Paul H. Schimpf

The Influence of Brain Tissue Anisotropy on Human EEG and MEG

The influence of gray and white matter tissue anisotropy on the human electroencephalogram (EEG) and magnetoencephalogram (MEG) was examined with a high resolution finite element model of the head of an adult male subject. The conductivity tensor data for gray and white matter were estimated from magnetic resonance diffusion tensor imaging. Simulations were carried out with single dipoles or small extended sources in the cortical gray matter. The inclusion of anisotropic volume conduction in the brain was found to have a minor influence on the topology of EEG and MEG (and hence source localization). We found a major influence on the amplitude of EEG and MEG (and hence source strength estimation) due to the change in conductivity and the inclusion of anisotropy. We expect that inclusion of tissue anisotropy information will improve source estimation procedures.

Dipole models for the EEG and MEG

The current dipole is a widely used source model in forward and inverse electroencephalography and magnetoencephalography applications. Analytic solutions to the governing field equations have been developed for several approximations of the human head using ideal dipoles as the source model. Numeric approaches such as the finite-element and finite-difference methods have become popular because they allow the use of anatomically realistic head models and the increased computational power that they require has become readily available. Although numeric methods can represent more realistic domains, the sources in such models are an approximation of the ideal dipole. In this paper, we examine several methods for representing dipole sources in finite-element models and compare the resulting surface potentials and external magnetic field with those obtained from analytic solutions using ideal dipoles.

How Working Memory and the Cerebellum Collaborate to Produce Creativity and Innovation

Abstract It is proposed that (a) creativity and innovation are the result of continuously repetitive processes of working memory that are learned as cognitive control models in the cerebellum, (b) that these cerebellar control models consist of multiple-paired predictor (forward) models within the MOdular Selection and Identification for Control (MOSAIC) and hierarchical MOSAIC (HMOSAIC) cerebellar architectures that explore and test problem-solving requirements, and (c) when resulting newly formed predictor/controller models are fed forward to more efficiently control the operations of working memory, they lead to creative and innovative problem solving (including the experiences of “insight” and “intuition”). Within this framework, three of Einsteins classic autobiographical accounts of creative discovery are analyzed. It is concluded that the working memory/cerebellar explanation of creativity and innovation can begin to tie together: (1) behavioral and neuroimaging studies of working memory, (2) behavioral, clinical and neuroimaging studies of the cognitive functions of the cerebellum, and (3) autobiographical accounts of creativity. It is suggested that newly developed electromagnetic inverse techniques will be a necessary complement to functional brain imaging studies to further establish the validity of the theory.

Standardized shrinking LORETA-FOCUSS (SSLOFO): a new algorithm for spatio-temporal EEG source reconstruction

This paper presents a new algorithm called Standardized Shrinking LORETA-FOCUSS (SSLOFO) for solving the electroencephalogram (EEG) inverse problem. Multiple techniques are combined in a single procedure to robustly reconstruct the underlying source distribution with high spatial resolution. This algorithm uses a recursive process which takes the smooth estimate of sLORETA as initialization and then employs the re-weighted minimum norm introduced by FOCUSS. An important technique called standardization is involved in the recursive process to enhance the localization ability. The algorithm is further improved by automatically adjusting the source space according to the estimate of the previous step, and by the inclusion of temporal information. Simulation studies are carried out on both spherical and realistic head models. The algorithm achieves very good localization ability on noise-free data. It is capable of recovering complex source configurations with arbitrary shapes and can produce high quality images of extended source distributions. We also characterized the performance with noisy data in a realistic head model. An important feature of this algorithm is that the temporal waveforms are clearly reconstructed, even for closely spaced sources. This provides a convenient way to estimate neural dynamics directly from the cortical sources.

Role of Soft Bone, CSF and Gray Matter in EEG Simulations

Effects of soft skull bone, cerebrospinal fluid (CSF) and gray matter on scalp potentials were examined with highly heterogeneous finite element models of an adult male subject. These models were constructed from segmented T1 weighted magnetic resonance images. Models had voxel resolutions of 1x1x3.2 mm with a total of about 1.5 million voxels. The scalp potentials, due to a dipolar source in the motor cortex area, were computed with an adaptive finite element solver. It was found that the scalp potentials were significantly affected by the soft bone, CSF and gray matter tissue boundaries in the models.

Predicting cardiothoracic voltages during high energy shocks: methodology and comparison of experimental to finite element model data

Finite element modeling has been used as a method to investigate the voltage distribution within the thorax during high energy shocks. However, there have been few quantitative methods developed to assess how well the calculations derived from the models correspond to measured voltages. Here, the authors present a methodology for recording thoracic voltages and the results of comparisons of these voltages to those predicted by finite element models. The authors constructed detailed 3D subject-specific thorax models of 6 pigs based on their individual CT images. The models were correlated with the results of experiments conducted on the animals to measure the voltage distribution in the thorax at 52 locations during synchronized high energy shocks. One transthoracic and two transvenous electrode configurations were used in the study. The measured voltage values were compared to the model predictions resulting in a correlation coefficient of 0.927/spl plusmn/0.036 (average/spl plusmn/standard deviation) and a relative rms error of 22.13/spl plusmn/5.99%. The model predictions of voltage gradient within the myocardium were also examined revealing differences in the percent of the myocardium above a threshold value for various electrode configurations and variability between individual animals. This variability reinforces the potential benefit of patient-specific modeling.<<ETX>>

A recursive algorithm for the three-dimensional imaging of brain electric activity: shrinking LORETA-FOCUSS

Estimation of intracranial electric activity from the scalp electroencephalogram (EEG) requires a solution to the EEG inverse problem, which is known as an ill-conditioned problem. In order to yield a unique solution, weighted minimum norm least square (MNLS) inverse methods are generally used. This paper proposes a recursive algorithm, termed Shrinking LORETA-FOCUSS, which combines and expands upon the central features of two well-known weighted MNLS methods: LORETA and FOCUSS. This recursive algorithm makes iterative adjustments to the solution space as well as the weighting matrix, thereby dramatically reducing the computation load, and increasing local source resolution. Simulations are conducted on a 3-shell spherical head model registered to the Talairach human brain atlas. A comparative study of four different inverse methods, standard Weighted Minimum Norm, L1-norm, LORETA-FOCUSS and Shrinking LORETA-FOCUSS are presented. The results demonstrate that Shrinking LORETA-FOCUSS is able to reconstruct a three-dimensional source distribution with smaller localization and energy errors compared to the other methods.

Realistic computer modelling of electric and magnetic fields of human head and torso

Abstract Anatomically realistic computer models are needed for an accurate modeling of electric and magnetic fields of the human head and torso. Their applications are in the emerging field of functional tomography for non-invasive medical diagnostics. Some of these models tend to become very large and require supercomputers for solution. The future supercomputing challenge is to solve these models in a time frame such that patient specific models can be used for online clinical diagnostics and treatment planning. In this paper we present methods and tools for developing anatomically realistic torso and head models, numerical techniques to solve for potentials and currents in the models, and their clinical applications.

Geometric effects on resistivity measurements with four-electrode probes in isotropic and anisotropic tissues

We studied via computer simulation the effects of electrode diameter, electrode length, interelectrode spacing, and tissue size on the accuracy of measured tissue resistivities and anisotropy ratios obtained with the widely used four-electrode technique. Such measurements commonly assume an ideal situation in which the four electrodes are infinitesimally small and the tissue is semi-infinite. Our study shows that these geometric factors can significantly affect measured resistivities, particularly for anisotropic tissues. The measured anisotropy ratio is decreased by either (1) increasing the electrode diameter or length relative to the interelectrode spacing of the probe or (2) decreasing tissue size. We have provided an equation for estimating errors in the measured anisotropy ratio from the parameters of electrode and tissue geometries. The simulation findings are supported by our in vitro experimental results.

Object-free adaptive meshing in highly heterogeneous 3-D domains

Traditional approaches to the generation of finite element meshes are well suited for modeling the homogeneous or mildly heterogeneous domains presented by man-made objects, but are difficult to apply to the complex 3-D domains encountered in some biomedical applications. In this paper, we describe an adaptive algorithm that automates the modeling of these domains. The method differs from traditional approaches in that no explicit description is required of the boundaries between objects with dissimilar material properties. The algorithm uses images of the tissue class to build irregular meshes, and continuity is enforced by constraining the solution at irregular nodes. Local estimates of the error in the flux solution are used to refine the mesh. For an analytic problem with a rapid change along a spherical boundary, the adaptive method converges to a 1% voltage error using 25% of the degrees of freedom required by a uniform refinement, and to a 5% voltage gradient error using 11% of the degrees of freedom. For a defibrillation model in a pig thorax, the voltage gradient solution in the ventricles of the heart converges to within 5% of a uniform mesh solution using less than 8% of the memory and processing resources required by a uniform mesh, which has been the only practical alternative for subject-specific modeling.