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At (ultra)high magnetic fields the artifact sensitivity of ECG recordings increases. This bears the risk of R-wave mis-registration which has been consistently reported for ECG triggered CMR at 7.0T. Realizing the constraints of conventional ECG, acoustic cardiac triggering (ACT) has been proposed. The clinical ACT has not been carefully examined yet. For this reason, this work scrutinizes the suitability, accuracy and reproducibility of ACT for CMR at 7.0T. For this purpose, the trigger reliability and trigger detection variance are examined together with an qualitative and quantitative assessment of image quality of the heart at 7.0T.
We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.