This article deals with germanisms in Czech. Frequencies of 26 different new High German loanwords were analyzed in the Czech National Corpus. These borrowed words were standing in competition with their Czech synonyms. This comparison is used to study the question of whether germanisms or their equivalents in Czech are more used by native speakers. For this analysis new High German loanwords were deliberately selected in order to verify the actuality of the topic. But the major part of the study was examined in a diachronic period. This shows not only the current situation but in most cases the frequency of the selected loanwords throughout their existence. The calculations of the average frequency are made for each century (since 1650), and also in the recent modern period (from 1947 to 2008). and Článek se zabývá germanizmy v češtině. Prostřednictvím Českého národního korpusu byly zjišťovány různé frekvence 26 novohornoněmeckých výpůjček a jim konkurujících českých synonym. Článek se na základě frekvenčních srovnání snaží odpovědět na otázku, zda čeští rodilí mluvčí preferují germanizmy či dávají přednost jejich českým ekvivalentům. Článek analyzuje nejen aktuální situaci, ale ve většině případů ukazuje frekvenci vybraných germanizmů z diachronního hlediska, po celou dobu jejich existence. Byla vypočtena průměrná frekvence za každé století (od roku 1650), včetně posledního moderního období (od roku 1947 do roku 2008).
Diagnostic support for psychiatric disorders is a very interesting goal because of the lack of biological markers with sufficient sensitivity and specificity in psychiatry. The approach consists of a feature extraction process based on the results of Pearson correlation of known measures of white matter integrity obtained from diffusion weighted images: fractional anisotropy (FA) and mean diffusivity (MD), followed by a classification step performed by statistical support vector machines (SVM), different implementations of artificial neural networks (ANN) and learn vector quantization (LVQ) classifiers. The most discriminant voxels were found in frontal and temporal white matter. A total of 100% classification accuracy was achieved in almost every case, although the features extracted from the FA data yielded the best results. The study has been performed on publicly available diffusion weighted images of 20 male subjects.
In paper connections among dissociation, neural and EEG complexity are presented. They implicate the EEG correlate for dissociated rnental representations of neural assemblies which actually act in the brain-mind system. As a consequence of dissociation among these rnental representations biirst EEG activity is present. Burst activity is explained as a consequence of deterrninistic chaos, which leads to emerging of the underlying order of attractors in brain physiology. This chaos is comparable to the world of possibilities and their collapse in quanturn theory. The chaos may thus serve to link quanturn events to globál brain dyriamics and rriay be connected to the quanturn superposition of brain States and the collapse.
Several diffusion tensor imaging (DTI) studies have reported on the anatomical neural tracts between the amygdala and specific brain regions. However, no study on the neural connectivity of the amygdala has been reported. In the current study, using probabilistic DTI tractography, we attempted to investigate the neural connectivity of the amygdala in normal subjects. Forty eight healthy subjects were recruited for this study. A seed region of interest was drawn at the amygdala using the FMRIB Software Library based on probabilistic DTI tractography. Connectivity was defined as the incidence of connection between the amygdala and each brain region at the threshold of 1 and 5 streamlines. The amygdala showed 100% connectivity to the hippocampus, thalamus, hypothalamus, and medial temporal cortex regardless of the thresholds. In contrast, regarding the thresholds of 1 and 5 streamlines, the amygdala showed high conncetivity (over 60%) to the globus pallidus (100% and 92.7%), brainstem (83.3% and 78.1%), putamen (72.9% and 63.5%), occipito-temporal cortex (72.9% and 67.7%), orbitofrontal cortex (70.8 and 43.8%), caudate nucleus (63.5% and 45.8%), and ventromedial prefrontal cortex (63.5% and 31.3%), respectively. The amygdala showed high connectivity to the hippocampus, thalamus, hypothalamus, medial temporal cortex, basal ganglia, brainstem, occipito-temporal cortex, orbitofrontal cortex, and ventromedial prefrontal cortex. We believe that the methods and results of this study provide useful information to clinicians and researchers studying the amygdala.