Spatio-temporal Source Localization and Granger Causality in Ictal Source Analysis

2006 ◽  
Author(s):  
L. Ding ◽  
G.A. Worrell ◽  
T.D. Lagerlund ◽  
B. He
Keyword(s):  
Granger Causality ◽  
Source Localization ◽  
Source Analysis ◽  
Spatio Temporal

scholarly journals Electric Source Localization Adds Evidence for Task-Specific CNVs

1998 ◽  
Vol 11 (1) ◽  
pp. 21-28 ◽  
Author(s):  
Ina M. Tarkka ◽  
Luis F. H. Basile
Keyword(s):  
Pattern Recognition ◽  
Source Localization ◽  
Clinical Neurophysiology ◽  
Recognition Task ◽  
Source Analysis ◽  
Visual Spatial ◽  
Spatio Temporal ◽  
Electric Source ◽  
Pattern Recognition Task ◽  
Spatial Recognition

This study was an attempt to replicate recent magnetoencephalographic (MEG) findings on human task-specific CNV sources (Basile et al., Electroencephalography and Clinical Neurophysiology 90, 1994, 157–165) by means of a spatio-temporal electric source localization method (Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 62, 1985, 32–44; Scherg and von Cramon, Electroencephalography and Clinical Neurophysiology 65, 1986, 344-360; Scherg and Berg, Brain Electric Source Analysis Handbook, Version 2). The previous MEG results showed CNV sources in the prefrontal cortex of the two hemispheres for two tasks used, namely visual pattern recognition and visual spatial recognition tasks. In the right hemisphere, the sources were more anterior and inferior for the spatial recognition task than for the pattern recognition task. In the present study we obtained CNVs in five subjects during two tasks identical to the MEG study. The elicited electric potentials were modeled with four spatio-temporal dipoles for each task, three of which accounted for the visual evoked response and one that accounted for the CNV. For all subjects the dipole explaining the CNV was always localized in the frontal region of the head, however, the dipole obtained during the visual spatial recognition task was more anterior than the one obtained during the pattern recognition task. Thus, task-specific CNV sources were again observed, although the stable model consisted of only one dipole located close to the midline instead of one dipole in each hemisphere. This was a major difference in the CNV sources between the previous MEG and the present electric source analysis results. We discuss the possible basis for the difference between the two methods used to study slow brain activity that is believed to originate from extended cortical patches.

Spatio-temporal source analysis

1997 ◽  
Vol 103 (1) ◽  
pp. 47
Author(s):  
M Scherq
Keyword(s):  
Source Analysis ◽  
Spatio Temporal

Signal source localization from spatio-temporal biomagnetic data by signal subspace method

1996 ◽  
Vol 27 (2) ◽  
pp. 12-25
Author(s):  
Hiroyuki Kudo ◽  
Tsuneo Saito ◽  
Takashi Maemura
Keyword(s):  
Source Localization ◽  
Signal Subspace ◽  
Signal Source ◽  
Subspace Method ◽  
Spatio Temporal

Spatio-Temporal Causal Relations at Urban Road Networks; Granger Causality Based Networks as an Insight to Urban Traffic Dynamics

2021 ◽  
pp. 791-804
Author(s):  
Glykeria Myrovali ◽  
Theodoros Karakasidis ◽  
Georgia Ayfantopoulou ◽  
Maria Morfoulaki
Keyword(s):  
Granger Causality ◽  
Road Networks ◽  
Urban Traffic ◽  
Traffic Dynamics ◽  
Causal Relations ◽  
Urban Road ◽  
Spatio Temporal

scholarly journals Closely Spaced MEG Source Localization and Functional Connectivity Analysis Using a New Prewhitening Invariance of Noise Space Algorithm

2016 ◽  
Vol 2016 ◽  
pp. 1-12 ◽  
Author(s):  
Junpeng Zhang ◽  
Yuan Cui ◽  
Lihua Deng ◽  
Ling He ◽  
Junran Zhang ◽  
...  
Keyword(s):  
Source Localization ◽  
Neural Plasticity ◽  
Source Analysis ◽  
Correlated Noise ◽  
Localization Accuracy ◽  
Cortical Sources ◽  
Noise Simulation ◽  
Time Courses ◽  
Adverse Influence ◽  
Highly Correlated

This paper proposed a prewhitening invariance of noise space (PW-INN) as a new magnetoencephalography (MEG) source analysis method, which is particularly suitable for localizing closely spaced and highly correlated cortical sources under real MEG noise. Conventional source localization methods, such as sLORETA and beamformer, cannot distinguish closely spaced cortical sources, especially under strong intersource correlation. Our previous work proposed an invariance of noise space (INN) method to resolve closely spaced sources, but its performance is seriously degraded under correlated noise between MEG sensors. The proposed PW-INN method largely mitigates the adverse influence of correlated MEG noise by projecting MEG data to a new space defined by the orthogonal complement of dominant eigenvectors of correlated MEG noise. Simulation results showed that PW-INN is superior to INN, sLORETA, and beamformer in terms of localization accuracy for closely spaced and highly correlated sources. Lastly, source connectivity between closely spaced sources can be satisfactorily constructed from source time courses estimated by PW-INN but not from results of other conventional methods. Therefore, the proposed PW-INN method is a promising MEG source analysis to provide a high spatial-temporal characterization of cortical activity and connectivity, which is crucial for basic and clinical research of neural plasticity.

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