Modelling cardiac signal as a confound in EEG-fMRI and its application in focal epilepsy studies.

Cardiac noise has been shown to reduce the sensitivity of functional Magnetic Resonance Imaging (fMRI) to an experimental effect due to its confounding presence in the blood oxygenation level-dependent (BOLD) signal. Its effect is most severe in particular regions of the brain and a method is yet to take it into account in routine fMRI analysis. This paper reports the development of a general and robust technique to improve the reliability of EEG-fMRI studies to BOLD signal correlated with interictal epileptiform discharges (IEDs). In these studies, ECG is routinely recorded, enabling cardiac effects to be modelled, as effects of no interest. Our model is based on an over-complete basis set covering a linear relationship between cardiac-related MR signal and the phase of the cardiac cycle or time after pulse (TAP). This method showed that, on average, 24.6 +/- 10.9% of grey matter voxels contained significant cardiac effects and 22.3 +/- 24.1% of those voxels exhibiting significantly IED-correlated BOLD signal also contained significant cardiac effects. We quantified the improvement of the TAP model over the original model, without cardiac effects, by evaluating changes in efficiency, with respect to estimating the contrast of the effects of interest. Over voxels containing significant, cardiac-related signal, efficiency was improved by 18.5 +/- 4.8%. Over the remaining voxels, no improvement was demonstrated. This suggests that, while improving sensitivity in particular regions of the brain, there is no risk that the TAP model will reduce sensitivity elsewhere.

The effect of layers in imaging brain function using electrical impedance tomograghy.

Electrical impedance tomography (EIT) has promise for imaging brain function with rings of scalp electrodes, but hitherto human images have been collected and reconstructed using a simple algorithm in which the head was modelled as a homogeneous sphere. The purpose of this work was to assess the improvement in image quality which could be achieved by adding layers to represent the cerebro-spinal fluid (CSF), skull and scalp in the forward model employed by the reconstruction algorithm. Solutions to the forward model were produced analytically and using the linear finite element method (FEM). This was undertaken for computer simulated data when a spherical conductivity change of 10%, radius 5 mm, was moved through 29 positions within a head modelled as four concentric spheres of radius 80-92 mm in order to verify the accuracy of the linear FEM by comparison with the analytical method. Test data were also recorded in a 93.5 mm, spherical, saline-filled tank in which the skull was simulated by a hollow sphere of plaster of Paris, 5 mm thick and a 20 x 20 mm right-cylindrical Perspex object, a 100% conductivity decrease, was moved through 39 positions. The best images were achieved by reconstruction with a four- or three-shell analytical model, giving a spatial accuracy of 5.8 +/- 2.2 mm for computer simulated or 14.0 +/- 5.8 mm for tank data. Mean FWHM was 57 mm and 91 mm in the XY-plane and along the z-axis, respectively. Reconstruction with a homogeneous analytical model gave localization errors greater by about 50-300%, but a reduction in FWHM of about 5% of the image diameter. Unexpectedly, reconstruction with FEM models gave poorer results similar to the analytical homogeneous case. This confirms that addition of shells to the forward model improves image quality as expected with an analytical model for reconstruction, but that the FEM method employed, which used a medium mesh and a linear element computation, requires improvement in order to yield the expected benefits.

Neuromagnetic field strength outside the human head due to impedance changes from neuronal depolarization.

The holy grail of neuroimaging would be to have an imaging system, which could image neuronal electrical activity over milliseconds. One way to do this would be by imaging the impedance changes associated with ion channels opening in neuronal membranes in the brain during activity. In principle, we could measure this change by using electrical impedance tomography (EIT) but it is close to its threshold of detectability. With the inherent limitation in the use of electrodes, we propose a new scheme based on recording the magnetic field resulting from an injected current with superconducting quantum interference devices (SQUIDs), used in magnetoencephalography (MEG). We have performed a feasibility study using computer simulation. The head was modelled as concentric spheres to mimic the scalp, skull, cerebrospinal fluid and brain using the finite element method. The magnetic field 1 cm away from the scalp was estimated. An impedance change of 1% in a 2 cm radius volume in the brain was modelled as the region of depolarization. A constant current of 100 microA was injected into the head from diametrically opposite electrodes. The model predicts that the standing magnetic field is about 10 pT and changed by about 3 fT (0.03%) on depolarization. The spectral noise density in a typical MEG system in the frequency band 1-100 Hz is about 7 fT, so this places the change at the limit of detectability. This is similar to electrical recording, as in conventional EIT systems, but there may be advantages to MEG in that the magnetic field directly traverses the skull and instrumentation errors from the electrode-skin interface will be obviated.

A multi-shell algorithm to reconstruct EIT images of brain function.

Electrical impedance tomography (EIT) may be used to image brain function, but an important consideration is the effect of the highly resistive skull and other extracerebral layers on the flow of injected current. We describe a new reconstruction algorithm, based on a forward solution which models the head as four concentric, spherical shells, with conductivities of the brain, cerebrospinal fluid, skull and scalp. The model predicted that the mean current travelling in the brain in the diametric plane for current injection from polar electrodes was 5.6 times less than if the head was modelled as a homogeneous sphere; this suggests that an algorithm based on this should be more accurate than one based on a homogeneous sphere model. In images reconstructed from computer-simulated data or data from a realistic saline-filled tank containing a real skull, a Perspex rod was localized to within 17% or 20% of the tank diameter of its true position, respectively. Contrary to expectation, the tank images were less accurate than those obtained with a reconstruction algorithm based on a homogeneous sphere. It is not yet clear if the theoretical advantages of this algorithm will yield practical advantages for head EIT imaging; it may be necessary to proceed to more complex algorithms based on numerical models which incorporate realistic head geometry. If so, this analytical forward model and algorithm may be used to validate numerical solutions.