Uniform approach to linear and nonlinear interrelation patterns in multivariate time series

Currently, a variety of linear and nonlinear measures is in use to investigate spatiotemporal interrelation patterns of multivariate time series. Whereas the former are by definition insensitive to nonlinear effects, the latter detect both nonlinear and linear interrelation. In the present contribution we employ a uniform surrogate-based approach, which is capable of disentangling interrelations that significantly exceed random effects and interrelations that significantly exceed linear correlation. The bivariate version of the proposed framework is explored using a simple model allowing for separate tuning of coupling and nonlinearity of interrelation. To demonstrate applicability of the approach to multivariate real-world time series we investigate resting state functional magnetic resonance imaging (rsfMRI) data of two healthy subjects as well as intracranial electroencephalograms (iEEG) of two epilepsy patients with focal onset seizures. The main findings are that for our rsfMRI data interrelations can be described by linear cross-correlation. Rejection of the null hypothesis of linear iEEG interrelation occurs predominantly for epileptogenic tissue as well as during epileptic seizures.

Evolution of genuine cross-correlation strength of focal onset seizures

To quantify the evolution of genuine zero-lag cross-correlations of focal onset seizures, we apply a recently introduced multivariate measure to broad band and to narrow-band EEG data. For frequency components below 12.5 Hz, the strength of genuine cross-correlations decreases significantly during the seizure and the immediate postseizure period, while higher frequency bands show a tendency of elevated cross-correlations during the same period. We conclude that in terms of genuine zero-lag cross-correlations, the electrical brain activity as assessed by scalp electrodes shows a significant spatial fragmentation, which might promote seizure offset.

Genuine cross-correlations: Which surrogate based measure reproduces analytical results best?

The analysis of short segments of noise-contaminated, multivariate real world data constitutes a challenge. In this paper we compare several techniques of analysis, which are supposed to correctly extract the amount of genuine cross-correlations from a multivariate data set. In order to test for the quality of their performance we derive time series from a linear test model, which allows the analytical derivation of genuine correlations. We compare the numerical estimates of the four measures with the analytical results for different correlation pattern. In the bivariate case all but one measure performs similarly well. However, in the multivariate case measures based on the eigenvalues of the equal-time cross-correlation matrix do not extract exclusively information about the amount of genuine correlations, but they rather reflect the spatial organization of the correlation pattern. This may lead to failures when interpreting the numerical results as illustrated by an application to three electroencephalographic recordings of three patients suffering from pharmacoresistent epilepsy.

Comparison of different radiography systems in an experimental study for detection of forearm fractures and evaluation of the Müller-AO and Frykman classification for distal radius fractures

OBJECTIVES: We sought to compare the diagnostic performance of screen-film radiography, storage-phosphor radiography, and a flat-panel detector system in detecting forearm fractures and to classify distal radius fractures according to the Müller-AO and Frykman classifications compared with the true extent, depicted by anatomic preparation. MATERIALS AND METHODS: A total of 71 cadaver arms were fractured in a material testing machine creating different fractures of the radius and ulna as well as of the carpal bones. Radiographs of the complete forearm were evaluated by 3 radiologists, and anatomic preparation was used as standard of reference in a receiver operating curve analysis. RESULTS: The highest diagnostic performance was obtained for the detection of distal radius fractures with area under the receiver operating curve (AUC) values of 0.959 for screen-film radiography, 0.966 for storage-phosphor radiography, and 0.971 for the flat-panel detector system (P > 0.05). Exact classification was slightly better for the Frykman (kappa values of 0.457-0.478) compared with the Müller-AO classification (kappa values of 0.404-0.447), but agreement can be considered as moderate for both classifications. CONCLUSIONS: The 3 imaging systems showed a comparable diagnostic performance in detecting forearm fractures. A high diagnostic performance was demonstrated for distal radius fractures and conventional radiography can be routinely performed for fracture detection. However, compared with anatomic preparation, depiction of the true extent of distal radius fractures was limited and the severity of distal radius fractures tends to be underestimated.