions of interest identified as significant using standard voxel-wise techniques suggests that including reliability analyses may add useful scientific information in neurobehavioral research.Building and measuring a high performance network architecture Our results support the use of resting fMRI as a means to evaluate neuropsychiatric states and motor disease progression in Parkinson disease, and the clinical and epidemiologic observation that apathy and depression are distinct pathological entities. A number of additional regions are also statistically (but not reliably) correlated with our neuropsychological measures and disease severity. Disease severity was best predicted by ALFF signal in the right putamen. Using this approach, we show that the apathy score in this sample is best predicted by ALFF signal in the left supplementary motor cortex, the right orbitofrontal cortex, and the right middle frontal cortex, whereas depression score is best predicted by ALFF signal in the right subgenual cingulate. For this, we develop and introduce a cross validation approach we term the “Regional Mapping of Reliable Differences” (RMRD) method to evaluate reliability of regions of interest deemed “significant” by standard voxel-wise techniques. We first evaluated if the resting ALFF signal is a reliable measure for our clinical question. We evaluate the relationship between apathy, depression, and motor severity of disease in PD, focusing on the relationship between these factors and the amplitude of the low frequency fluctuation (ALFF) in the resting state.
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Parkinson disease (PD) is a disorder in which apathy and depression co-exist in a single population. No functional imaging study has attempted to separate brain regions altered in apathy from those altered in depression in a clinical population. This clinical overlap leads to problems with classification and diagnosis in clinical populations. Apathy and depression are heterogeneous syndromes with symptoms that overlap clinically.