Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In thi...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Xu Lei, Dirk Ostwald, Jiehui Hu, Chuan Qiu, Camillo Porcaro, Andrew P Bagshaw, Dezhong Yao
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/06829ae9c1974ce1943efc8ec86bb540
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:06829ae9c1974ce1943efc8ec86bb540
record_format	dspace
spelling	oai:doaj.org-article:06829ae9c1974ce1943efc8ec86bb5402021-11-04T06:08:04ZMultimodal functional network connectivity: an EEG-fMRI fusion in network space.1932-620310.1371/journal.pone.0024642https://doaj.org/article/06829ae9c1974ce1943efc8ec86bb5402011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21961040/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.Xu LeiDirk OstwaldJiehui HuChuan QiuCamillo PorcaroAndrew P BagshawDezhong YaoPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 9, p e24642 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Xu Lei Dirk Ostwald Jiehui Hu Chuan Qiu Camillo Porcaro Andrew P Bagshaw Dezhong Yao Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
description	EEG and fMRI recordings measure the functional activity of multiple coherent networks distributed in the cerebral cortex. Identifying network interaction from the complementary neuroelectric and hemodynamic signals may help to explain the complex relationships between different brain regions. In this paper, multimodal functional network connectivity (mFNC) is proposed for the fusion of EEG and fMRI in network space. First, functional networks (FNs) are extracted using spatial independent component analysis (ICA) in each modality separately. Then the interactions among FNs in each modality are explored by Granger causality analysis (GCA). Finally, fMRI FNs are matched to EEG FNs in the spatial domain using network-based source imaging (NESOI). Investigations of both synthetic and real data demonstrate that mFNC has the potential to reveal the underlying neural networks of each modality separately and in their combination. With mFNC, comprehensive relationships among FNs might be unveiled for the deep exploration of neural activities and metabolic responses in a specific task or neurological state.
format	article
author	Xu Lei Dirk Ostwald Jiehui Hu Chuan Qiu Camillo Porcaro Andrew P Bagshaw Dezhong Yao
author_facet	Xu Lei Dirk Ostwald Jiehui Hu Chuan Qiu Camillo Porcaro Andrew P Bagshaw Dezhong Yao
author_sort	Xu Lei
title	Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
title_short	Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
title_full	Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
title_fullStr	Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
title_full_unstemmed	Multimodal functional network connectivity: an EEG-fMRI fusion in network space.
title_sort	multimodal functional network connectivity: an eeg-fmri fusion in network space.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/06829ae9c1974ce1943efc8ec86bb540
work_keys_str_mv	AT xulei multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT dirkostwald multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT jiehuihu multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT chuanqiu multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT camilloporcaro multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT andrewpbagshaw multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace AT dezhongyao multimodalfunctionalnetworkconnectivityaneegfmrifusioninnetworkspace
_version_	1718445132439617536

Multimodal functional network connectivity: an EEG-fMRI fusion in network space.

Ejemplares similares