Present techniques for functional MRI rely on detecting changes in hemodynamics that result as a consequence of brain activation. It would be useful if MRI techniques could be developed that enable imaging of a parameter directly related to neuronal activity. Influx of calcium into neurons is necessary for release of neurotransmitters. Divalent manganese ions (Mn") can enter cells through voltage-gated calcium channels and Mn2+ is paramagnetic. Mn2+ accumulation in br.ain due to activation should alter relaxation times offering an approach to sensitize MRI to calcium influx in the brain. To test this idea, T,-weighted MRI was obtained from the rat brain in the presence of a continuous intravenous infusion of 3.6 pnol/min MnCI,. In the anesthetized rat brain, signal enhancement was detected in regions corresponding to ventricles. Activation of the brain with glutamate led to increase in MRI signal intensity in the brain to 238 2 23% of the original. This increase in signal was dependent on the presence of MnCI, and was not due to changes in blood flow. It was necessary to break the blood brain barrier with mannitol to make Mn" accessible to the active sites for efficient detection. Enhancement of MRI signal in the brain was also detected with decreasing anesthesia and with somatosensory stimulation. Due to the slow clearance of Mn2+ from the stimulated region of the brain, MRI enhancement could also be detected after stimulation that occurred on awake, behaving rats outside the magnet. These data indicate that MnCI, shows potential as a MRI contrast agent that is directly sensitive to brain activation.
Abstract-Clustering is an important research topic in wireless networks, because cluster structures can facilitate resource reuse and increase system capacity. In this paper, we present a new clustering algorithm that considers both node position and node mobility in vehicular ad hoc environments. The proposed algorithm intends to create stable clusters by reducing reclustering overhead, prolonging cluster lifetime, and shortening the average distance between cluster heads and their cluster members. Most important, this algorithm supports single and multiple cluster heads. Simulation results show the superiority of our clustering algorithm over the other three well-known algorithms.
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