Comparison of Different Methods for the Evaluation of Treatment Effects from the Sleep EEG of Patients with Major Depression

In healthy subjects, sleep has a typical structure of three to five cyclic transitions between different sleep states. In major depression, this regular pattern is often destroyed but can be reestablished during successful treatment. The differences between healthy and abnormal sleep are generally assessed in a time-consuming process, which consists of determining the nightly variations of the sleep states (the hypnogram) based on visual inspection of the electroencephalogram (EEG), electrooculogram, and electromyogram. In this study, three different methods of sleep EEG analysis (spectrum, outlier, and recurrence analysis) have been examined with regard to their ability to extract information about treatment effects in patients with major depression. Our data suggest that improved sleep patterns during treatment with antidepressant medication can be identified with an appropriate analysis of the EEG. By comparing different methods, we have found that many treatment effects identified by spectrum analysis can be reproduced by the much simpler technique of outlier analysis. Finally, the cyclic structure of sleep and its modification by antidepressant treatment is best illustrated by a non-linear approach, the so-called recurrence method.     

Is Bagging Effective in the Classification of Small-Sample Genomic and Proteomic Data?

There has been considerable interest recently in the application of bagging in the classification of both gene-expression data and protein-abundance mass spectrometry data. The approach is often justified by the improvement it produces on the performance of unstable, overfitting classification rules under small-sample situations. However, the question of real practical interest is whether the ensemble scheme will improve performance of those classifiers sufficiently to beat the performance of single stable, nonoverfitting classifiers, in the case of small-sample genomic and proteomic data sets. To investigate that question, we conducted a detailed empirical study, using publicly-available data sets from published genomic and proteomic studies. We observed that, under t-test and RELIEF filter-based feature selection, bagging generally does a good job of improving the performance of unstable, overfitting classifiers, such as CART decision trees and neural networks, but that improvement was not sufficient to beat the performance of single stable, nonoverfitting classifiers, such as diagonal and plain linear discriminant analysis, or 3-nearest neighbors. Furthermore, as expected, the ensemble method did not improve the performance of these classifiers significantly. Representative experimental results are presented and discussed in this work.     

Effects of carbon dioxide exposure on early brain development in rats

The developing brain is vulnerable to environmental factors. We investigated the effects of air that contained 0.05, 0.1 and 0.3% CO₂ on the hippocampus, prefrontal cortex (PFC) and amygdala. We focused on the circuitry involved in the neurobiology of anxiety, spatial learning, memory, and on insulin-like growth factor-1 (IGF-1), which is known to play a role in early brain development in rats. Spatial learning and memory were impaired by exposure to 0.3% CO₂ air, while exposure to 0.1 and 0.3% CO₂ air elevated blood corticosterone levels, intensified anxiety behavior, increased superoxide dismutase (SOD) enzyme activity and MDA levels in hippocampus and PFC; glutathione peroxidase (GPx) enzyme activity decreased in the PFC with no associated change in the hippocampus. IGF-1 levels were decreased in the blood, PFC and hippocampus by exposure to both 0.1 and 0.3% CO₂. In addition, apoptosis was increased, while cell numbers were decreased in the CA1 regions of hippocampus and PFC after 0.3% CO₂ air exposure in adolescent rats. A positive correlation was found between the blood IGF-1 level and apoptosis in the PFC. We found that chronic exposure to 0.3% CO₂ air decreased IGF-1 levels in the serum, hippocampus and PFC, and increased oxidative stress. These findings were associated with increased anxiety behavior, and impaired memory and learning.     

Reliability of the calculated maximal lactate steady state in amateur cyclists

Complex performance diagnostics in sports medicine should contain maximal aerobic and maximal anaerobic performance. The requirements on appropriate stress protocols are high. To validate a test protocol quality criteria like objectivity and reliability are necessary. Therefore, the present study was performed in intention to analyze the reliability of maximal lactate production rate (V.L_amax) by using a sprint test, maximum oxygen consumption (V.O_2max) by using a ramp test and, based on these data, resulting power in calculated maximum lactate-steady-state (PMLSS) especially for amateur cyclists. All subjects (n = 23, age 26 ± 4 years) were leisure cyclists. At three different days they completed first a sprint test to approximate V.La_max. After 60 min of recreation time a ramp test to assess V.O_2max was performed. The results of V.La_max-test and V.O_2max-test and the body weight were used to calculate PMLSS for all subjects. The intra class correlation (ICC) for V.La_max and V.O_2max was 0.904 and 0.987, respectively, coefficient of variation (CV) was 6.3% and 2.1%, respectively. Between the measurements the reliable change index of 0.11 mmol·l ^-1s ^-1 for V.La_max and 3.3 mlkg ^-1min ^-1 for V.O_2max achieved significance. The mean of the calculated PMLSS was 237 ± 72 W with an RCI of 9 W and reached with ICC = 0.985 a very high reliability. Both metabolic performance tests and the calculated PMLSS are reliable for leisure cyclists.     

Stimulation of Cortisol During Mental Task Performance in a Provocative Virtual Environment

Fully immersive and stereoscopic Virtual Environments (VE) represent a powerful multimedia tool for laboratory-based simulations of distinct scenarios including scenarios for evaluating stressful situations resembling reality. Thus far, cortisol secretion as a neuroendocrine parameter of stress has not been evaluated within a Virtual Reality (VR)-based paradigm. In this study 94 healthy volunteers were subjected to a provocative VR-paradigm and a cognitive stress task. Provocative in this context means the VE was designed to provoke physiological reactions (cortisol secretion) within the respective users by purpose. It was tested (a) if a fully dynamic VE as opposed to a static VE can be regarded as a stressor and (b) if such a fully dynamic VE can modify an additional response to a cognitive stressor presented within the VE additionally. Furthermore, possible gender-related impacts on cortisol responses were assessed. A significant cortisol increase was observed only after the combined application of the fully dynamic VE and the cognitive stressor, but not after application of the dynamic VE or the cognitive stressor alone. Cortisol reactivity was greater for men than for women. We conclude that a fully dynamic VE does not affect cortisol secretion per se, but increases cortisol responses to a dual task paradigm that includes performance of a stressful mental task. This provides the basis for the application of VR-based technologies in neuroscientific research, including the assessment of the human Hypothalamus-Pituitary-Adrenal (HPA) axis regulation.     

Human plasma DSIP decreases at the initiation of sleep at different circadian times

Nocturnal plasma delta sleep-inducing peptide-like immunoreactivity (DSIP-LI) was determined serially in seven healthy male subjects. Time courses during nocturnal sleep (2300-0800 h), nocturnal sleep deprivation (2300-0500 h), and morning recovery sleep (0500–0800 h) after sleep deprivation were compared. A significant decrease in plasma DSIP-LI was found at the transition from wakefulness to sleep in both evening sleep (2300 h) and morning recovery sleep (0500 h). Time courses were accompanied by physiological changes in sleep electroencephalographic slow-wave activity, and in plasma concentrations of cortisol and human growth hormone. No sleep stage specificity was found. It is concluded that DSIP is influenced by the initiation of sleep.