In spite of remarkable advances in neuroimaging technologies over the last two decades, no single method provides everything that we desire for basic research or best clinical practice. Structural Magnetic Resonance Imaging (MRI) provides exquisite images of the head and brain that provide a powerful anatomical framework for functional mapping. Functional MRI provides lower resolution functional images based on metabolic or hemodynamic responses to brain activation, but does not provide information on the timescales most relevant for neural function, and may not match activity maps based on electrophysiological criteria. Magneto- and electroencephalography (MEG and EEG) provide excellent measures of neural population dynamics, but neural electromagnetic source localization depends on model-based solutions of an ill-posed inverse problem. MRI can provide geometry and conductivity information to significantly improve biophysical models of the head volume conductor, required for source localization. Bayesian Inference techniques for MEG and EEG analysis provide a probabilistic approach for source localization and timecourse estimation, explicitly treating the ambiguity and uncertainty inherent in the inverse problem. Such methods allow formally rigorous strategies for integrating dynamic measures with spatial estimates of neural sources provided by fMRI or probabilistic functional atlas data, constrained by individual anatomy. Although these integrated methods fall short of true tomographic imaging they provide the best available techniques for noninvasive imaging of dynamic neural function. However, cutting edge methods may allow direct imaging of neural currents by MRI, eventually providing the ultimate tools for functional imaging of the human brain. |