253 research outputs found
Identifying phase synchronization clusters in spatially extended dynamical systems
We investigate two recently proposed multivariate time series analysis
techniques that aim at detecting phase synchronization clusters in spatially
extended, nonstationary systems with regard to field applications. The starting
point of both techniques is a matrix whose entries are the mean phase coherence
values measured between pairs of time series. The first method is a mean field
approach which allows to define the strength of participation of a subsystem in
a single synchronization cluster. The second method is based on an eigenvalue
decomposition from which a participation index is derived that characterizes
the degree of involvement of a subsystem within multiple synchronization
clusters. Simulating multiple clusters within a lattice of coupled Lorenz
oscillators we explore the limitations and pitfalls of both methods and
demonstrate (a) that the mean field approach is relatively robust even in
configurations where the single cluster assumption is not entirely fulfilled,
and (b) that the eigenvalue decomposition approach correctly identifies the
simulated clusters even for low coupling strengths. Using the eigenvalue
decomposition approach we studied spatiotemporal synchronization clusters in
long-lasting multichannel EEG recordings from epilepsy patients and obtained
results that fully confirm findings from well established neurophysiological
examination techniques. Multivariate time series analysis methods such as
synchronization cluster analysis that account for nonlinearities in the data
are expected to provide complementary information which allows to gain deeper
insights into the collective dynamics of spatially extended complex systems
Quantification of depth of anesthesia by nonlinear time series analysis of brain electrical activity
We investigate several quantifiers of the electroencephalogram (EEG) signal
with respect to their ability to indicate depth of anesthesia. For 17 patients
anesthetized with Sevoflurane, three established measures (two spectral and one
based on the bispectrum), as well as a phase space based nonlinear correlation
index were computed from consecutive EEG epochs. In absence of an independent
way to determine anesthesia depth, the standard was derived from measured blood
plasma concentrations of the anesthetic via a pharmacokinetic/pharmacodynamic
model for the estimated effective brain concentration of Sevoflurane. In most
patients, the highest correlation is observed for the nonlinear correlation
index D*. In contrast to spectral measures, D* is found to decrease
monotonically with increasing (estimated) depth of anesthesia, even when a
"burst-suppression" pattern occurs in the EEG. The findings show the potential
for applications of concepts derived from the theory of nonlinear dynamics,
even if little can be assumed about the process under investigation.Comment: 7 pages, 5 figure
Controversies on the network theory of epilepsy : Debates held during the ICTALS 2019 conference
Acknowledgements We would like to acknowledge the contributions of the discussants to the exposition and discussion of the six debate topics. The discussants for debates 1-6 were Fabrice Wendling, Mark Cook, Mark Richardson, Thorsten Rings, Klaus Lehnertz and Piotr Suffczynski, respectively. Funding for ICTALS 2019 was received from the following foundations and industry partners: UCB S.A. (Belgium), American Epilepsy Society (AES), Epilepsy Innovation Institute (Ei2) and Epilepsy Foundation of America (EFA), NeuraLynx (Bozeman, MT, USA) and LivaNova (London, UK). The contribution of HZ was supported by award R01NS109062 from the National Institutes of Health, MG by the EPSRC via grants EP/P021417/1 and EP/N014391/1 and a Wellcome Trust Institutional Strategic Support Award (WT105618MA), and PJ by awards from the Ministry of Health of the Czech Republic AZV 17-28427A and the Czech Science Foundation 20-25298S. The opinions expressed in this article do not necessarily reflect the view of the National Institutes of Health, the Department of Health and Human Services, or the United States government.Peer reviewedPostprin
Controversies in epilepsy: Debates held during the Fourth International Workshop on Seizure Prediction
Debates on six controversial topics were held during the Fourth International Workshop on Seizure Prediction (IWSP4) convened in Kansas City, KS, USA, July 4–7, 2009. The topics were (1) Ictogenesis: Focus versus Network? (2) Spikes and Seizures: Step-relatives or Siblings? (3) Ictogenesis: A Result of Hyposynchrony? (4) Can Focal Seizures Be Caused by Excessive Inhibition? (5) Do High-Frequency Oscillations Provide Relevant Independent Information? (6) Phase Synchronization: Is It Worthwhile as Measured? This article, written by the IWSP4 organizing committee and the debaters, summarizes the arguments presented during the debates
Failure of adaptive self-organized criticality during epileptic seizure attacks
Critical dynamics are assumed to be an attractive mode for normal brain
functioning as information processing and computational capabilities are found
to be optimized there. Recent experimental observations of neuronal activity
patterns following power-law distributions, a hallmark of systems at a critical
state, have led to the hypothesis that human brain dynamics could be poised at
a phase transition between ordered and disordered activity. A so far unresolved
question concerns the medical significance of critical brain activity and how
it relates to pathological conditions. Using data from invasive
electroencephalogram recordings from humans we show that during epileptic
seizure attacks neuronal activity patterns deviate from the normally observed
power-law distribution characterizing critical dynamics. The comparison of
these observations to results from a computational model exhibiting
self-organized criticality (SOC) based on adaptive networks allows further
insights into the underlying dynamics. Together these results suggest that
brain dynamics deviates from criticality during seizures caused by the failure
of adaptive SOC.Comment: 7 pages, 5 figure
Seizure prediction : ready for a new era
Acknowledgements: The authors acknowledge colleagues in the international seizure prediction group for valuable discussions. L.K. acknowledges funding support from the National Health and Medical Research Council (APP1130468) and the James S. McDonnell Foundation (220020419) and acknowledges the contribution of Dean R. Freestone at the University of Melbourne, Australia, to the creation of Fig. 3.Peer reviewedPostprin
The methyltransferase G9a regulates HoxA9-dependent transcription in AML
Chromatin modulators are emerging as attractive drug targets, given their widespread implication in human cancers and susceptibility to pharmacological inhibition. Here we establish the histone methyltransferase G9a/EHMT2 as a selective regulator of fast proliferating myeloid progenitors with no discernible function in hematopoietic stem cells (HSCs). In mouse models of acute myeloid leukemia (AML), loss of G9a significantly delays disease progression and reduces leukemia stem cell (LSC) frequency. We connect this function of G9a to its methyltransferase activity and its interaction with the leukemogenic transcription factor HoxA9 and provide evidence that primary human AML cells are sensitive to G9A inhibition. Our results highlight a clinical potential of G9A inhibition as a means to counteract the proliferation and self-renewal of AML cells by attenuating HoxA9-dependent transcription
dSETDB1 and SU(VAR)3–9 Sequentially Function during Germline-Stem Cell Differentiation in Drosophila melanogaster
Germline-stem cells (GSCs) produce gametes and are thus true “immortal stem cells”. In Drosophila ovaries, GSCs divide asymmetrically to produce daughter GSCs and cystoblasts, and the latter differentiate into germline cysts. Here we show that the histone-lysine methyltransferase dSETDB1, located in pericentric heterochromatin, catalyzes H3-K9 trimethylation in GSCs and their immediate descendants. As germline cysts differentiate into egg chambers, the dSETDB1 function is gradually taken over by another H3-K9-specific methyltransferase, SU(VAR)3–9. Loss-of-function mutations in dsetdb1 or Su(var)3–9 abolish both H3K9me3 and heterochromatin protein-1 (HP1) signals from the anterior germarium and the developing egg chambers, respectively, and cause localization of H3K9me3 away from DNA-dense regions in most posterior germarium cells. These results indicate that dSETDB1 and SU(VAR)3–9 act together with distinct roles during oogenesis, with dsetdb1 being of particular importance due to its GSC-specific function and more severe mutant phenotype
Scaling Effects and Spatio-Temporal Multilevel Dynamics in Epileptic Seizures
Epileptic seizures are one of the most well-known dysfunctions of the nervous system. During a seizure, a highly synchronized behavior of neural activity is observed that can cause symptoms ranging from mild sensual malfunctions to the complete loss of body control. In this paper, we aim to contribute towards a better understanding of the dynamical systems phenomena that cause seizures. Based on data analysis and modelling, seizure dynamics can be identified to possess multiple spatial scales and on each spatial scale also multiple time scales. At each scale, we reach several novel insights. On the smallest spatial scale we consider single model neurons and investigate early-warning signs of spiking. This introduces the theory of critical transitions to excitable systems. For clusters of neurons (or neuronal regions) we use patient data and find oscillatory behavior and new scaling laws near the seizure onset. These scalings lead to substantiate the conjecture obtained from mean-field models that a Hopf bifurcation could be involved near seizure onset. On the largest spatial scale we introduce a measure based on phase-locking intervals and wavelets into seizure modelling. It is used to resolve synchronization between different regions in the brain and identifies time-shifted scaling laws at different wavelet scales. We also compare our wavelet-based multiscale approach with maximum linear cross-correlation and mean-phase coherence measures
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