International Federation of Clinical Neurophysiology (IFCN) - EEG research workgroup: Recommendations on frequency and topographic analysis of resting state EEG rhythms. Part 1: Applications in clinical research studies.

In 1999, the International Federation of Clinical Neurophysiology (IFCN) published "IFCN Guidelines for topographic and frequency analysis of EEGs and EPs" (Nuwer et al., 1999). Here a Workgroup of IFCN experts presents unanimous recommendations on the following procedures relevant for the topographic and frequency analysis of resting state EEGs (rsEEGs) in clinical research defined as neurophysiological experimental studies carried out in neurological and psychiatric patients: (1) recording of rsEEGs (environmental conditions and instructions to participants; montage of the EEG electrodes; recording settings); (2) digital storage of rsEEG and control data; (3) computerized visualization of rsEEGs and control data (identification of artifacts and neuropathological rsEEG waveforms); (4) extraction of "synchronization" features based on frequency analysis (band-pass filtering and computation of rsEEG amplitude/power density spectrum); (5) extraction of "connectivity" features based on frequency analysis (linear and nonlinear measures); (6) extraction of "topographic" features (topographic mapping; cortical source mapping; estimation of scalp current density and dura surface potential; cortical connectivity mapping), and (7) statistical analysis and neurophysiological interpretation of those rsEEG features. As core outcomes, the IFCN Workgroup endorsed the use of the most promising "synchronization" and "connectivity" features for clinical research, carefully considering the limitations discussed in this paper. The Workgroup also encourages more experimental (i.e. simulation studies) and clinical research within international initiatives (i.e., shared software platforms and databases) facing the open controversies about electrode montages and linear vs. nonlinear and electrode vs. source levels of those analyses.

What electrophysiology tells us about Alzheimer's disease: a window into the synchronization and connectivity of brain neurons.

Electrophysiology provides a real-time readout of neural functions and network capability in different brain states, on temporal (fractions of milliseconds) and spatial (micro, meso, and macro) scales unmet by other methodologies. However, current international guidelines do not endorse the use of electroencephalographic (EEG)/magnetoencephalographic (MEG) biomarkers in clinical trials performed in patients with Alzheimer's disease (AD), despite a surge in recent validated evidence. This position paper of the ISTAART Electrophysiology Professional Interest Area endorses consolidated and translational electrophysiological techniques applied to both experimental animal models of AD and patients, to probe the effects of AD neuropathology (i.e., brain amyloidosis, tauopathy, and neurodegeneration) on neurophysiological mechanisms underpinning neural excitation/inhibition and neurotransmission as well as brain network dynamics, synchronization, and functional connectivity, reflecting thalamocortical and corticocortical residual capacity. Converging evidence shows relationships between abnormalities in EEG/MEG markers and cognitive deficits in groups of AD patients at different disease stages. The supporting evidence for the application of electrophysiology in AD clinical research as well as drug discovery pathways warrants an international initiative to include the use of EEG/MEG biomarkers in the main multicentric projects planned in AD patients, to produce conclusive findings challenging the present regulatory requirements and guidelines for AD studies.

Epilepsy as a manifestation of a multistate network of oscillatory systems.

The human brain, largely accepted as the most complex biological system known, is still far from being understood in its parts or as a whole. More specifically, biological mechanisms of epileptic states and state transitions are not well understood. Here, we explore the concept of the epilepsy as a manifestation of a multistate network composed of coupled oscillatory units. We also propose that functional coupling between neuroglial elements is a dynamic process, characterized by temporal changes both at short and long time scales. We review various experimental and modelling data suggesting that epilepsy is a pathological manifestation of such a multistate network - both when viewed as a coupled oscillatory network, and as a system of multistate stable state attractors. Based on a coupled oscillators model, we propose a significant role for glial cells in modulating hyperexcitability of the neuroglial networks of the brain. Also, using these concepts, we explain a number of observable phenomena such as propagation patterns of bursts within a seizure in the isolated intact hippocampus in vitro, postictal generalized suppression in human encephalographic seizure data, and changes in seizure susceptibility in epileptic patients. Based on our conceptual model we propose potential clinical applications to estimate brain closeness to ictal transition by means of active perturbations and passive measures during on-going activity.

Phase clustering in transcranial magnetic stimulation-evoked EEG responses in genetic generalized epilepsy and migraine.

BACKGROUND: Epilepsy and migraine are paroxysmal neurological conditions associated with disturbances of cortical excitability. No useful biomarkers to monitor disease activity in these conditions are available. Phase clustering was previously described in electroencephalographic (EEG) responses to photic stimulation and may be a potential epilepsy biomarker. OBJECTIVE: The objective of this study was to investigate EEG phase clustering in response to transcranial magnetic stimulation (TMS), compare it with photic stimulation in controls, and explore its potential as a biomarker of genetic generalized epilepsy or migraine with aura. METHODS: People with (possible) juvenile myoclonic epilepsy (JME), migraine with aura, and healthy controls underwent single-pulse TMS with concomitant EEG recording during the interictal period. We compared phase clustering after TMS with photic stimulation across the groups using permutation-based testing. RESULTS: We included eight people with (possible) JME (five off medication, three on), 10 with migraine with aura, and 37 controls. The TMS and photic phase clustering spectra showed significant differences between those with epilepsy without medication and controls. Two phase clustering-based indices successfully captured these differences between groups. One participant was tested multiple times. In this case, the phase clustering-based indices were inversely correlated with the dose of antiepileptic medication. Phase clustering did not differ between people with migraine and controls. CONCLUSION: We present methods to quantify phase clustering using TMS-EEG and show its potential value as a measure of brain network activity in genetic generalized epilepsy. Our results suggest that the higher propensity to phase clustering is not shared between genetic generalized epilepsy and migraine.

Idiopathic generalised epilepsies of adult onset: a reappraisal and literature review.

Idiopathic generalised epilepsies are characterised by widespread, symmetric, bilateral spike-and-wave discharges on EEG. Onset typically occurs in children and adolescents, but may also start in adulthood. These forms of adult onset constitute the focus of this review. A critical analysis of the medical literature was conducted through a narrative review search of PubMed and Medline databases. Cases of idiopathic generalised epilepsies with adult onset, in general, are not considered to be independent nosological entities. The "grand mal on awakening" seems to prevail among the idiopathic syndromes of adult onset. The EEG findings that question the diagnosis of late-onset idiopathic generalised epilepsies consist mainly of patterns interpreted as representing focal epileptiform activity. Normal brain MRI and typical EEG abnormalities are essential for diagnosis. For all cases with symptomatology of suspected adult-onset idiopathic generalised epilepsy, it is mandatory to exclude neurological conditions that may be associated with epileptic seizures which appear in this age group. A correct diagnosis of adult-onset idiopathic generalised epilepsy alleviates concern for a symptomatic origin, leading to appropriate antiepileptic treatment.

EEG synchronization measures predict epilepsy-related BOLD-fMRI fluctuations better than commonly used univariate metrics.

OBJECTIVE: We hypothesize that the hypersynchronization associated with epileptic activity is best described by EEG synchronization measures, and propose to use these as predictors of epilepsy-related BOLD fluctuations. METHODS: We computed the phase synchronization index (PSI) and global field synchronization (GFS), within two frequency bands, a broadband (1-45 Hz) and a narrower band focused on the presence of epileptic activity (3-10 Hz). The associated epileptic networks were compared with those obtained using conventional unitary regressors and two power-weighted metrics (total power and root mean square frequency), on nine simultaneous EEG-fMRI datasets from four epilepsy patients, exhibiting inter-ictal epileptiform discharges (IEDs). RESULTS: The average PSI within 3-10 Hz achieved the best performance across several measures reflecting reliability in all datasets. The results were cross-validated through electrical source imaging of the IEDs. The applicability of PSI when no IEDs are recorded on the EEG was evaluated on three additional patients, yielding partially plausible networks in all cases. CONCLUSIONS: Epileptic networks can be mapped based on the EEG PSI metric within an IED-specific frequency band, performing better than commonly used EEG metrics. SIGNIFICANCE: This is the first study to investigate EEG synchronization measures as potential predictors of epilepsy-related BOLD fluctuations.

Dynamics of convulsive seizure termination and postictal generalized EEG suppression.

It is not fully understood how seizures terminate and why some seizures are followed by a period of complete brain activity suppression, postictal generalized EEG suppression. This is clinically relevant as there is a potential association between postictal generalized EEG suppression, cardiorespiratory arrest and sudden death following a seizure. We combined human encephalographic seizure data with data of a computational model of seizures to elucidate the neuronal network dynamics underlying seizure termination and the postictal generalized EEG suppression state. A multi-unit computational neural mass model of epileptic seizure termination and postictal recovery was developed. The model provided three predictions that were validated in EEG recordings of 48 convulsive seizures from 48 subjects with refractory focal epilepsy (20 females, age range 15-61 years). The duration of ictal and postictal generalized EEG suppression periods in human EEG followed a gamma probability distribution indicative of a deterministic process (shape parameter 2.6 and 1.5, respectively) as predicted by the model. In the model and in humans, the time between two clonic bursts increased exponentially from the start of the clonic phase of the seizure. The terminal interclonic interval, calculated using the projected terminal value of the log-linear fit of the clonic frequency decrease was correlated with the presence and duration of postictal suppression. The projected terminal interclonic interval explained 41% of the variation in postictal generalized EEG suppression duration (P < 0.02). Conversely, postictal generalized EEG suppression duration explained 34% of the variation in the last interclonic interval duration. Our findings suggest that postictal generalized EEG suppression is a separate brain state and that seizure termination is a plastic and autonomous process, reflected in increased duration of interclonic intervals that determine the duration of postictal generalized EEG suppression.

Automated Video Detection of Epileptic Convulsion Slowing as a Precursor for Post-Seizure Neuronal Collapse.

Automated monitoring and alerting for adverse events in people with epilepsy can provide higher security and quality of life for those who suffer from this debilitating condition. Recently, we found a relation between clonic slowing at the end of a convulsive seizure (CS) and the occurrence and duration of a subsequent period of postictal generalized EEG suppression (PGES). Prolonged periods of PGES can be predicted by the amount of progressive increase of interclonic intervals (ICIs) during the seizure. The purpose of the present study is to develop an automated, remote video sensing-based algorithm for real-time detection of significant clonic slowing that can be used to alert for PGES. This may help preventing sudden unexpected death in epilepsy (SUDEP). The technique is based on our previously published optical flow video sequence processing paradigm that was applied for automated detection of major motor seizures. Here, we introduce an integral Radon-like transformation on the time-frequency wavelet spectrum to detect log-linear frequency changes during the seizure. We validate the automated detection and quantification of the ICI increase by comparison to the results from manually processed electroencephalography (EEG) traces as "gold standard". We studied 48 cases of convulsive seizures for which synchronized EEG-video recordings were available. In most cases, the spectral ridges obtained from Gabor-wavelet transformations of the optical flow group velocities were in close proximity to the ICI traces detected manually from EEG data during the seizure. The quantification of the slowing-down effect measured by the dominant angle in the Radon transformed spectrum was significantly correlated with the exponential ICI increase factors obtained from manual detection. If this effect is validated as a reliable precursor of PGES periods that lead to or increase the probability of SUDEP, the proposed method would provide an efficient alerting device.

Preventive and Abortive Strategies for Stimulation Based Control of Epilepsy: A Computational Model Study.

Epilepsy is a condition in which periods of ongoing normal EEG activity alternate with periods of oscillatory behavior characteristic of epileptic seizures. The dynamics of the transitions between the two states are still unclear. Computational models provide a powerful tool to explore the underlying mechanisms of such transitions, with the purpose of eventually finding therapeutic interventions for this debilitating condition. In this study, the possibility to postpone seizures elicited by a decrease of inhibition is investigated by using external stimulation in a realistic bistable neuronal model consisting of two interconnected neuronal populations representing pyramidal cells and interneurons. In the simulations, seizures are induced by slowly decreasing the conductivity of GABA[Formula: see text] synaptic channels over time. Since the model is bistable, the system will change state from the initial steady state (SS) to the limit cycle (LS) state because of internal noise, when the inhibition falls below a certain threshold. Several state-independent stimulations paradigms are simulated. Their effectiveness is analyzed for various stimulation frequencies and intensities in combination with periodic and random stimulation sequences. The distributions of the time to first seizure in the presence of stimulation are compared with the situation without stimulation. In addition, stimulation protocols targeted to specific subsystems are applied with the objective of counteracting the baseline shift due to decreased inhibition in the system. Furthermore, an analytical model is used to investigate the effects of random noise. The relation between the strength of random noise stimulation, the control parameter of the system and the transitions between steady state and limit cycle are investigated. The study shows that it is possible to postpone epileptic activity by targeted stimulation in a realistic neuronal model featuring bistability and that it is possible to stop seizures by random noise in an analytical model.

Dynamics of epileptic activity in a peculiar case of childhood absence epilepsy and correlation with thalamic levels of GABA.

OBJECTIVES: Childhood absence epilepsy (CAE) is a syndrome with well-defined electroclinical features but unknown pathological basis. An increased thalamic tonic GABA inhibition has recently been discovered on animal models (Cope et al., 2009), but its relevance for human CAE is unproven. METHODS: We studied an 11-year-old boy, presenting the typical clinical features of CAE, but spike-wave discharges (SWD) restricted to one hemisphere. RESULTS: High-resolution EEG failed to demonstrate independent contralateral hemisphere epileptic activity. Consistently, simultaneous EEG-fMRI revealed the typical thalamic BOLD activation, associated with caudate and default mode network deactivation, but restricted to the hemisphere with SWD. Cortical BOLD activations were localized on the ipsilateral pars transverse. Magnetic resonance spectroscopy, using MEGA-PRESS, showed that the GABA/creatine ratio was 2.6 times higher in the hemisphere with SWD than in the unaffected one, reflecting a higher GABA concentration. Similar comparisons for the patient's occipital cortex and thalamus of a healthy volunteer yielded asymmetries below 25%. SIGNIFICANCE: In a clinical case of CAE with EEG and fMRI-BOLD manifestations restricted to one hemisphere, we found an associated increase in thalamic GABA concentration consistent with a role for this abnormality in human CAE.

Spatio-temporal common pattern: A companion method for ERP analysis in the time domain.

BACKGROUND: Already used at the incept of research on event-related potentials (ERP) over half a century ago, the arithmetic mean is still the benchmark for ERP estimation. Such estimation, however, requires a large number of sweeps and/or a careful rejection of artifacts affecting the electroencephalographic recording. NEW METHOD: In this article we propose a method for estimating ERPs as they are naturally contaminated by biological and instrumental artifacts. The proposed estimator makes use of multivariate spatio-temporal filtering to increase the signal-to-noise ratio. This approach integrates a number of relevant advances in ERP data analysis, such as single-sweep adaptive estimation of amplitude and latency and the use of multivariate regression to account for ERP overlapping in time. RESULTS: We illustrate the effectiveness of the proposed estimator analyzing a dataset comprising 24 subjects involving a visual odd-ball paradigm, without performing any artifact rejection. COMPARISON WITH EXISTING METHOD(S): As compared to the arithmetic average, a lower number of sweeps is needed. Furthermore, artifact rejection can be performed roughly using permissive automatic procedures. CONCLUSION: The proposed ensemble average estimator yields a reference companion to the arithmetic ensemble average estimation, suitable both in clinical and research settings. The method can be applied equally to event related fields (ERF) recorded by means of magnetoencephalography. In this article we describe all necessary methodological details to promote testing and comparison of this proposed method by peers. Furthermore, we release a MATLAB toolbox, a plug-in for the EEGLAB software suite and a stand-alone executable application.

Which insights have we gained from the kindling and post-status epilepticus models?

Experimental animal epilepsy research got a big boost since the discovery that daily mild and short (seconds) tetanic stimulations in selected brain regions led to seizures with increasing duration and severity. This model that was developed by Goddard (1967) became known as the kindling model for epileptogenesis and has become a widely used model for temporal lobe epilepsy with complex partial seizures. During the late ninety-eighties the number of publications related to electrical kindling reached its maximum. However, since the kindling procedure is rather labor intensive and animals only develop spontaneous seizures (epilepsy) after hundreds of stimulations, research has shifted toward models in which the animals exhibit spontaneous seizures after a relatively short latent period. This led to post-status epilepticus (SE) models in which animals experience SE after injection of pharmacological compounds (e.g. kainate or pilocarpine) or via electrical stimulation of (limbic) brain regions. These post-SE models are the most widely used models in epilepsy research today. However, not all aspects of mesial temporal lobe epilepsy (MTLE) are reproduced and the widespread brain damage is often a caricature of the situation in the patient. Therefore, there is a need for models that can better replicate the disease. Kindling, although already a classic model, can still offer valid clues in this context. In this paper, we review different aspects of the kindling model with emphasis on experiments in the rat. Next, we review characteristic properties of the post-SE models and compare the neuropathological, electrophysiological and molecular differences between kindling and post-SE epilepsy models. Finally, we shortly discuss the advantages and disadvantages of these models.

Dynamics of collective multi-stability in models of multi-unit neuronal systems.

In this study, we investigate the correspondence between dynamic patterns of behavior in two types of computational models of neuronal activity. The first model type is the realistic neuronal model; the second model type is the phenomenological or analytical model. In the simplest model set-up of two interconnected units, we define a parameter space for both types of systems where their behavior is similar. Next we expand the analytical model to two sets of 90 fully interconnected units with some overlap, which can display multi-stable behavior. This system can be in three classes of states: (i) a class consisting of a single resting state, where all units of a set are in steady state, (ii) a class consisting of multiple preserving states, where subsets of the units of a set participate in limit cycle, and (iii) a class consisting of a single saturated state, where all units of a set are recruited in a global limit cycle. In the third and final part of the work, we demonstrate that phase synchronization of units can be detected by a single output unit.

EEG and MEG: relevance to neuroscience.

To understand dynamic cognitive processes, the high time resolution of EEG/MEG is invaluable. EEG/MEG signals can play an important role in providing measures of functional and effective connectivity in the brain. After a brief description of the foundations and basic methodological aspects of EEG/MEG signals, the relevance of the signals to obtain novel insights into the neuronal mechanisms underlying cognitive processes is surveyed, with emphasis on neuronal oscillations (ultra-slow, theta, alpha, beta, gamma, and HFOs) and combinations of oscillations. Three main functional roles of brain oscillations are put in evidence: (1) coding specific information, (2) setting and modulating brain attentional states, and (3) assuring the communication between neuronal populations such that specific dynamic workspaces may be created. The latter form the material core of cognitive functions.

Epileptic neuronal networks: methods of identification and clinical relevance.

The main objective of this paper is to examine evidence for the concept that epileptic activity should be envisaged in terms of functional connectivity and dynamics of neuronal networks. Basic concepts regarding structure and dynamics of neuronal networks are briefly described. Particular attention is given to approaches that are derived, or related, to the concept of causality, as formulated by Granger. Linear and non-linear methodologies aiming at characterizing the dynamics of neuronal networks applied to EEG/MEG and combined EEG/fMRI signals in epilepsy are critically reviewed. The relevance of functional dynamical analysis of neuronal networks with respect to clinical queries in focal cortical dysplasias, temporal lobe epilepsies, and "generalized" epilepsies is emphasized. In the light of the concepts of epileptic neuronal networks, and recent experimental findings, the dichotomic classification in focal and generalized epilepsy is re-evaluated. It is proposed that so-called "generalized epilepsies," such as absence seizures, are actually fast spreading epilepsies, the onset of which can be tracked down to particular neuronal networks using appropriate network analysis. Finally new approaches to delineate epileptogenic networks are discussed.

Automatic segmentation of episodes containing epileptic clonic seizures in video sequences.

Epilepsy is a neurological disorder characterized by sudden, often unexpected transitions from normal to pathological behavioral states called epileptic seizures. Some of these seizures are accompanied by uncontrolled, often rhythmic movements of body parts when seizure activity propagates to brain areas responsible for the initiation and control of movement. The dynamics of these transitions is, in general, unknown. As a consequence, individuals have to be monitored for long periods in order to obtain sufficient data for adequate diagnosis and to plan therapeutic strategy. Some people may require long-term care in special units to allow for timely intervention in case seizures get out of control. Our goal is to present a method by which a subset of motor seizures can be detected using only remote sensing devices (i.e., not in contact with the subject) such as video cameras. These major motor seizures (MMS) consist of clonic movements and are often precursors of generalized tonic-clonic (convulsive) seizures, sometimes leading to a condition known as status epilepticus, which is an acute life-threatening event. We propose an algorithm based on optical flow, extraction of global group transformation velocities, and band-pass temporal filtering to identify occurrence of clonic movements in video sequences. We show that for a validation set of 72 prerecorded epileptic seizures in 50 people, our method is highly sensitive and specific in detecting video segments containing MMS with clonic movements.

High frequency spectral components after secobarbital: the contribution of muscular origin--a study with MEG/EEG.

OBJECTIVES: Previously we found that benzodiazepines not only provoke beta-activity in the EEG, but also higher frequency activity. Knowing the origin of this high frequency activity is crucial if localisation of epileptogenic brain tissue is the query. We attempt to differentiate cerebral from muscular origin of such activity. METHODS: We postulate that EEG and MEG have similar sensitivity to brain activity, but different sensitivity to muscle activity, and compare co-recorded EEG and MEG signals in a group of five patients who had received short-lasting barbiturates to induce sleep. We performed principal components analysis over time and subtract the results for MEG from the EEG to see where the frequency spectra differ. RESULTS: The EEG showed activity in the gamma bands up to 270Hz for all patients; the MEG significantly less. We find no differences in the lower frequency bands. Topographically the differences localized over the frontotemporal regions. CONCLUSIONS: In the EEG benzodiazepines and/or barbiturates are not only associated with frequencies in the beta band, but also with wide range gamma activity. The latter seems to be of muscular origin. SIGNIFICANCE: Our study suggests that gamma activity in such measurements may not be cerebral in origin. MEG is less susceptible to contamination from muscle activity than the EEG.

Mechanisms of physiological and epileptic HFO generation.

High frequency oscillations (HFO) have a variety of characteristics: band-limited or broad-band, transient burst-like phenomenon or steady-state. HFOs may be encountered under physiological or under pathological conditions (pHFO). Here we review the underlying mechanisms of oscillations, at the level of cells and networks, investigated in a variety of experimental in vitro and in vivo models. Diverse mechanisms are described, from intrinsic membrane oscillations to network processes involving different types of synaptic interactions, gap junctions and ephaptic coupling. HFOs with similar frequency ranges can differ considerably in their physiological mechanisms. The fact that in most cases the combination of intrinsic neuronal membrane oscillations and synaptic circuits are necessary to sustain network oscillations is emphasized. Evidence for pathological HFOs, particularly fast ripples, in experimental models of epilepsy and in human epileptic patients is scrutinized. The underlying mechanisms of fast ripples are examined both in the light of animal observations, in vivo and in vitro, and in epileptic patients, with emphasis on single cell dynamics. Experimental observations and computational modeling have led to hypotheses for these mechanisms, several of which are considered here, namely the role of out-of-phase firing in neuronal clusters, the importance of strong excitatory AMPA-synaptic currents and recurrent inhibitory connectivity in combination with the fast time scales of IPSPs, ephaptic coupling and the contribution of interneuronal coupling through gap junctions. The statistical behaviour of fast ripple events can provide useful information on the underlying mechanism and can help to further improve classification of the diverse forms of HFOs.