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Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space

Lookup NU author(s): Dr Camillo Porcaro



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.

Publication metadata

Author(s): Lei X, Ostwald D, Hu JH, Qiu C, Porcaro C, Bagshaw AP, Yao DZ

Publication type: Article

Publication status: Published

Journal: PLoS One

Year: 2011

Volume: 6

Issue: 9

Print publication date: 22/09/2011

ISSN (print): 1932-6203

ISSN (electronic):

Publisher: Public Library of Science


DOI: 10.1371/journal.pone.0024642


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Funder referenceFunder name
2009AA02Z301863 Project
2011CB707803973 project
60736029National Nature Science Foundation of China
EP/F023057/1UK Engineering and Physical Sciences Research Council