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Biomedical Functional Imaging and Neuroengineering Laboratory

College of Science & Engineering > Dept. of Biomedical Engineering

Multimodal Neuroimaging

We have been developing a novel multimodal spatio-temporal electrophysiological magnetic resonance imaging (eMRITM) technology integrating electrophysiological source imaging and fMRI. The eMRITM approach is aimed at greatly enhancing the temporal resolution of fMRI and the spatial resolution of EEG or MEG source imaging (for review, see He & Liu, IEEE Rev BME, 2008, Abstract).

We have been investigating the simultaneous EEG and fMRI during the same experimental session and have demonstrated the feasibility in a 3T MRI system. The developed experimental multimodal imaging framework has greatly facilitated our investigations on brain functions, such as the retinotopic mapping in human visual system (Im et al, J Neurosci Meth, 2006, Abstract; 2007, Abstract) and the cortical visual pathways and dynamic visual interactions.

We have also been investigating theoretical models in order to integrate BOLD fMRI and EEG in a principled way. Our work suggests that the BOLD effect size is proportional to the time integral of power of synaptic currents. We have experimentally tested our approach in human subjects using a visual paradigm, which led to significantly improved performance (Liu & He, NeuroImage, 2008, Abstract).

The multimodal neuroimaging approach has been further used to image functional connectivity of the brain under motor tasks (Babiloni et al., NeuroImage, 2005, Abstract). The availability of such high-resolution spatio-temporal functional neuroimaging technology would provide an important advancement in brain research, and improve clinical diagnosis and management of neurological and psychiatric disorders.

Fig. 1 (A) The pattern-reversal checkerboard visual stimulation, (B) fMRI activation map with a corrected threshold p<0.01, and (C) the global field power of VEP and the dynamic cortical source distribution at three VEP latencies (76, 112, 212 ms after the visual onset) imaged from EEG alone (1st row) or fMRI–EEG integration using our proposed adaptive wiener filter (2nd row) and the conventional 90% fMRI-weighted algorithm (3rd row). Both the source images and the fMRI activation map are visualized on an inflated representation of cortical surface. (from Liu ZM & He B: “fMRI-EEG Integrated Cortical Source Imaging by use of Time-Variant Spatial Constraints,” NeuroImage, 39(3): 1198-214, 2008, with permission from Elsevier)

Fig. 2 Cortical connectivity imaging based on cortical current density imaging results. (Left two columns) Connectivity patterns estimated in the alpha (A) and gamma (B) frequency bands during finger movement before or after movement onset. (Right four columns) Information i nflow and outflow patterns obtained from connectivity imaging. (from Babiloni F, Babiloni C, Carducci F, Cincotti F, Astolfi L, Basilisco A, Rossini PM, Ding L, Ni Y, Cheng J, Christine K, Sweeney J, and He B: "Assessing time-varying cortical functional connectivity with the multimodal integration of high resolution EEG and fMRI data by Directed Transfer Function," NeuroImage, 24(1):118-131, 2005, with permission from Elsevier)