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

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.

Effective connectivity and cortical information flow under visual stimulation in migraine with aura

“…The beta rhythm is generally associated with a state of cortical activation, and up to now few reports have described its changes under intermittent visual stimulation [23]. In this study GC and TE measured the total amount of information transfer across electrodes in any direction, which may be interpreted as a sign of activation of different interacting functional networks [24]. In particular, we found that strong causal connections were activated in migraine with aura across posterior and anterior electrodes.…”

Machine Learning with Brain Graphs: Predictive Modeling Approaches for Functional Imaging in Systems Neuroscience

“…For example, it was shown that very different graphs are obtained from MEG and fMRI data, even when the graph structure learning algorithm is the same [49]. This indicates that interpretation of brain graphs and their properties must always consider the limitations of the modality used, but also that multimodal graph analysis methods might bring additional insight [50].…”

Parallel processing of filtered queries in attributed semantic graphs

“…Similarly, the voxel correlations in fMRI and DTI are defined differently, 13 requiring different edge types. The ability to declare edge and vertex types dynamically in Python allows co-analysis of different brain networks and overcomes the limitations of using a single modality [28], and we plan to leverage our described methodology for forthcoming investigations of computational neuropathology.…”