In this paper, we present a novel approach to imaging sparse and focal neural current sources from MEG (magnetoencephalography) data. Using the framework of Tikhonov regularization theory, we introduce a new stabilizer that uses the concept of controlled support to incorporate a priori assumptions about the area occupied by focal sources. The paper discusses the underlying Tikhonov theory and its relationship to a Bayesian formulation which in turn allows us to interpret and better understand other related algorithms.
Bibliographical noteFunding Information:
This work was partially supported under NIH grant P41 RR12553-03 and also by grants from the Whitaker Foundation and NIH (R01DC004855) to S.N. The authors would like to thank Dr. M. Funke from the University of Utah Department of Radiology for his help providing the realistic MEG array geometry and Blythe Nobleman from Scientific Computing and Imaging Institute at the University of Utah, for her many useful suggestions pertaining to the manuscript.
All Science Journal Classification (ASJC) codes
- Cognitive Neuroscience