Analyzing spatio-temporal patterns of genuine cross-correlations

In multivariate time series analysis, the equal-time cross-correlation is a classic and computationally efficient measure for quantifying linear interrelations between data channels. When the cross-correlation coefficient is estimated using a finite amount of data points, its non-random part may be strongly contaminated by a sizable random contribution, such that no reliable conclusion can be drawn about genuine mutual interdependencies. The random correlations are determined by the signals' frequency content and the amount of data points used. Here, we introduce adjusted correlation matrices that can be employed to disentangle random from non-random contributions to each matrix element independently of the signal frequencies. Extending our previous work these matrices allow analyzing spatial patterns of genuine cross-correlation in multivariate data regardless of confounding influences. The performance is illustrated by example of model systems with known interdependence patterns. Finally, we apply the methods to electroencephalographic (EEG) data with epileptic seizure activity.

Parameter-free extraction of the functional network topology from intracranial EEG recordings: a time-resolved study of graph properties in focal onset seizures

Rationale: Focal onset epileptic seizures are due to abnormal interactions between distributed brain areas. By estimating the cross-correlation matrix of multi-site intra-cerebral EEG recordings (iEEG), one can quantify these interactions. To assess the topology of the underlying functional network, the binary connectivity matrix has to be derived from the cross-correlation matrix by use of a threshold. Classically, a unique threshold is used that constrains the topology [1]. Our method aims to set the threshold in a data-driven way by separating genuine from random cross-correlation. We compare our approach to the fixed threshold method and study the dynamics of the functional topology. Methods: We investigate the iEEG of patients suffering from focal onset seizures who underwent evaluation for the possibility of surgery. The equal-time cross-correlation matrices are evaluated using a sliding time window. We then compare 3 approaches assessing the corresponding binary networks. For each time window: * Our parameter-free method derives from the cross-correlation strength matrix (CCS)[2]. It aims at disentangling genuine from random correlations (due to finite length and varying frequency content of the signals). In practice, a threshold is evaluated for each pair of channels independently, in a data-driven way. * The fixed mean degree (FMD) uses a unique threshold on the whole connectivity matrix so as to ensure a user defined mean degree. * The varying mean degree (VMD) uses the mean degree of the CCS network to set a unique threshold for the entire connectivity matrix. * Finally, the connectivity (c), connectedness (given by k, the number of disconnected sub-networks), mean global and local efficiencies (Eg, El, resp.) are computed from FMD, CCS, VMD, and their corresponding random and lattice networks. Results: Compared to FMD and VMD, CCS networks present: *topologies that are different in terms of c, k, Eg and El. *from the pre-ictal to the ictal and then post-ictal period, topological features time courses that are more stable within a period, and more contrasted from one period to the next. For CCS, pre-ictal connectivity is low, increases to a high level during the seizure, then decreases at offset. k shows a ‘‘U-curve’’ underlining the synchronization of all electrodes during the seizure. Eg and El time courses fluctuate between the corresponding random and lattice networks values in a reproducible manner. Conclusions: The definition of a data-driven threshold provides new insights into the topology of the epileptic functional networks.

Postmortem CT versus forensic autopsy: frequent discrepancies of tracheobronchial content findings.

In their daily forensic casework, the authors experienced discrepancies of tracheobronchial content findings between postmortem computed tomography (PMCT) and autopsy to an extent previously unnoticed in the literature. The goal of this study was to evaluate such discrepancies in routine forensic cases. A total of 327 cases that underwent PMCT prior to routine forensic autopsy were retrospectively evaluated for tracheal and bronchial contents according to PMCT and autopsy findings. Hounsfield unit (HU) values of tracheobronchial contents, causes of death, and presence of pulmonary edema were assessed in mismatching and matching cases. Comparing contents in PMCT and autopsy in each of the separately evaluated compartments of the respiratory tract low positive predictive values were assessed (trachea, 38.2 %; main bronchi, 40 %; peripheral bronchi, 69.1 %) indicating high discrepancy rates. The majority of tracheobronchial contents were viscous stomach contents in matching cases and low radiodensity materials (i.e., HU < 30) in mismatching cases. The majority of causes of death were cardiac related in the matching cases and skull/brain trauma in the mismatching cases. In mismatching cases, frequency of pulmonary edema was significantly higher than in matching cases. It can be concluded that discrepancies in tracheobronchial contents observed between PMCT and routine forensic autopsy occur in a considerable number of cases. Discrepancies may be explained by the runoff of contents via nose and mouth during external examination and the flow back of tracheal and main bronchial contents into the lungs caused by upright movement of the respiratory tract at autopsy.