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Spatiotemporal Imaging of Human Brain Activity Using Functional MRI Constrained Magnetoencephalography Data: Monte Carlo Simulations
Arthur K. Liu, John W. Belliveau and Anders M. Dale
Proceedings of the National Academy of Sciences of the United States of America
Vol. 95, No. 15 (Jul. 21, 1998), pp. 8945-8950
Published by: National Academy of Sciences
Stable URL: http://www.jstor.org/stable/45865
Page Count: 6
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The goal of our research is to develop an experimental and analytical framework for spatiotemporal imaging of human brain function. Preliminary studies suggest that noninvasive spatiotemporal maps of cerebral activity can be produced by combining the high spatial resolution (millimeters) of functional MRI (fMRI) with the high temporal resolution (milliseconds) of electroencephalography (EEG) and magnetoencephalography (MEG). Although MEG and EEG are sensitive to millisecond changes in mental activity, the ability to resolve source localization and timing is limited by the ill-posed ``inverse'' problem. We conducted Monte Carlo simulations to evaluate the use of MRI constraints in a linear estimation inverse procedure, where fMRI weighting, cortical location and orientation, and sensor noise statistics were realistically incorporated. An error metric was computed to quantify the effects of fMRI invisible (``missing'') sources, ``extra'' fMRI sources, and cortical orientation errors. Our simulation results demonstrate that prior anatomical and functional information from MRI can be used to regularize the EEG/MEG inverse problem, giving an improved solution with high spatial and temporal resolution. An fMRI weighting of approximately 90% was determined to provide the best compromise between separation of activity from correctly localized sources and minimization of error caused by missing sources. The accuracy of the estimate was relatively independent of the number and extent of the sources, allowing for incorporation of physiologically realistic multiple distributed sources. This linear estimation method provides an operator-independent approach for combining information from fMRI, MEG, and EEG and represents a significant advance over traditional dipole modeling.
Proceedings of the National Academy of Sciences of the United States of America © 1998 National Academy of Sciences