Electromagnetic brain imaging based on standardized resting-state networks

We have recently proposed a electromagnetic brain imaging based on multiple fMRI spatial priors: NEtwork-based SOurce Imaging (NESOI) [Lei et al. "fMRI Functional Networks for EEG Source Imaging," Human Brain Mapping, vol. 32, pp. 1141-1160, 2011]. In our previous method, the spatial priors is extracted from the fMRI dataset on the same subject within the same paradigm. In this paper, we present a resting-state NEtwork-based SOurce Imaing (rsNESOI), which includes fMRI priors without extra fMRI scan. While simultaneous electroencephalogram (EEG) recording within fMRI scanner is available currently, rsNESOI is more convenient for EEG source reconstruction. The real data test suggests that rsNESOI is distinctly valuable in improvement of distributed source localization in contrast to the previous methods.

[1]  Stephen M Smith,et al.  Correspondence of the brain's functional architecture during activation and rest , 2009, Proceedings of the National Academy of Sciences.

[2]  Adriano B. L. Tort,et al.  Dynamic cross-frequency couplings of local field potential oscillations in rat striatum and hippocampus during performance of a T-maze task , 2008, Proceedings of the National Academy of Sciences.

[3]  Dezhong Yao,et al.  EEG/fMRI fusion based on independent component analysis: integration of data-driven and model-driven methods. , 2012, Journal of integrative neuroscience.

[4]  Kevin Whittingstall,et al.  Evaluating the spatial relationship of event‐related potential and functional MRI sources in the primary visual cortex , 2007, Human brain mapping.

[5]  A K Liu,et al.  Spatiotemporal imaging of human brain activity using functional MRI constrained magnetoencephalography data: Monte Carlo simulations. , 1998, Proceedings of the National Academy of Sciences of the United States of America.

[6]  Dezhong Yao,et al.  Incorporating fMRI Functional Networks in EEG Source Imaging: A Bayesian Model Comparison Approach , 2011, Brain Topography.

[7]  Karl J. Friston,et al.  Systematic Regularization of Linear Inverse Solutions of the EEG Source Localization Problem , 2002, NeuroImage.

[8]  Ping Yang,et al.  An Empirical Bayesian Framework for Brain–Computer Interfaces , 2009, IEEE Transactions on Neural Systems and Rehabilitation Engineering.

[9]  Dezhong Yao,et al.  A parallel framework for simultaneous EEG/fMRI analysis: Methodology and simulation , 2010, NeuroImage.

[10]  Karl J. Friston,et al.  Multiple sparse priors for the M/EEG inverse problem , 2008, NeuroImage.

[11]  R. Henson,et al.  Electrophysiological and haemodynamic correlates of face perception, recognition and priming. , 2003, Cerebral cortex.

[12]  Josef Parvizi,et al.  Resting oscillations and cross-frequency coupling in the human posteromedial cortex , 2012, NeuroImage.

[13]  E. Halgren,et al.  Dynamic Statistical Parametric Mapping Combining fMRI and MEG for High-Resolution Imaging of Cortical Activity , 2000, Neuron.

[14]  Karl J. Friston,et al.  An empirical Bayesian solution to the source reconstruction problem in EEG , 2005, NeuroImage.

[15]  Dezhong Yao,et al.  Gaussian source model based iterative algorithm for EEG source imaging , 2009, Comput. Biol. Medicine.

[16]  P. Argibay,et al.  Episodic-like memory: new perspectives from a behavioral test in rats. , 2012, Journal of integrative neuroscience.

[17]  Dezhong Yao,et al.  fMRI functional networks for EEG source imaging , 2011, Human brain mapping.

[18]  N. Tzourio-Mazoyer,et al.  Automated Anatomical Labeling of Activations in SPM Using a Macroscopic Anatomical Parcellation of the MNI MRI Single-Subject Brain , 2002, NeuroImage.