Inferring brain dynamics using granger causality on fMRI data

Author

Cecchi, Guillermo A. ; Garg, Rahul ; Rao, A. Ravishankar

Author_Institution

Comput. Biol. Center, IBM T.J. Watson Res. Center, Yorktown Heights, NY

fYear

2008

fDate

14-17 May 2008

Firstpage

604

Lastpage

607

Abstract

Here we present a scalable method to compute the structure of causal links over large scale dynamical systems that achieves high efficiency in discovering actual functional connections. The method is based on the Granger causality analysis of multivariate linear models, solved by means of a sparse regression approach, and can deal with autoregressive models of more than 10,000 variables.

Keywords

biomedical MRI; brain; large-scale systems; neurophysiology; Granger causality analysis; autoregressive models; brain dynamics; fMRI data; large scale dynamical systems; multivariate linear model; neurophysiology; sparse regression approach; Biological system modeling; Computational biology; High-resolution imaging; Independent component analysis; Large-scale systems; Magnetic resonance imaging; Mathematical model; Neuroscience; Random variables; Stochastic processes; Functional Imaging; Image interpretation; Magnetic Resonance Imaging;

fLanguage

English

Publisher

ieee

Conference_Titel

Biomedical Imaging: From Nano to Macro, 2008. ISBI 2008. 5th IEEE International Symposium on

Conference_Location

Paris

Print_ISBN

978-1-4244-2002-5

Electronic_ISBN

978-1-4244-2003-2

Type

conf

DOI

10.1109/ISBI.2008.4541068

Filename

4541068

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1821236