Pauly Ossenblok

Onset and propagation of spike and slow wave discharges in human absence epilepsy: A MEG study

Purpose:  A nonlinear association and a source localization technique were used to describe the onset and propagation of spike‐and‐slow‐wave discharges (SWDs) in children with absence seizures. Previous studies have emphasized a leading cortical role in the generation of absence seizures in genetic epileptic rats.

Magnetoencephalography is more successful for screening and localizing frontal lobe epilepsy than electroencephalography.

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.

Space–time network connectivity and cortical activations preceding spike wave discharges in human absence epilepsy: a MEG study

To describe the spatial and temporal profiles of connectivity networks and sources preceding generalized spike-and-wave discharges (SWDs) in human absence epilepsy. Nonlinear associations of MEG signals and cluster indices obtained within the framework of graph theory were determined, while source localization in the frequency domain was performed in the low frequency bands with dynamic imaging of coherent sources. The results were projected on a three-dimensional surface rendering of the brain using a semi-realistic head model and MRI images obtained for each of the five patients studied. An increase in clustering and a decrease in path length preceding SWD onset and a rhythmic pattern of increasing and decreasing connectivity were seen during SWDs. Beamforming showed a consistent appearance of a low frequency frontal cortical source prior to the first generalized spikes. This source was preceded by a low frequency occipital source. The changes in the connectivity networks with the onset of SWDs suggest a pathologically predisposed state towards synchronous seizure networks with increasing connectivity from interictal to preictal and ictal state, while the occipital and frontal low frequency early preictal sources demonstrate that SWDs are not suddenly arising but gradually build up in a dynamic network.

Use of routine MEG in the primary diagnostic process of epilepsy.

At present, in epilepsy, magnetoencephalography (MEG) is mostly used for presurgical evaluations. It has proven to be robust for detecting and localizing interictal epileptiform discharges. Whether this is also true for first-line investigation in the diagnosis of epilepsy has not been investigated yet. We present our data on the usefulness of MEG in the earliest phase of diagnosing epilepsy. We examined 51 patients with suspicion of neocortical epilepsy and an inconclusive routine EEG. A method to integrate MEG in daily routine was developed. Results of visually assessed MEG recordings were compared, retrospectively, with clinical data and with the results of EEG after sleep deprivation. After a finding of inconclusive, routine MEG generated a gain in diagnostic value of 63% when compared with “final” clinical diagnosis. This is comparable with the added value of EEG after sleep deprivation recorded previously in the same patients. However, MEG is less of a burden for patient and hospital and has no association with risk of increase in seizure frequency. The routine MEG with visual assessment only is a reliable diagnostic tool in the routine diagnosis of epilepsy and may replace or precede EEG after sleep deprivation in daily clinical practice. Furthermore, MEG together with MRI enables magnetic source imaging and, thus, may provide additional information on the cortical localization of the epilepsy of a patient.

Novel artefact removal algorithms for co-registered EEG/fMRI based on selective averaging and subtraction.

Co-registered EEG and functional MRI (EEG/fMRI) is a potential clinical tool for planning invasive EEG in patients with epilepsy. In addition, the analysis of EEG/fMRI data provides a fundamental insight into the precise physiological meaning of both fMRI and EEG data. Routine application of EEG/fMRI for localization of epileptic sources is hampered by large artefacts in the EEG, caused by switching of scanner gradients and heartbeat effects. Residuals of the ballistocardiogram (BCG) artefacts are similarly shaped as epileptic spikes, and may therefore cause false identification of spikes. In this study, new ideas and methods are presented to remove gradient artefacts and to reduce BCG artefacts of different shapes that mutually overlap in time. Gradient artefacts can be removed efficiently by subtracting an average artefact template when the EEG sampling frequency and EEG low-pass filtering are sufficient in relation to MR gradient switching (Gonçalves et al., 2007). When this is not the case, the gradient artefacts repeat themselves at time intervals that depend on the remainder between the fMRI repetition time and the closest multiple of the EEG acquisition time. These repetitions are deterministic, but difficult to predict due to the limited precision by which these timings are known. Therefore, we propose to estimate gradient artefact repetitions using a clustering algorithm, combined with selective averaging. Clustering of the gradient artefacts yields cleaner EEG for data recorded during scanning of a 3T scanner when using a sampling frequency of 2048 Hz. It even gives clean EEG when the EEG is sampled with only 256 Hz. Current BCG artefacts-reduction algorithms based on average template subtraction have the intrinsic limitation that they fail to deal properly with artefacts that overlap in time. To eliminate this constraint, the precise timings of artefact overlaps were modelled and represented in a sparse matrix. Next, the artefacts were disentangled with a least squares procedure. The relevance of this approach is illustrated by determining the BCG artefacts in a data set consisting of 29 healthy subjects recorded in a 1.5 T scanner and 15 patients with epilepsy recorded in a 3 T scanner. Analysis of the relationship between artefact amplitude, duration and heartbeat interval shows that in 22% (1.5T data) to 30% (3T data) of the cases BCG artefacts show an overlap. The BCG artefacts of the EEG/fMRI data recorded on the 1.5T scanner show a small negative correlation between HBI and BCG amplitude. In conclusion, the proposed methodology provides a substantial improvement of the quality of the EEG signal without excessive computer power or additional hardware than standard EEG-compatible equipment.

The influence of construction methodology on structural brain network measures: A review

Structural brain networks based on diffusion MRI and tractography show robust attributes such as small-worldness, hierarchical modularity, and rich-club organization. However, there are large discrepancies in the reports about specific network measures. It is hypothesized that these discrepancies result from the influence of construction methodology. We surveyed the methodological options and their influences on network measures. It is found that most network measures are sensitive to the scale of brain parcellation, MRI gradient schemes and orientation model, and the tractography algorithm, which is in accordance with the theoretical analysis of the small-world network model. Different network weighting schemes represent different attributes of brain networks, which makes these schemes incomparable between studies. Methodology choice depends on the specific study objectives and a clear understanding of the pros and cons of a particular methodology. Because there is no way to eliminate these influences, it seems more practical to quantify them, optimize the methodologies, and construct structural brain networks with multiple spatial resolutions, multiple edge densities, and multiple weighting schemes.

Evaluating Contextual Processing in Diffusion MRI: Application to Optic Radiation Reconstruction for Epilepsy Surgery

Diffusion MRI and tractography allow for investigation of the architectural configuration of white matter in vivo, offering new avenues for applications like presurgical planning. Despite the promising outlook, there are many pitfalls that complicate its use for (clinical) application. Amongst these are inaccuracies in the geometry of the diffusion profiles on which tractography is based, and poor alignment with neighboring profiles. Recently developed contextual processing techniques, including enhancement and well-posed geometric sharpening, have shown to result in sharper and better aligned diffusion profiles. However, the research that has been conducted up to now is mainly of theoretical nature, and so far these techniques have only been evaluated by visual inspection of the diffusion profiles. In this work, the method is evaluated in a clinically relevant application: the reconstruction of the optic radiation for epilepsy surgery. For this evaluation we have developed a framework in which we incorporate a novel scoring procedure for individual pathways. We demonstrate that, using enhancement and sharpening, the extraction of an anatomically plausible reconstruction of the optic radiation from a large amount of probabilistic pathways is greatly improved in three healthy controls, where currently used methods fail to do so. Furthermore, challenging reconstructions of the optic radiation in three epilepsy surgery candidates with extensive brain lesions demonstrate that it is beneficial to integrate these methods in surgical planning.

Structural Brain Network: What is the Effect of LiFE Optimization of Whole Brain Tractography?

Structural brain networks constructed based on diffusion-weighted MRI (dMRI) have provided a systems perspective to explore the organization of the human brain. Some redundant and nonexistent fibers, however, are inevitably generated in whole brain tractography. We propose to add one critical step while constructing the networks to remove these fibers using the linear fascicle evaluation (LiFE) method, and study the differences between the networks with and without LiFE optimization. For a cohort of nine healthy adults and for 9 out of the 35 subjects from Human Connectome Project, the T1-weighted images and dMRI data are analyzed. Each brain is parcellated into 90 regions-of-interest, whilst a probabilistic tractography algorithm is applied to generate the original connectome. The elimination of redundant and nonexistent fibers from the original connectome by LiFE creates the optimized connectome, and the random selection of the same number of fibers as the optimized connectome creates the non-optimized connectome. The combination of parcellations and these connectomes leads to the optimized and non-optimized networks, respectively. The optimized networks are constructed with six weighting schemes, and the correlations of different weighting methods are analyzed. The fiber length distributions of the non-optimized and optimized connectomes are compared. The optimized and non-optimized networks are compared with regard to edges, nodes and networks, within a sparsity range of 0.75–0.95. It has been found that relatively more short fibers exist in the optimized connectome. About 24.0% edges of the optimized network are significantly different from those in the non-optimized network at a sparsity of 0.75. About 13.2% of edges are classified as false positives or the possible missing edges. The strength and betweenness centrality of some nodes are significantly different for the non-optimized and optimized networks, but not the node efficiency. The normalized clustering coefficient, the normalized characteristic path length and the small-worldness are higher in the optimized network weighted by the fiber number than in the non-optimized network. These observed differences suggest that LiFE optimization can be a crucial step for the construction of more reasonable and more accurate structural brain networks.

Spatiotemporal mapping of interictal epileptiform discharges in human absence epilepsy: A MEG study

PURPOSE Although absence epilepsy is considered to be a prototypic type of generalized epilepsy, it is still under debate whether generalized 3 Hz spike-and-wave discharges (SWDs) might have a cortical focal origin. Here it is investigated whether focal interictal epileptiform discharges (IEDs), which typically occur in the electro- (EEG) and magnetoencephalogram (MEG) in case of focal epilepsy, are present in the MEG of children with absence epilepsy. Next, the location of the sources of the IEDs is established, and it is investigated whether the location is concordant to the earlier established focal cortical regions involved in the generalized SWDs of these children. METHODS Whole head MEG recordings of seven children with absence epilepsy were reviewed with respect to the presence of IEDs (spikes and sharp waves). These IEDs were grouped into distinct clusters, in which each contribution to a cluster yields a comparable magnetic field distribution. Source localization was then performed onto the average signal of each cluster using an equivalent current dipole model and a realistic head model of the cortical surface. RESULTS IEDs were detected in 6 out of 7 patients. Source reconstruction indicated most often frontal, central or parietal origins of the IED in either the left and or right hemisphere. Spatiotemporal assessment of the IEDs indicated a stable location of the averages of these discharges, indicating a single underlying cortical source. DISCUSSION The outcome of this pilot study shows that MEG is well suited for the detection of IEDs and suggests that their estimated sources coincide rather well with the cortical regions involved during the spikes of the SWDs. It is discussed whether the presence of IEDs, classically seen as a marker of focal epilepsies, indicate that absence epilepsy should be considered as a focal type of epilepsy, in which changes in the network are evolving rapidly.

Towards clinical standards for EEG/fMRI

Once certain technical issues are properly addressed, simultaneous recording of EEG and fMRI (EEG/fMRI) provides very important opportunities to resolve longstanding questions concerning the interpretation of EEG. Without the need of questionable assumptions about the character of the underlying current sources to solve the inverse problem, one can exploit the high temporal resolution of EEG to detect certain phenomena and use the localizing power of fMRI to determine where in the brain the sources are. An important clinical application of EEG/fMRI is the promise to localize the generators of interictal spikes in order to plan surgical interventions in patients with epilepsy who are not responding to drugs. Furthermore, the co-registration of EEG and fMRI provides a wealth of information about the fMRI signal itself. The fMRI signal measures a T2* effect and therefore it is a very indirect measure of neural activity and is highly susceptible to motion and physiological artifacts due to respiratory and cardiac cycles. Apart from safety aspects (Lemieux et al., 1997), most technical issues related to EEG/fMRI are related to artifact removal (e.g. Bénar et al., 2003; Gotman et al., 2004; Gonçalves et al., 2007). Scanner artifacts on the EEG are huge, but very regular and different methods have been proposed to remove them. Also the beating heart produces an artifact on the EEG, which is much smaller than the gradient artifact but less regular. Therefore, removal of heart beat artifacts is much harder and because of its peaked shape this artifact can easily be mistaken for an epileptic event. A third type of artifact present in the EEG recorded during fMRI scanning is caused by small motions of the subject. These artifacts have been overlooked until now, because until recently there was no easy way to detect subject motion other than from the motion correction of the series of fMRI scans. However, motions taking place at a shorter time scale than the repetition time of the MRI sequence remain unnoticed by this method. Recently, Masterton et al. (2007) have found a very elegant solution for motion detection, using three small cupper loops that are fixed to the MRI compatible electrode cap. Because the small motion artifacts may have large similarity with epileptic events, Flanagan et al. (2009) explored the effect on the EEG/fMRI correlation analysis when true spikes are mixed up with motion artifacts. In their experiment number 2 the authors show convincingly that small motions in healthy subjects are presented in the EEG as waveforms that are very similar to epileptic spikes and a correlation analysis of these events with the co-registered fMRI gives significant brain areas that could in some cases be mistaken as a part of an epileptic network. The most realistic experiment of Flanagan et al. (2009) was their analysis of the situation that a mixture of