Global, regional, and local measures of complexity of multichannel electroencephalography in acute, neuroleptic-naive, first-break schizophrenics

Background: Schizophrenic symptoms commonly are felt to indicate a loosened coordination, i.e. a decreased connectivity of brain processes. Methods: To address this hypothesis directly, global and regional multichannel electroencephalographic (EEG) complexities (omega complexity and dimensional complexity) and single channel EEG dimensional complexities were calculated from 19-channel EEG data from 9 neuroleptic-naive, first-break, acute schizophrenics and 9 age- and sex-matched controls. Twenty artifact-free 2 second EEG epochs during resting with closed eyes were analyzed (2–30 Hz bandpass, average reference for global and regional complexities, local EEG gradient time series for single channels). Results: Anterior regional Omega-Complexity was significantly increased in schizophrenics compared with controls (p < 0.001) and anterior regional Dimensional Complexity showed a trend for increase. Single channel Dimensional Complexity of local gradient waveshapes was prominently increased in the schizophrenics at the right precentral location (p = 0.003). Conclusions: The results indicate a loosened cooperativity or coordination (vice versa: an increased independence) of the active brain processes in the anterior brain regions of the schizophrenics.

Building a soil spectral library for Tajikistan comparing local and global modeling approaches

Over the last ~20 years, soil spectral libraries storing near-infrared reflectance (NIR) spectra from diverse soil samples have been built for many places, since almost 10 years also for Tajikistan. Many calibration approaches have been reported and used for prediction from large and heterogeneous libraries, but most are hampered by the high diversity of the soils, where the mineral background is heavily influencing spectral features. In such cases, local learning strategies have the advantage of building locally adapted calibrations, which can deal better with nonlinearities. Therefore, it was our major aim to identify the most efficient approach to develop an accurate and stable locally weigthed calibration model using a spectral library compiled over the past years. Keywords: Tajikistan, Near-Infrared spectroscopy (NIRS), soil organic carbon, locally weighted regression, regional and local spectral library.

Repeatability analysis of global and local metrics of brain structural networks

Computational network analysis provides new methods to analyze the human connectome. Brain structural networks can be characterized by global and local metrics that recently gave promising insights for diagnosis and further understanding of neurological, psychiatric and neurodegenerative disorders. In order to ensure the validity of results in clinical settings the precision and repeatability of the networks and the associated metrics must be evaluated. In the present study, nineteen healthy subjects underwent two consecutive measurements enabling us to test reproducibility of the brain network and its global and local metrics. As it is known that the network topology depends on the network density, the effects of setting a common density threshold for all networks were also assessed. Results showed good to excellent repeatability for global metrics, while for local metrics it was more variable and some metrics were found to have locally poor repeatability. Moreover, between subjects differences were slightly inflated when the density was not fixed. At the global level, these findings confirm previous results on the validity of global network metrics as clinical biomarkers. However, the new results in our work indicate that the remaining variability at the local level as well as the effect of methodological characteristics on the network topology should be considered in the analysis of brain structural networks and especially in networks comparisons.

From local perception to global perspective

Recent sociological studies show that over short time periods the large day-to-day, month-to-month or year-to-year variations in weather at a specific location can influence and potentially bias our perception of climate change, a more long-term and global phenomenon. By weighting local temperature anomalies with the number of people that experience them and considering longer time periods, we illustrate that the share of the world population exposed to warmer-than-normal temperatures has steadily increased during the past few decades. Therefore, warming is experienced by an increasing number of individuals, counter to what might be simply inferred from global mean temperature anomalies. This behaviour is well-captured by current climate models, offering an opportunity to increase confidence in future projections of climate change irrespective of the personal local perception of weather.