A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.

Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal c...

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Main Authors:	Tian Ge, Keith M Kendrick, Jianfeng Feng
Format:	Article
Language:	English
Published:	Public Library of Science (PLoS) 2009-11-01
Series:	PLoS Computational Biology
Online Access:	http://europepmc.org/articles/PMC2777405?pdf=render

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spelling	doaj-2a2f2c1d23cc49f09668f351cec2eb672020-11-25T01:42:05ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582009-11-01511e100057010.1371/journal.pcbi.1000570A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.Tian GeKeith M KendrickJianfeng FengTwo main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning.http://europepmc.org/articles/PMC2777405?pdf=render
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Tian Ge Keith M Kendrick Jianfeng Feng
spellingShingle	Tian Ge Keith M Kendrick Jianfeng Feng A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning. PLoS Computational Biology
author_facet	Tian Ge Keith M Kendrick Jianfeng Feng
author_sort	Tian Ge
title	A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.
title_short	A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.
title_full	A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.
title_fullStr	A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.
title_full_unstemmed	A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.
title_sort	novel extended granger causal model approach demonstrates brain hemispheric differences during face recognition learning.
publisher	Public Library of Science (PLoS)
series	PLoS Computational Biology
issn	1553-734X 1553-7358
publishDate	2009-11-01
description	Two main approaches in exploring causal relationships in biological systems using time-series data are the application of Dynamic Causal model (DCM) and Granger Causal model (GCM). These have been extensively applied to brain imaging data and are also readily applicable to a wide range of temporal changes involving genes, proteins or metabolic pathways. However, these two approaches have always been considered to be radically different from each other and therefore used independently. Here we present a novel approach which is an extension of Granger Causal model and also shares the features of the bilinear approximation of Dynamic Causal model. We have first tested the efficacy of the extended GCM by applying it extensively in toy models in both time and frequency domains and then applied it to local field potential recording data collected from in vivo multi-electrode array experiments. We demonstrate face discrimination learning-induced changes in inter- and intra-hemispheric connectivity and in the hemispheric predominance of theta and gamma frequency oscillations in sheep inferotemporal cortex. The results provide the first evidence for connectivity changes between and within left and right inferotemporal cortexes as a result of face recognition learning.
url	http://europepmc.org/articles/PMC2777405?pdf=render
work_keys_str_mv	AT tiange anovelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning AT keithmkendrick anovelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning AT jianfengfeng anovelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning AT tiange novelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning AT keithmkendrick novelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning AT jianfengfeng novelextendedgrangercausalmodelapproachdemonstratesbrainhemisphericdifferencesduringfacerecognitionlearning
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A novel extended Granger Causal Model approach demonstrates brain hemispheric differences during face recognition learning.

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