Blood flow to the brain responds to changes in neuronal activity and, thus, metabolic demand. In earlier work, we observed correlation between cerebral blood flow and spontaneous electroencephalogram (EEG) activity in neonates. Using coherence, we now found that during Trace/spl acute/ Alternant EEG activity in quiet sleep of normal term neonates, this correlation is strongest at frequencies around 0.1 Hz, reaching statistical significance (p<0.05) in six of the nine subjects studied (p<0.07 in eight subjects). Due to noise, artifact, and spontaneous changes in the subjects' EEG patterns, the signals investigated included epochs of missing samples. We, therefore, developed a novel algorithm for the estimation of coherence in such data and applied a Monte Carlo (surrogate data) method for its statistical analysis. This process provides a test for the statistical significance of the maximum coherence within a selected frequency band. In addition to permitting further insight into the mechanisms of cerebral blood flow control, these algorithms are potentially of great benefit in a wide range of biomedical applications, where interrupted (gapped) recordings are often a problem
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