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Abstract

An improved method, termed 'statistical synthetic aperture magnetometry' (SSAM) of transforming magnetoencephalographic (MEG) measurements into corresponding three-dimensional images of the electrophysiological activity within the brain. The computed images are static, representing the time-integrated brain activity over a selected period. By selecting the time periods and frequency bands of interest, the SSAM method selectively images brain activity relating to different types of brain pathology or to cognitive events. Unlike prior art methods, the SSAM method compensates for the growth of ionic signal source strength estimates with depth into the head, resulting, in part, from the declining sensitivity of the MEG sensors. This is achieved by computing and displaying functions of the ratio of source strength to its noise for each element comprising the image. That is, a functional image is determined by an array of voxels where each voxel is based upon a function of source signal-to-noise ratio (SNR) rather than the source strength, alone. By using functions of SNR to represent source activity, the SSAM method achieves more accurate and higher resolution source localization. Each voxel is represented as a function of the ratio of a source power estimate to a source noise variance estimate. Such functions are found to be maximum at the true locations of sources, whereas plots of source power alone (as in prior art methods), show maxima which appear deeper and more diffuse in the brain than they in fact are.

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First Claim