Inferring effective connectivity in epilepsy using dynamic causal modeling

IRBM ◽  
2015 ◽  
Vol 36 (6) ◽  
pp. 335-344 ◽  
Author(s):  
W. Xiang ◽  
C. Yang ◽  
J.-J. Bellanger ◽  
H. Shu ◽  
R. Le Bouquin Jeannès
Keyword(s):  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling

Effective connectivity in target stimulus processing: A dynamic causal modeling study of visual oddball task

NeuroImage ◽  
2007 ◽  
Vol 35 (2) ◽  
pp. 827-835 ◽  
Author(s):  
Milan Brázdil ◽  
Michal Mikl ◽  
Radek Mareček ◽  
Petr Krupa ◽  
Ivan Rektor
Keyword(s):  
Target Stimulus ◽  
Effective Connectivity ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Stimulus Processing ◽  
Oddball Task ◽  
Visual Oddball ◽  
Modeling Study

Neural Correlates of Mental Workload During Multitasking: a Dynamic Causal Modeling Study

2021 ◽  
Vol 65 (1) ◽  
pp. 1337-1341
Author(s):  
Jiali Huang ◽  
Zach Traylor ◽  
Sanghyun Choo ◽  
Chang S. Nam
Keyword(s):  
Mental Workload ◽  
Effective Connectivity ◽  
Neural Correlates ◽  
Causal Modeling ◽  
Dynamic Causal Modeling ◽  
Augmented Cognition ◽  
Connection Pattern ◽  
High Workload ◽  
Electroencephalogram Eeg ◽  
Brain Connections

The goal of this study is to examine the neural correlates of different mental workload levels. Electroencephalogram (EEG) signals were recorded when participants perform a set of tasks simultaneously with low and high levels of mental workload. Brain connections for each workload level were estimated using Dynamic Causal Modeling (DCM), which is an effective connectivity method to reveal causal relationships between brain sources. The result showed a backward-only, left-lateralized connection pattern for high workload condition, compared to the bidirectional, two-sided connection pattern for low workload condition.These findings of the mental workload effect on neural mechanisms may be utilized in applications of the augmented cognition program.

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