J.C. de Munck

Magnetoencephalographic evaluation of resting-state functional connectivity in Alzheimer's disease

Statistical interdependencies between magnetoencephalographic signals recorded over different brain regions may reflect the functional connectivity of the resting-state networks. We investigated topographic characteristics of disturbed resting-state networks in Alzheimers disease patients in different frequency bands. Whole-head 151-channel MEG was recorded in 18 Alzheimer patients (mean age 72.1 years, SD 5.6; 11 males) and 18 healthy controls (mean age 69.1 years, SD 6.8; 7 males) during a no-task eyes-closed resting state. Pair-wise interdependencies of MEG signals were computed in six frequency bands (delta, theta, alpha1, alpha2, beta and gamma) with the synchronization likelihood (a nonlinear measure) and coherence and grouped into long distance (intra- and interhemispheric) and short distance interactions. In the alpha1 and beta band, Alzheimer patients showed a loss of long distance intrahemispheric interactions, with a focus on left fronto-temporal/parietal connections. Functional connectivity was increased in Alzheimer patients locally in the theta band (centro-parietal regions) and the beta and gamma band (occipito-parietal regions). In the Alzheimer group, positive correlations were found between alpha1, alpha2 and beta band synchronization likelihood and MMSE score. Resting-state functional connectivity in Alzheimers disease is characterized by specific changes of long and short distance interactions in the theta, alpha1, beta and gamma bands. These changes may reflect loss of anatomical connections and/or reduced central cholinergic activity and could underlie part of the cognitive impairment.

Correlating the alpha rhythm to BOLD using simultaneous EEG/fMRI: Inter-subject variability

Simultaneous recording of electroencephalogram/functional magnetic resonance images (EEG/fMRI) was applied to identify blood oxygenation level-dependent (BOLD) changes associated with spontaneous variations of the alpha rhythm, which is considered the hallmark of the brain resting state. The analysis was focused on inter-subject variability associated with the resting state. Data from 7 normal subjects are presented. Confirming earlier findings, three subjects showed a negative correlation between the BOLD signal and the average power time series within the alpha band (8--12 Hz) in extensive areas of the occipital, parietal and frontal lobes. In small thalamic areas, the BOLD signal was positively correlated with the alpha power. For subjects 3 and 4, who displayed two different states during the data acquisition time, it was shown that the corresponding correlation patterns were different, thus demonstrating the state dependency of the results. In subject 5, the changes in BOLD were observed mainly in the frontal and temporal lobes. Subject 6 only showed positive correlations, thus contradicting the negative BOLD alpha power cortical correlations that were found in most subjects. Results suggest that the resting state varies over subjects and, sometimes, even within one subject. As the resting state plays an important role in many fMRI experiments, the inter-subject variability of this state should be addressed when comparing fMRI results from different subjects.

The electric resistivity of human tissues (100 Hz-10 MHz): a meta-analysis of review studies

The electric resistivity of various human tissues has been reported in many studies, but on comparison large differences appear between these studies. The aim of this study was to investigate systematically the resistivities of human tissues as published in review studies (100 Hz-10 MHz). A data set of 103 resistivities for 21 different human tissues was compiled from six review studies. For each kind of tissue the mean and its 95% confidence interval were calculated. Moreover, an analysis of covariance showed that the calculated means were not statistically different for most tissues, namely skeletal (171 omega cm) and cardiac (175 omega cm) muscle, kidney (211 omega cm), liver (342 omega cm), lung (157 omega cm) and spleen (405 omega cm), with bone (> 17,583 omega cm), fat (3,850 omega cm) and, most likely, the stratum corneum of the skin having higher resistivities. The insignificance of differences between various tissue means could imply an equality of their resistivities, or, alternatively, could be the result of the large confidence intervals which obscured real existing differences. In either case, however, the large 95% confidence intervals reflected large uncertainties in our knowledge of resistivities of human tissues. Applications based on these resistivities in bioimpedance methods, EEG and EKG, should be developed and evaluated with these uncertainties in mind.

Mathematical dipoles are adequate to describe realistic generators of human brain activity

It is investigated whether a mathematical dipole description is adequate for the localization of brain activity on the basis of visually evoked potentials (VEPs). Extended sources (dipole disks and dipole annuli) are stimulated and fitted with a mathematical dipole. It is found that the deviation between the positions of the disks and annuli and the equivalent dipole is very small. Also, the differences in the direction and amplitude may be neglected. The position of the extended source with respect to the electrode grid does not much influence these conclusions.<<ETX>>

The potential distribution in a layered anisotropic spheroidal volume conductor

Formulas are derived that express the potential distribution in a layered spherical and spheroidal anisotropic volume conductor: the number of layers is arbitrary and each layer may be anisotropic. These expressions are a generalization of existing formulas for localization of sources underlying evoked potentials or electro‐encephalographic data. The formulas are presented in a convenient form so that if they are applied in practice the likelihood of software errors is minimal.

A linear discretization of the volume conductor boundary integral equation using analytically integrated elements (electrophysiology application)

A method is presented to compute the potential distribution on the surface of a homogeneous isolated conductor of arbitrary shape. The method is based on an approximation of a boundary integral equation as a set of linear algebraic equations. The potential is described as a piecewise linear or quadratic function. The matrix elements of the discretized equation are expressed as analytical formulas. >

The hemodynamic response of the alpha rhythm: An EEG/fMRI study

EEG was recorded during fMRI scanning of 16 normal controls in resting condition with eyes closed. Time variations of the occipital alpha band amplitudes were correlated to the fMRI signal variations to obtain insight into the hemodynamic correlates of the EEG alpha activity. Contrary to earlier studies, no a priori assumptions were made on the expected shape of the alpha band response function (ARF). The ARF of different brain regions and subjects were explored and compared. It was found that: (1) the ARF of the thalamus is mainly positive. (2) The ARFs at the occipital and left and right parietal points are similar in amplitude and timing. (3) The peak time of the thalamus is a few seconds earlier than that of occipital and parietal cortex. (4) No systematic BOLD activity was found preceding the alpha band activity, although in the two subjects with the strongest alpha band power such correlation was present. (5) There is a strong and immediate positive correlation at the eyeball, and a strong negative correlation at the back of the eye. Furthermore, it was found that in one subject the cortical ARF was positive, contrary to the other subjects. Finally, a cluster analysis of the observed ARF, in combination with a Modulated Sine Model (MSM) fit to the estimated ARF, revealed that within the cortex the ARF peak time shows a spatial pattern that may be interpreted as a traveling wave. The spatial pattern of alpha band response function represents the combined effect of local differences in electrical alpha band activity and local differences in the hemodynamic response function (HRF) onto these electrical activities. To disentangle the contributions of both factors, more advanced integration of EEG inverse modeling and hemodynamic response modeling is required in future studies.

In vivo measurement of the brain and skull resistivities using an EIT-based method and realistic models for the head

In vivo measurements of equivalent resistivities of skull (/spl rho//sub skull/) and brain (/spl rho//sub brain/) are performed for six subjects using an electric impedance tomography (EIT)-based method and realistic models for the head. The classical boundary element method (BEM) formulation for EIT is very time consuming. However, the application of the Sherman-Morrison formula reduces the computation time by a factor of 5. Using an optimal point distribution in the BEM model to optimize its accuracy, decreasing systematic errors of numerical origin, is important because cost functions are shallow. Results demonstrate that /spl rho//sub skull///spl rho//sub brain/ is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in /spl rho//sub brain/ (average = 301 /spl Omega/ /spl middot/ cm, SD = 13%) and /spl rho//sub skull/ (average = 12230 /spl Omega/ /spl middot/ cm, SD = 18%) is decreased by half, when compared with the results using the sphere model, showing that the correction for geometry errors is essential to obtain realistic estimations. However, a factor of 2.4 may still exist between values of /spl rho//sub skull///spl rho//sub brain/ corresponding to different subjects. Earlier results show the necessity of calibrating /spl rho//sub brain/ and /spl rho//sub skull/ by measuring them in vivo for each subject, in order to decrease errors associated with the electroencephalogram inverse problem. We show that the proposed method is suited to this goal.

A fast method to compute the potential in the multisphere model (EEG application)

A series expansion is derived for the potential distribution, caused by a dipole source in a multilayered sphere with piecewise constant conductivity. When the radial coordinate of the source approaches the radial coordinate of the field point the spherical harmonics expansion converges only very slowly. It is shown how the convergence can be improved by first calculating an asymptotic approximation of the potential and using the so-called addition-subtraction method. Since the asymptotic solution is an approximation of the true solution, it gives some insight on the dependence of the potential on the conductivities. The formulas are given in Cartesian coordinates, so that difficulties with coordinate transformations are avoided. Attention is paid to the (fast) computation of the partial derivatives of the potential, which is useful for inverse algorithms.<<ETX>>

Interactions between different EEG frequency bands and their effect on alpha-fMRI correlations

In EEG/fMRI correlation studies it is common to consider the fMRI BOLD as filtered version of the EEG alpha power. Here the question is addressed whether other EEG frequency components may affect the correlation between alpha and BOLD. This was done comparing the statistical parametric maps (SPMs) of three different filter models wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. EEG and fMRI were co-registered in a 30 min resting state condition in 15 healthy young subjects. Power variations in the delta, theta, alpha, beta and gamma bands were extracted from the EEG and used as regressors in a general linear model. Statistical parametric maps (SPMs) were computed using three different filter models, wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. Results show that the SPMs of different EEG frequency bands, when significant, are very similar to that of the alpha rhythm. This is true in particular for the beta band, despite the fact that the alpha harmonics were discarded. It is shown that inclusion of EEG frequency bands as confounder in the fMRI-alpha correlation model has a large effect on the resulting SPM, in particular when for each frequency band the HRF is extracted from the data. We conclude that power fluctuations of different EEG frequency bands are mutually highly correlated, and that a multi frequency model is required to extract the SPM of the frequency of interest from EEG/fMRI data. When no constraints are put on the shapes of the HRFs of the nuisance frequencies, the correlation model looses so much statistical power that no correlations can be detected.