The Feasibility of Fast Neural Magnetic Detection Electrical Impedance Tomography: A Modelling Study

Magnetic Detection Electrical Impedance Tomography (MDEIT) is a possible method to non-invasively image fast neural activity in the human brain by injecting current with scalp electrodes and measuring the change in the magnetic field due to neural activity. A modelling study was performed on an anatomically realistic head model, assessing the SNR and reconstructed image quality for MDEIT and EIT with 3 different realistic noise cases. EIT produced a larger SNR than MDEIT for 2 out of the 3 noise cases. However, MDEIT was found to reconstruct images with a significantly lower error for all the reconstruction cases considered (P< 0.001)

Characterising the frequency response of impedance changes during evoked physiological activity in the rat brain

OBJECTIVE: Electrical impedance tomography (EIT) can image impedance changes associated with evoked physiological activity in the cerebral cortex using an array of epicortical electrodes. An impedance change is observed as the externally applied current, normally confined to the extracellular space is admitted into the conducting intracellular space during neuronal depolarisation. The response is largest at DC and decreases at higher frequencies due to capacitative transfer of current across the membrane. Biophysical modelling has shown that this effect becomes significant above 100 Hz. Recordings at DC, however, are contaminated by physiological endogenous evoked potentials. By moving to 1.7 kHz, images of somatosensory evoked responses have been produced down to 2 mm with a resolution of 2 ms and 200 μm. Hardware limitations have so far restricted impedance measurements to frequencies  2 kHz using improved hardware. APPROACH: Impedance changes were recorded during forepaw somatosensory stimulation in both cerebral cortex and the VPL nucleus of the thalamus in anaesthetised rats using applied currents of 1 kHz to 10 kHz. MAIN RESULTS: In the cortex, impedance changed by -0.04 ± 0.02 % at 1 kHz, reached a peak of -0.13 ± 0.05 % at 1475 Hz and decreased to -0.05 ± 0.02 % at 10 kHz. At these frequencies, changes in the thalamus were -0.26 ± 0.1%, -0.4 ± 0.15 % and -0.08 ± 0.03 % respectively. The signal-to-noise ratio was also highest at 1475 Hz with values of -29.5 ± 8 and -31.6 ±10 recorded from the cortex and thalamus respectively. Signficance: This indicates that the optimal frequency for imaging cortical and thalamic evoked activity using fast neural EIT is 1475 Hz

Investigation of optimal parameters for finite element solution of the forward problem in magnetic field tomography based on magnetoencephalography

This paper presents an investigation of optimal parameters for finite element (FE) solution of the forward problem in magnetic field tomography (MFT) brain imaging based on magnetoencephalography (MEG). It highlights detailed analyses of the main parameters involved and evaluates their optimal values for various cases of FE model solutions (e.g., steady-state, transient, etc.). In each case, a detail study of some of the main parameters and their effects on FE solution and its accuracy are carefully tested and evaluated. These parameters include: total number and size of 3D FE elements used, number and size of elements used in surface discretisation (of both white and grey matters of the brain), number and size of elements used for approximation of current sources, number of anisotropic properties used in steady-state and transient solutions, and the time steps used in transient analyses. The optimal values of these parameters in relation to solution accuracy and mesh convergence criteria have been found and presented

Are patient specific meshes required for EIT head imaging?

Head imaging with electrical impedance tomography (EIT) is usually done with time-differential measurements, to reduce time-invariant modelling errors. Previous research suggested that more accurate head models improved image quality, but no thorough analysis has been done on the required accuracy. We propose a novel pipeline for creation of precise head meshes from magnetic resonance imaging and computed tomography scans, which was applied to four different heads. Voltages were simulated on all four heads for perturbations of different magnitude, haemorrhage and ischaemia, in five different positions and for three levels of instrumentation noise. Statistical analysis showed that reconstructions on the correct mesh were on average 25% better than on the other meshes. However, the stroke detection rates were not improved. We conclude that a generic head mesh is sufficient for monitoring patients for secondary strokes following head trauma

Selective Neuromodulation of the Vagus Nerve

Vagus nerve stimulation (VNS) is an effective technique for the treatment of refractory epilepsy and shows potential for the treatment of a range of other serious conditions. However, until now stimulation has generally been supramaximal and non-selective, resulting in a range of side effects. Selective VNS (sVNS) aims to mitigate this by targeting specific fiber types within the nerve to produce functionally specific effects. In recent years, several key paradigms of sVNS have been developed-spatially selective, fiber-selective, anodal block, neural titration, and kilohertz electrical stimulation block-as well as various stimulation pulse parameters and electrode array geometries. sVNS can significantly reduce the severity of side effects, and in some cases increase efficacy of the treatment. While most studies have focused on fiber-selective sVNS, spatially selective sVNS has demonstrated comparable mitigation of side-effects. It has the potential to achieve greater specificity and provide crucial information about vagal nerve physiology. Anodal block achieves strong side-effect mitigation too, but is much less specific than fiber- and spatially selective paradigms. The major hurdle to achieving better selectivity of VNS is a limited knowledge of functional anatomical organization of vagus nerve. It is also crucial to optimize electrode array geometry and pulse shape, as well as expand the applications of sVNS beyond the current focus on cardiovascular disease

Solution of the Forward Problem in Magnetic-Field Tomography (MFT) Based on Magnetoencephalography (MEG)

This paper presents the methodology and some of the results of accurate solution of the forward problem in magnetic-field tomography based on magnetoencephalography for brain imaging. The solution is based on modeling and computation of magnetic-field distribution in and around the head produced by distributed 2-D cortical and 3-D volume lead current sources. The 3-D finite-element model of the brain incorporates realistic geometry based on accurate magnetic resonance imaging data and inhomogeneous conductivity properties. The model allows arbitrary placement of line, surface, and volume current sources. This gives flexibility in the source current approximation in terms of size, orientation, placement, and spatial distribution

EIT-MESHER – Segmented FEM Mesh Generation and Refinement

EIT-MESHER (https://github.com/EIT-team/Mesher) is C++ software, based on the CGAL library, which generates high quality Finite Element Model tetrahedral meshes from binary masks of 3D volume segmentations. Originally developed for biomedical applications in Electrical Impedance Tomography (EIT) to address the need for custom, non-linear refinement in certain areas (e.g. around electrodes), EIT-MESHER can also be used in other fields where custom FEM refinement is required, such as Diffuse Optical Tomography (DOT)

Imaging of focal seizures with Electrical Impedance Tomography and depth electrodes in real time

Intracranial EEG is the current gold standard technique for localising seizures for surgery, but it can be insensitive to tangential dipole or distant sources. Electrical Impedance Tomography (EIT) offers a novel method to improve coverage and seizure onset localisation. The feasibility of EIT has been previously assessed in a computer simulation, which revealed an improved accuracy of seizure detection with EIT compared to intracranial EEG. In this study, slow impedance changes, evoked by cell swelling occurring over seconds, were reconstructed in real time by frequency division multiplexing EIT using depth and subdural electrodes in a swine model of epilepsy. EIT allowed to generate repetitive images of ictal events at similar time course to fMRI but without its significant limitations. EIT was recorded with a system consisting of 32 parallel current sources and 64 voltage recorders. Seizures triggered with intracranial injection of benzylpenicillin (BPN) in five pigs caused a repetitive peak impedance increase of 3.4±1.5 mV and 9.5±3% (N=205 seizures); the impedance signal change was seen already after a single, first seizure. EIT enabled reconstruction of the seizure onset 9±1.5 mm from the BPN cannula and 7.5±1.1 mm from the closest SEEG contact (p<0.05, n=37 focal seizures in three pigs) and it could address problems with sampling error in intracranial EEG. The amplitude of the impedance change correlated with the spread of the seizure on the SEEG (p <0.001, n=37). The results presented here suggest that combining a parallel EIT system with intracranial EEG monitoring has a potential to improve the diagnostic yield in epileptic patients and become a vital tool in improving our understanding of epilepsy