Electroencephalographic Source Reconstruction by the Finite-Element Approximation of the Elliptic Cauchy Problem

Yttrium-Iron Garnet Film Magnetometer for Registration of Magnetic Nano- and Submicron Particles: In Vitro and In Vivo Studies

In the current article, we present a new kind of magnetometer for quantitative detection of magnetic objects (magnetic nano- and submicron particles) in biological fluids and tissues. The sensor is based on yttrium-iron garnet film with optical signal registration system. Inheriting the working principle of a fluxgate magnetometers, the sensor works at a room-temperature, its wide dynamic range allows the measurements in an unshielded environment. A small size of sensitive element combined with a short recovery time after the excitation coils are off provide us with a potentially high spatial and temporal resolution of measurements. We show the feasibility of the developed devices by sensing the remanent magnetization of magnetic nanoparticles (MNPs) both in vitro (test tubes, dry MNPs) and in vivo (local injection of the MNPs into mice).

Conservative Finite Element Modeling of EEG and MEG on Unstructured Grids

For interpretation of electroencephalography (EEG) and magnetoencephalography (MEG) data, multiple solutions of the respective forward problems are needed. In this paper, we assess performance of the mixed-hybrid finite element method (MHFEM) applied to EEG and MEG modeling. The method provides an approximate potential and induced currents and results in a system with a positive semi-definite matrix. The system thus can be solved with a variety of standard methods (e.g. the preconditioned conjugate gradient method). The induced currents satisfy discrete charge conservation law making the method conservative. We studied its performance on unstructured tetrahedral grids for a layered spherical head model as well as a realistic head model. We also compared its accuracy versus the conventional nodal finite element method ( P₁ FEM). To avoid modeling singular sources, we completed our computations with a subtraction approach; the derived expression for the MEG response different from earlier published and involves integration of finite quantities only. We conclude that although the MHFEM is more computationally demanding than the P₁ FEM, its use is justified for EEG and MEG modeling on low-resolution head models where P₁ FEM loses accuracy.