Epileptic Discharges Affect the Default Mode Network – fMRI and Intracerebral EEG Evidence

Functional neuroimaging studies of epilepsy patients often show, at the time of epileptic activity, deactivation in default mode network (DMN) regions, which is hypothesized to reflect altered consciousness. We aimed to study the metabolic and electrophysiological correlates of these changes in the DMN regions. We studied six epilepsy patients that underwent scalp EEG-fMRI and later stereotaxic intracerebral EEG (SEEG) sampling regions of DMN (posterior cingulate cortex, Pre-cuneus, inferior parietal lobule, medial prefrontal cortex and dorsolateral frontal cortex) as well as non-DMN regions. SEEG recordings were subject to frequency analyses comparing sections with interictal epileptic discharges (IED) to IED-free baselines in the IED-generating region, DMN and non-DMN regions. EEG-fMRI and SEEG were obtained at rest. During IEDs, EEG-fMRI demonstrated deactivation in various DMN nodes in 5 of 6 patients, most frequently the pre-cuneus and inferior parietal lobule, and less frequently the other DMN nodes. SEEG analyses demonstrated decrease in gamma power (50–150 Hz), and increase in the power of lower frequencies (<30 Hz) at times of IEDs, in at least one DMN node in all patients. These changes were not apparent in the non-DMN regions. We demonstrate that, at the time of IEDs, DMN regions decrease their metabolic demand and undergo an EEG change consisting of decreased gamma and increased lower frequencies. These findings, specific to DMN regions, confirm in a pathological condition a direct relationship between DMN BOLD activity and EEG activity. They indicate that epileptic activity affects the DMN, and therefore may momentarily reduce the consciousness level and cognitive reserve.     

Des sources neuronales aux signaux EEG : modèle et interprétation des activités épileptiques

This work deals with the interpretation of electrophysiological patients recorded in epileptic patients candidate to surgery. This issue is addressed through a physiologically relevant model for the generation of scalp and intracerebral electroencephalographic (EEG) signals. The proposed model is based on a spatiotemporal representation of the sources of brain activity, which combines a distributed dipole source model and a model of coupled neuronal populations. Signals recorded by sensors (scalp and intracerebral) are then computed by solving the forward problem in the head volume conductor. In this paper, the EEG generation model is used to study the influence of some source-related parameters (spatial extent, position, synchronization) on simulated signals, during epileptic transient activity (interictal spikes). Results show that the model allows for studying, on the one hand, the relationship between the spatiotemporal organization of neuronal sources and the properties of the observed signals and, on the other hand, the relationship between surface and depth EEG signals.     

Localizing confined epileptic foci in patients with an unclear focus or presumed multifocality using a component-based EEG-fMRI method

Precise localization of epileptic foci is an unavoidable prerequisite in epilepsy surgery. Simultaneous EEG-fMRI recording has recently created new horizons to locate foci in patients with epilepsy and, in comparison with single-modality methods, has yielded more promising results although it is still subject to limitations such as lack of access to information between interictal events. This study assesses its potential added value in the presurgical evaluation of patients with complex source localization. Adult candidates considered ineligible for surgery on account of an unclear focus and/or presumed multifocality on the basis of EEG underwent EEG-fMRI. Adopting a component-based approach, this study attempts to identify the neural behavior of the epileptic generators and detect the components-of-interest which will later be used as input in the GLM model, substituting the classical linear regressor. Twenty-eight sets interictal epileptiform discharges (IED) from nine patients were analyzed. In eight patients, at least one BOLD response was significant, positive and topographically related to the IEDs. These patients were rejected for surgery because of an unclear focus in four, presumed multifocality in three, and a combination of the two conditions in two. Component-based EEG-fMRI improved localization in five out of six patients with unclear foci. In patients with presumed multifocality, component-based EEG-fMRI advocated one of the foci in five patients and confirmed multifocality in one of the patients. In seven patients, component-based EEG-fMRI opened new prospects for surgery and in two of these patients, intracranial EEG supported the EEG-fMRI results. In these complex cases, component-based EEG-fMRI either improved source localization or corroborated a negative decision regarding surgical candidacy. As supported by the statistical findings, the developed EEG-fMRI method leads to a more realistic estimation of localization compared to the conventional EEG-fMRI approach, making it a tool of high value in pre-surgical evaluation of patients with refractory epilepsy. To ensure proper implementation, we have included guidelines for the application of component-based EEG-fMRI in clinical practice.     

Decomposition of Neurological Multivariate Time Series by State Space Modelling

Decomposition of multivariate time series data into independent source components forms an important part of preprocessing and analysis of time-resolved data in neuroscience. We briefly review the available tools for this purpose, such as Factor Analysis (FA) and Independent Component Analysis (ICA), then we show how linear state space modelling, a methodology from statistical time series analysis, can be employed for the same purpose. State space modelling, a generalization of classical ARMA modelling, is well suited for exploiting the dynamical information encoded in the temporal ordering of time series data, while this information remains inaccessible to FA and most ICA algorithms. As a result, much more detailed decompositions become possible, and both components with sharp power spectrum, such as alpha components, sinusoidal artifacts, or sleep spindles, and with broad power spectrum, such as FMRI scanner artifacts or epileptic spiking components, can be separated, even in the absence of prior information. In addition, three generalizations are discussed, the first relaxing the independence assumption, the second introducing non-stationarity of the covariance of the noise driving the dynamics, and the third allowing for non-Gaussianity of the data through a non-linear observation function. Three application examples are presented, one electrocardigram time series and two electroencephalogram (EEG) time series. The two EEG examples, both from epilepsy patients, demonstrate the separation and removal of various artifacts, including hum noise and FMRI scanner artifacts, and the identification of sleep spindles, epileptic foci, and spiking components. Decompositions obtained by two ICA algorithms are shown for comparison.     

癫痫病人脑电信号的奇异谱

癫痫是一种常见的神经系统疾患,其唯一客观证据为脑电图的癫痫样发放。在癫痫发作间期,仅有偶发的很难辨别的癫痫样放电,为了正确诊断癫痫病,往往需要医生长时间监测病人的脑电信号,在对脑电信号进行相空间重构,进而对其进行奇异系统分析,发现癫痫病人无论在癫痫发作前、发作中、发作后,其脑电信号的奇异谱曲线不存在噪声平台,明显区别于正常人。是否可以认为脑电信号的奇异谱正代表着大脑的一种基本状态,癫痫患者在未发作时,大脑的基本状态已经处于异常。无论如休,奇异系统分析方法使得可以利用很短的一段脑电数据诊断癫痫。无疑为癫痫病人的临床诊断提供了一条简单、有效的途径。     

Estimating short-run and long-run interaction mechanisms in interictal state

We address the issue of analyzing electroencephalogram (EEG) from seizure patients in order to test, model and determine the statistical properties that distinguish between EEG states (interictal, pre-ictal, ictal) by introducing a new class of time series analysis methods. In the present study: firstly, we employ statistical methods to determine the non-stationary behavior of focal interictal epileptiform series within very short time intervals; secondly, for such intervals that are deemed non-stationary we suggest the concept of Autoregressive Integrated Moving Average (ARIMA) process modelling, well known in time series analysis. We finally address the queries of causal relationships between epileptic states and between brain areas during epileptiform activity. We estimate the interaction between different EEG series (channels) in short time intervals by performing Granger-causality analysis and also estimate such interaction in long time intervals by employing Cointegration analysis, both analysis methods are well-known in econometrics. Here we find: first, that the causal relationship between neuronal assemblies can be identified according to the duration and the direction of their possible mutual influences; second, that although the estimated bidirectional causality in short time intervals yields that the neuronal ensembles positively affect each other, in long time intervals neither of them is affected (increasing amplitudes) from this relationship. Moreover, Cointegration analysis of the EEG series enables us to identify whether there is a causal link from the interictal state to ictal state.     

Spike-induced interference in auditory sensory processing in Landau–Kleffner syndrome

Objectives: Landau–Kleffner Syndrome (LKS) is an epileptic syndrome characterised by a deficit in language comprehension and production, paroxysmal epileptiform activity in the posterior temporal leads, and by the inconsistent presence of epileptic fits. Its interest lies in the fact that it stands as a model for the study of interference of epileptiform activity on cognitive function, although the pathophysiology of the decline in language skills that follows its onset has not yet been clarified.Methods: We have recorded spike-triggered auditory evoked responses in a group of 6 children with LKS, to investigate whether the occurrence of individual EEG paroxysms is able per se to induce a decline in the response of the auditory cortex.Results: Results have indicated that left hemisphere spikes are associated with a greater reduction in amplitude and an increase in latency of the N1, than spikes occurring in the right hemisphere. No stable change in the evoked response has been detected outside of the EEG paroxysm.Conclusions: We postulate EEG interictal activity is able to induce impairment in processing auditory information and that this may play a role in the pathogenesis of language deficit (deficiency?) in LKS.     

2 types of chronic epileptogenesis in rabbits during kindling stimulation of the hippocampus

The development of convulsant readiness in rabbits during kindling electrical stimulation of the hippocamp was studied as was the dependence of the motor seizure pattern on the degree of epileptiform activity generalization in the CNS. The kindling electrical stimulation of the hippocamp gave rise to the formation in different rabbits of the two main types of afterdischarges. One of them was characterized by high-frequency and high-amplitude spikes (total duration 8-30 s) and the other one by continuous, rather long (50-100 s) hypersynchronous paroxysms. In the interictal period, the animals with the first type demonstrated the occurrence of spontaneous spikes (in all the brain structures under study) that sometimes progressed to a more or less prolonged seizure discharges. At the same time in the animals with the second type of afterdischarges the EEG in the interictal period was slightly different from normal. Despite this fact the seizures induced by electrical stimulation ran a milder course (short-term clonic seizures) in animals with the first type of afterdischarges as compared to those with the second type (long-term clonicotonic seizures). It is assumed that the severity of the motor seizure does not depend on the degree of epileptic activity generalization in the CNS.     

Functional Brain Dysfunction in Patients with Benign Childhood Epilepsy as Revealed by Graph Theory

There is growing evidence that brain networks are altered in epileptic subjects. In this study, we investigated the functional connectivity and brain network properties of benign childhood epilepsy with centrotemporal spikes using graph theory. Benign childhood epilepsy with centrotemporal spikes is the most common form of idiopathic epilepsy in young children under the age of 16 years. High-density EEG data were recorded from patients and controls in resting state with eyes closed. Data were preprocessed and spike and spike-free segments were selected for analysis. Phase locking value was calculated for all paired combinations of channels and for five frequency bands (δ, θ, α, β₁ and β₂). We computed the degree and small-world parameters—clustering coefficient (C) and path length (L)—and compared the two patient conditions to controls. A higher degree at epileptic zones during interictal epileptic spikes (IES) was observed in all frequency bands. Both patient conditions reduced connection at the occipital and right frontal regions close to the epileptic zone in the α band. The “small-world” features (high C and short L) were deviated in patients compared to controls. A changed from an ordered network in the δ band to a more randomly organized network in the α band was observed in patients compared to healthy controls. These findings show that the benign epileptic brain network is disrupted not only at the epileptic zone, but also in other brain regions especially frontal regions.     

Combined EEG/MEG Can Outperform Single Modality EEG or MEG Source Reconstruction in Presurgical Epilepsy Diagnosis

We investigated two important means for improving source reconstruction in presurgical epilepsy diagnosis. The first investigation is about the optimal choice of the number of epileptic spikes in averaging to (1) sufficiently reduce the noise bias for an accurate determination of the center of gravity of the epileptic activity and (2) still get an estimation of the extent of the irritative zone. The second study focuses on the differences in single modality EEG (80-electrodes) or MEG (275-gradiometers) and especially on the benefits of combined EEG/MEG (EMEG) source analysis. Both investigations were validated with simultaneous stereo-EEG (sEEG) (167-contacts) and low-density EEG (ldEEG) (21-electrodes). To account for the different sensitivity profiles of EEG and MEG, we constructed a six-compartment finite element head model with anisotropic white matter conductivity, and calibrated the skull conductivity via somatosensory evoked responses. Our results show that, unlike single modality EEG or MEG, combined EMEG uses the complementary information of both modalities and thereby allows accurate source reconstructions also at early instants in time (epileptic spike onset), i.e., time points with low SNR, which are not yet subject to propagation and thus supposed to be closer to the origin of the epileptic activity. EMEG is furthermore able to reveal the propagation pathway at later time points in agreement with sEEG, while EEG or MEG alone reconstructed only parts of it. Subaveraging provides important and accurate information about both the center of gravity and the extent of the epileptogenic tissue that neither single nor grand-averaged spike localizations can supply.     

Automatic detection of epileptic EEG signals using higher order cumulant features

The unpredictability of the occurrence of epileptic seizures makes it difficult to detect and treat this condition effectively. An automatic system that characterizes epileptic activities in EEG signals would allow patients or the people near them to take appropriate precautions, would allow clinicians to better manage the condition, and could provide more insight into these phenomena thereby revealing important clinical information. Various methods have been proposed to detect epileptic activity in EEG recordings. Because of the nonlinear and dynamic nature of EEG signals, the use of nonlinear Higher Order Spectra (HOS) features is a seemingly promising approach. This paper presents the methodology employed to extract HOS features (specifically, cumulants) from normal, interictal, and epileptic EEG segments and to use significant features in classifiers for the detection of these three classes. In this work, 300 sets of EEG data belonging to the three classes were used for feature extraction and classifier development and evaluation. The results show that the HOS based measures have unique ranges for the different classes with high confidence level (p-value < 0.0001). On evaluating several classifiers with the significant features, it was observed that the Support Vector Machine (SVM) presented a high detection accuracy of 98.5% thereby establishing the possibility of effective EEG segment classification using the proposed technique.     

Concordance of Epileptic Networks Associated with Epileptic Spikes Measured by High-Density EEG and Fast fMRI

Objective

The present study aims to investigate whether a newly developed fast fMRI called MREG (magnetic resonance encephalography) measures metabolic changes related to interictal epileptic discharges (IED). For this purpose BOLD changes are correlated with the IED distribution and variability.

Methods

Patients with focal epilepsy underwent EEG-MREG using a 64 channel cap. IED voltage maps were generated using 32 and 64 channels and compared regarding their correspondence to the BOLD response. The extents of IEDs (defined as number of channels with >50% of maximum IED negativity) were correlated with the extents of positive and negative BOLD responses. Differences in inter-spike variability were investigated between interictal epileptic discharges (IED) sets with and without concordant positive or negative BOLD responses.

Results

17 patients showed 32 separate IED types. In 50% of IED types the BOLD changes could be confirmed by another independent imaging method. The IED extent significantly correlated with the positive BOLD extent (p = 0.04). In 6 patients the 64-channel EEG voltage maps better reflected the positive or negative BOLD response than the 32-channel EEG; in all others no difference was seen. Inter-spike variability was significantly lower in IED sets with than without concordant positive or negative BOLD responses (with p = 0.04).

Significance

Higher density EEG and fast fMRI seem to improve the value of EEG-fMRI in epilepsy. The correlation of positive BOLD and IED extent could suggest that widespread BOLD responses reflect the IED network. Inter-spike variability influences the likelihood to find IED concordant positive or negative BOLD responses, which is why single IED analysis may be promising.     

An avian model of genetic reflex epilepsy

The Fayoumi strain of chickens (Fepi) carries a recessive autosomal gene mutation in which homozygotes are afflicted with a photogenic and audiogenic reflex epilepsy. Seizures consist of stimulus-locked motor symptoms followed by generalized self sustained convulsions. EEG recordings show spikes and spike and waves patterns at rest which are suppressed during seizures and replaced by a desynchronized pattern of activity. Neurones of the prosencephalon discharge in bursts at rest, while neurones of the mesencephalon are bursting during seizures. Living neural chimeras were obtained by replacing specific embryonic brain vesicles in a normal chicken embryo with equivalent vesicles from a Fepi donor. These chimeras show that the epileptic phenotype can be totally or partially transferred from the Fepi to the normal chickens. Total transfer of photogenic and audiogenic seizures was obtained by substitution of both the prosencephalon and mesencephalon, while substitution of the prosencephalon alone resulted in transfer of interictal paroxysmal activity and substitution of the mesencephalon alone resulted principally in transfer of ictal motor symptoms. Increased expression of the c-fos protooncogene, as revealed by the western blot technique, confirmed the distinct encephalic localizations of the symptoms of the photogenic and audiogenic reflex epilepsy of the Fepi shown with the methods of electrophysiology and brain chimeras. We conclude that the Fepi is a good model of brain stem reflex epilepsy and suggest that the brain stem is a generator of some other animal and human genetic reflex "epileptic syndromes".     

In vivo optical mapping of epileptic foci and surround inhibition in ferret cerebral cortex

The population of neurons participating in an epileptiform event varies from moment to moment. Most techniques currently used to localize epileptiform events in vivo have spatial and/or temporal sampling limitations. Here we show in an animal model that optical imaging based on intrinsic signals is an excellent method for in vivo mapping of clinically relevant epileptiform events, such as interictal spikes, ictal onsets, ictal spread and secondary homotopic foci. In addition, a decrease in the optical signal correlates spatially with a decrease in neuronal activity recorded from cortex surrounding an epileptic focus. Optical mapping of epilepsy might be a useful adjunct in the surgical treatment of neocortical epilepsy, which critically depends on the precise localization of intrinsically epileptogenic neurons.     

Application of a dissimilarity index of EEG and its sub-bands on prediction of induced epileptic seizures from rat's EEG signals

ObjectiveEpileptic seizures are defined as manifest of excessive and hyper-synchronous activity of neurons in the cerebral cortex that cause frequent malfunction of the human central nervous system. Therefore, finding precursors and predictors of epileptic seizure is of utmost clinical relevance to reduce the epileptic seizure induced nervous system malfunction consequences. Researchers for this purpose may even guide us to a deep understanding of the seizure generating mechanisms. The goal of this paper is to predict epileptic seizures in epileptic rats.MethodsSeizures were induced in rats using pentylenetetrazole (PTZ) model. EEG signals in interictal, preictal, ictal and postictal periods were then recorded and analyzed to predict epileptic seizures. Epileptic seizures were predicted by calculating an index in consecutive windows of EEG signal and comparing the index with a threshold. In this work, a newly proposed dissimilarity index called Bhattacharyya Based Dissimilarity Index (BBDI), dynamical similarity index and fuzzy similarity index were investigated.ResultsBBDI, dynamical similarity index and fuzzy similarity index were examined on case and control groups and compared to each other. The results show that BBDI outperforms dynamical and fuzzy similarity indices. In order to improve the results, EEG sub-bands were also analyzed. The best result achieved when the proposed dissimilarity index was applied on Delta sub-band that predicts epileptic seizures in all rats with a mean of 299.5 s.ConclusionThe dissimilarity of neural network activity between reference window and present window of EEG signal has a significant increase prior to an epileptic seizure and the proposed dissimilarity index (BBDI) can reveal this variation to predict epileptic seizures. In addition, analyzing EEG sub-bands results in more accurate information about constituent neuronal activities underlying the EEG since certain changes in EEG signal may be amplified when each sub-band is analyzed separately.SignificanceThis paper presents application of a dissimilarity index (BBDI) on EEG signals and its sub-bands to predict PTZ-induced epileptic seizures in rats. Based on the results of this work, BBDI will predict epileptic seizures more accurately and more reliably compared to current indices that increases epileptic patient comfort and improves patient outcomes.     

Selecting EEG components using time series analysis in brain death diagnosis

In diagnosis of brain death for human organ transplant, EEG (electroencephalogram) must be flat to conclude the patient’s brain death but it has been reported that the flat EEG test is sometimes difficult due to artifacts such as the contamination from the power supply and ECG (electrocardiogram, the signal from the heartbeat). ICA (independent component analysis) is an effective signal processing method that can separate such artifacts from the EEG signals. Applying ICA to EEG channels, we obtain several separated components among which some correspond to the brain activities while others contain artifacts. This paper aims at automatic selection of the separated components based on time series analysis. In the flat EEG test in brain death diagnosis, such automatic component selection is helpful.     

Epilepsy due to malformations of cortical development--correlation of clinical, MRI and Tc-99mECD SPECT findings

Malformations of cortical development (MCD) have been increasingly recognized as an important cause of intractable epilepsy. The aim of our study was to define epileptogenicity of MCDs by correlating MRI, EEG and semiology of epileptic attacks, and to determine the effect of MCD on drug resistant epilepsy. We also intended to reveal the utility of interictal single photo emission computed tomography (SPECT) in verification of MCD lesions and relative prevalence of different MCDs. Based on interictal EEG finding, semiology of the epileptic attacks and brain magnetic resonance imaging (MRI) "electroclinical epileptogenicity" of MCD was defined. Brain MRI revealed cortical dysplasia (CD) in nine patients, polymicrogyria in four patients, lissencephaly and schizencephaly in one patient each. Three patients had a combination of malformations. The localization of SPECT hypoperfusion corresponded to MCD lesion in ten (66.67%) patients. Electroclinically confirmed epileptogenicity of MCD overlapped with MR and interictal SPECT findings in fourteen (93.3%) and nine (60.0%) patients, respectively. Our study results demonstrated the MCD lesions to be highly epileptogenic and a frequent cause of intractability.     

Removal of Pulse Artefact from EEG Data Recorded in MR Environment at 3T. Setting of ICA Parameters for Marking Artefactual Components: Application to Resting-State Data

Simultaneous electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) allow for a non-invasive investigation of cerebral functions with high temporal and spatial resolution. The main challenge of such integration is the removal of the pulse artefact (PA) that affects EEG signals recorded in the magnetic resonance (MR) scanner. Often applied techniques for this purpose are Optimal Basis Set (OBS) and Independent Component Analysis (ICA). The combination of OBS and ICA is increasingly used, since it can potentially improve the correction performed by each technique separately. The present study is focused on the OBS-ICA combination and is aimed at providing the optimal ICA parameters for PA correction in resting-state EEG data, where the information of interest is not specified in latency and amplitude as in, for example, evoked potential. A comparison between two intervals for ICA calculation and four methods for marking artefactual components was performed. The performance of the methods was discussed in terms of their capability to 1) remove the artefact and 2) preserve the information of interest. The analysis included 12 subjects and two resting-state datasets for each of them. The results showed that none of the signal lengths for the ICA calculation was highly preferable to the other. Among the methods for the identification of PA-related components, the one based on the wavelets transform of each component emerged as the best compromise between the effectiveness in removing PA and the conservation of the physiological neuronal content.     

Localizational concepts in epilepsy: past, present and future

The clinical seizure pattern, particularly the initial phenomena, plus the EEG, when satisfactory recording of the seizure onset can be achieved, determine the primary localization of epileptic phenomena. The EEG has also demonstrated, by the presence of interictal epileptiform spike discharges, the presence of a second-order localization of epileptic phenomena, namely, the location and extent of cortex adjacent to the site of origin of the neuronal seizure discharge that is recruited into action in a clinical epileptic seizure. Experience with cortical resection in the treatment of focal epilepsy has demonstrated the importance of a third-order localization of epileptic phenomena, namely, how much of the potentially epileptogenic cortex must be excised in order to produce a satisfactory reduction of the seizure tendency.     

基于时频分析检测EEG中癫痫样棘/尖波的方法

提出了一种基于Ｃｈｏｉ－Ｗｉｌｌｉａｍｓ分布检测ＥＥＧ中癫痫样棘波／尖波的方法。该方法通过计算ＥＥＧ信号的时频分布,得到一段信号在各个时刻上沿频率方向上的能量分布。这种能量分布相当于一种瞬时频谱,反映了ＥＥＧ信号在局部时间范围里的波形特征。以一段ＥＥＧ信号在各个时刻的瞬时频谱的平均作为这段脑电的背景信号频谱,通过计算每一时刻的瞬时频谱与背景信号频谱之间的频谱差,检测这段信号中的棘波／尖波。对临床Ｅ