Chow–Liu trees are sufficient predictive models for reproducing key features of functional networks of periictal EEG time-series

Seizure freedom in patients suffering from pharmacoresistant epilepsies is still not achieved in 20–30% of all cases. Hence, current therapies need to be improved, based on a more complete understanding of ictogenesis. In this respect, the analysis of functional networks derived from intracranial electroencephalographic (iEEG) data has recently become a standard tool. Functional networks however are purely descriptive models and thus are conceptually unable to predict fundamental features of iEEG time-series, e.g., in the context of therapeutical brain stimulation. In this paper we present some first steps towards overcoming the limitations of functional network analysis, by showing that its results are implied by a simple predictive model of time-sliced iEEG time-series. More specifically, we learn distinct graphical models (so called Chow–Liu (CL) trees) as models for the spatial dependencies between iEEG signals. Bayesian inference is then applied to the CL trees, allowing for an analytic derivation/prediction of functional networks, based on thresholding of the absolute value Pearson correlation coefficient (CC) matrix. Using various measures, the thus obtained networks are then compared to those which were derived in the classical way from the empirical CC-matrix. In the high threshold limit we find (a) an excellent agreement between the two networks and (b) key features of periictal networks as they have previously been reported in the literature. Apart from functional networks, both matrices are also compared element-wise, showing that the CL approach leads to a sparse representation, by setting small correlations to values close to zero while preserving the larger ones. Overall, this paper shows the validity of CL-trees as simple, spatially predictive models for periictal iEEG data. Moreover, we suggest straightforward generalizations of the CL-approach for modeling also the temporal features of iEEG signals.

Virtopsy: postmortem minimally invasive angiography using cross section techniques--implementation and preliminary results.

Postmortem investigation is increasingly supported by Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). This led to the idea to implement a noninvasive or minimally invasive autopsy technique. Therefore, a minimally invasive angiography technique becomes necessary, in order to support the vascular cross section diagnostic. Preliminary experiments investigating different contrast agents for CT and MRI and their postmortem applicability have been performed using an ex-vivo porcine coronary model. MSCT and MRI angiography was performed in the porcine model. Three human corpses were investigated using minimally invasive MSCT angiography. Via the right femoral artery a plastic tube was advanced into the aortic arch. Using a flow adjustable pump the radiopaque contrast agent meglumine-ioxithalamate was injected. Subsequent MSCT scanning provided an excellent anatomic visualization of the human arterial system including intracranial and coronary arteries. Vascular pathologies such as calcification, stenosis and injury were detected. Limitations of the introduced approach are cases of major vessel injury and cases that show an advanced stage of decay.

Measurement of the νμ charged current quasielastic cross section on carbon with the T2K on-axis neutrino beam

We report a measurement of the νµ charged current quasi-elastic cross-sections on carbon in the T2K on-axis neutrino beam. The measured charged current quasi-elastic cross-sections on carbon at mean neutrino energies of 1.94 GeV and 0.93 GeV are (11.95 ± 0.19(stat.) +1.82−1.47(syst.)) ×10^−39 cm^2/neutron, and (10.64 ± 0.37(stat.)+2.03−1.65(syst.)) × 10^−39 cm^2/neutron, respectively. These results agree well with the predictions of neutrino interaction models. In addition, we investigated the effects of the nuclear model and the multi-nucleon interaction.