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Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain

GuoRong Wu, Fuyong Chen, Dezhi Kang, Hiangyang Zang, Daniele Marinazzo UGent and Huafu Chen (2011) IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 58(11). p.3088-3096
abstract
Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.
Please use this url to cite or link to this publication:
author
organization
year
type
journalArticle (original)
publication status
published
subject
keyword
TIME-SERIES ANALYSIS, TEMPORAL-LOBE EPILEPSY, GRANGER CAUSALITY, CORTICAL INTERACTIONS, SMALL-WORLD, FMRI DATA, MODEL, ONSET, SETS, IDENTIFICATION, Canonical correlation analysis, depth-EEG, multivariate Granger causality
journal title
IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING
IEEE Trans. Biomed. Eng.
volume
58
issue
11
pages
3088 - 3096
Web of Science type
Article
Web of Science id
000296019500006
JCR category
ENGINEERING, BIOMEDICAL
JCR impact factor
2.278 (2011)
JCR rank
22/72 (2011)
JCR quartile
2 (2011)
ISSN
0018-9294
DOI
10.1109/TBME.2011.2162669
project
The integrative neuroscience of behavioral control (Neuroscience)
language
English
UGent publication?
yes
classification
A1
copyright statement
I have transferred the copyright for this publication to the publisher
id
1957873
handle
http://hdl.handle.net/1854/LU-1957873
date created
2011-12-01 20:09:50
date last changed
2013-02-27 09:07:03
@article{1957873,
  abstract     = {Multivariate Granger causality is a well-established approach for inferring information flow in complex systems, and it is being increasingly applied to map brain connectivity. Traditional Granger causality is based on vector autoregressive (AR) or mixed autoregressive moving average (ARMA) model, which are potentially affected by errors in parameter estimation and may be contaminated by zero-lag correlation, notably when modeling neuroimaging data. To overcome this issue, we present here an extended canonical correlation approach to measure multivariate Granger causal interactions among time series. The procedure includes a reduced rank step for calculating canonical correlation analysis (CCA), and extends the definition of causality including instantaneous effects, thus avoiding the potential estimation problems of AR (or ARMA) models. We tested this approach on simulated data and confirmed its practical utility by exploring local network connectivity at different scales in the epileptic brain analyzing scalp and depth-EEG data during an interictal period.},
  author       = {Wu, GuoRong and Chen, Fuyong and Kang, Dezhi and Zang, Hiangyang and Marinazzo, Daniele and Chen, Huafu},
  issn         = {0018-9294},
  journal      = {IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING},
  keyword      = {TIME-SERIES ANALYSIS,TEMPORAL-LOBE EPILEPSY,GRANGER CAUSALITY,CORTICAL INTERACTIONS,SMALL-WORLD,FMRI DATA,MODEL,ONSET,SETS,IDENTIFICATION,Canonical correlation analysis,depth-EEG,multivariate Granger causality},
  language     = {eng},
  number       = {11},
  pages        = {3088--3096},
  title        = {Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain},
  url          = {http://dx.doi.org/10.1109/TBME.2011.2162669},
  volume       = {58},
  year         = {2011},
}

Chicago
Wu, GuoRong, Fuyong Chen, Dezhi Kang, Hiangyang Zang, Daniele Marinazzo, and Huafu Chen. 2011. “Multiscale Causal Connectivity Analysis by Canonical Correlation: Theory and Application to Epileptic Brain.” Ieee Transactions on Biomedical Engineering 58 (11): 3088–3096.
APA
Wu, GuoRong, Chen, F., Kang, D., Zang, H., Marinazzo, D., & Chen, H. (2011). Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 58(11), 3088–3096.
Vancouver
1.
Wu G, Chen F, Kang D, Zang H, Marinazzo D, Chen H. Multiscale causal connectivity analysis by canonical correlation: theory and application to epileptic brain. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. 2011;58(11):3088–96.
MLA
Wu, GuoRong, Fuyong Chen, Dezhi Kang, et al. “Multiscale Causal Connectivity Analysis by Canonical Correlation: Theory and Application to Epileptic Brain.” IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING 58.11 (2011): 3088–3096. Print.