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

X Lei, Dirk Ostwald, J Hu, C Qiu, Camillo Porcaro, Andrew Bagshaw, D Yao

Research output: Contribution to journalArticle

35 Citations (Scopus)

Abstract

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.
Original languageEnglish
Pages (from-to)e24642
JournalPLoS ONE
Volume6
Issue number9
DOIs
Publication statusPublished - 1 Sept 2011

Fingerprint

Dive into the research topics of 'Multimodal Functional Network Connectivity: An EEG-fMRI Fusion in Network Space'. Together they form a unique fingerprint.

Cite this