Data from magnetoencephalography (MEG) and electroencephalography (EEG) suffer from a rather limited signal-to-noise-ratio (SNR) due to cortical background activities and other artifacts. In order to study the effect of the SNR on the size and distribution of dipole clusters reconstructed from interictal epileptic spikes, we performed simulations using realistically shaped volume conductor models and extended cortical sources with different sensor configurations. Head models and cortical surfaces were derived from an averaged magnetic resonance image dataset (Montreal Neurological Institute). Extended sources were simulated by spherical patches with Gaussian current distributions on the folded cortical surface. Different patch sizes were used to investigate cancellation effects from opposing walls of sulcal foldings and to estimate corresponding changes in MEG and EEG sensitivity distributions. Finally, white noise was added to the simulated fields and equivalent current dipole reconstructions were performed to determine size and shape of the resulting dipole clusters. Neuronal currents are oriented perpendicular to the local cortical surface and show cancellation effects of source components on opposing sulcal walls. Since these mostly tangential aspects from large cortical patches cancel out, large extended sources exhibit more radial components in the head geometry. This effect has a larger impact on MEG data as compared to EEG, because in a spherical head model radial currents do not yield any magnetic field. Confidence volumes of single reconstructed dipoles from simulated data at different SNRs show a good correlation with the extension of clusters from repeated dipole reconstructions. Size and shape of dipole clusters reconstructed from extended cortical sources do not only depend on spike and timepoint selection, but also strongly on the SNR of the measured interictal MEG or EEG data. In a linear approximation the size of the clusters is proportional to the inverse SNR.
Artifacts ; Dataset ; Electroencephalography* ; Head ; Magnetic Fields ; Magnetoencephalography ; Neurons ; Noise

Establishing the significance of observed effects is a preliminary requirement for any meaningful interpretation of clinical and experimental Electroencephalography or Magnetoencephalography (MEG) data. We propose a method to evaluate significance on the level of sensors whilst retaining full temporal or spectral resolution. Input data are multiple realizations of sensor data. In this context, multiple realizations may be the individual epochs obtained in an evoked-response experiment, or group study data, possibly averaged within subject and event type, or spontaneous events such as spikes of different types. In this contribution, we apply Statistical non-Parametric Mapping (SnPM) to MEG sensor data. SnPM is a non-parametric permutation or randomization test that is assumption-free regarding distributional properties of the underlying data. The method, referred to as Maps SnPM, is demonstrated using MEG data from an auditory mismatch negativity paradigm with one frequent and two rare stimuli and validated by comparison with Topographic Analysis of Variance (TANOVA). The result is a time- or frequency-resolved breakdown of sensors that show consistent activity within and/or differ significantly between event or spike types. TANOVA and Maps SnPM were applied to the individual epochs obtained in an evoked-response experiment. The TANOVA analysis established data plausibility and identified latencies-of-interest for further analysis. Maps SnPM, in addition to the above, identified sensors of significantly different activity between stimulus types.
Electroencephalography ; Magnetoencephalography ; Methods ; Random Allocation

OBJECTIVETo investigate differences between hepatic and biliary lipid metabolism and secretion of genetically gallstone-susceptible (C57L) and resistant (AKR) mice and the mechanism of cholesterol gallstone formation.

METHODSThe inbred C57L and AKR mice were fed a lithogenic diet containing 15% fat, 1.25% cholesterol and 0.5% cholic acid for four weeks. Hepatic cholesterol content and secretion rates of biliary lipids, as well as phenotypes of the liver and gallbladder were determined and examined before and after the feeding of the lithogenic diet.

RESULTSBoth before and after ingestion of the lithogenic diet, hepatic secretion rates of all biliary lipids in C57L mice were markedly higher than that of AKR mice (P < 0.05, P < 0.01, respectively), whereas hepatic cholesterol contents of C57L mice were significantly lower than that of AKR mice (P < 0.05). Furthermore, after consumption of the lithogenic diet, the increase in hepatic secretion rate of biliary cholesterol in C57L mice was significantly higher than that in AKR mice (P < 0.01). Cholesterol gallstones formed in C57L mice and fatty livers developed in AKR mice.

CONCLUSIONSBiliary cholesterol hypersecretion is the key pathophysiological defect of gallstone formation, lith genes have effects on biliary cholesterol hypersecretion and susceptibility to cholesterol gallstone formation in C57L mice. Lithogenic bile is formed at the canalicular membrane and precedes the development of cholesterol gallstones. It is most likely that cholesterol and bile acid hyposecretion make the AKR strain susceptible to the development of fatty livers and resistant to gallstone formation.

Animals ; Bile ; metabolism ; Cholelithiasis ; genetics ; metabolism ; Cholesterol ; analysis ; metabolism ; Fatty Liver ; etiology ; Genetic Predisposition to Disease ; Lipid Metabolism ; Liver ; metabolism ; Male ; Mice ; Mice, Inbred AKR ; Mice, Inbred C57BL

PURPOSE: To identify baseline prognostic factors for survival in patients with disease progression, during or after chemotherapy for the treatment of advanced gastric or gastroesophageal junction (GEJ) cancer. MATERIALS AND METHODS: We pooled data from patients randomized between 2009 and 2012 in 2 phase III, global double-blind studies of ramucirumab for the treatment of advanced gastric or GEJ adenocarcinoma following disease progression on first-line platinum- and/or fluoropyrimidine-containing therapy (REGARD and RAINBOW). Forty-one key baseline clinical and laboratory factors common in both studies were examined. Model building started with covariate screening using univariate Cox models (significance level=0.05). A stepwise multivariable Cox model identified the final prognostic factors (entry+exit significance level=0.01). Cox models were stratified by treatment and geographic region. The process was repeated to identify baseline prognostic quality of life (QoL) parameters. RESULTS: Of 1,020 randomized patients, 953 (93%) patients without any missing covariates were included in the analysis. We identified 12 independent prognostic factors of poor survival: 1) peritoneal metastases; 2) Eastern Cooperative Oncology Group (ECOG) performance score 1; 3) the presence of a primary tumor; 4) time to progression since prior therapy <6 months; 5) poor/unknown tumor differentiation; abnormally low blood levels of 6) albumin, 7) sodium, and/or 8) lymphocytes; and abnormally high blood levels of 9) neutrophils, 10) aspartate aminotransferase (AST), 11) alkaline phosphatase (ALP), and/or 12) lactate dehydrogenase (LDH). Factors were used to devise a 4-tier prognostic index (median overall survival [OS] by risk [months]: high=3.4, moderate=6.4, medium=9.9, and low=14.5; Harrell's C-index=0.66; 95% confidence interval [CI], 0.64–0.68). Addition of QoL to the model identified patient-reported appetite loss as an independent prognostic factor. CONCLUSIONS: The identified prognostic factors and the reported prognostic index may help clinical decision-making, patient stratification, and planning of future clinical studies.
Adenocarcinoma ; Alkaline Phosphatase ; Appetite ; Aspartate Aminotransferases ; Clinical Decision-Making ; Disease Progression ; Double-Blind Method ; Drug Therapy ; Esophagogastric Junction ; Factor Analysis, Statistical* ; Humans ; L-Lactate Dehydrogenase ; Lymphocytes ; Mass Screening ; Neoplasm Metastasis ; Neutrophils ; Prognosis ; Proportional Hazards Models ; Quality of Life ; Sodium ; Stomach Neoplasms*