Targeting deep brain regions in transcranial electrical neuromodulation using the reciprocity principle

MARIANO FERNÁNDEZ CORAZZA; SERGEI TUROVETS; PHAN LUU; ERIK ANDERSON; DON TUCKER

San Diego

Conferencia; International Conference con Computational Science (ICCS 2016); 2016

Targeting deep regions in the brain is a key challenge in noninvasive transcranialelectrical neuromodulation. We explore this problem by means of computersimulations within a detailed seven-tissue finite element head model (2 millionstetrahedrons) constructed from high resolution MRI and CT volumes. We solve theforward electrical stimulation and EEG problems governed by the quasi-static Poissonequation numerically using the first order Finite Element Method (FEM) with theGalerking approach. Given a dense array of EEG-electrode layout and location ofregions of interest inside the brain, we compute optimal current injection patternsbased on the reciprocity principle in EEG and compare results with optimization basedon the Least Squares (LS) or Linearly Constrained Minimum Variance (LCMV)algorithms. It is found that the reciprocity algorithms show good performancecomparable to the LCMV and LS solutions for deep brain targets which are generallycomputationally more expensive to obtain.