Anatomical constraints on source models for high-resolution EEG and MEG derived from MRI.

Electroencephalography (EEG) remains the primary tool for measuring changes in dynamic brain function due to disease state with the millisecond temporal resolution of neuronal activity. In recent decades EEG has been supplanted by CT and MRI for the localization of tumors and lesions in the brain. In contrast to the excellent temporal resolution of EEG, the spatial information in EEG is limited by the volume conduction of currents through the tissues of the head. We have extracted source models (position and orientation) from MRI scans to investigate the theoretical relationship between brain sources and EEG recorded on the scalp. Although detailed information about the boundaries between different tissues can also be obtained from MRI, these models are only approximate because of our relatively poor knowledge of the conductivities of the different tissue compartments in living heads. We also compare the resolution of EEG with magnetoecephalography (MEG), which offers the advantage of requiring less detail about volume conduction in the head. The brain's magnetic field depends only on the position of sources in the brain and the position and orientation of the sensors. We demonstrate that EEG and MEG space average neural activity over comparably large volumes of the brain; however, they are preferentially sensitive to sources of different orientation suggesting a complementary role for EEG and MEG. High-resolution EEG methods potentially yield much better localization of source activity in superficial brain areas. These methods do not make any assumptions about the sources, and can be easily co-registered with the brain surface derived from MRI. While there is much information to be gained by using anatomical MRI to develop models of the generators of EEG/MEG, functional neuroimaging (e.g., fMRI) signals and EEG/MEG signals are not easily related.

[1]  D. P. Russell,et al.  Increased Synchronization of Neuromagnetic Responses during Conscious Perception , 1999, The Journal of Neuroscience.

[2]  Thom F. Oostendorp,et al.  The conductivity of the human skull: results of in vivo and in vitro measurements , 2000, IEEE Transactions on Biomedical Engineering.

[3]  S. Raghavachari,et al.  Gating of Human Theta Oscillations by a Working Memory Task , 2001, The Journal of Neuroscience.

[4]  E. Harth,et al.  Electric Fields of the Brain: The Neurophysics of Eeg , 2005 .

[5]  P. Nunez Toward a quantitative description of large-scale neocortical dynamic function and EEG , 2000, Behavioral and Brain Sciences.

[6]  W. Sutherling,et al.  Conductivities of Three-Layer Human Skull , 2004, Brain Topography.

[7]  P. Nunez,et al.  On the Relationship of Synaptic Activity to Macroscopic Measurements: Does Co-Registration of EEG with fMRI Make Sense? , 2004, Brain Topography.

[8]  P. Nunez,et al.  Spatial filtering and neocortical dynamics: estimates of EEG coherence , 1998, IEEE Transactions on Biomedical Engineering.

[9]  G. Ojemann,et al.  Changes in power and coherence of brain activity in human sensorimotor cortex during performance of visuomotor tasks. , 2001, Bio Systems.

[10]  R. Srinivasan Methods to Improve the Spatial Resolution of EEG , 1999 .

[11]  P. Nunez,et al.  A theoretical and experimental study of high resolution EEG based on surface Laplacians and cortical imaging. , 1994, Electroencephalography and clinical neurophysiology.

[12]  D. Cohen,et al.  MEG versus EEG localization test using implanted sources in the human brain , 1990, Annals of neurology.

[13]  P. Nunez A Method to Estimate Local Skull Resistance in Living Subjects , 1987, IEEE Transactions on Biomedical Engineering.

[14]  M R Nuwer,et al.  Evaluation of stroke using EEG frequency analysis and topographic mapping , 1987, Neurology.

[15]  Prof. Dr. Valentino Braitenberg,et al.  Anatomy of the Cortex , 1991, Studies of Brain Function.

[16]  D. Tucker,et al.  EEG coherency. I: Statistics, reference electrode, volume conduction, Laplacians, cortical imaging, and interpretation at multiple scales. , 1997, Electroencephalography and clinical neurophysiology.

[17]  David Poeppel,et al.  How can EEG/MEG and fMRI/PET data be combined? , 2002, Human brain mapping.

[18]  Don M. Tucker,et al.  Localizing Acute Stroke-related EEG Changes:: Assessing the Effects of Spatial Undersampling , 2001, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[19]  S. Ueno,et al.  Impedance magnetic resonance imaging: A method for imaging of impedance distributions based on magnetic resonance imaging , 1998 .

[20]  P. V. van Rijen,et al.  Measurement of the Conductivity of Skull, Temporarily Removed During Epilepsy Surgery , 2004, Brain Topography.

[21]  D. Tucker,et al.  Spatial sampling and filtering of EEG with spline Laplacians to estimate cortical potentials , 2005, Brain Topography.

[22]  W. Freeman,et al.  Spatio-temporal correlations in human gamma band electrocorticograms. , 1996, Electroencephalography and clinical neurophysiology.

[23]  K. Nagata,et al.  Topographic Electroencephalographic Study of Cerebral Infarction Using Computed Mapping of the EEG , 1982, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[24]  R. Ilmoniemi,et al.  Magnetoencephalography-theory, instrumentation, and applications to noninvasive studies of the working human brain , 1993 .

[25]  R. T. Hart,et al.  Finite-element model of the human head: scalp potentials due to dipole sources , 1991, Medical and Biological Engineering and Computing.

[26]  A. Urbano,et al.  Spline Laplacian estimate of EEG potentials over a realistic magnetic resonance-constructed scalp surface model. , 1996, Electroencephalography and clinical neurophysiology.