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A unique whole-head Magnetoencephalography (MEG) system incorporating a superconducting imaging surface (SIS) has been designed and built at Los Alamos with the goal of dramatically improving source localization accuracy while mitigating limitations of current systems (e.g. low signal-to-noise, cost, bulk). Magnetoencephalography (MEG) measures the weak magnetic fields emanating from the brain as a direct consequence of the neuronal currents resulting from brain function[1]. The extraordinarily weak magnetic fields are measured by an array of SQUID (Superconducting QUantum Interference Device) sensors. The position and vector characteristics of these neuronal sources can be estimated from the inverse solution of the field distribution at the surface of the head. In addition, MEG temporal resolution is unsurpassed by any other method currently used for brain imaging. Although MEG source reconstruction is limited by solutions of the electromagnetic inverse problem, constraints used for source localization produce reliable results. The Los Alamos SIS-MEG system[2] is based on the principal that fields from nearby sources measured by a SQUID sensor array while the SIS shields the sensor array from distant noise fields. In general, Meissner currents flow in the surface of superconductors, preventing any significant penetration of magnetic fields. A hemispherical SIS with a brim, or helmet, more » surrounds the SQUID sensor array largely sheilding the SQUIDs from sources outside the helmet while measuring fields from nearby sources within the helmet. We have implemented a finite element model (FEM) description of the SIS using the exact as-built geometry to accurately describe how the SIS impacts the forward physics of source models. The FEM is used to calculate the distribution of Meissner currents in the complicated surface geometry of the SIS such that B{perpendicular} = 0 at the surface. This model of the forward physics is described elsewhere in these proceedings [3]. In this paper, we present the results of localizing well characterized phantom sources using the SIS-MEG system, the SIS forward model, and a simple inverse method. « less

Source Localization Precision of the Superconducting Imaging-Surface MEG System