Background: Advanced methods of signal analysis of the preictal and ictal activity dynamics characterizing absence epilepsy in humans with absences and in genetic animal models have revealed new and unknown electroencephalographic characteristics, that has led to new insights and theories.
New Method: Taking into account that some network associations can be considered as nonlinear, an adaptive nonlinear Granger causality approach was developed and applied to analyze cortico-cortical, cortico-thalamic and intrathalamic network interactions from local field potentials (LFPs). The outcomes of adaptive nonlinear models, constructed based on the properties of electroencephalographic signal and on statistical criteria to optimize the number of coefficients in the models, were compared with the outcomes of linear Granger causality.
Results: The nonlinear adaptive method showed statistically significant preictal changes in Granger causality in almost all pairs of channels, as well as ictal changes in cortico-cortical, cortico-thalamic and intrathalamic networks. Current results suggest rearrangement of interactions in the thalamo-cortical network accompanied the transition from preictal to ictal phase.
Comparison With Existing Method(s): The linear method revealed no preictal and less ictal changes in causality.
Conclusions: Achieved results suggest that this proposed adaptive nonlinear method is more sensitive than the linear one to dynamics of network properties. Since changes in coupling were found before the seizure-related increase of LFP signal amplitude and also based on some additional tests it seems likely that they were not spurious and could not result from signal to noise ratio change.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1016/j.jneumeth.2014.01.028 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
2D MoS-based reconfigurable analog hardware.
Nat Commun
January 2025
School of Integrated Circuits and Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, Hubei, 430074, China.
Biological neural circuits demonstrate exceptional adaptability to diverse tasks by dynamically adjusting neural connections to efficiently process information. However, current two-dimension materials-based neuromorphic hardware mainly focuses on specific devices to individually mimic artificial synapse or heterosynapse or soma and encoding the inner neural states to realize corresponding mock object function. Recent advancements suggest that integrating multiple two-dimension material devices to realize brain-like functions including the inter-mutual connecting assembly engineering has become a new research trend.
View Article and Find Full Text PDFBottom-up Multiscale Simulation of Nonlinear Rheology for Entangled Polymer Melts: Using Atactic Polystyrene as An Example.
Macromolecules
December 2024
Pritzker School of Molecular Engineering, The University of Chicago, Chicago, Illinois 60637, United States.
Article Synopsis
- A multiscale modeling framework is developed to predict the nonlinear shear rheology of entangled polymer melts, starting from an all-atom model and transitioning to coarser models through a detailed parametrization process.
- The study focuses on atactic polystyrene melts, where the steady shear viscosity is accurately calculated across various shear rates, matching experimental data without needing adjustable parameters.
- The analysis reveals a transition from power-law behavior at high shear rates to a plateau regime at lower rates, influenced by molecular weight, allowing for comprehensive viscosity predictions over a wide range of conditions.
Research on short-term precipitation forecasting method based on CEEMDAN-GRU algorithm.
Sci Rep
December 2024
School of Information Science and Control Engineering, Liaoning Petrochemical University, Fushun, 113005, China.
Precipitation forecasting is vital for managing disasters, urban traffic, and agriculture. This study develops an improved model for short-term precipitation forecasting by combining Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and Gated Recurrent Unit (GRU). Using precipitation data from January 1, 2019, to December 31, 2022, as a sample, the model capitalizes on CEEMDAN's superior signal decomposition capabilities and GRU's ability to capture nonlinear dynamic patterns in time series.
View Article and Find Full Text PDFA novel hybrid methodology for wind speed and solar irradiance forecasting based on improved whale optimized regularized extreme learning machine.
Sci Rep
December 2024
Center for Research on Microgrids (UPC CROM), Department of Electronic Engineering, Technical University of Catalonia, 08019, Barcelona, Spain.
With rising demand for electricity, integrating renewable energy sources into power networks has become a key challenge. The fast incorporation of clean energy sources, particularly solar and wind power, into the existing power grid in the last several years has raised a major problem in controlling and managing the power grid due to the intermittent nature of these sources. Therefore, in order to ensure the safe RES integration providing high-quality power at a fair price and for the secure and reliable functioning of electrical systems, a precise one-day-ahead solar irradiation and wind speed forecast is essential for a stable and safe hybrid energy system.
View Article and Find Full Text PDFA sample size analysis of a mathematical model of longitudinal tumor volume and progression-free survival for Bayesian individual dynamic predictions in recurrent high-grade glioma.
CPT Pharmacometrics Syst Pharmacol
December 2024
Department of Radiation Oncology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
Patients with recurrent high-grade glioma (rHGG) have a poor prognosis with median progression-free survival (PFS) of <7 months. Responses to treatment are heterogenous, suggesting a clinical need for prognostic models. Bayesian data analysis can exploit individual patient follow-up imaging studies to adaptively predict the risk of progression.
View Article and Find Full Text PDFWant AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!