In functional magnetic resonance imaging (fMRI) coherent oscillations of the blood oxygen level-dependent (BOLD) signal can be detected. These arise when brain regions respond to external stimuli or are activated by tasks. The same networks have been characterized during wakeful rest when functional connectivity of the human brain is organized in generic resting-state networks (RSN). Alterations of RSN emerge as neurobiological markers of pathological conditions such as altered mental state. In single-subject fMRI data the coherent components can be identified by blind source separation of the pre-processed BOLD data using spatial independent component analysis (ICA) and related approaches. The resulting maps may represent physiological RSNs or may be due to various artifacts. In this methodological study, we propose a conceptually simple and fully automatic time course based filtering procedure to detect obvious artifacts in the ICA output for resting-state fMRI. The filter is trained on six and tested on 29 healthy subjects, yielding mean filter accuracy, sensitivity and specificity of 0.80, 0.82, and 0.75 in out-of-sample tests. To estimate the impact of clearly artifactual single-subject components on group resting-state studies we analyze unfiltered and filtered output with a second level ICA procedure. Although the automated filter does not reach performance values of visual analysis by human raters, we propose that resting-state compatible analysis of ICA time courses could be very useful to complement the existing map or task/event oriented artifact classification algorithms.
The Status Epilepticus Severity Score (STESS) is one of the most well-known clinical scoring systems to predict mortality in status epilepticus (SE). The objective of this study was to validate STESS in a Colombian population. Method: We evaluated historical data of adult patients (age ≥16 years) with a clinical or electroencephalographic diagnosis of SE admitted between 2014 and 2017. Prospectively, we included patients admitted from January to June of 2018. The primary outcome was in-hospital mortality. Receiver operating characteristic (ROC)-analysis, determination of best cutoff values, sensitivity, specificity, and positive and negative likelihood ratios were performed. Results: The sample was 395 patients, with in-hospital mortality of 16.8 %. The area under the ROC curve for STESS was 0.84. A cutoff point of ≥3 produced the highest sensitivity of 84.9 % (95 % CI 73.9 %-92.5 %) and a specificity of 65.7 % (95 % CI 60.2 %-70.8 %), with a positive likelihood ratio of 2.5 and a negative likelihood ratio of 0.2. Conclusions: STESS is a useful tool to predict mortality in patients with SE. In Medellin, Colombia, a STESS < 3 allows the identification of the patients who survive reliably. Those patients with a score <3 may have a better prognosis, and treatment with fewer side effects than anaesthetics could be suggested, always remembering the importance of the treating physician's clinical judgement.
The characterization of the functional network of the brain dynamics has become a prominent tool to illuminate novel aspects of brain functioning. Due to its excellent time resolution, such research is oftentimes based on electroencephalographic recordings (EEG). However, a particular EEG-reference might cause crucial distortions of the spatiotemporal interrelation pattern and may induce spurious correlations as well as diminish genuine interrelations originally present in the dataset. Here we investigate in which manner correlation patterns are affected by a chosen EEG reference. To this end we evaluate the influence of 7 popular reference schemes on artificial recordings derived from well controlled numerical test frameworks. In this respect we are not only interested in the deformation of spatial interrelations, but we test additionally in which way the time evolution of the functional network, estimated via some bi-variate interrelation measures, gets distorted. It turns out that the median reference as well as the global average show the best performance in most situations considered in the present study. However, if a collective brain dynamics is present, where most of the signals get correlated, these schemes may also cause crucial deformations of the functional network, such that the parallel use of different reference schemes seems advisable.
The cytoskeleton is the main intracellular structure that determines the morphology of neurons and maintains their integrity. Therefore, disruption of its structure and function may underlie several neurodegenerative diseases. This review summarizes the current literature on the tau protein, microtubule-associated protein 2 (MAP2) and neurofilaments as common denominators in pathological conditions such as Alzheimer's disease (AD), cerebral ischemia, and multiple sclerosis (MS). Insights obtained from experimental models using biochemical and immunocytochemical techniques highlight that changes in these proteins may be potentially used as protein targets in clinical settings, which provides novel opportunities for the detection, monitoring and treatment of patients with these neurodegenerative diseases.
Contents1. Introduction 2. Cytoskeletal damage and neurodegeneration 3. Alzheimer's disease 4. Cerebral ischemia 5. Multiple sclerosis 6. Conclusions
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