Progress and Challenges of Multimodal Imaging using MEG, EEG, MRI, and fMRI by Matti S. Hämäläinen

Independently, electromagnetic and hemodynamic measurements of brain activity offer compromises between spatial and temporal resolution. fMRI is temporally limited by the slow time course of the hemodynamic response, but can provide a spatial sampling on a millimeter scale. EEG and MEG in turn provide a temporal resolution of milliseconds, but the localization of sources is more complicated because of the ill-posed electromagnetic inverse problem. Elucidating the spatial distribution and temporal orchestration of human brain regions is thus facilitated by combining information provided by both anatomical and functional MRI with EEG/MEG data.It was recognized very early on by the MEG researchers that the spatiotemporal distribution of the magnetic field can be used to estimate the sources of the underlying brain activity. This information can be integrated with anatomical MRI data to associate the source locations with anatomical structures. In addition, anatomical MRI data are now employed routinely to delineate boundaries between regions of different electrical conductivities for forward field computations, to restrict the locations and orientations of the sources, and in advanced visualization techniques involving three-dimensional renderings of the cortical mantle and other structures.The fusion of electromagnetic and hemodynamic data is still in its infancy. In the presently available modeling methods, this is usually accomplished by confining the sources to the cortical gray matter and by computing a distributed current estimate with a stronger a priori weighting at locations with significant fMRI activity. More elaborate methods which attempt to model the two data sets jointly under a common framework are also emerging. Furthermore, basic studies which aim at understanding the relationship between the hemodynamic and electromagnetic signals are ongoing and will eventually result in physiologically motivated rather than partly heuristic source estimation models.Rather surprisingly, relatively little effort has been devoted to combination of MEG with EEG, its most obvious companion. This has been due to difficulties in collecting both types of data simultaneously with truly indentical preprocessing and to challenges in combined modelling of the two data sets. Both simulations and analyses of actual data sets have shown that the combination of these two methods yields more reliable estimates of the sources than using one modality alone. Furthermore, these studies indicate that the improvement is not due to the increased number of measurement channels but is attributable to the different sensitivities of MEG and EEG to the cerebral current sources.Host: Richard Leahy, x04659, leahy@sipi.usc.edu

Location: Hedco Neurosciences Building (HNB) - 100

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