Next-generation human brain neuroimaging and the role of high-performance computing

Advances in human brain neuroimaging to achieve high-temporal and high-spatial resolution will depend on computational approaches to localize EEG signals to their sources in the cortex. The source localization inverse problem is inherently ill-posed and depends critically on the modeling of human head electromagnetics. In this paper we present a systematic methodology to analyze the main factors and parameters that affect the accuracy of the EEG source-mapping solutions. We argue that these factors are not independent and their effect must be evaluated in a unified way. To do so requires significant computational capabilities to explore the landscape of the problem, to quantify uncertainty effects, and to evaluate alternative algorithms. We demonstrate that bringing HPC to this domain will enable such investigation and will allow new avenues for neuroinformatics research. Two algorithms to the electromagnetics forward problem (the heart of the source localization inverse), incorporating tissue inhomogeneity and impedance anisotropy, are presented and their parallel implementations described. The head model forward solvers are evaluated and their performance analyzed.