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Uncovering interactions in the frequency domain
Oscillatory activity plays a critical role in regulating biological processes at levels ranging from subcellular, cellular, and
network to the whole organism, and often involves a large number of interacting elements. We shed light on this issue by
introducing a novel approach called partial Granger causality to reliably reveal interaction patterns in multivariate data with
exogenous inputs and latent variables in the frequency domain. The method is extensively tested with toy models, and
successfully applied to experimental datasets, including (1) gene microarray data of HeLa cell cycle; (2) in vivo multielectrode
array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of a sheep; and (3) in vivo LFPs
recorded from distributed sites in the right hemisphere of a macaque monkey
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