scholarly journals Assessment of Effective Network Connectivity among MEG None Contaminated Epileptic Transitory Events

2021 ◽  
Vol 2021 ◽  
pp. 1-14
Author(s):  
Abir Hadriche ◽  
Ichrak Behy ◽  
Amal Necibi ◽  
Abdennaceur Kachouri ◽  
Chokri Ben Amar ◽  
...  
Keyword(s):  
Inverse Problem ◽  
Clinical Performance ◽  
Signal To Noise Ratio ◽  
Statistical Parametric Mapping ◽  
Simulated Data ◽  
Network Connectivity ◽  
Propagation Delay ◽  
Connection Strength ◽  
Advanced Technique ◽  
Epileptiform Discharges

Characterizing epileptogenic zones EZ (sources responsible of excessive discharges) would assist a neurologist during epilepsy diagnosis. Locating efficiently these abnormal sources among magnetoencephalography (MEG) biomarker is obtained by several inverse problem techniques. These techniques present different assumptions and particular epileptic network connectivity. Here, we proposed to evaluate performances of distributed inverse problem in defining EZ. First, we applied an advanced technique based on Singular Value Decomposition (SVD) to recover only pure transitory activities (interictal epileptiform discharges). We evaluated our technique’s robustness in separation between transitory and ripples versus frequency range, transitory shapes, and signal to noise ratio on simulated data (depicting both epileptic biomarkers and respecting time series and spectral properties of realistic data). We validated our technique on MEG signal using detector precision on 5 patients. Then, we applied four methods of inverse problem to define cortical areas and neural generators of excessive discharges. We computed network connectivity of each technique. Then, we confronted obtained noninvasive networks to intracerebral EEG transitory network connectivity using nodes in common, connection strength, distance metrics between concordant nodes of MEG and IEEG, and average propagation delay. Coherent Maximum Entropy on the Mean (cMEM) proved a high matching between MEG network connectivity and IEEG based on distance between active sources, followed by Exact low-resolution brain electromagnetic tomography (eLORETA), Dynamical Statistical Parametric Mapping (dSPM), and Minimum norm estimation (MNE). Clinical performance was interesting for entire methods providing in an average of 73.5% of active sources detected in depth and seen in MEG, and vice versa, about 77.15% of active sources were detected from MEG and seen in IEEG. Investigated problem techniques succeed at least in finding one part of seizure onset zone. dSPM and eLORETA depict the highest connection strength among all techniques. Propagation delay varies in this range [18, 25]ms, knowing that eLORETA ensures the lowest propagation delay (18 ms) and the closet one to IEEG propagation delay.

Multilag Correlation Estimators for Polarimetric Radar Measurements in the Presence of Noise

2012 ◽  
Vol 29 (6) ◽  
pp. 772-795 ◽  
Author(s):  
Lei Lei ◽  
Guifu Zhang ◽  
Richard J. Doviak ◽  
Robert Palmer ◽  
Boon Leng Cheong ◽  
...  
Keyword(s):  
Theoretical Analysis ◽  
Data Quality ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Radar Data ◽  
Polarimetric Radar ◽  
Signal To Noise ◽  
Spectral Moments ◽  
Radar Measurements

Abstract The quality of polarimetric radar data degrades as the signal-to-noise ratio (SNR) decreases. This substantially limits the usage of collected polarimetric radar data to high SNR regions. To improve data quality at low SNRs, multilag correlation estimators are introduced. The performance of the multilag estimators for spectral moments and polarimetric parameters is examined through a theoretical analysis and by the use of simulated data. The biases and standard deviations of the estimates are calculated and compared with those estimates obtained using the conventional method.

scholarly journals Reconstruction of Initial Wave Field of a Nonsteady-State Wave Propagation from Limited Measurements at a Specific Spatial Point Based on Stochastic Inversion

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
S. L. Han ◽  
Takeshi Kinoshita
Keyword(s):  
Inverse Problem ◽  
Wave Propagation ◽  
Wave Field ◽  
Simulated Data ◽  
Initial Surface ◽  
Reconstruction Procedure ◽  
Inference Problem ◽  
State Wave ◽  
Initial Wave ◽  
Nonsteady State

This paper studies an inverse problem that can be used for reconstructing initial wave field of a nonsteady-state wave propagation. The inverse problem is ill posed in the sense that small changes in the input data can greatly affect the solution of the problem. To address the difficulty, the problem is formulated in the form of an inference problem in an appropriately constructed stochastic model. It is shown that the stochastic inverse model enables the initial surface disturbance to be reconstructed, including its confidence intervals given the noisy measurements. The reconstruction procedure is illustrated through applications to some simulated data for two- and three-dimensional problem.

scholarly journals Antiepileptic Efficacy and Network Connectivity Modulation of Repetitive Transcranial Magnetic Stimulation by Vertex Suppression

2021 ◽  
Vol 15 ◽  
Author(s):  
Cong Fu ◽  
Aikedan Aisikaer ◽  
Zhijuan Chen ◽  
Qing Yu ◽  
Jianzhong Yin ◽  
...  
Keyword(s):  
Transcranial Magnetic Stimulation ◽  
Seizure Frequency ◽  
Magnetic Stimulation ◽  
Repetitive Transcranial Magnetic Stimulation ◽  
Reduction Rate ◽  
Network Connectivity ◽  
Hamilton Depression Scale ◽  
Depression And Anxiety ◽  
Epileptiform Discharges ◽  
The Impact

A core feature of drug-resistant epilepsy is hyperexcitability in the motor cortex, and low-frequency repetitive transcranial magnetic stimulation (rTMS) is a suitable treatment for seizures. However, the antiepileptic effect causing network reorganization has rarely been studied. Here, we assessed the impact of rTMS on functional network connectivity (FNC) in resting functional networks (RSNs) and their relation to treatment response. Fourteen patients with medically intractable epilepsy received inhibitive rTMS with a figure-of-eight coil over the vertex for 10 days spread across two weeks. We designed a 6-week follow-up phase divided into four time points to investigate FNC and rTMS-induced timing-dependent plasticity, such as seizure frequency and abnormal interictal discharges on electroencephalography (EEG). For psychiatric comorbidities, the Hamilton Depression Scale (HAM-D) and the Hamilton Anxiety Scale (HAM-A) were applied to measure depression and anxiety before and after rTMS. FNC was also compared to that of a cohort of 17 healthy control subjects. The after-effects of rTMS included all subjects that achieved the significant decrease rate of more than 50% in interictal epileptiform discharges and seizure frequency, 12 (14) patients with the reduction rate above 50% compared to the baseline, as well as emotional improvements in depression and anxiety (p < 0.05). In the analysis of RSNs, we found a higher synchronization between the sensorimotor network (SMN) and posterior default-mode network (pDMN) in epileptic patients than in healthy controls. In contrast to pre-rTMS, the results demonstrated a weaker FNC between the anterior DMN (aDMN) and SMN after rTMS, while the FNC between the aDMN and dorsal attention network (DAN) was greater (p < 0.05, FDR corrected). Importantly, the depressive score was anticorrelated with the FNC of the aDMN-SMN (r = −0.67, p = 0.0022), which was markedly different in the good and bad response groups treated with rTMS (p = 0.0115). Based on the vertex suppression by rTMS, it is possible to achieve temporary clinical efficacy by modulating network reorganization in the DMN and SMN for patients with refractory epilepsy.

scholarly journals The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 815 ◽  
Author(s):  
Shanshan Du ◽  
Liangyun Liu ◽  
Xinjie Liu ◽  
Xinwei Zhang ◽  
Xianlian Gao ◽  
...  
Keyword(s):  
Chlorophyll Fluorescence ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Terrestrial Ecosystem ◽  
Global Scale ◽  
Terrestrial Vegetation ◽  
Atmospheric Transmission ◽  
Imaging Spectrometer ◽  
Ecosystem Carbon ◽  
Carbon Inventory

The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.

Heterologous double-determinant immunoradiometric assay CA 125 II: reliable second-generation immunoassay for determining CA 125 in serum

1993 ◽  
Vol 39 (12) ◽  
pp. 2509-2513 ◽  
Author(s):  
P Kenemans ◽  
G J van Kamp ◽  
P Oehr ◽  
R A Verstraeten
Keyword(s):  
Clinical Performance ◽  
Signal To Noise Ratio ◽  
Linear Regression Analysis ◽  
Ca 125 ◽  
Reference Ranges ◽  
Immunoradiometric Assay ◽  
Receiver Operating Characteristic Curves ◽  
Analysis Of Results ◽  
Capture Antibody ◽  
Healthy Females

Abstract The new CA 125 II (Centocor) serum assay utilizing the M11 mouse monoclonal antibody as capture antibody and OC125 as tracer antibody, was investigated for its technical and clinical performance against the original CA 125 assay. The CA 125 II test revealed a quadruple increase in signal-to-noise ratio, good intra- and interassay precision (with CVs < 5% and 7%, respectively), improved dilution linearity, and a minimal detectable dose of 0.38 units/mL. Sera were obtained from healthy females (n = 192), women with benign conditions (n = 208), and patients with various cancers (n = 379). Both assays measured highly similar CA 125 distributions with equal reference ranges and nearly identical positivity (> 35 units/mL) rates, resulting in similar receiver-operating characteristic curves and monitoring graphs. Linear regression analysis of results by the two assays (CA 125 = x, CA 125 II = y) in ovarian cancer patients showed, for CA 125 assay values between 0 and 1000 units/mL, a slope of 1.00 and a y-intercept of 12.6 (n = 254, r = 0.8617, P < 0.0001). The heterologous CA 125 II assay appeared to be more accurate in patients who had human anti-mouse antibodies after immunoscintigraphy. The CA 125 II immunoradiometric assay is sensitive and reliable for measuring serum CA 125, and fully retains the cutoff values of 35 and 65 units/mL that were defined with the original CA 125 immunoradiometric assay.

scholarly journals Configuration and Registration of Multi-Camera Spectral Image Database of Icon Paintings

Computation ◽  
2019 ◽  
Vol 7 (3) ◽  
pp. 47
Author(s):  
Arash Mirhashemi
Keyword(s):  
Dynamic Range ◽  
Specular Reflection ◽  
Signal To Noise Ratio ◽  
Simulated Data ◽  
Image Database ◽  
High Dynamic Range ◽  
Registration Method ◽  
Spatial Registration ◽  
Feature Based ◽  
The Cost

At the cost of added complexity and time, hyperspectral imaging provides a more accurate measure of the scene’s irradiance compared to an RGB camera. Several camera designs with more than three channels have been proposed to improve the accuracy. The accuracy is often evaluated based on the estimation quality of the spectral data. Currently, such evaluations are carried out with either simulated data or color charts to relax the spatial registration requirement between the images. To overcome this limitation, this article presents an accurately registered image database of six icon paintings captured with five cameras with different number of channels, ranging from three (RGB) to more than a hundred (hyperspectral camera). Icons are challenging topics because they have complex surfaces that reflect light specularly with a high dynamic range. Two contributions are proposed to tackle this challenge. First, an imaging configuration is carefully arranged to control the specular reflection, confine the dynamic range, and provide a consistent signal-to-noise ratio for all the camera channels. Second, a multi-camera, feature-based registration method is proposed with an iterative outlier removal phase that improves the convergence and the accuracy of the process. The method was tested against three other approaches with different features or registration models.

EEG SOURCE LOCALIZATION: A COMPARATIVE STUDY OF CLASSICAL AND NEURAL NETWORK METHODS

2001 ◽  
Vol 11 (04) ◽  
pp. 349-359 ◽  
Author(s):  
UDANTHA R. ABEYRATNE ◽  
G. ZHANG ◽  
P. SARATCHANDRAN
Keyword(s):  
Neural Network ◽  
Inverse Problem ◽  
Signal To Noise Ratio ◽  
Closed Form Solution ◽  
Form Solution ◽  
Hybrid Technique ◽  
Eeg Signals ◽  
Rbf Network ◽  
Network Methods ◽  
Lm Algorithm

We address the problem of estimating biopotential sources within the brain, based on EEG signals observed on the scalp. This problem, known as the inverse problem of electrophysiology, has no closed-form solution, and requires iterative techniques such as the Levenberg – Marquardt (LM) algorithm. Considering the nonlinear nature of the inverse problem, and the low signal to noise ratio inherent in EEG signals, a backpropagation neural network (BPN) has been recently proposed as a solution. The technique has not been properly compared with classical techniques such as the LM method, or with more recent neural network techniques such as the Radial Basis Function (RBF) network. In this paper, we provide improved strategies based on BPN and consider RBF networks in solving the inverse problem. We compare the performances of BPN, RBF and a hybrid technique with that of the classical LM method.

scholarly journals Ability of Granger Causality Analysis to Detect Indirect Links: A Simulation Study

2020 ◽  
Vol 23 (2) ◽  
pp. 121-124
Author(s):  
N. W. Falasca ◽  
R. Franciotti
Keyword(s):  
Time Series ◽  
Granger Causality ◽  
Time Domain ◽  
Simulated Data ◽  
Biological Data ◽  
Connection Strength ◽  
Connectivity Analysis ◽  
Causality Analysis ◽  
Granger Causality Analysis ◽  
Virtual Time

Granger causality (G-causality) has emerged as a useful tool to investigate the influence that one system can exert over another system, but challenges remain when applying it to biological data. Specifically, it is not clear if G-causality can distinguish between direct and indirect influences. In this study time domain G-causality connectivity analysis was performed on simulated electroencephalographic cerebral signals. Conditional multivariate autoregressive model was applied to 19 virtual time series (nodes) to identify the effects of direct and indirect links while varying one of the following variables: the length of the time series, the lags between interacting nodes, the connection strength of the links, and the noise. Simulated data revealed that weak indirect influences are not identified by Gcausality analysis when applied on covariance stationary, non-correlated electrophysiological time series.

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