Modeling transcranial magnetic stimulation from the induced electric fields to the membrane potentials along tractography-based white matter fiber tracts

Objective. Transcranial magnetic stimulation (TMS) is a promising non-invasive tool for modulating the brain activity. Despite the widespread therapeutic and diagnostic use of TMS in neurology and psychiatry, its observed response remains hard to predict, limiting its further development and applications. Although the stimulation intensity is always maximum at the cortical surface near the coil, experiments reveal that TMS can affect deeper brain regions as well. Approach. The explanation of this spread might be found in the white matter fiber tracts, connecting cortical and subcortical structures. When applying an electric field on neurons, their membrane potential is altered. If this change is significant, more likely near the TMS coil, action potentials might be initiated and propagated along the fiber tracts towards deeper regions. In order to understand and apply TMS more effectively, it is important to capture and account for this interaction as accurately as possible. Therefore, we compute, next to the induced electric fields in the brain, the spatial distribution of the membrane potentials along the fiber tracts and its temporal dynamics. Main results. This paper introduces a computational TMS model in which electromagnetism and neurophysiology are combined. Realistic geometry and tissue anisotropy are included using magnetic resonance imaging and targeted white matter fiber tracts are traced using tractography based on diffusion tensor imaging. The position and orientation of the coil can directly be retrieved from the neuronavigation system. Incorporating these features warrants both patient- and case-specific results. Significance. The presented model gives insight in the activity propagation through the brain and can therefore explain the observed clinical responses to TMS and their inter- and/or intra-subject variability. We aspire to advance towards an accurate, flexible and personalized TMS model that helps to understand stimulation in the connected brain and to target more focused and deeper brain regions

Low-parametric Induced Current-Magnetic Resonance Electrical Impedance Tomography for quantitative conductivity estimation of brain tissues using a priori information: a simulation study

Accurate estimation of the human head conductivity is important for the diagnosis and therapy of brain diseases. Induced Current - Magnetic Resonance Electrical Impedance Tomography (IC-MREIT) is a recently developed non-invasive technique for conductivity estimation. This paper presents a formulation where a low number of material parameters need to be estimated, starting from MR eddy-current field maps. We use a parameterized frequency dependent 4-Cole-Cole material model, an efficient independent impedance method for eddy-current calculations and a priori information through the use of voxel models. The proposed procedure circumvents the ill-posedness of traditional IC-MREIT and computational efficiency is obtained by using an efficient forward eddy-current solver

In vivo electrical conductivity imaging of animal tumor model at 7T using electrical properties tomography

Ex vivo studies have shown that various diseases alter the electrical properties of tissues compared to healthy nearby tissues. Therefore, electrical conductivity can be used as a diagnostic parameter for e.g. tumor diagnosis. For in vivo measurements, magnetic resonance electrical properties tomography (MREPT) was used and electrical conductivity was reconstructed from the B1+ phase. The technique was first evaluated using homogeneous and heterogeneous phantoms. Then a mouse with a tumor was scanned and the conductivity is reconstructed from the B1+ phase map. The reconstructed conductivity in the phantom experiments was in good agreement with the target conductivity map and the conductivity map of the animal revealed good agreement with the co-axial probe measurement. Our work confirms the possibility of accurate in vivo conductivity assessment in disease

Design and calibration of a mm-wave personal exposure meter for 5G exposure assessment in indoor diffuse environments

For the first time, a mm-wave personal exposure meter (mm-PEM) for the 5th generation of mobile networks (5G) exposure assessment in indoor diffuse fields is presented. The design is based on simulations and on-phantom calibration measurements in a mm-wave reverberation chamber (RC) at 60 GHz. The mm-PEM consists of an array of nine antennas on the body. Using the mm-PEM, the incident power density (IPD) is measured in the unloaded RC, for the antenna(s) on the phantom and RC loaded with phantom. The uncertainty of the mm-PEM is then determined in terms of its response, which is defined as the ratio of antenna aperture for the above measurement scenarios. Using nine antennas, the designed meter has a response of 1.043 (0.17 dB) at 60 GHz, which is very close to 1 (0 dB), the desired ideal response value. The mm-PEM measured an IPD of 96.6 W m(-2) at 60 GHz in the RC, for an input power of 1 W. In addition, the average absorption cross-section of the phantom is determined as 225 cm(2), which is an excellent agreement with its physical dimensions