Gender differences in brain functional organization during verbal and spatial cognitive challenges.

This is a quantitative EEG study of gender-related differences in brain function. It is novel in that to elicit gender differences, it was necessary to apply a spatial filter to the EEGs that was effective for suppressing components common to different cognitive states. The study involved estimates of both the source-current power density in the brain and the complex coherence between different regions in the brain, the latter probably unique in EEG source analysis. Gender effects are shown in terms of differences in both lateralized source power and complex coherence in response to verbal and spatial cognitive challenges. The results provide evidence that verbal and spatial challenges are more lateralized in males than in females, that females are more verbal than males, that males are more spatial than females, that females verbalize more interpretively than males and that males verbalize more consequentially than females.

EEG source analysis of chronic fatigue syndrome.

Sixty-one dextral, unmedicated women with chronic fatigue syndrome (CFS) diagnosed according to the Fukuda criteria (1994) and referred for investigation by rheumatologists and internists were studied with quantitative EEG (43 channels) at rest with eyes open and during verbal and spatial cognitive activation. The EEGs from the patients were compared with recordings from 80 dextral healthy female controls. Only those subjects who could provide 20 1-s artefact-free segments of EEG were admitted into the study. The analysis consisted of the identification of the spatial patterns in the EEGs that maximally differentiated the two groups and the estimation of the cortical source distributions underlying these patterns. Spatial patterns were analyzed in the alpha (8-13Hz) and beta (14-20Hz) bands and the source distributions were estimated using the Borgiotti-Kaplan BEAMFORMER algorithm. The results indicate that the spatial patterns identified were effective in separating the two groups, providing a minimum correct retrospective classification rate of 72% in both frequency bands while the subjects were at rest to a maximum of 83% in the alpha band during the verbal cognitive condition. Underlying cortical source distributions showed significant differences between the two groups in both frequency bands and in all cognitive conditions. Lateralized cortical differences were evident between the two groups in the both frequency bands during both the verbal and spatial cognitive conditions. During these active cognitive conditions, the CFS group showed significantly greater source-current activity than the controls in the left frontal-temporal-parietal regions of the cortex.

The effect of tissue anisotropy on the EEG inverse problem.

Electroencephalogram (EEG) source analysis is typically done using head models with isotropic tissue conductivities. This ignores the fact that some tissues are known to be highly anisotropic. In this study, we investigate the effect that tissue anisotropy has on the EEG inverse problem. EEG electrode voltages are simulated using the forward problem solution for an anisotropic head model, while the inverse problem is solved for an isotropic head model. The error associated with neglecting anisotropy is quantified with the source localization error. A realistic head model is generated from magnetic resonance images and anisotropic conductivity values are estimated from diffusion tensor images. All calculations are done with the finite volume method on a cubic grid with 1 mm resolution. We determine that neglecting to account for anisotropy can cause considerable source localization errors, indicating that the anisotropic conductivities should not be ignored in EEG source analysis.

A combined SPM-ICA approach to fMRI.

Independent component analysis (ICA) and statistical parametric mapping (SPM) are two commonly used methods of analyzing fMRI measurements. Typically, these methods are applied separately to the measurements to produce brain maps indicating active brain regions in response to a stimulus or a performed task. However, ICA can also be used to develop a hemodynamic response model that can be used as a regressor in SPM of fMRI measurements. This may lead to a more accurate method of localizing brain activity that corresponds to performing a task or to various pathologies. In this study, BOLD fMRI data were acquired from a subject performing a finger flexion task in a block design paradigm. Both spatial and temporal ICA was performed on the subject's BOLD fMRI measurements. Two hemodynamic response model signals were generated from ICA results to use as regressors in SPM of the subject data. IC maps and SPM-generated brain maps of the subject data using the canonical hemodynamic response model and the ICA-derived models were compared. In all cases, there was significant overlap in voxel activations.

A comparison of adaptive and non-adaptive EEG source localization algorithms using a realistic head model.

An accurate and robust electroencephalogram (EEG) source localization algorithm would be a definite asset for the surgical treatment of patients with epilepsy. Due to the underdetermined nature of the EEG inverse problem, a variety of algorithms with unique constraints and assumptions are applied to select the current dipole source distribution that best accounts for the scalp recordings. We investigated four algorithms: two non-adaptive algorithms: the minimum norm and LORETA as well as two adaptive algorithms: the Borgiotti-Kaplan and eigenspace projection beamformers. Compared over a range of SNR values and single source locations, we found that the eigenspace projection beamformer exhibited superior localizing capabilities compared to the other three algorithms while minimizing source current dispersion. The size of the data window required to accurately localize using the adaptive beamformers was also investigated to improve algorithm efficiency and minimize stationary source assumptions.

A high-resolution anisotropic finite-volume head model for EEG source analysis.

Solution of the electroencephalogram (EEG) forward problem in a realistic head model is necessary for accurate source analysis. Realistic head models are usually derived from volumetric magnetic resonance images that provide a voxel resolution of about 1 mm3. The availability of an electrical head model with this resolution would therefore be extremely advantageous. Head models with resolution in the millimeter range that incorporate the anisotropic properties of their elements have been formulated with the finite element method (FEM). However, these FEM models are fraught with complications related to irregular grids and meshes, along with the incumbent segmentation problems. Presented here is a finite volume method (FVM) formulation of the realistic head model in cubic elements that can ameliorate some of these problems, can incorporate tissue anisotropy, and is both physically intuitive and simple to implement.

A computationally efficient method for accurately solving the EEG forward problem in a finely discretized head model.

OBJECTIVE: Solution of the forward problem using realistic head models is necessary for accurate EEG source analysis. Realistic models are usually derived from volumetric magnetic resonance images that provide a voxel resolution of about 1 mm3. Electrical models could, therefore contain, for a normal adult head, over 4 million elements. Solution of the forward problem using models of this magnitude has so far been impractical due to issues of computation time and memory. METHODS: A preconditioner is proposed for the conjugate-gradient method that enables the forward problem to be solved using head models of this magnitude. It is applied to the system matrix constructed from the head anatomy using finite differences. The preconditioner is not computed explicitly and so is very efficient in terms of memory utilization. RESULTS: Using a spherical head model discretized into over 4 million volumes, we have been able to obtain accurate forward solutions in about 60 min on a 1 GHz Pentium III. L2 accuracy of the solutions was better than 2%. CONCLUSIONS: Accurate solution of the forward problem in EEG in a finely discretized head model is practical in terms of computation time and memory. SIGNIFICANCE: The results represent an important step in head modeling for EEG source analysis.

A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated men with schizophrenia.

Imaging studies and quantitative electroencephalography (EEG) have often, but not consistently, implicated the left hemisphere and the prefrontal cortex in schizophrenia. To help clarify this picture, a spatial filter shown to be effective for enhancing differences between EEG populations was combined with low-resolution electromagnetic tomography and used to compare the source-current densities from a group of 57 male subjects with schizophrenia and a group of 65 matched controls. To elicit differences, comparisons were made during resting conditions and during verbal and spatial cognitive challenges to the subjects. Estimates of the source-current density were derived from 43-electrode recordings of the EEG reduced to the delta, alpha and beta frequency bands. The patients were unmedicated and were selected according to DSM-IV criteria. As a group, they were severe, chronic states with both deficit negative and superimposed florid psychotic symptomatology. The results confirm that schizophrenia is a left-hemispheric disorder centered in the temporal and frontal lobes. They also suggest that, in schizophrenia, functions normally performed by these regions in controls are assumed by homologous regions in the opposite hemispheres.

A source-imaging (low-resolution electromagnetic tomography) study of the EEGs from unmedicated males with depression.

Imaging studies and quantitative EEG have often, but not consistently, implicated the right hemisphere and the left prefrontal cortex in depression. To help clarify this picture, a spatial filter shown to be effective for enhancing differences between EEG populations was combined with an electrical tomographic approach called low-resolution electromagnetic tomography and used to compare the source-current densities from a group of 25 male subjects with depression and a group of 65 matched controls. To elicit differences, comparisons were made during resting conditions and during verbal and spatial cognitive challenges to the subjects. Estimates of the source-current density were derived from 43-electrode recordings of the EEG reduced to the delta, alpha and beta frequency bands. The depressed subjects were unmedicated and selected according to DSM IV criteria. Regions of significantly increased current density in depression compared to controls were generally right hemispheric, while regions of significantly decreased current density were generally frontal and left hemispheric. A within-group comparison of the depressed subjects during the two cognitive challenges suggested a left anterior functional hypoactivation in depression. Retrospective classification of the two groups indicated that the spatial challenge best separated the groups irrespective of frequency band.