286 research outputs found
How important is the seizure onset zone for seizure dynamics?
Purpose: Research into epileptic networks has recently allowed deeper
insights into the epileptic process. Here we investigated the importance of
individual network nodes for seizure dynamics.
Methods: We analysed intracranial electroencephalographic recordings of 86
focal seizures with different anatomical onset locations. With time-resolved
correlation analyses, we derived a sequence of weighted epileptic networks
spanning the pre-ictal, ictal, and post-ictal period, and each recording site
represents a network node. We assessed node importance with commonly used
centrality indices that take into account different network properties.
Results: A high variability of temporal evolution of node importance was
observed, both intra- and interindividually. Nevertheless, nodes near and far
off the seizure onset zone (SOZ) were rated as most important for seizure
dynamics more often (65% of cases) than nodes from within the SOZ (35% of
cases).
Conclusion: Our findings underline the high relevance of brain outside of the
SOZ but within the large-scale epileptic network for seizure dynamics.
Knowledge about these network constituents may elucidate targets for
individualised therapeutic interventions that aim at preventing seizure
generation and spread.Comment: In press (Seizure
Flux control of cytochrome c oxidase in human skeletal muscle
In the present work, by titrating cytochrome c oxidase (COX) with the specific inhibitor KCN, the flux control coefficient and the metabolic reserve capacity of COX have been determined in human saponin-permeabilized muscle fibers. In the presence of the substrates glutamate and malate, a 2.3 ± 0.2-fold excess capacity of COX was observed in ADP-stimulated human skeletal muscle fibers. This value was found to be dependent on the mitochondrial substrate supply. In the combined presence of glutamate, malate, and succinate, which supported an approximately 1.4-fold higher rate of respiration, only a 1.4 ± 0.2-fold excess capacity of COX was determined. In agreement with these findings, the flux control of COX increased, in the presence of the three substrates, from 0.27 ± 0.03 to 0.36 ± 0.08. These results indicate a tight in vivo control of respiration by COX in human skeletal muscle. This tight control may have significant implications for mitochondrial myopathies. In support of this conclusion, the analysis of skeletal muscle fibers from two patients with chronic progressive external ophthalmoplegia, which carried deletions in 11 and 49% of their mitochondrial DNA, revealed a substantially lowered reserve capacity and increased flux control coefficient of COX, indicating severe rate limitations of oxidative phosphorylation by this enzyme
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Getting the best outcomes from epilepsy surgery.
Neurosurgery is an underutilized treatment that can potentially cure drug-refractory epilepsy. Careful, multidisciplinary presurgical evaluation is vital for selecting patients and to ensure optimal outcomes. Advances in neuroimaging have improved diagnosis and guided surgical intervention. Invasive electroencephalography allows the evaluation of complex patients who would otherwise not be candidates for neurosurgery. We review the current state of the assessment and selection of patients and consider established and novel surgical procedures and associated outcome data. We aim to dispel myths that may inhibit physicians from referring and patients from considering neurosurgical intervention for drug-refractory focal epilepsies. Ann Neurol 2018;83:676-690
Enhancement of Neocortical-Medial Temporal EEG Correlations during Non-REM Sleep
Interregional interactions of oscillatory activity are crucial for the integrated processing of multiple brain regions. However, while the EEG in virtually all brain structures passes through substantial modifications during sleep, it is still an open question whether interactions between neocortical and medial temporal EEG oscillations also depend on the state of alertness. Several previous studies in animals and humans suggest that hippocampal-neocortical interactions crucially depend on the state of alertness (i.e., waking state or sleep). Here, we analyzed scalp and intracranial EEG recordings during sleep and waking state in epilepsy patients undergoing presurgical evaluation. We found that the amplitudes of oscillations within the medial temporal lobe and the neocortex were more closely correlated during sleep, in particular during non-REM sleep, than during waking state. Possibly, the encoding of novel sensory inputs, which mainly occurs during waking state, requires that medial temporal dynamics are rather independent from neocortical dynamics, while the consolidation of memories during sleep may demand closer interactions between MTL and neocortex
Graded Effects of Social Conformity on Recognition Memory
Previous studies have shown that the opinion of confederates in a group influences recognition memory, but inconsistent results have been obtained concerning the question of whether recognition of items as old and new are affected similarly, possibly because only one or two confederates are present during the recognition phase. Here, we present data from a study where recognition of novel faces was tested in the presence of four confederates. In a long version of this experiment, recognition of items as old and new was similarly affected by group responses. However, in the short version, recognition of old items depended proportionally on the number of correct group responses, while rejection of new items only decreased significantly when all confederates gave an incorrect response. These findings indicate that differential effects of social conformity on recognition of items as old and new occur in situations with an intermediate level of group pressure
Computing the distribution of a tree metric
The Robinson-Foulds (RF) distance is by far the most widely used measure of dissimilarity between trees. Although the distribution of these distances has been investigated for twenty years, an algorithm that is explicitly polynomial time has yet to be described for computing this distribution (which is also the dis- tribution of trees around a given tree under the popular Robinson-Foulds metric). In this paper we derive a polynomial-time algorithm for this distribution. We show how the distribution can be approximated by a Poisson distribution determined by the proportion of leaves that lie in ‘cherries’ of the given tree. We also describe how our results can be used to derive normalization constants that are required in a recently-proposed maximum likelihood approach to supertree construction
Performance of ECG-based seizure detection algorithms strongly depends on training and test conditions
Objective
To identify non-EEG-based signals and algorithms for detection of motor and non-motor seizures in people lying in bed during video-EEG (VEEG) monitoring and to test whether these algorithms work in freely moving people during mobile EEG recordings.
Methods
Data of three groups of adult people with epilepsy (PwE) were analyzed. Group 1 underwent VEEG with additional devices (accelerometry, ECG, electrodermal activity); group 2 underwent VEEG; and group 3 underwent mobile EEG recordings both including one-lead ECG. All seizure types were analyzed. Feature extraction and machine-learning techniques were applied to develop seizure detection algorithms. Performance was expressed as sensitivity, precision, F score, and false positives per 24 hours.
Results
The algorithms were developed in group 1 (35 PwE, 33 seizures) and achieved best results (F score 56%, sensitivity 67%, precision 45%, false positives 0.7/24 hours) when ECG features alone were used, with no improvement by including accelerometry and electrodermal activity. In group 2 (97 PwE, 255 seizures), this ECG-based algorithm largely achieved the same performance (F score 51%, sensitivity 39%, precision 73%, false positives 0.4/24 hours). In group 3 (30 PwE, 51 seizures), the same ECG-based algorithm failed to meet up with the performance in groups 1 and 2 (F score 27%, sensitivity 31%, precision 23%, false positives 1.2/24 hours). ECG-based algorithms were also separately trained on data of groups 2 and 3 and tested on the data of the other groups, yielding maximal F1 scores between 8% and 26%.
Significance
Our results suggest that algorithms based on ECG features alone can provide clinically meaningful performance for automatic detection of all seizure types. Our study also underscores that the circumstances under which such algorithms were developed, and the selection of the training and test data sets need to be considered and limit the application of such systems to unseen patient groups behaving in different conditions
Ethical, Legal and Social Issues of Big Data - A Comprehensive Overview
The ELSI White Paper is the final achievement of the ELSI Task Force for the National Research Programme “Big Data” (NRP 75). It is an informational document that provides an overview of the key ethical, legal, and social challenges of big data and provides guidance for the collection, use, and sharing of big data. The document aims to bring together the expertise of the ELSI Task Force members rather than exhaustively covering all topics in big data relating to ethical, legal, and social issues (ELSI).
The white paper comprises two parts: main articles and commentaries on it. The main articles give an overview of the major concerns associated with the use of big data, based on the assessment of the participating researchers. The commentary articles either examine in depth one or more of the issues that are presented in the main articles or highlight other issues that are considered relevant by their authors but are not covered in the main articles.
The main articles are divided into three sections corresponding to the three ELSI levels of analysis. In the section on ethics, Marcello Ienca explores the threat of big data to ethics commissions, privacy rights, personal autonomy, and equality in the healthcare sector and biomedical research. Bernice Elger focuses on the need to address informed consent differently and complement it with additional mechanisms in the big data context. In the legal section, Christophe Schneble explores whether current Swiss data protection laws adequately regulate and protect individuals’ data. Eleonora Viganò analyses the threat of big data to state sovereignty and explore the two contrasting acceptations of the term “digital sovereignty” in the context of big data. In the section on social issues, Markus Christen addresses the big data divide, namely, the uneven distribution of benefits and harms from big data and the connected issue of the transparency asymmetry between data givers and data owners. Michele Loi delves into the debate on fair algorithms, presenting the risks of discriminating against certain groups when adopting big data-based predictive algorithms, such as those for predicting inmates’ recidivism.
The second part of the ELSI White Paper contains three commentaries. In the first, Mira Burri focuses on the viability of new approaches to global trade governance that seek to address big data issues and makes recommendations for a better informed and more proactive Swiss approach. In the second commentary, David Shaw explores the lack of protection for vulnerable groups in big data research and the temporospatial and moral distance between researchers and participants that increases the risk of exploitation. In the third commentary, Christian Hauser tackles big data from the perspective of business ethics and provides guidance to companies employing big data.
Keywords: Big data, informed consent, data protection law, big data divide, digital sovereignty, health data, discrimination, big data research, big data in industry
JEL Classification: O3, F1
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