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A method based on the granger causality and graph kernels for discriminating resting state from attentional task

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Main Authors: Danesh Shahnazian, Fatemeh, Mokhtari, Hossein-Zadeh, Gholam-Ali
Other Authors: d.shahnazian@ut.ac.ir
Format: Working Paper
Language:English
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Subjects:
Functional magnetic resonance imaging
Effective brain connectivity
Granger causality
Graph kernels
Discriminating brain state
Online Access:http://dspace.unimap.edu.my/xmlui/handle/123456789/20724
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spelling my.unimap-207242012-08-22T03:06:00Z A method based on the granger causality and graph kernels for discriminating resting state from attentional task Danesh Shahnazian Fatemeh, Mokhtari Hossein-Zadeh, Gholam-Ali d.shahnazian@ut.ac.ir f.mokhtari@ece.ut.ac.ir ghzadeh@ut.ac.ir Functional magnetic resonance imaging Effective brain connectivity Granger causality Graph kernels Discriminating brain state Link to publisher's homepage at http://ieeexplore.ieee.org/ Exploring the directional connections between brain regions is of great importance in understanding the brain function. As a method of this exploration, Granger causality is defined in terms of the amount of improvement in the estimation of a signal by past samples of another signal (cause). This method produced reliable results in various applications. In current study, we use connections of directed graphs as the features for discriminating two brain states, rest and attentional cueing task, in a block design fMRI dataset. We apply a support vector machine (SVM) which is enriched by graph kernels like random walk, graphlet and sub-tree kernels on directed graphs of different brain states. Graph kernel methods are a branch of graph matching methods and have recently been proposed as a theoretically sound and promising approach to the problem of graph comparison. They measure the inexact similarity between graphs. For the first time, we apply graph kernels on graphs of brain’s effective connectivity. We achieved classification accuracy of 100% in discrimination of resting state from attentional task. We also obtain one graph for each brain state representing causal connections between brain regions. From the networks obtained for each state, we can infer that caudate is the source of information in both states and Left ventromedial prefrontal is the sink of information in the resting state. 2012-08-22T03:06:00Z 2012-08-22T03:06:00Z 2012-02-27 Working Paper p. 83-88 978-145771989-9 http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6178960 http://hdl.handle.net/123456789/20724 en Proceedings of the International Conference on Biomedical Engineering (ICoBE 2012) Institute of Electrical and Electronics Engineers (IEEE)
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Functional magnetic resonance imaging
Effective brain connectivity
Granger causality
Graph kernels
Discriminating brain state
spellingShingle Functional magnetic resonance imaging
Effective brain connectivity
Granger causality
Graph kernels
Discriminating brain state
Danesh Shahnazian
Fatemeh, Mokhtari
Hossein-Zadeh, Gholam-Ali
A method based on the granger causality and graph kernels for discriminating resting state from attentional task
description Link to publisher's homepage at http://ieeexplore.ieee.org/
author2 d.shahnazian@ut.ac.ir
author_facet d.shahnazian@ut.ac.ir
Danesh Shahnazian
Fatemeh, Mokhtari
Hossein-Zadeh, Gholam-Ali
format Working Paper
author Danesh Shahnazian
Fatemeh, Mokhtari
Hossein-Zadeh, Gholam-Ali
author_sort Danesh Shahnazian
title A method based on the granger causality and graph kernels for discriminating resting state from attentional task
title_short A method based on the granger causality and graph kernels for discriminating resting state from attentional task
title_full A method based on the granger causality and graph kernels for discriminating resting state from attentional task
title_fullStr A method based on the granger causality and graph kernels for discriminating resting state from attentional task
title_full_unstemmed A method based on the granger causality and graph kernels for discriminating resting state from attentional task
title_sort method based on the granger causality and graph kernels for discriminating resting state from attentional task
publisher Institute of Electrical and Electronics Engineers (IEEE)
publishDate 2012
url http://dspace.unimap.edu.my/xmlui/handle/123456789/20724
_version_ 1643793163189485568
score 13.222552

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