The long term goal of this project is to establish a multimodal functional neuroimaging methodology for noninvasively imaging brain activity and connectivity with high spatial and temporal resolution. We propose to develop and evaluate novel methods to integrate high-temporal-resolution electroencephalography (EEG) and high-spatial-resolution blood oxygen level dependent (BOLD) functional magnetic resonance imaging (fMRI) for imaging neural activations and their interactions in humans. To achieve these goals, the following specific aims will be addressed: 1) Develop multimodal imaging methods to integrate EEG and fMRI for imaging brain activity. We will develop and refine novel neuroimaging methods for reconstructing current density distributions from integration of EEG and BOLD-fMRI. Of innovation is the proposed strategy to estimate time-variant source co-variance from both EEG and quantified BOLD responses, and to extend the method to the spatio- temporal-frequency domain. We will rigorously evaluate the proposed multimodal neuroimaging methods by means of systematic computer simulations and refine the strategy of integrating BOLD-fMRI with EEG. 2) Evaluate multimodal brain activity imaging through well-controlled human experimentation. We will evaluate our modeling assumptions with regard to the relationship between the BOLD response and the event-related electrophysiological response in a group of human subjects. We will evaluate the proposed methods using visual and motor paradigms in a group of healthy subjects. We will evaluate the proposed imaging approach using independent subdural potential recordings on 30 epilepsy patients performing the same motor and somatosensory tasks as in corresponding BOLD-fMRI and EEG studies. 3) Multimodal imaging of brain functional connectivity. We will extend the use of EEG and BOLD-fMRI for the estimation of brain functional connectivity among regions of interest. We will develop time-varying connectivity estimation methods and rigorous graph theory based analysis methods to assess the connectivity estimates. We will rigorously evaluate the fMRI-EEG integrated brain connectivity estimation methods by computer simulations and human experimentation including healthy subjects and patients undergoing surgical evaluation. The successful completion of the proposed research will: (1) enable us to address an important question in functional neuroimaging as to whether, and to what extent, multimodal integration of fMRI and EEG can further improve the performance of spatiotemporal neuroimaging;(2) enable us to develop and evaluate a novel high- resolution spatiotemporal functional neuroimaging approach, which promises to have great potential in terms of mapping human brain activity and connectivity in both healthy subjects and patients suffering from various neurological and psychiatric disorders.

Public Health Relevance

The proposed work aims at developing and evaluating a high-resolution multimodal neuroimaging technology, which may provide a significantly enhanced ability to image dynamic brain function. The establishment of such a high-resolution spatio-temporal imaging modality may greatly enhance our ability to tackle and manage a number of neurological and mental disorders, and provide a significant benefit to patient healthcare.

National Institute of Health (NIH)
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Research Project (R01)
Project #
Application #
Study Section
Biomedical Imaging Technology Study Section (BMIT)
Program Officer
Pai, Vinay Manjunath
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Minnesota Twin Cities
Biomedical Engineering
Schools of Engineering
United States
Zip Code
Edelman, Bradley J; Baxter, Bryan; He, Bin (2016) EEG Source Imaging Enhances the Decoding of Complex Right-Hand Motor Imagery Tasks. IEEE Trans Biomed Eng 63:4-14
Sohrabpour, Abbas; Lu, Yunfeng; Worrell, Gregory et al. (2016) Imaging brain source extent from EEG/MEG by means of an iteratively reweighted edge sparsity minimization (IRES) strategy. Neuroimage 142:27-42
Sohrabpour, Abbas; Ye, Shuai; Worrell, Gregory A et al. (2016) Noninvasive Electromagnetic Source Imaging and Granger Causality Analysis: An Electrophysiological Connectome (eConnectome) Approach. IEEE Trans Biomed Eng 63:2474-2487
Mariappan, Leo; Shao, Qi; Jiang, Chunlan et al. (2016) Magneto acoustic tomography with short pulsed magnetic field for in-vivo imaging of magnetic iron oxide nanoparticles. Nanomedicine 12:689-699
Huishi Zhang, Clara; Sohrabpour, Abbas; Lu, Yunfeng et al. (2016) Spectral and spatial changes of brain rhythmic activity in response to the sustained thermal pain stimulation. Hum Brain Mapp 37:2976-91
Shan, Haijun; Xu, Haojie; Zhu, Shanan et al. (2015) A novel channel selection method for optimal classification in different motor imagery BCI paradigms. Biomed Eng Online 14:93
Jamison, Keith W; Roy, Abhrajeet V; He, Sheng et al. (2015) SSVEP signatures of binocular rivalry during simultaneous EEG and fMRI. J Neurosci Methods 243:53-62
Liu, Jiaen; Zhang, Xiaotong; Schmitter, Sebastian et al. (2015) Gradient-based electrical properties tomography (gEPT): A robust method for mapping electrical properties of biological tissues in vivo using magnetic resonance imaging. Magn Reson Med 74:634-46
Zhang, Clara Huishi; Lu, Yunfeng; Brinkmann, Benjamin et al. (2015) Lateralization and localization of epilepsy related hemodynamic foci using presurgical fMRI. Clin Neurophysiol 126:27-38
Zhang, Clara Huishi; Sha, Zhiyi; Mundahl, John et al. (2015) Thalamocortical relationship in epileptic patients with generalized spike and wave discharges--A multimodal neuroimaging study. Neuroimage Clin 9:117-27

Showing the most recent 10 out of 74 publications