Multimodal Integration of fMRI and EEG Data for High Spatial and Temporal Resolution Analysis of Brain Networks |
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Authors: | D Mantini L Marzetti M Corbetta C Del Gratta |
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Institution: | (1) Department of Clinical Sciences and Bio-imaging, University “G. D’Annunzio”, Chieti, Italy;(2) ITAB—Institute for Advanced Biomedical Technologies, “G. D’Annunzio University” Foundation, 66013 Chieti, Italy;(3) Laboratory for Neuro-psychophysiology, K.U.Leuven Medical School, Leuven, Belgium;(4) Department of Neurology, Washington University, St. Louis, MO, USA;(5) Department of Radiology, Washington University, St. Louis, Missouri, USA |
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Abstract: | Two major non-invasive brain mapping techniques, electroencephalography (EEG) and functional magnetic resonance imaging (fMRI),
have complementary advantages with regard to their spatial and temporal resolution. We propose an approach based on the integration
of EEG and fMRI, enabling the EEG temporal dynamics of information processing to be characterized within spatially well-defined
fMRI large-scale networks. First, the fMRI data are decomposed into networks by means of spatial independent component analysis
(sICA), and those associated with intrinsic activity and/or responding to task performance are selected using information
from the related time-courses. Next, the EEG data over all sensors are averaged with respect to event timing, thus calculating
event-related potentials (ERPs). The ERPs are subjected to temporal ICA (tICA), and the resulting components are localized
with the weighted minimum norm (WMNLS) algorithm using the task-related fMRI networks as priors. Finally, the temporal contribution
of each ERP component in the areas belonging to the fMRI large-scale networks is estimated. The proposed approach has been
evaluated on visual target detection data. Our results confirm that two different components, commonly observed in EEG when
presenting novel and salient stimuli, respectively, are related to the neuronal activation in large-scale networks, operating
at different latencies and associated with different functional processes. |
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