Evaluation of Functional Network Connectivity in Event-related FMRI Data Based on ICA and Time-frequency Granger Causality

被引：0
|
作者：
Havlicek, M. [1 ,2 ]
Jan, J. [1 ]
Calhoun, V. D. [2 ,3 ]
Brazdil, M. [4 ]
Mikl, M. [1 ,4 ]
机构：
[1] Brno Univ Technol, Dept Biomed Engn, Kolejni 4, Brno, Czech Republic
[2] Univ New Mexico, Mind Res Network, Albuquerque, NM 87131 USA
[3] Univ New Mexico, Dept Elect & Comp Engn, Albuquerque, NM 87131 USA
[4] St Annes Univ Hosp, Dept Neurol, Brno, Czech Republic
来源：
WORLD CONGRESS ON MEDICAL PHYSICS AND BIOMEDICAL ENGINEERING, VOL 25, PT 4: IMAGE PROCESSING, BIOSIGNAL PROCESSING, MODELLING AND SIMULATION, BIOMECHANICS | 2010年 / 25卷
关键词：
fMRI; ICA; spectral; Granger; adaptive; BLIND SEPARATION; COMPONENTS;
D O I：
暂无
中图分类号：
R318 [生物医学工程];
学科分类号：
0831 ;
摘要：
In this article we show that Adaptive Multivariate Autoregressive (AMVAR) modeling accompanied by proper preprocessing is an effective technique for evaluation of spectral Granger causality among functional brain networks identified by independent component analysis from event-related fMRI data.
引用
收藏
页码：716 / 719
页数：4
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