R.M. Heethaar

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.

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.

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.

Improved harmonic phase myocardial strain maps

Magnetic resonance tagging has proven a valuable tool in the quantification of myocardial deformation. However, time‐consuming postprocessing has discouraged the use of this technique in clinical routine. Recently, the harmonic phase (HARP) technique was introduced for automatic calculation of myocardial strain maps from tagged images. In this study, a comparison was made between HARP instantaneous strain maps calculated from single tagged images (SPAMM) and those calculated from subtracted tagged images (CSPAMM). The performance was quantified using simulated images of an incompressible cylinder in the ‘end‐systolic’ state with realistic image contrast and noise. The error in the second principal stretch ratio was 0.009 ± 0.032 (mean ± SD) for the SPAMM acquisition, and 0.007 ± 0.016 for CSPAMM at identical contrast‐to‐noise ratio. Furthermore, differences between the methods were illustrated with in vivo strain maps. Those calculated from CSPAMM images showed fewer artifacts and were less sensitive to the choice of cut‐off frequencies in the HARP band‐pass filter. A prerequisite for the method to become practical is that the CSPAMM images should be acquired in a single breathhold. Magn Reson Med 46:993–999, 2001.

The influence of through-plane motion on left ventricular volumes measured by magnetic resonance imaging: implications for image acquisition and analysis.

In the evaluation of the left ventricular (LV) function using magnetic resonance imaging (MRI), a stack of parallel short-axis (SA) cine images is acquired that covers the whole LV. The aim of this study is to quantify the contribution to the LV volume parameters, provided by the most basal image plane that shows the LV wall only in end diastole (ED) but not in end systole (ES). In 57 healthy volunteers (31 men, mean body surface area 1.87 m2), a complete set of parallel SA images was acquired (10-mm slice distance) by breathhold segmented k-space cine MRI (7 ky lines per beat). The LV end-diastolic volume (EDV), stroke volume (SV), ejection fraction (EF), and cardiac output (CO) were determined by slice summation. Calculations were performed both with and without inclusion of the most basal slice. With inclusion of the most basal slice, all parameters were significantly (p < 0.001) larger compared with the values obtained by excluding this slice. EDV was 134 +/- 29 ml versus 113 +/- 26 ml; SV was 93 +/- 18 ml versus 72 +/- 16 ml; EF was 70 +/- 4% versus 64 +/- 4%; and CO was 5.3 +/- 1.4 l/min versus 4.1 +/- 1.1 l/min. The inclusion of the most basal slice leads to significantly larger values of LV volume parameters. Thus, this most basal SA image slice should be included in calculating the EDV. Whether or not this basal SA slice also contributes to the ES volume should be decided by using anatomical criteria on the ES image. The projection line onto the ES image of a long-axis view provides an additional criterion.

MRI-derived left ventricular function parameters and mass in healthy young adults: Relation with gender and body size

Purpose: To obtain normal values of left ventricular (LV) end-diastolic volume (EDV), stroke volume (SV), cardiac output (CO) and LV mass, in relation to gender, weight (W), length (L) and body surface area (BSA). Methods: Sixty-one healthy volunteers (32 male, 22.4 ± 2.2 years) were examined, weight was 70.9 ± 12.2 kg, length was 1.78 ± 0.09 m, BSA was 1.88 ± 0.19 m2. Segmented k-space breathhold cine MRI was used to obtain a stack of parallel short-axis images, from which LV volumes and end-diastolic mass were derived by slice summation. Four different body size indices were studied: W, L, L2 and BSA. Results: After indexing for L, L2 and BSA, the gender differences in all LV parameters are still persisting. After indexing for W, gender differences persist for EDV and EDM, but are no longer observed for SV and CO. Separate regression analyses for males and females were performed. EDV, SV, CO and EDM correlated significantly with each body size index, both in males and in females. L or BSA were in general better predictors for LV parameters than W. Linear regression equations of EDV (ml) vs. L(m) were for males: EDV = 275 × L − 359 and for females: EDV = 190 × L − 215. Equations of SV(ml) vs. L were for males: SV = 186 × L − 237 and for females: SV = 118 × L − 121. Equations of LV mass(g) vs. L were for males: Mass = 175 × L − 179 and for females: Mass = 65.8 × L − 10.9. Conclusion: Most gender differences in LV parameters remain even after correction for body size indices. Normal reference values for LV parameters are given in relation to body size indices, by calculating regression coefficients separately for males and females. These normal values serve to obtain more accurate reference values for a patient with given gender, weight and length, and thus to improve the differentiation between normal and abnormal LV parameters.

The localization of spontaneous brain activity: first results in patients with cerebral tumors

OBJECTIVE From EEG studies, it is known that structural brain lesions are accompanied by abnormal rhythmic electric activity. With the better spatial resolution of MEG, MEG dipole analysis can extend the knowledge based on EEG power spectra. This study presents the first results of a completely automatic analysis method applied to spontaneous MEG. METHODS Spontaneous MEG data of 5 patients with cerebral brain tumors and 4 controls were collected using a whole-head MEG system. Signals were bandpass-filtered with cut-off frequencies according to standard EEG bands. A moving dipole model was fitted to samples with at least twice the average sample power. Dipoles explaining 90% or more of the magnetic variance were projected onto a matched MR scan. RESULTS In controls, dipole distributions are symmetrical with respect to the mid-sagittal plane whereas distributions in patients often are asymmetrical to it. Dipoles describing gamma activity were located contralateral, and dipoles describing delta and theta activity were located ipsilateral to lesions. CONCLUSIONS The automatic method gives plausible 3-dimensional information about generator foci of abnormal slow waves and other rhythms with respect to lesion foci and thereby adds physiological knowledge to that derived from EEG power spectra.

The application of electrical impedance tomography to reduce systematic errors in the EEG inverse problem - a simulation study

In this paper we propose a new method, using the principles of electrical impedance tomography (EIT), to correct for the systematic errors in the inverse problem (IP) of electroencephalography (EEG) that arise from the wrong specification of the electrical conductivities of the head compartments. By injecting known currents into pairs of electrodes and measuring the resulting potential differences recorded from the other electrodes, the equivalent conductivities of brain (sigma3), skull (sigma2) and scalp (sigma1) can be estimated. Since the geometry of the head is assumed to be known, the electrical conductivities remain as the only unknown parameters to be estimated. These conductivities can then be used in the inverse problem of EEG. The simulations performed in this study, using a three-layer sphere to model the head, prove the feasibility of the method, theoretically. Even in the presence of simulated noise with a value of signal-to-noise ratio (SNR) equal to 10, estimations of the electrical conductivities within 5% of the true values were obtained. Simulations showed the existence of a strong relation between errors in the skull thickness and the EIT estimated conductivities. If the skull thickness is wrongly specified, for example overestimated by a factor of two, the conductivity determined by EIT is also overestimated by a factor of two. Simulations showed that this compensation effect also works in the inverse problem of EEG. Application of the proposed method reduces systematic errors in the dipole localization, up to an amount of 1 cm. However it proved to be ineffective to decrease the dipole strength error.