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PurposeTo investigate the diagnostic accuracy of an image-based classifier to distinguish between Alzheimer disease (AD) and behavioral variant frontotemporal dementia (bvFTD) in individual patients by using gray matter (GM) density maps computed from standard T1-weighted structural images obtained with multiple imagers and with independent training and prediction data.Materials and MethodsThe local institutional review board approved the study. Eighty-four patients with AD, 51 patients with bvFTD, and 94 control subjects were divided into independent training (n = 115) and prediction (n = 114) sets with identical diagnosis and imager type distributions. Training of a support vector machine (SVM) classifier used diagnostic status and GM density maps and produced voxelwise discrimination maps. Discriminant function analysis was used to estimate suitability of the extracted weights for single-subject classification in the prediction set. Receiver operating characteristic (ROC) curves and area under the ROC curve (AUC) were calculated for image-based classifiers and neuropsychological z scores.ResultsTraining accuracy of the SVM was 85% for patients with AD versus control subjects, 72% for patients with bvFTD versus control subjects, and 79% for patients with AD versus patients with bvFTD (P ≤ .029). Single-subject diagnosis in the prediction set when using the discrimination maps yielded accuracies of 88% for patients with AD versus control subjects, 85% for patients with bvFTD versus control subjects, and 82% for patients with AD versus patients with bvFTD, with a good to excellent AUC (range, 0.81–0.95; P ≤ .001). Machine learning-based categorization of AD versus bvFTD based on GM density maps outperforms classification based on neuropsychological test results.ConclusionThe SVM can be used in single-subject discrimination and can help the clinician arrive at a diagnosis. The SVM can be used to distinguish disease-specific GM patterns in patients with AD and those with bvFTD as compared with normal aging by using common T1-weighted structural MR imaging.

Inter subject variability of skull conductivity highly constraints the possibility to use EEG (Electro Encephalography) amplitude parameters as a biomarker to compare the amount neural activity between different patients. The same inter subject variability limits the accuracy by which it is possible to localize the current sources underlying EEG using forward models with standardized parameters for the conductivity of brain, skull and skin. To solve this problem, it has been proposed to estimate conductivity parameters in vivo by analyzing the potentials generated by precisely known electric currents, injected into different pairs of EEG electrodes.

Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimer's disease (AD). Eigenvector centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. This study used voxel-wise EC mapping (ECM) to analyze individual whole-brain resting-state functional magnetic resonance imaging (MRI) scans in 39 AD patients (age 67 ± 8) and 43 healthy controls (age 69 ± 7). Between-group differences were assessed by a permutation-based method. Associations of EC with biomarkers for AD pathology in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE) scores were assessed using Spearman correlation analysis. Decreased EC was found bilaterally in the occipital cortex in AD patients compared to controls. Regions of increased EC were identified in the anterior cingulate and paracingulate gyrus. Across groups, frontal and occipital EC changes were associated with pathological concentrations of CSF biomarkers and with cognition. In controls, decreased EC values in the occipital regions were related to lower MMSE scores. Our main finding is that ECM, a hypothesis-free and computationally efficient analysis method of functional MRI (fMRI) data, identifies changes in brain network organization in AD patients that are related to cognition and underlying AD pathology. The relation between AD-like EC changes and cognitive performance suggests that resting-state fMRI measured EC is a potential marker of disease severity for AD.

EEG-correlated functional MRI (EEG-fMRI) visualizes brain regions associated with interictal epileptiform discharges (IEDs). This technique images the epileptiform network, including multifocal, superficial and deeply situated cortical areas. To understand the role of EEG-fMRI in presurgical evaluation, its results should be validated relative to a gold standard. For that purpose, EEG-fMRI data were acquired for a heterogeneous group of surgical candidates (n=16) who were later implanted with subdural grids and strips (ECoG). The EEG-fMRI correlation patterns were systematically compared with brain areas involved in IEDs ECoG, using a semi-automatic analysis method, as well as to the seizure onset zone, resected area, and degree of seizure freedom. In each patient at least one of the EEG-fMRI areas was concordant with an interictally active ECoG area, always including the early onset area of IEDs in the ECoG data. This confirms that EEG-fMRI reflects a pattern of onset and propagation of epileptic activity. At group level, 76% of the BOLD regions that were covered with subdural grids, were concordant with interictally active ECoG electrodes. Due to limited spatial sampling, 51% of the BOLD regions were not covered with electrodes and could, therefore, not be validated. From an ECoG perspective it appeared that 29% of the interictally active ECoG regions were missed by EEG-fMRI and that 68% of the brain regions were correctly identified as inactive with EEG-fMRI. Furthermore, EEG-fMRI areas included the complete seizure onset zone in 83% and resected area in 93% of the data sets. No clear distinction was found between patients with a good or poor surgical outcome: in both patient groups, EEG-fMRI correlation patterns were found that were either focal or widespread. In conclusion, by comparison of EEG-fMRI with interictal invasive EEG over a relatively large patient population we were able to show that the EEG-fMRI correlation patterns are spatially accurate at the level of neurosurgical units (i.e. anatomical brain regions) and reflect the underlying network of IEDs. Therefore, we expect that EEG-fMRI can play an important role for the determination of the implantation strategy.

One of the most important factors limiting the accuracy of source localization with EEG is the ignorance of the conductivity of brain, skull and skin. Because these parameters are highly subject dependent, it has been proposed to estimate them in vivo using an electrical impedance tomographic approach. Here the associated conductivity parameter estimation problem is analyzed in the context of different boundary element approaches. The numerical accuracies of the following combinations are compared: double layer and monolayer, linear and constant interpolation. Contrary to the superior behavior of the monolayer approach to predict potentials generated by dipolar sources, for potentials generated by injected currents hardly any improvement was found when comparing the double and monolayer BEM combined with constant potential interpolation. For linear potential interpolation, the numerical accuracy of the double layer approach was found much better than the monolayer approach.

Eigenvector centrality mapping (ECM) has recently emerged as a measure to spatially characterize connectivity in functional brain imaging by attributing network properties to voxels. The main obstacle for widespread use of ECM in functional magnetic resonance imaging (fMRI) is the cost of computing and storing the connectivity matrix. This article presents fast ECM (fECM), an efficient algorithm to estimate voxel-wise eigenvector centralities from fMRI time series. Instead of explicitly storing the connectivity matrix, fECM computes matrix-vector products directly from the data, achieving high accelerations for computing voxel-wise centralities in fMRI at standard resolutions for multivariate analyses, and enabling high-resolution analyses performed on standard hardware. We demonstrate the validity of fECM at cluster and voxel levels, using synthetic and in vivo data. Results from synthetic data are compared to the theoretical gold standard, and local centrality changes in fMRI data are measured after experimental intervention. A simple scheme is presented to generate time series with prescribed covariances that represent a connectivity matrix. These time series are used to construct a 4D dataset whose volumes consist of separate regions with known intra- and inter-regional connectivities. The fECM method is tested and validated on these synthetic data. Resting-state fMRI data acquired after real-versus-sham repetitive transcranial magnetic stimulation show fECM connectivity changes in resting-state network regions. A comparison of analyses with and without accounting for motion parameters demonstrates a moderate effect of these parameters on the centrality estimates. Its computational speed and statistical sensitivity make fECM a good candidate for connectivity analyses of multimodality and high-resolution functional neuroimaging data.

The analysis of simultaneous EEG and fMRI data is generally based on the extraction of regressors of interest from the EEG, which are correlated to the fMRI data in a general linear model setting. In more advanced approaches, the spatial information of EEG is also exploited by assuming underlying dipole models. In this study, we present a semi automatic and efficient method to determine electrode positions from electrode gel artifacts, facilitating the integration of EEG and fMRI in future EEG/fMRI data models. In order to visualize all electrode artifacts simultaneously in a single view, a surface rendering of the structural MRI is made using a skin triangular mesh model as reference surface, which is expanded to a "pancake view". Then the electrodes are determined with a simple mouse click for each electrode. Using the geometry of the skin surface and its transformation to the pancake view, the 3D coordinates of the electrodes are reconstructed in the MRI coordinate frame. The electrode labels are attached to the electrode positions by fitting a template grid of the electrode cap in which the labels are known. The correspondence problem between template and sample electrodes is solved by minimizing a cost function over rotations, shifts and scalings of the template grid. The crucial step here is to use the solution of the so-called "Hungarian algorithm" as a cost function, which makes it possible to identify the electrode artifacts in arbitrary order. The template electrode grid has to be constructed only once for each cap configuration. In our implementation of this method, the whole procedure can be performed within 15 min including import of MRI, surface reconstruction and transformation, electrode identification and fitting to template. The method is robust in the sense that an electrode template created for one subject can be used without identification errors for another subject for whom the same EEG cap was used. Furthermore, the method appears to be robust against spurious or missing artifacts. We therefore consider the proposed method as a useful and reliable tool within the larger toolbox required for the analysis of co-registered EEG/fMRI data. © 2011 Elsevier Inc.

The simultaneous recording of electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) can be used to localize interictal epileptiform discharges (IEDs). Previous studies have reported varying degrees of concordance of EEG-fMRI with electroclinical findings. The aim of the present study is to evaluate to what extent this variability is determined by the analytical strategy or by the properties of the EEG data. For that purpose, 42 IED sets obtained in 29 patients with epilepsy were reanalyzed using a finite impulse response approach, which estimates the hemodynamic response function (HRF) from the data and allows non-causal effects. Cardiac effects were treated as additional confounders in the model. This approach was compared to the classical approach assuming a fixed HRF for each voxel in the brain. The performance of each method was assessed by comparing the fMRI results to the EEG focus. The flexible model revealed more significantly activated voxels, which resulted in more activated brain regions concordant with the EEG focus (26 vs. 16). Correction for cardiac effects improved the results in 7 out of the 42 data sets. Furthermore, design theory for event-related experiments was applied in order to determine the influence of the number of IEDs and their temporal distribution on the success of an experiment. It appeared that this success is highly dependent upon the number of IEDs present during the recording and less on their temporal spacing. We conclude that the outcome of EEG-fMRI can be improved by using an optimized analytical strategy, but also depends on the number of IEDs occurring during the recording.

Introduction: Amyloid plaque deposition in the brain is an early pathological change in Alzheimer's disease (AD), causing disrupted synaptic connections. Brain network disruptions in AD have been demonstrated with eigenvector centrality (EC), a measure that identifies central regions within networks. Carrying an apolipoprotein (APOE)-ε4 allele is a genetic risk for AD, associated with increased amyloid deposition. We studied whether APOE-ε4 carriership is associated with EC disruptions in cognitively normal individuals. Methods: A total of 261 healthy middle-aged to older adults (mean age 56.6 years) were divided into high-risk (APOE-ε4 carriers) and low-risk (noncarriers) groups. EC was computed from resting-state functional MRI data. Clusters of between-group differences were assessed with a permutation-based method. Correlations between cluster mean EC with brain volume, CSF biomarkers, and psychological test scores were assessed. Results: Decreased EC in the visual cortex was associated with APOE-ε4 carriership, a genetic risk factor for AD. EC differences were correlated with age, CSF amyloid levels, and scores on the trail-making and 15-object recognition tests. Conclusion: Our findings suggest that the APOE-ε4 genotype affects brain connectivity in regions previously found to be abnormal in AD as a sign of very early disease-related pathology. These differences were too subtle in healthy elderly to use EC for single-subject prediction of APOE genotype.

Third harmonic generation (THG) microscopy shows great potential for instant pathology of brain tissue during surgery. However, the rich morphologies contained and the noise associated makes image restoration, necessary for quantification of the THG images, challenging. Anisotropic diffusion filtering (ADF) has been recently applied to restore THG images of normal brain, but ADF is hard‐to‐code, time‐consuming and only reconstructs salient edges. This work overcomes these drawbacks by expressing ADF as a tensor regularized total variation model, which uses the Huber penalty and the L1 norm for tensor regularization and fidelity measurement, respectively. The diffusion tensor is constructed from the structure tensor of ADF yet the tensor decomposition is performed only in the non‐flat areas. The resulting model is solved by an efficient and easy‐to‐code primal‐dual algorithm. Tests on THG brain tumor images show that the proposed model has comparable denoising performance as ADF while it much better restores weak edges and it is up to 60% more time efficient., Third harmonic generation (THG) microscopy is a label‐free imaging technique that shows great potential for in vivo instant pathology of human brain tissue. Here we have developed a robust and efficient image denoising method to facilitate the clinical applications of THG.

Summary Purpose: The diagnosis of frontal lobe epilepsy may be compounded by poor electroclinical localization, due to distributed or rapidly propagating epileptiform activity. This study aimed at developing optimal procedures for localizing interictal epileptiform discharges (IEDs) of patients with localization related epilepsy in the frontal lobe. To this end the localization results obtained for magnetoencephalography (MEG) and electroencephalography (EEG) were compared systematically using automated analysis procedures. Methods: Simultaneous recording of interictal EEG and MEG was successful for 18 out of the 24 patients studied. Visual inspection of these recordings revealed IEDs with varying morphology and topography. Cluster analysis was used to classify these discharges on the basis of their spatial distribution followed by equivalent dipole analysis of the cluster averages. The locations of the equivalent dipoles were compared with the location of the epileptogenic lesions of the patient or, if these were not visible at MRI with the location of the interictal onset zones identified by subdural electroencephalography. Results: Generally IEDs were more abundantly in MEG than in the EEG recordings. Furthermore, the duration of the MEG spikes, measured from the onset till the spike maximum, was in most patients shorter than the EEG spikes. In most patients, distinct spike subpopulations were found with clearly different topographical field maps. Cluster analysis of MEG spikes followed by dipole localization was successful (n = 14) for twice as many patients as for EEG source analysis (n = 7), indicating that the localizability of interictal MEG is much better than of interictal EEG. Conclusions: The automated procedures developed in this study provide a fast screening method for identifying the distinct categories of spikes and the brain areas responsible for these spikes. The results show that MEG spike yield and localization is superior compared with EEG. This finding is of importance for the diagnosis and preoperative evaluation of patients with frontal lobe epilepsy.

Fitting an equivalent current dipole (ECD) to magnetoencephalography (MEG) data is a widely used method for analyzing MEG data, especially in clinical applications. However, as is well known, the iterative fitting algorithms routinely employed can converge on a local minima far from the best fit. While a host of fitting algorithms have been proposed to remedy this problem, the simple exhaustive search optimization algorithm has not been considered in the literature — possibly because it is assumed to be too slow for routine use. Taking advantage of the speed of modern computers, it is demonstrated that using exhaustive search to fit the parameters of a single ECD to 151 MEG channels yields both robust and reproducible fits within reasonable computation times. On a high end personal computer, with a 2 mm grid spacing over the head, exhaustive search can fit 450 ECD's per hour with only an additional 5 min spent on calculating the lead fields. As the algorithm is fully automatic, it is very useful when the goal is to detect ECD's in a large dataset. Also, an interpreter can be provided with parameters from the ECD that fits for all latencies after stimulus, allowing a more informed decision of which latency to use for further analysis.

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.

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

Spatial independent component analysis (ICA) is increasingly being used to extract resting-state networks from fMRI data. Previous studies showed that ICA also reveals independent components (ICs) related to the seizure onset zone. However, it is currently unknown how these epileptic ICs depend on the presence of interictal epileptic discharges (IEDs) in the EEG. The goal of this study was to explore the relation between ICs obtained from fMRI epochs during the occurrence of IEDs in the EEG and those without IEDs. fMRI data sets with co-registered EEG were retrospectively selected of patients from whom the location of the epileptogenic zone was confirmed by outcome of surgery (n = 8). The fMRI data were split into two epochs: one with IEDs visible in scalp EEG and one without. Spatial ICA was applied to the fMRI data of each part separately. The maps of all resulting components were compared to the resection area and the EEG–fMRI correlation pattern by computing a spatial correlation coefficient to detect the epilepsy-related component. For all patients, except one, there was a remarkable resemblance between the epilepsy-related components selected during epochs with IEDs and those without IEDs. These findings suggest that epilepsy-related ICs are not dependent on the presence of IEDs in scalp EEG. Since these epileptic ICs showed partial overlap with resting-state networks of healthy volunteers (n = 10), our study supports the need for new ways to classify epileptic ICs.

In six healthy adults we examined the sources underlying P1 and N2 of the motion VEP. For this purpose we acquired, in addition to the VEP, MRI images and patterns of regional cerebral blood flow with SPECT for three of the subjects. With the same motion stimulus we also examined the spatial distribution of N2 in children. In both adults and children left and right half-field responses were compared. It was found that N2 is generated by extrastriate activity and that motion stimuli are not equivalently processed in the two cerebral hemispheres. In adults, N2 dominates in one hemisphere irrespective of the visual half-field used for stimulation whereas children show an ipsilateral maximum for N2 upon half-field presentation.

Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimer's disease (AD). Eigenvector centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient populations. This study used voxel-wise EC mapping (ECM) to analyze individual whole-brain resting-state functional magnetic resonance imaging (MRI) scans in 39 AD patients (age 67 ± 8) and 43 healthy controls (age 69 ± 7). Between-group differences were assessed by a permutation-based method. Associations of EC with biomarkers for AD pathology in cerebrospinal fluid (CSF) and Mini Mental State Examination (MMSE) scores were assessed using Spearman correlation analysis. Decreased EC was found bilaterally in the occipital cortex in AD patients compared to controls. Regions of increased EC were identified in the anterior cingulate and paracingulate gyrus. Across groups, frontal and occipital EC changes were associated with pathological concentrations of CSF biomarkers and with cognition. In controls, decreased EC values in the occipital regions were related to lower MMSE scores. Our main finding is that ECM, a hypothesis-free and computationally efficient analysis method of functional MRI (fMRI) data, identifies changes in brain network organization in AD patients that are related to cognition and underlying AD pathology. The relation between AD-like EC changes and cognitive performance suggests that resting-state fMRI measured EC is a potential marker of disease severity for AD.

Background This study examined whether chronic neuropathic pain, modulated by a local anesthetic block, is associated with cortical magnetic field changes. Methods In a group of 20 patients with pain caused by unilateral traumatic peripheral nerve injury, a local block with lidocaine 1% was administered and the cortical effects were measured and compared with a control group. The global field power (GFP), describing distribution of cortical activation after median and ulnar nerve stimulation, was plotted and calculated. The effects on the affected hemisphere and the unaffected hemisphere (UH) before and after a block of the injured nerve were statistically evaluated. Results Major differences based on the GFP curves, at a component between 50 ms - 90 ms (M70), were found in patients: in the affected hemisphere the M70 GFP peak values were statistically significantly larger in comparison with the UH, and the GFP curves differed morphologically. Interestingly, the mean UH responses were reduced in comparison with the control group, a finding suggesting that the UH is also part of the cortical changes. At M70, the GFP curves and values in the affected hemisphere were modulated by a local block of the median or the ulnar nerve. The most likely location of cortical adaptation is in the primary somatosensory cortex. Conclusions Cortical activation is enhanced in the affected hemisphere compared with the UH and is modulated by a local block. The UH in neuropathic pain changes as well. Evoked fields may offer an opportunity to monitor the effectiveness of treatments of neuropathic pain in humans.

EEG correlated functional MRI (EEG‐fMRI) allows the delineation of the areas corresponding to spontaneous brain activity, such as epileptiform spikes or alpha rhythm. A major problem of fMRI analysis in general is that spurious correlations may occur because fMRI signals are not only correlated with the phenomena of interest, but also with physiological processes, like cardiac and respiratory functions. The aim of this study was to reduce the number of falsely detected activated areas by taking the variation in physiological functioning into account in the general linear model (GLM). We used the photoplethysmogram (PPG), since this signal is based on a linear combination of oxy‐ and deoxyhemoglobin in the arterial blood, which is also the basis of fMRI. We derived a regressor from the variation in pulse height (VIPH) of PPG and added this regressor to the GLM. When this regressor was used as predictor it appeared that VIPH explained a large part of the variance of fMRI signals acquired from five epilepsy patients and thirteen healthy volunteers. As a confounder VIPH reduced the number of activated voxels by 30% for the healthy volunteers, when studying the generators of the alpha rhythm. Although for the patients the number of activated voxels either decreased or increased, the identification of the epileptogenic zone was substantially enhanced in one out of five patients, whereas for the other patients the effects were smaller. In conclusion, applying VIPH as a confounder diminishes physiological noise and allows a more reliable interpretation of fMRI results. Hum Brain Mapp, 2010. © 2009 Wiley‐Liss, Inc.

Objective: Synchronization between distributed rhythms in the brain is commonly assessed by estimating the synchronization strength from simultaneous measurements. This approach, however, does not elucidate the phase dynamics that underlies synchronization. For this, an explicit dynamical model is required. Based on the assumption that the recorded rhythms can be described as weakly coupled oscillators, we propose a method for characterizing their phase-interaction dynamics. Methods: We propose to model ongoing magnetoencephalographic (MEG) oscillations as weakly coupled oscillators. Based on this model, the phase interactions between simultaneously recorded signals are characterized by estimating the modulation in instantaneous frequency as a function of their phase difference. Furthermore, we mathematically derive the effect of volume conduction on the model and show how indices for strength and direction of coupling can be derived. Results: The methodology is tested using simulations and is applied to ongoing occipital–frontal MEG oscillations of healthy subjects in the alpha and beta bands during rest. The simulations show that the model is robust against the presence of noise, short observation times, and model violations. The application to MEG data shows that the model can reconstruct the observed occipital–frontal phase difference distributions. Furthermore, it suggests that phase locking in the alpha and beta band is established by qualitatively different mechanisms. Conclusion: When the recorded rhythms are assumed to be weakly coupled oscillators, a dynamical model for the phase interactions can be fitted to data. The model is able to reconstruct the observed phase difference distribution, and hence, provides a dynamical explanation for observed phase locking. Hum Brain Mapp, 2011. © 2011 Wiley-Liss, Inc.

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.

Simultaneous acquisition of EEG and fMRI data enables the investigation of the hemodynamic correlates of interictal epileptiform discharges (IEDs) during the resting state in patients with epilepsy. This paper addresses two issues: (1) the semi-automation of IED classification in statistical modelling for fMRI analysis and (2) the improvement of IED detection to increase experimental fMRI efficiency. For patients with multiple IED generators, sensitivity to IED-correlated BOLD signal changes can be improved when the fMRI analysis model distinguishes between IEDs of differing morphology and field. In an attempt to reduce the subjectivity of visual IED classification, we implemented a semi-automated system, based on the spatio-temporal clustering of EEG events. We illustrate the technique's usefulness using EEG–fMRI data from a subject with focal epilepsy in whom 202 IEDs were visually identified and then clustered semi-automatically into four clusters. Each cluster of IEDs was modelled separately for the purpose of fMRI analysis. This revealed IED-correlated BOLD activations in distinct regions corresponding to three different IED categories. In a second step, Signal Space Projection (SSP) was used to project the scalp EEG onto the dipoles corresponding to each IED cluster. This resulted in 123 previously unrecognised IEDs, the inclusion of which, in the General Linear Model (GLM), increased the experimental efficiency as reflected by significant BOLD activations. We have also shown that the detection of extra IEDs is robust in the face of fluctuations in the set of visually detected IEDs. We conclude that automated IED classification can result in more objective fMRI models of IEDs and significantly increased sensitivity.

Controversy remains regarding the preferred modality, magnetoencephalography (MEG) or EEG, for the presurgical evaluation of patients with epilepsy. In general, it appears that the spike yields for MEG and EEG are similar in patients with temporal lobe epilepsy, and that for neocortical epilepsy the MEG spike yields may be larger than for EEG. In general, MEG/EEG spike yields depend on factors such as (1) the number of sensors, (2) the source depth and orientation, (3) the background activity, and (4) the smearing of the potential fields due to variations in skull resistivity in EEG. Because the contribution of all these factors are of the same order of magnitude, the authors took them all into account to predict the signal-to-noise ratio (SNR) of hypothetical spikes in different brain areas. In this study, it was assumed that spike sensitivity (and therefore the spike yield) increases with SNR. The estimated SNR values at temporal areas were comparable for MEG and EEG, which is in agreement with clinical findings that spike yields in temporal lobe epilepsy are similar. Furthermore, the SNR of MEG was substantially higher in the frontal area, indicating that in frontal lobe epilepsy MEG may be highly relevant to prescreening of epilepsy patients. This model-based approach indicates that SNR mapping clarifies differences between MEG and EEG findings that are difficult to understand on the basis of patient studies only.

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. © 2004 Elsevier Inc. All rights reserved.

Objective : First, to determine the distribution of the estimated sources of sleep spindles, and alpha and mu rhythms based on whole-head magnetoencephalogram (MEG) recordings; second, to scrutinize the physiological relevance of the dipole fit algorithm in localizing on-going normal rhythmic activities. Methods : One hundred and fifty-one channels were used to record spontaneous MEG activity during wakefulness and superficial sleep in 4 normal subjects. The equivalent dipolar sources were estimated by a new ‘dipole fit algorithm' and projected on the corresponding magnetic resonance images. Results : Equivalent dipoles of MEG spindles were distributed over the centro-parietal region. Those of alpha rhythms were concentrated around the occipito-parietal sulcus and those of mu rhythms were confined to the area around the central sulcus. Conclusions : MEG sleep spindles, and alpha and mu rhythms have distinct spatial distributions of their equivalent dipolar sources. This demonstrates that various cortical regions that oscillate within the same frequency band have different spatial organizations and different functional aspects.

We investigated the accuracy of 3D image registration using markers that are repeatedly applied to external anatomical landmarks on the head. The purpose of this study is to establish a lower limit of the errors that would occur in, for instance, MRI-SPECT matching, which in some situations can only be achieved using external landmarks. Marker matching was compared with (single-modality) volume matching for 20 MRI scans. The results were compared with a published expression for the target registration error (TRE) which gives the 3D distribution of the mismatch between both scans. It was found that the main error source is reapplying the external markers on the anatomical landmarks. The published expression describes the relative distribution of the TRE in space well, but tends to underestimate the actual registration error. This deviation is due to anisotropy in the error distribution of the marker position (errors in the direction perpendicular to the skin surface are in general much smaller than errors in other directions). A simulation of marker matching with anisotropy in the errors confirmed this finding. With four reapplied markers, the TRE is 6 mm or smaller in most regions of the head.

Target volume delineation is an important source of uncertainty in radiotherapy. The usefulness of image registration to improve the quality of target volume delineation for treatment planning is well established [e.g., 1, 2]. However, it is an unsolved problem how to best utilize multiple image modalities for target volume delineation. Mostly, users would switch back and forth between modalities, copying previously delineated contours, or target volumes are combined using Boolean operators. Some delineation systems can handle two scans simultaneously, using linked cursors [3]. When utilizing more than two image modalities for delineation, the image registration is difficult (how to build a consistent set of N matched images from arbitrary matched pairs), but also the user interface is complex (how to present several scans simultaneously). The aim of this study is to develop a software system that allows N different scans or image modalities to be used simultaneously for delineation (in short: the system). In this paper, the emphasis will be on the object oriented software constructs which were developed to implement such a complex system.

A subject sitting at rest with electrodes attached to his or her head produces EEG-signals, even when he or she is not performing a specific task. These signals are irregular and it is impossible to predict how the signal will evolve in time with only the knowledge of their behavior in the past. This impossibility does not imply that the signals are completely random, because often characteristic rhythms can be observed when the condition of the subject is changed. A well known example is the appearance of the alpha-rhythm when the subject closes his eyes (e.g. Lopes da Silva, 1987). The precise shape of these EEG signals can not be predicted and the exact knowledge is not very useful. A meaningful description of spontaneous EEG signals can only be given in terms of their statistical properties. The same is true for the behavior of the EEG signals in the space domain. EEG maps can vary from very regular and dipolar patters to very irregular patterns that do not easily reveal where the underlying generators are located. To characterize the maps of spontaneous EEG one has to use statistics.

The purpose of this study is to develop a method for registration of CT and MR scans of the pelvis with minimal user interaction and to obtain a means for objective quantification of the registration accuracy of clinical data without markers. CT scans were registered with proton density MR scans using chamfer matching on automatically segmented bone. A fixed threshold was used to segment CT, while morphological filters were used to segment MR. The method was tested with transverse and coronal MR scans of 18 patients and sagittal MR scans of 8 patients. The registration accuracy was estimated by comparing (triangulating) registrations of a single CT scan with MR in different orientations in a "full circle." For example, CT is first matched on transverse MR, next transverse MR is matched independently on coronal MR, and finally coronal MR is matched independently on CT. The product of the three transformations is the identity if all matching steps are perfect. Deviations from identity occur both due to random errors and due to some types of systematic errors. MR was registered on MR (to close the "circle") by minimization of rms voxel value differences. CT-MR registration takes about 1 min, including user interaction. The random error for CT-MR registration with transverse or coronal MR was 0.5 mm in translation and 0.4 degree in rotation (standard deviation) for each axis. A systematic registration error of about 1 mm was demonstrated along the MR frequency encoding direction, which is attributed to the chemical shift. In conclusion, the presented algorithm efficiently and accurately registers pelvic CT and MR scans on bone. The "full circle" method provides an estimate of the registration accuracy on clinical data.

Ground truth measurements are necessary for the validation of remotely sensed data. Rapid ship or aircraft spectral measurements of the upwelling and downwelling (ir)radiance are needed to determine the reflectance of thewater column as well as to intercalibrate with satellite sensors. Inter calibrations are hindered by the application of different instruments with varying spectral bands. It has been found that high resolution ocean colour data (case2) do not contain more information on water quality than data from a limited amount of spectral bands. However the positioning of these spectral bands over the visible spectrum should be correctly chosen. The present work uses a reconstruction technique which would enable simple optical instruments with 5 specific bands to be used to determine water quality parameters and to validate satellite data even if the sensors differ in central wavelengths. It is shown that when an optical data bank (ODB) of high resolution spectra (400-720 nm) of a specific sea area is available it is possible to reconstruct complete reflectance spectra, accurate to within 1 %, out of the reflectance measured in 5 bands. Such an ODB could contain subsurface or airborne reflectance (or normalised radiance) data. The reconstruction technique used is based upon a multiple regression analysis (MRA) of the ODB. To validate this full reflectance spectrum reconstruction method spectral data collected with different radiometers in different locations were successfully regenerated from 5 specific bands (412, 492, 556, 620 and 672 nm). Another advantage of the reconstruction technique is the storage of high resolution spectral data by means of only 5 bands. It is proposed that airborne spectral reflectance measurements could stay limited to only 5 specific spectral bands.

The influence of parameters describing the head on the localization of two simultaneous active dipoles is studied. The head is described by a set of five concentric anisotropic spheres. The uncertainties in the conductivity parameters and their effects on the dipole parameters are discussed. Finally the applicability of EEG and MEG is compared.

It is known that teleosts, without Weberian ossicles but with a swimbladder, can detect the direction of and, under appropriate conditions, the distance to a sound source [e.g., Schuijf and Hawkins, Nature 302, 143-144 (1983)]. It is hypothesized here that the underlying mechanism is the analysis of the parameters of the elliptical movement of the hair cells with respect to the otoliths. This movement results from the displacement wave impinging directly upon the labyrinth and the response displacement wave reradiated by the swimbladder. For a given swimbladder geometry, given the positions of the maculae of both labyrinths with respect to the swimbladder and the damping of the swimbladder, the displacement orbits of the maculae can be calculated [de Munck and Schellart, J. Acoust. Soc. Am. 81, 556-560 (1987)]. These calculations were made for the cod and the trout with the frequency, direction, and distance between the fish and the sound source as parameters with the source within the same horizontal plane as the fish. The orbit model predicts that the utriculus has the most strategic position to detect direction and distance of such a sound source. Moreover, the model predicts that this could basically be done monaurally. A hypothesis is proposed to describe how the utricular system analyzes the orbit parameters. The model is evaluated in relation to the results of behavioral experiments described in the literature.