Biomedical subjects

Jan C de Munck

Publications and source records attributed to Jan C de Munck.

13 recordsLinked to original sources

PubMed2006

Disturbed functional connectivity in brain tumour patients: evaluation by graph analysis of synchronization matrices.

OBJECTIVE: Cerebral functions are based on the functional interactions between multiple distinct specialized regions of the brain. Functional interactions require anatomical connections as well as the synchronization of brain oscillations. The present work aims at evaluating the impact of brain tumours on spatial patterns of functional connectivity of the brain measured at rest by MEG. METHODS: We analyzed the statistical dependency (by computing the synchronization likelihood (SL, a measure of generalized synchronization)) between MEG signals at rest, in 17 patients with a brain tumour and in 15 healthy controls. Following an approach that derives from graph theory, we also analyzed the architectural properties of the networks by computing two parameters from the SL matrix, the cluster coefficient C and the characteristic path length L. RESULTS: Alterations in synchronization levels were found in the patients and were not focal but involved intra-hemispheric connectivity. Effects were different considering the frequencies sub-bands, predominating in a decrease in high frequencies bands for long-distance connections and an increase in slower bands for local connectivity. In addition, graph analysis reveals changes in the normal "small-world" network architecture in addition to changes in synchronization levels with some differences according to the studied frequency sub-bands. CONCLUSIONS: Brain tumours alter the functional connectivity and the "network" architecture of the brain. These alterations are not focal and effects are different considering the frequencies sub-bands. SIGNIFICANCE: These neurophysiological changes may contribute to the cognitive alterations observed in patients with brain tumours.

Adult↗

PubMed2006

How do brain tumors alter functional connectivity? A magnetoencephalography study.

OBJECTIVE: This study was undertaken to test the hypothesis that brain tumors interfere with normal brain function by disrupting functional connectivity of brain networks. METHODS: Functional connectivity was assessed by computing the synchronization likelihood in a broad band (0.5-60Hz) or in the gamma band (30-60Hz) between all pairwise combinations of magnetoencephalography signals. Magnetoencephalography recordings were made at rest in 17 brain tumor patients and 15 healthy control subjects. For a given threshold of synchronization likelihood values, graphs of the suprathreshold connections between each magnetoencephalography channel and the others channels were built. RESULTS: In some regions, a variable number of channels without connectivity (missing connective points) at this threshold was found. The number of missing connective points was higher in patients with brain tumors than in control subjects (p < 0.0001, broad and gamma band) and was higher for left-sided than right-sided tumors (p = 0.008, broad band; p < 0.0001, gamma band). Individual results analysis indicates that the majority of brain tumor patients display several regions with missing connective point alterations in the affected and in the contralateral hemisphere. INTERPRETATION: Our findings suggest that brain tumors induce a loss of functional connectivity that affects multiple brain regions, and that left side brain tumors have the more severe consequences in this respect.

Adult↗

PubMed2005

The spatiotemporal MEG covariance matrix modeled as a sum of Kronecker products.

The single Kronecker product (KP) model for the spatiotemporal covariance of MEG residuals is extended to a sum of Kronecker products. This sum of KP is estimated such that it approximates the spatiotemporal sample covariance best in matrix norm. Contrary to the single KP, this extension allows for describing multiple, independent phenomena in the ongoing background activity. Whereas the single KP model can be interpreted by assuming that background activity is generated by randomly distributed dipoles with certain spatial and temporal characteristics, the sum model can be physiologically interpreted by assuming a composite of such processes. Taking enough terms into account, the spatiotemporal sample covariance matrix can be described exactly by this extended model. In the estimation of the sum of KP model, it appears that the sum of the first 2 KP describes between 67% and 93%. Moreover, these first two terms describe two physiological processes in the background activity: focal, frequency-specific alpha activity, and more widespread non-frequency-specific activity. Furthermore, temporal nonstationarities due to trial-to-trial variations are not clearly visible in the first two terms, and, hence, play only a minor role in the sample covariance matrix in terms of matrix power. Considering the dipole localization, the single KP model appears to describe around 80% of the noise and seems therefore adequate. The emphasis of further improvement of localization accuracy should be on improving the source model rather than the covariance model.

Algorithms↗

PubMed2005

Age distribution of MEG spontaneous theta activity in healthy subjects.

This study investigates the possible relevance of distribution and age variation of spontaneous theta activity (4-8 Hz) in normal subjects using magnetoencephalography (MEG) recordings. Spontaneous theta was recorded with a 151-channel MEG in healthy subjects; moreover, in a group of 10 subjects, simultaneous MEG-EEG was recorded in order to compare the two methods. Theta was divided in two sub-bands: T(A) (4-6 Hz) and T(B) (6-8 Hz). The pre-processed data were transformed into the frequency domain by Fast Fourier Transform (FFT)-based software by subdividing the data in epochs of 5 sec, on which FFT amplitudes are computed. Moreover, on all trials a simple model of a single electric current embedded in a spherically symmetric conductor was fitted automatically to the magnetic fields and projected onto an averaged MRI. The results obtained show that FFT-based theta power spectrum was distributed in adults with the highest power over the posterior parietal and occipital areas with T(B) dominance. The dipole analysis resulted in a mid-sagittal distribution, though the youngest group displayed theta dipoles fitting more posteriorly respect to the adults and the elderly. These results suggest that spontaneous theta activity is a diffuse and pervasive rhythm which shows some different topographical distribution among the age groups. Whether the prevalent posterior distribution of theta is the expression of distinct networks or the outcome of complex dynamics are questions of possible relevance in the organization of higher order processes.

Adult↗

PubMed2004

The coupled dipole model: an integrated model for multiple MEG/EEG data sets.

Often MEG/EEG is measured in a few slightly different conditions to investigate the functionality of the human brain. This kind of data sets show similarities, though are different for each condition. When solving the inverse problem (IP), performing the source localization, one encounters the problem that this IP is ill-posed: constraints are necessary to solve and stabilize the solution to the IP. Moreover, a substantial amount of data is needed to avoid a signal to noise ratio (SNR) that is too poor for source localizations. In the case of similar conditions, this common information can be exploited by analyzing the data sets simultaneously. The here proposed coupled dipole model (CDM) provides an integrated method in which these similarities between conditions are used to solve and stabilize the inverse problem. The coupled dipole model is applicable when data sets contain common sources or common source time functions. The coupled dipole model uses a set of common sources and a set of common source time functions (STFs) to model all conditions in one single model. The data of each condition are mathematically described as a linear combination of these common spatial and common temporal components. This linear combination is specified in a coupling matrix for each data set. The coupled dipole model was applied in two simulation studies and in one experimental study. The simulations show that the errors in the estimated spatial and temporal parameters decrease compared to the standard separate analyses. A decrease in position error of a factor of 10 was shown for the localization of two nearby sources. In the experimental application, the coupled dipole model was shown to be necessary to obtain a plausible solution in at least 3 of 15 conditions investigated. Moreover, using the CDM, a direct comparison between parameters in different conditions is possible, whereas in separate models, the scaling of the amplitude parameters varies in general from data set to data set.

Algorithms↗

PubMed2003

A mathematical approach to the temporal stationarity of background noise in MEG/EEG measurements.

The general spatiotemporal covariance matrix of the background noise in MEG/EEG signals is huge. To reduce the dimensionality of this matrix it is modeled as a Kronecker product of a spatial and a temporal covariance matrix. When the number of time samples is larger than, say, J = 500, the iterative Maximum Likelihood estimation of these two matrices is still too time-consuming to be useful on a routine basis. In this study we looked for methods to circumvent this computationally expensive procedure by using a parametric model with subject-dependent parameters. Such a model would additionally help with interpreting MEG/EEG signals. For the spatial covariance, models have been derived already and it has been shown that measured MEG/EEG signals can be understood spatially as random processes, generated by random dipoles. The temporal covariance, however, has not been modeled yet, therefore we studied the temporal covariance matrix in several subjects. For all subjects the temporal covariance shows an alpha oscillation and vanishes for large time lag. This gives rise to a temporal noise model consisting of two components: alpha activity and additional random noise. The alpha activity is modeled as randomly occurring waves with random phase and the covariance of the additional noise decreases exponentially with lag. This model requires only six parameters instead of 12 J(J + 1). Theoretically, this model is stationary but in practice the stationarity of the matrix is highly influenced by the baseline correction. It appears that very good agreement between the data and the parametric model can be obtained when the baseline correction window is taken into account properly. This finding implies that the background noise is in principle a stationary process and that nonstationarities are mainly caused by the nature of the preprocessing method. When analyzing events at a fixed sample after the stimulus (e.g., the SEF N20 response) one can take advantage of this nonstationarity by optimizing the baseline window to obtain a low noise variance at this particular sample.

Algorithms↗

PubMed2003

Localization of slow wave activity in patients with tumor-associated epilepsy.

OBJECTIVE: Brain tumors are frequently accompanied by abnormal low frequency magnetic activity (ALFMA). The prevalence and clinical meaning of ALFMA are not well known, although a relation with epileptic brain tissue has been suggested. We studied the prevalence, characteristics and clinical correlates of ALFMA in 20 patients with brain tumors. METHODS: In 20 patients with clinical seizures due to a supratentorial tumor, MEG was performed, followed by MR imaging. MEG signals were band pass-filtered (1-4 Hz); the sources of this activity were localized and projected onto the MRI of the patient. RESULTS: Peritumoral ALFMA could be detected in 13 of 20 patients. A pattern of ALFMA distribution around the tumor could be recognized. In eight cases ALFMA also appeared to be localized within the tumor. In three cases ALFMA was also detected in peritumoral white matter. CONCLUSIONS: Automatic detection of abnormal delta-activity in patients with a brain tumor and seizures can be performed in a clinical setting. When detected, ALFMA is mostly present in circumscribed regions around the tumor. Presence of ALFMA within the tumor might be an important warning signal for the neurosurgeon that the tumor area comprises functional brain tissue.

Adult↗

PubMed2003

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 (rho(skull)) and brain (rho(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 rho(skull)/rho(brain) is more likely to be within 20 and 50 rather than equal to the commonly accepted value of 80. The variation in rho(brain)(average = 301 omega x cm, SD = 13%) and rho(skull)(average = 12230 omega x 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 rho(skull)/rho(brain) corresponding to different subjects. Earlier results show the necessity of calibrating rho(brain) and rho(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.

Adult↗

PubMed2002

Generalized synchronization of MEG recordings in Alzheimer's Disease: evidence for involvement of the gamma band.

The purpose of this study was to investigate interdependencies in whole-head magnetoencephalography (MEG) of Alzheimer patients and healthy control subjects. Magnetoencephalograms were recorded in 20 Alzheimer patients (11 men; mean age, 69.0 years [standard deviation, 8.2 years]); Mini-Mental State Examination score, 21.3 points; range, 15 to 27 points) and 20 healthy control subjects (9 men; mean age, 66.4 years [standard deviation, 9.0 years]) during a no-task eyes-closed condition with a 151 channel whole-head MEG system. Synchronization likelihood (a new measure for linear as well as nonlinear interdependencies between signals) and coherence were computed for each channel in different frequency bands (2 to 6, 6 to 10, 10 to 14, 14 to 18, 18 to 22, 22 to 40 Hz). Synchronization was lower in Alzheimer patients in the upper alpha band (10 to 14 Hz), the upper beta band (18 to 22 Hz), and the gamma band (22 to 40 Hz). In contrast, coherence did not show significant group differences at the p<0.05 level. The synchronization likelihood showed a spatial pattern (high synchronization central, parietal and right frontal; low synchronization, occipital and temporal). This study confirms a widespread loss of functional interactions in the alpha and beta bands, and provides the first evidence for loss of gamma band synchronization in Alzheimer's disease. Synchronization likelihood may be more sensitive to detect such changes than the commonly used coherence analysis.

Aged↗

PubMed2002

Spatiotemporal EEG/MEG source analysis based on a parametric noise covariance model.

A method is described to incorporate the spatiotemporal noise covariance matrix into a spatiotemporal source analysis. The essential feature is that the estimation problem is split into two parts. First, a model is fitted to the observed noise covariance matrix. This model is a Kronecker product of a spatial and a temporal matrix. The spatial matrix models the spatial covariances by a function dependent on sensor distance. The temporal matrix models the temporal covariances as lag dependent. In the second part, sources are estimated given this noise model, which can be done very efficiently due to the Kronecker formulation. An application to real electroencephalogram (EEG) data shows that the noise model fits the data very well. Simulation results show that the resulting source estimates are more precise than those obtained from a standard analysis neglecting the noise covariance. In addition, the estimated standard errors of the source parameter estimates are far more precise than those obtained from a standard analysis. Finally, the source parameter standard errors are used to investigate the effects of temporal sampling. It is shown that increasing the sampling by a factor x, decreases the standard errors of all source parameters with the square root of x.

Computer Simulation↗

PubMed2002

Model selection in electromagnetic source analysis with an application to VEFs.

In electromagnetic source analysis, it is necessary to determine how many sources are required to describe the electroencephalogram or magnetoencephalogram adequately. Model selection procedures (MSPs) or goodness of fit procedures give an estimate of the required number of sources. Existing and new MSPs are evaluated in different source and noise settings: two sources which are close or distant and noise which is uncorrelated or correlated. The commonly used MSP residual variance is seen to be ineffective, that is it often selects too many sources. Alternatives like the adjusted Hotelling's test, Bayes information criterion and the Wald test on source amplitudes are seen to be effective. The adjusted Hotelling's test is recommended if a conservative approach is taken and MSPs such as Bayes information criterion or the Wald test on source amplitudes are recommended if a more liberal approach is desirable. The MSPs are applied to empirical data (visual evoked fields).

Algorithms↗

PubMed2002

Matching PET and CT scans of the head and neck area: development of method and validation.

Positron emission tomography (PET) provides important information on tumor biology, but lacks detailed anatomical information. Our aim in the present study was to develop and validate an automatic registration method for matching PET and CT scans of the head and neck. Three difficulties in achieving this goal are (1) nonrigid motions of the neck can hamper the use of automatic ridged body transformations; (2) emission scans contain too little anatomical information to apply standard image fusion methods; and (3) no objective way exists to quantify the quality of the match results. These problems are solved as follows: accurate and reproducible positioning of the patient was achieved by using a radiotherapy treatment mask. The proposed method makes use of the transmission rather than the emission scan. To obtain sufficient (anatomical) information for matching, two bed positions for the transmission scan were included in the protocol. A mutual information-based algorithm was used as a registration technique. PET and CT data were obtained in seven patients. Each patient had two CT scans and one PET scan. The datasets were used to estimate the consistency by matching PET to CT1, CT1 to CT2, and CT2 to PET using the full circle consistency test. It was found that using our method, consistency could be obtained of 4 mm and 1.3 degrees on average. The PET voxels used for registration were 5.15 mm, so the errors compared quite favorably with the voxel size. Cropping the images (removing the scanner bed from images) did not improve the consistency of the algorithm. The transmission scan, however, could potentially be reduced to a single position using this approach. In conclusion, the represented algorithm and validation technique has several features that are attractive from both theoretical and practical point of view, it is a user-independent, automatic validation technique for matching CT and PET scans of the head and neck, which gives the opportunity to compare different image enhancements.

Algorithms↗

PubMedDate not supplied

What does an epileptiform spike look like in MEG? Comparison between coincident EEG and MEG spikes.

Recent investigations suggest that there are differences between the characteristics of EEG and MEG epileptiform spikes. The authors performed an objective characterization of the morphology of epileptiform spikes recorded simultaneously in both EEG and MEG to determine whether they present the same morphologic characteristics. Based on a stepwise approach, the authors performed a computer analysis of EEG and MEG of a set of coincident epileptiform transients selected by a senior clinical neurophysiologist in recordings of three patients with drug-resistant epilepsy. A computer-based algorithm was applied to extract parameters that could be used to describe quantitatively the morphology of the transients, followed by a statistical comparison over the extracted metrics of the EEG and MEG waveforms. EEG and MEG coincident events were statistically different with respect to several morphologic characteristics, such as duration, sharpness, and shape. The differences found appear to be a consequence of MEG signals not being influenced by volume propagation through the tissues with different conductivities that surround the brain, compared with EEG, and of the different orientation of the underlying dipolar sources. The results indicate that visual inspection of MEG spikes and automatic spike-detector algorithms should use criteria adapted to the specific characteristics of the MEG, and not simply those used on conventional EEG.

Adult↗