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In aged humans, stroke is a major cause of disability for which no neuroprotective measures are available. In animal studies of focal ischemia, short-term hypothermia often reduces infarct size. Nevertheless, efficient neuroprotection requires long-term, regulated lowering of whole-body temperature. Previously, it is reported that post-stroke exposure to hydrogen sulfide (H2S) effectively lowers whole-body temperature and confers neuroprotection in aged animals. Here we report for the first time that the animals exposed to H2S the normal sleep–wake oscillations are replaced by a low-amplitude EEG dominated by a 4-Hz rhythmicactivity, reminiscent of EEG recordings in hibernating animals. In the present study using magnetic resonance imaging, reverse transcriptase polymerase chain reaction, western blotting and immunofluorescence, we characterized the central nervous system response to H2S -induced hypothermia and report, that annexin A1, a major constituent of peripheral leukocytes that is upregulated after stroke, was consistently downregulated in polymorphonuclear cells in the peri-lesional cortex of post-ischemic, aged rat brain after 48 hours of hypothermia induced by exposure to H2S. This might be due to the reduced kinetics of recruitment, adherence and infiltration of PMN cells by H2S -induced hypothermia. Our findings further suggest that, in contrast to monotherapies that have thus far uniformly failed in clinical practice, prolonged hypothermia has pleiotropic effects on brain physiology that may be necessary for effective protection of the brain after stroke.
Manual sleep scoring for research purposes and for the diagnosis of sleep disorders is labor-intensive and often varies significantly between scorers, which has motivated many attempts to design automatic sleep stage classifiers. With the recent introduction of large, publicly available hand-scored polysomnographic data, and concomitant advances in machine learning methods to solve complex classification problems with supervised learning, the problem has received new attention, and a number of new classifiers that provide excellent accuracy. Most of these however have non-trivial barriers to use. We introduce the Greifswald Sleep Stage Classifier (GSSC), which is free, open source, and can be relatively easily installed and used on any moderately powered computer. In addition, the GSSC has been trained to perform well on a large variety of electrode set-ups, allowing high performance sleep staging with portable systems. The GSSC can also be readily integrated into brain-computer interfaces for real-time inference. These innovations were achieved while simultaneously reaching a level of accuracy equal to, or exceeding, recent state of the art classifiers and human experts, making the GSSC an excellent choice for researchers in need of reliable, automatic sleep staging.
Separating EEG correlates of stress: Cognitive effort, time pressure, and social‐evaluative threat
(2022)
Abstract
The prefrontal cortex is a key player in stress response regulation. Electroencephalographic (EEG) responses, such as a decrease in frontal alpha and an increase in frontal beta power, have been proposed to reflect stress‐related brain activity. However, the stress response is likely composed of different parts such as cognitive effort, time pressure, and social‐evaluative threat, which have not been distinguished in previous studies. This distinction, however, is crucial if we aim to establish reliable tools for early detection of stress‐related conditions and monitoring of stress responses throughout treatment. This randomized cross‐over study (N = 38) aimed to disentangle EEG correlates of stress. With linear mixed models accounting for missing values in some conditions, we found a decrease in frontal alpha and increase in beta power when performing the Paced Auditory Serial Addition Test (PASAT; cognitive effort; n = 32) compared to resting state (n = 33). No change in EEG power was found when the PASAT was performed under time pressure (n = 29) or when adding social‐evaluative threat (video camera; n = 29). These findings suggest that frontal EEG power can discriminate stress from resting state but not more fine‐grained differences of the stress response.