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Objective: Epilepsy surgery can potentially cure drug-resistant epilepsy, but careful presurgical evaluation is vital to select patients who will profit from such an intervention. Many epilepsy surgery programs offer extensive presurgical evaluation including several days of video-EEG monitoring. Non-lesional epilepsy cases are rare among epilepsy surgery patients. We set up a lesion-orientated paediatric epilepsy surgery program for patients with clearly localized lesions with limited presurgical diagnostics, in particular, with a maximum of 48 hours of non-invasive EEG monitoring that did not necessarily include ictal EEGs., Methods: We retrospectively evaluated the outcome of patients who were operated on within our epilepsy surgery program with respect to seizure freedom., Results: Fifty-two children and adolescents with MRI lesions at a mean age of 8.27 ±4.83 years (range: 0.17-18.87) underwent a resective procedure. The most frequent surgery was a hemispherotomy. Overall seizure freedom was 81.8% after 12 months and 85.6% after a median observation period of 20.45 months. Seizure frequency was reduced >50% in all other patients. Preoperative recording of an ictal EEG on the side of surgery had no effect on postoperative seizure outcome (p= 0.697), nor did recording of epileptiform discharges on the ipsilateral (p= 0.538) and contralateral side (p= 0.147)., Significance: Our findings highlight the high success rate using a lesion-orientated epilepsy surgical approach with reduced presurgical video-EEG monitoring in the paediatric epilepsy population. Our data show that it is possible to reduce the complex pre-surgical work-up for epilepsy in children and adolescents by asking the basic question: "Is there any reason why the lesion should not be resected".

Objective: Limbic encephalitis (LE) is defined by mesiotemporal lobe structure abnormalities, seizures, memory, and psychiatric disturbances. This study aimed to identify the long-term clinical and neuropsychological outcome of selective amygdalohippocampectomy (sAH) in drug-resistant patients with temporal lobe epilepsy due to known or later diagnosed subacute LE not responding to immunotherapy associated with neuronal autoantibodies., Methods: In seven patients with temporal lobe epilepsy due to antibody positive LE (glutamic acid decarboxylase (GAD65): n=5; voltage-gated potassium channel complex (VGKC), N-methyl d-aspartate receptor (NMDAR): n=1; Ma-2/Ta: n=1) sAH (6 left, 1 right) was performed. Those patients underwent repeated electroencephalography (EEG) recordings, magnetic resonance imaging (MRI) volumetry of the amygdala and hippocampus, and neuropsychological examinations and were followed up for 6-7years on average., Results: Verbal memory and figural memory were affected in 57% of patients at baseline and 71% at the last follow-up. At the last follow-up, 14% of the patients had declined in verbal memory and figural memory. We observed improved memory in 43% of patients regarding figural memory, but not in a single patient regarding verbal memory. Repeated evaluations across the individual courses reveal cognitive and MRI dynamics that appear to be unrelated to surgery and drug treatment. Three of the seven patients with LE with different antibodies (NMDAR: n=1, Ma-2/Ta: n=1 and GAD65: n=1) achieved persistent seizure freedom along with no accelerated memory decline after surgery. Two of the five GAD65-antibody patients positive with LE showed progressive memory decline and a long-term tendency to contralateral hippocampus atrophy., Conclusions: While memory demonstrated some decline in the long run, what is most important is that a progressive decline in memory is seldom found after sAH in patients with LE. Moreover, the dynamics in performance and MRI before and after surgery reveal disease dynamics independent of surgery. Selective amygdalohippocampectomy can lead to seizure freedom, but should be considered as a last resort treatment option for drug-resistant patients with temporal lobe epilepsy due to LE. Particular caution is recommended in patients with GAD65-LE., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Epilepsy can be regarded as a network phenomenon with functionally and/or structurally aberrant connections in the brain. Over the past years, concepts and methods from network theory substantially contributed to improve the characterization of structure and function of these epileptic networks and thus to advance understanding of the dynamical disease epilepsy. We extend this promising line of research and assess-with high spatial and temporal resolution and using complementary analysis approaches that capture different characteristics of the complex dynamics-both strength and direction of interactions in evolving large-scale epileptic brain networks of 35 patients that suffered from drug-resistant focal seizures with different anatomical onset locations. Despite this heterogeneity, we find that even during the seizure-free interval the seizure onset zone is a brain region that, when averaged over time, exerts strongest directed influences over other brain regions being part of a large-scale network. This crucial role, however, manifested by averaging on the population-sample level only - in more than one third of patients, strongest directed interactions can be observed between brain regions far off the seizure onset zone. This may guide new developments for individualized diagnosis, treatment and control.

Objectives: Postictal generalized EEG suppression (PGES) frequently occurs after generalized convulsive seizures (GCS) and may be involved in the pathophysiology of sudden unexpected death in epilepsy (SUDEP). It is usually determined using conventional scalp EEG which is likely to miss cerebral activity in deeper brain structures. Here, we examined intracranial EEG activity after GCS to unravel the pattern and extent of local brain activity during apparent PGES on scalp EEG (s-PGES)., Methods: We retrospectively reviewed electroencephalographic data of people with chronic epilepsy who had GCS during presurgical video-EEG monitoring using simultaneous intracranial and scalp EEG (10-20 system) electrodes., Results: Twenty-five GCS (20 with s-PGES) of 15 patients with an average number of 88±42 intracranial electrode contacts were included. The majority of GCS with s-PGES (18 of 20) displayed persisting or reemerging intracranial EEG activity during apparent PGES on scalp EEG. Three patterns were identified: Pattern 1 (11 GCS, 6 patients) consisted of continuous local interictal activity; Pattern 2 (5 GCS, 5 patients) displayed suppressed EEG activity at all intracranial contacts in the early phase of s-PGES, but reemerging local brain activity before s-PGES dissolved; and Pattern 3 (2 GCS, 2 patients) showed persistent local ictal activity during s-PGES. Persisting intracranial EEG activity at PGES onset on scalp EEG was present in 10±14% (range: 0 to 42%) of all intracranial contacts and mostly in the temporal lobe., Conclusions: Our results reveal that, during apparently generalized postictal EEG suppression, local brain activity persists or reemerges in most GCS. Possible implications of this localized neuronal activity in the context of SUDEP are discussed in the paper., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Drug-resistant, multifocal, non-resectable epilepsies are among the most difficult epileptic disorders to manage. An approach to control previously uncontrollable seizures in epilepsy patients would consist of identifying seizure precursors in critical brain areas combined with delivering a counteracting influence to prevent seizure generation. Predictability of seizures with acceptable levels of sensitivity and specificity, even in an ambulatory setting, has been repeatedly shown, however, in patients with a single seizure focus only. We did a study to assess feasibility of state-of-the-art, electroencephalogram-based seizure-prediction techniques in patients with uncontrollable multifocal seizures. We obtained significant predictive information about upcoming seizures in more than two thirds of patients. Unexpectedly, the emergence of seizure precursors was confined to non-affected brain areas. Our findings clearly indicate that epileptic networks, spanning lobes and hemispheres, underlie generation of seizures. Our proof-of-concept study is an important milestone towards new therapeutic strategies based on seizure-prediction techniques for clinical practice.

Objective: To investigate determining factors of the ictal scalp EEG pattern at seizure onset and its predictive value for postsurgical outcome in people with unilateral MTLE due to hippocampal sclerosis (MTLE-HS)., Methods: Review of consecutive people with chronic MTLE-HS undergoing presurgical video-EEG telemetry. Exclusion criteria were additional epileptogenic lesions or seizure generators or compromised EEG traces at seizure-onset. Mixed linear or logistic regression models were used., Results: Inclusion of 63 patients with 219 seizures with a favorable outcome (no seizures or auras only) in 43 patients at last follow-up. Rhythmic activity at seizure-onset (RA) had a frequency of 4.7±1.5/s (range 1-8/s), mostly localized in the anterior temporal region. Postsurgical seizure outcome was not associated with any clinical or electrophysiological feature. RA in the delta-band was more often observed with shorter epilepsy duration (p=0.002)., Conclusions: RA on scalp EEG gets faster with increasing epilepsy duration, possibly via time-dependent alterations of epileptogenic networks. Neither the frequency of RA nor other EEG-features appeared to predict postsurgical seizure outcome., Significance: The results challenge the view that if patients with apparent MTLE display RA in the delta-band, seizure-onset in neocortical structures rather than in temporo-mesial tissue should be considered and further investigations should be prompted., (Copyright © 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.)

Objective: To determine the consistency and facilitating cofactors of postictal generalized EEG suppression (PGES) of >20 seconds after convulsive seizures (CS), a suggested predictor of sudden unexpected death in epilepsy risk., Methods: We retrospectively reviewed video-EEG data of people with ≥2 recorded CS. Presence and duration of PGES were assessed by 2 independent observers blinded to patient status. Intraindividual consistency of PGES >20 seconds was determined and correlations with clinical characteristics were analyzed after correction for individual effects and the varying number of seizures., Results: One hundred fifty-four seizures in 59 people were analyzed. PGES >20 seconds was found in 37 individuals (63%) and 57 (37%) of CS. The proportion of persons in whom PGES occurred consistently (presence or absence of PGES >20 seconds in all CS) was lower in those with more CS. PGES of >20 seconds was more frequent in seizures arising from sleep (odds ratio 3.29, 95% confidence interval 1.21-8.96) and when antiepileptic medication was tapered (odds ratio 4.80, 95% confidence interval 1.27-18.14)., Conclusion: Apparent PGES consistency was less frequent in people with more CS recorded, suggesting that PGES is an inconsistent finding in any one individual. Thus, we believe that PGES >20 seconds is not a reliable predictor of sudden unexpected death in epilepsy. Sleep and antiepileptic drug reduction appear to facilitate the occurrence of PGES.

Despite general agreement on shared syntactic resources in music and language, the neuroanatomical underpinnings of this overlap remain largely unexplored. While previous studies mainly considered frontal areas as supramodal grammar processors, the domain-general syntactic role of temporal areas has been so far neglected. Here we capitalized on the excellent spatial and temporal resolution of subdural EEG recordings to co-localize low-level syntactic processes in music and language in the temporal lobe in a within-subject design. We used Brain Surface Current Density mapping to localize and compare neural generators of the early negativities evoked by violations of phrase structure grammar in both music and spoken language. The results show that the processing of syntactic violations relies in both domains on bilateral temporo-fronto-parietal neural networks. We found considerable overlap of these networks in the superior temporal lobe, but also differences in the hemispheric timing and relative weighting of their fronto-temporal constituents. While alluding to the dissimilarity in how shared neural resources may be configured depending on the musical or linguistic nature of the perceived stimulus, the combined data lend support for a co-localization of early musical and linguistic syntax processing in the temporal lobe., (Copyright © 2012 Elsevier Inc. All rights reserved.)

Not only poststimulus, but also prestimulus neural activity has been shown to be predictive for later successful memory encoding. However, it is still not clear which medial temporal lobe processes precede effective memory formation. Here, our aim was to investigate whether such prestimulus markers for successful memory encoding can be specified based on intracranial recordings directly from the hippocampus and rhinal cortex. For this purpose, we analyzed subsequent memory effects during a continuous word recognition paradigm in 31 presurgical epilepsy patients. We found that rhinal and hippocampal theta and successive alpha power enhancement before word presentation predicted successful memory encoding. Previous studies suggest that stimulus-triggered hippocampal theta activity is particularly related to memory retrieval and activation of a mnemonic context, whereas the alpha rhythm reflects inhibitory top-down control of task processing and executive functioning. In line with these suggestions, we propose that the observed medial temporal theta and alpha power increases before stimulus presentation reflect activation of contextual information and inhibitory top-down control processes preparing for stimulus-triggered memory processing.

The face inversion effect (FIE) refers to a disproportionate disruption of the processing of face information by inverting faces. We investigated the FIE in epilepsy patients by simultaneous intracranial and scalp recordings of event-related potentials (ERPs). In scalp recordings, a typical FIE on ERPs was observed with increased latencies and amplitudes of the positive counterpart of the occipito-temporal N170, namely, the vertex positive potential (VPP), in response to inverted faces. Similar amplitude and latency increases were revealed for the intracranial N200 recorded over face-sensitive and non-face-sensitive areas in the lateral occipital cortex, but not in the ventral temporal cortex. Peak latencies did not differ between the scalp VPP and intracranial N200. Findings indicate that the lateral occipital cortex but not the ventral temporal cortex contributes primarily to the FIE observed in scalp recordings.

The role of neuroscience for the understanding of cognitive processes is a matter of controversial discussions. While it is widely accepted that neuroscientific data do contribute to theories on cognition in some way, their specific value is most often not explicitly described. One central issue is the validity of the inference from neuroscientific data to underlying cognitive processes, which depends on the characteristic properties of the respective neuroscientific method. In the first part of this review, we discuss the conditions under which data from functional MRI (fMRI), surface EEG, and intracranial EEG recordings may be interpreted with respect to associated cognitive processes. We will show that due to the different signal characteristics in each domain, cognitive processes at different levels can be captured. In the second part, we address the specific contribution made by neuroscientific data to the understanding of cognition. We show that neuroscientific findings may move beyond psychological theories based on purely behavioral data in several respects, which again depend on the imaging modality. Taken together, we suggest that neuroscientific data contribute to the understanding of cognition by adding specific biological constraints and by extending the explanatory potential of psychological theories.

The nucleus accumbens is critical for reward-guided learning and decision-making. It is thought to "gate" the flow of a diverse range of information (e.g., rewarding, aversive, and novel events) from limbic afferents to basal ganglia outputs. Gating and information encoding may be achieved via cross-frequency coupling, in which bursts of high-frequency activity occur preferentially during specific phases of slower oscillations. We examined whether the human nucleus accumbens engages such a mechanism by recording electrophysiological activity directly from the accumbens of human patients undergoing deep brain stimulation surgery. Oscillatory activity in the gamma (40-80 Hz) frequency range was synchronized with the phase of simultaneous alpha (8-12 Hz) waves. Further, losing and winning small amounts of money elicited relatively increased gamma oscillation power prior to and following alpha troughs, respectively. Gamma-alpha synchronization may reflect an electrophysiological gating mechanism in the human nucleus accumbens, and the phase differences in gamma-alpha coupling may reflect a reward information coding scheme similar to phase coding.

Electroencephalogram oscillations recorded both within and over the medial frontal cortex have been linked to a range of cognitive functions, including positive and negative feedback processing. Medial frontal oscillatory characteristics during decision making remain largely unknown. Here, we examined oscillatory activity of the human medial frontal cortex recorded while subjects played a competitive decision-making game. Distinct patterns of power and cross-trial phase coherence in multiple frequency bands were observed during different decision-related processes (e.g., feedback anticipation vs. feedback processing). Decision and feedback processing were accompanied by a broadband increase in cross-trial phase coherence at around 220 msec, and dynamic fluctuations in power. Feedback anticipation was accompanied by a shift in the power spectrum from relatively lower (delta and theta) to higher (alpha and beta) power. Power and cross-trial phase coherence were greater following losses compared to wins in theta, alpha, and beta frequency bands, but were greater following wins compared to losses in the delta band. Finally, we found that oscillation power in alpha and beta frequency bands were synchronized with the phase of delta and theta oscillations ("phase-amplitude coupling"). This synchronization differed between losses and wins, suggesting that phase-amplitude coupling might reflect a mechanism of feedback valence coding in the medial frontal cortex. Our findings link medial frontal oscillations to decision making, with relations among activity in different frequency bands suggesting a phase-utilizing coding of feedback valence information.

For patients with schizophrenia, a deficient gating (or filtering) of sensory input has been described. One major approach to study this sensory gating is to measure event-related potentials (ERPs) in response to paired clicks. In these experiments, sensory gating is quantified as amplitude reduction of the ERP components P50 and N100 from the 1st to the 2nd stimulus. In ERP studies brain electrical signals are averaged over single trials. Alterations in phase locking might be one factor contributing to the observed deficits in sensory gating, but findings have been inconclusive as yet. In particular, the contribution of different frequency bands to the deficit required further investigation. We studied N100 gating by intracranial recordings in a sample of epilepsy patients and subdivided the group into good and poor gators of the intracranial ERP component N100. Data were evaluated by frequency specific wavelet-based phase and power analyses. Poor N100 gators had an increased phase locking in the frequency range from 6.0-15.1 Hz after the 2nd stimulus, as compared to good gators. Other group differences were apparent already before the 2nd stimulus. Poor gators had less phase locked beta band activity (20.2-30.0 Hz) than good gators 200-315 ms after the onset of the 1st stimulus. Within the group of poor gators, lower values of phase locking in this frequency range were also associated with lower gating ratios. The reduced beta band response in response to the 1st stimulus may reflect poorer memory encoding of the 1st stimulus in poor gators. This in turn might lead to increased demands to process the 2nd stimulus.

The posterior superior temporal sulcus (pSTS) is a key structure for our ability to infer others' mental states based on social cues including facial expressions, body posture, and gestures ("mentalizing"), but the neural mechanisms of this ability remain largely unknown. We recorded electrocorticogram directly from the pSTS in humans to show that enhanced neural oscillations in the gamma frequency range (35-55 Hz) accompany mentalizing. One patient with a lesion in pSTS was tested behaviorally on this task; he was unable to infer a virtual character's preferences from nonverbal social cues. Enhanced coherent gamma oscillations in the patients with intact pSTS may reflect a process by which social signals are bound into a unified representation to support mentalizing. This may be relevant for other social cognitive processes, as well as to the study of autism spectrum disorders, for which both mentalizing deficits and abnormal gamma activity have been reported.

We assess electrical brain dynamics before, during, and after 100 human epileptic seizures with different anatomical onset locations by statistical and spectral properties of functionally defined networks. We observe a concave-like temporal evolution of characteristic path length and cluster coefficient indicative of a movement from a more random toward a more regular and then back toward a more random functional topology. Surprisingly, synchronizability was significantly decreased during the seizure state but increased already prior to seizure end. Our findings underline the high relevance of studying complex systems from the viewpoint of complex networks, which may help to gain deeper insights into the complicated dynamics underlying epileptic seizures., ((c) 2008 American Institute of Physics.)

It is a fundamental question whether the medial temporal lobe (MTL) supports only long-term memory encoding, or contributes to working memory (WM) processes as well. Recent data suggest that the MTL is activated whenever multiple items or item features are being maintained in WM. This may rely on interactions between the MTL or the prefrontal cortex (PFC) and content-specific areas in the inferior temporal (IT) cortex. Here, we investigated the neural mechanism through which the MTL, PFC, and IT cortex interact during WM maintenance. First, we quantified phase synchronization of intracranial EEG data in epilepsy patients with electrodes in both regions. Second, we used directional coupling analysis to study whether oscillatory activity in the IT cortex drives the MTL or vice versa. Finally, we investigated functional connectivity in functional magnetic resonance imaging data of healthy subjects with seeds in the MTL and PFC. With increasing load, EEG phase synchronization between the IT cortex and anterior parahippocampal gyrus and within the MTL increased. Coupling was bidirectional in all load conditions, but changed toward an increased top-down (anterior parahippocampal gyrus --> IT) coupling in the high gamma range (51-75 Hz) with increasing load. Functional connectivity between the MTL seed and the visual association cortex increased with load, but activity within the MTL and the PFC correlated with fewer voxels, suggesting that more specific neural networks were engaged. These data indicate that WM for multiple items depends on an increased strength of top-down control of activity within the IT cortex by the MTL.

Two step theories of memory formation assume that an initial learning phase is followed by a consolidation stage. Memory consolidation has been suggested to occur predominantly during sleep. Very recent findings, however, suggest that important steps in memory consolidation occur also during waking state but may become saturated after some time awake. Sleep, in this model, specifically favors restoration of synaptic plasticity and accelerated memory consolidation while asleep and briefly afterwards. To distinguish between these different views, we recorded intracranial electroencephalograms from the hippocampus and rhinal cortex of human subjects while they retrieved information acquired either before or after a "nap" in the afternoon or on a control day without nap. Reaction times, hippocampal event-related potentials, and oscillatory gamma activity indicated a temporal gradient of hippocampal involvement in information retrieval on the control day, suggesting hippocampal-neocortical information transfer during waking state. On the day with nap, retrieval of recent items that were encoded briefly after the nap did not involve the hippocampus to a higher degree than retrieval of items encoded before the nap. These results suggest that sleep facilitates rapid processing through the hippocampus but is not necessary for information transfer into the neocortex per se.

Interregional interactions of oscillatory activity are crucial for the integrated processing of multiple brain regions. However, while the EEG in virtually all brain structures passes through substantial modifications during sleep, it is still an open question whether interactions between neocortical and medial temporal EEG oscillations also depend on the state of alertness. Several previous studies in animals and humans suggest that hippocampal-neocortical interactions crucially depend on the state of alertness (i.e., waking state or sleep). Here, we analyzed scalp and intracranial EEG recordings during sleep and waking state in epilepsy patients undergoing presurgical evaluation. We found that the amplitudes of oscillations within the medial temporal lobe and the neocortex were more closely correlated during sleep, in particular during non-REM sleep, than during waking state. Possibly, the encoding of novel sensory inputs, which mainly occurs during waking state, requires that medial temporal dynamics are rather independent from neocortical dynamics, while the consolidation of memories during sleep may demand closer interactions between MTL and neocortex.

Purpose: To assess whether EEG synchronization changes during short-term low-frequency electrical stimulation of the seizure onset zone., Methods: In 10 patients (34+/-11 years) with pharmaco-resistant epilepsy the seizure onset zone (9 temporal lobe, 1 frontal lobe) was electrically stimulated at 1Hz for 5min via intracranial electrodes. Bipolar stimuli were applied and four pulse widths (0.05, 0.1, 0.5, and 1.0ms) were tested. Stimulation amplitudes were held fixed at 1mA for strip electrodes and at 2mA for depth electrodes. Changes of EEG synchronization were assessed by the eigenvalue dynamics of the cross-correlation matrix computed from a 2.5s sliding window., Results: 37 stimulations were performed. We observed EEG desynchronization in 49% (18/37), an increase of EEG synchronization in 27% (10/37) and an EEG pattern with no significant change of synchronization in 24% (9/37). EEG synchronization most frequently occurred when stimulating with a pulse width of 0.5ms. In a patient with bilateral independent seizure onsets stimulation effects on EEG synchronization were different for each side. In the patient with the shortest duration of temporal lobe epilepsy, stimulation triggered periodic epileptic spikes phase-locked to stimulation. One patient experienced an aura during stimulation, which did not evolve into a seizure, and in one patient a sub-clincial seizure occurred., Discussion: Low-frequency stimulation of the seizure onset zone is associated with different changes of EEG synchronization and its effects depend on the widths of the stimulation pulses. It may be an appropriate stimulation technique for long-term studies assessing whether synchronized or desynchronized brain dynamics prevent seizure occurrence.

We propose a method for estimating nonlinear interdependences between time series using cellular nonlinear networks. Our approach is based on the nonlinear dynamics of interacting nonlinear elements. We apply it to time series of coupled nonlinear model systems and to electroencephalographic time series from an epilepsy patient, and we show that an accurate approximation of symmetric and asymmetric realizations of a nonlinear interdependence measure can be achieved, thus allowing one to detect the strength and direction of couplings.

The filtering of sensory information, also referred to as "sensory gating", is impaired in various neuropsychiatric diseases. In the auditory domain, sensory gating is investigated mainly as a response decrease of the auditory evoked potential component P50 from one click to the second in a double-click paradigm. In order to relate deficient sensory gating to anatomy, it is essential to identify the cortical structures involved in the generation of P50. However, the exact cerebral topography of P50 gating remains largely unknown. In a group of 17 patients with drug-resistant focal epilepsy, P50 was recorded invasively via subdural electrodes, and the topography of functionally indispensable ("eloquent") cortices was obtained by electrical stimulation mapping. These eloquent areas were involved in language, motor, and sensory functions. P50 could be identified in 13 patients in either temporal (n=8) or midfrontal sites (n=5). There were six occurrences (in five patients) of overlap of sites with maximal P50 responses and eloquent areas. Those were auditory (n=1), supplementary sensorimotor (n=3), primary motor (n=1), and supplementary negative motor (n=1). Results suggest that the early stage of sensory gating already involves a top-down modulation of sensory input by frontal areas.

We compared surface and intracranial electroencephalogram recordings of mediotemporal structures. These structures are critically involved in declarative memory formation and memory consolidation during sleep. As memory processing is suggested to involve the interplay between fast and slow oscillations, we hypothesized different correlations between frequency bands in surface versus mediotemporal electroencephalogram recordings. Polysomnographic recordings obtained in 10 patients with unilateral temporal lobe epilepsy were analyzed. In accordance with earlier studies, we observed that power density in surface electroencephalogram is organized reciprocally between delta/theta and fast frequencies above 16 Hz during non-rapid-eye-movement sleep (negative correlations). In contrast, we found that within the hippocampus delta/theta power alternated in parallel with fast oscillations above 16 Hz during non-rapid-eye-movement sleep (positive correlations).

The ability to evaluate outcomes of previous decisions is critical to adaptive decision-making. The feedback-related negativity (FRN) is an event-related potential (ERP) modulation that distinguishes losses from wins, but little is known about the effects of outcome probability on these ERP responses. Further, little is known about the frequency characteristics of feedback processing, for example, event-related oscillations and phase synchronizations. Here, we report an EEG experiment designed to address these issues. Subjects engaged in a probabilistic reinforcement learning task in which we manipulated, across blocks, the probability of winning and losing to each of two possible decision options. Behaviorally, all subjects quickly adapted their decision-making to maximize rewards. ERP analyses revealed that the probability of reward modulated neural responses to wins, but not to losses. This was seen both across blocks as well as within blocks, as learning progressed. Frequency decomposition via complex wavelets revealed that EEG responses to losses, compared to wins, were associated with enhanced power and phase coherence in the theta frequency band. As in the ERP analyses, power and phase coherence values following wins but not losses were modulated by reward probability. Some findings between ERP and frequency analyses diverged, suggesting that these analytic approaches provide complementary insights into neural processing. These findings suggest that the neural mechanisms of feedback processing may differ between wins and losses.

The sudden and apparently unpredictable nature of seizures is one of the most disabling aspects of the disease epilepsy. A method capable of predicting the occurrence of seizures from the electroencephalogram (EEG) of epilepsy patients would open new therapeutic possibilities. Since the 1970s investigations on the predictability of seizures have advanced from preliminary descriptions of seizure precursors to controlled studies applying prediction algorithms to continuous multi-day EEG recordings. While most of the studies published in the 1990s and around the turn of the millennium yielded rather promising results, more recent evaluations could not reproduce these optimistic findings, thus raising a debate about the validity and reliability of previous investigations. In this review, we will critically discuss the literature on seizure prediction and address some of the problems and pitfalls involved in the designing and testing of seizure-prediction algorithms. We will give an account of the current state of this research field, point towards possible future developments and propose methodological guidelines for future studies on seizure prediction.

Epileptic seizures are commonly characterized as 'hypersynchronous states'. This habit is doubly misleading, because seizures are not necessarily synchronous and are not unchanging 'states' but dynamic processes. Here the temporal evolution of the correlation structure in the course of 100 focal onset seizures of 60 patients recorded by intracranial multichannel EEG was assessed. To this end a multivariate method was applied that at its core consists of computing the eigenvalue spectrum of the zero-lag correlation matrix of a short sliding window. Our results show that there are clearly observable and statistically significant changes of the correlation structure of focal onset seizures. Specifically, these changes indicate that the zero-lag correlation of multi-channel EEG either remains approximately unchanged or-especially in the case of secondary generalization-decreases during the first half of the seizures. Then correlation gradually increases again before the seizures terminate. This development was qualitatively independent of the anatomical location of the seizure onset zone and therefore seems to be a generic property of focal onset seizures. We suggest that the decorrelation of EEG activity is due to the different propagation times of locally synchronous ictal discharges from the seizure onset zone to other brain areas. Furthermore we speculate that the increase of correlation during the second half of the seizures may be causally related to seizure termination.

Hippocampal DC shifts have been observed under various physiological and pathological conditions. Here, we studied the interconnection of slow shifts (0.01 Hz high-pass) in surface EEG and hippocampal shifts as emerging in an event-related EEG biofeedback paradigm. Hippocampal EEG activity was monitored by depth electrodes implanted in four epilepsy patients for presurgical evaluation. Trials were sorted according to the near-DC shifts occurring at the surface position Cz, which was the feedback electrode, into positive, indistinct (i.e., small or biphasic) and negative shifts. We found significant hippocampal near-DC shifts being positively or negatively correlated to the shifts in surface EEG in all four patients. The amplitudes of the hippocampal near-DC shifts were several times larger than the surface shifts. The polarity of the shifts appears to depend on the location of the electrode contacts with respect to the hippocampal subfields. The finding that neocortical and hippocampal near-DC shifts are interconnected may open new perspectives for the prediction and control of mediotemporal lobe seizures., ((c) 2007 Wiley-Liss, Inc.)

An advanced characterization of the complicated dynamical system brain is one of science's biggest challenges. Nonlinear time series analysis allows characterizing nonlinear dynamical systems in which low-dimensional nonlinearity gives rise to complex and irregular behavior. While several studies indicate that nonlinear methods can extract valuable information from neuronal dynamics, others doubt their necessity and conjecture that the same information can be obtained using classical linear techniques. To address this issue, we compared these two concepts, but included furthermore a combination of nonlinear measures with surrogates, an approach that has been designed to specifically focus on nonlinearity. As a benchmark we used the discriminative power to detect the seizure-generating hemisphere in medically intractable mesial temporal lobe epilepsy. We analyzed intracranial electroencephalographic recordings from the seizure-free interval of 29 patients. While the performance of both linear and nonlinear measures was weak, if not insignificant, a very high performance was obtained by the use of surrogate-corrected measures. Focusing on nonlinearity by using a combination of nonlinear measures with surrogates appears as the key to a successful characterization of the spatial distribution of the epileptic process.

The detection of patterns embedded within a complex, nonstationary, and noisy background activity is a crucial and important task in EEG analysis. The authors present a biologically inspired, analog approach to EEG analysis that is conceptually different from a variety of statistical approaches currently used. A nonlinear, excitable, spatially extended medium that is composed of diffusively coupled model neurons is considered. When EEG recordings are applied as local perturbations to such an excitable neural tissue, the induced transient changes in the dynamics of the perturbed system can be regarded as an instantaneous characterization of transient processes in the brain reflected by the EEG, e.g., in the form of a sequence of correlated dynamical events (patterns). Nonlinear excitable media can be implemented in form of an array of locally coupled integrated analog nonlinear electrical circuits called cellular neural networks, which represent a next evolutionary step in the parallel analog computer architecture. Using cellular neural networks, the authors show that the concept of signal-induced pattern generation allows an almost instantaneous and unsupervised detection of seizure onsets in EEG recordings. In addition, they show that a cellular neural network can be trained in a supervised way to approximate the degree of synchronization in EEG recordings. The resulting pattern-recognition device may be suitable for the prediction of epileptic seizures.

Objective: An important issue in epileptology is the question whether information extracted from the EEG of epilepsy patients can be used for the prediction of seizures. Several studies have claimed evidence for the existence of a pre-seizure state that can be detected using different characterizing measures. In this paper, we evaluate the predictability of seizures by comparing the predictive performance of a variety of univariate and bivariate measures comprising both linear and non-linear approaches., Methods: We compared 30 measures in terms of their ability to distinguish between the interictal period and the pre-seizure period. After completely analyzing continuous inctracranial multi-channel recordings from five patients lasting over days, we used ROC curves to distinguish between the amplitude distributions of interictal and preictal time profiles calculated for the respective measures. We compared different evaluation schemes including channelwise and seizurewise analysis plus constant and adaptive reference levels. Particular emphasis was placed on statistical validity and significance., Results: Univariate measures showed statistically significant performance only in a channelwise, seizurewise analysis using an adaptive baseline. Preictal changes for these measures occurred 5-30 min before seizures. Bivariate measures exhibited high performance values reaching statistical significance for a channelwise analysis using a constant baseline. Preictal changes were found at least 240 min before seizures. Linear measures were found to perform similar or better than non-linear measures., Conclusions: Results provide statistically significant evidence for the existence of a preictal state. Based on our findings, the most promising approach for prospective seizure anticipation could be a combination of bivariate and univariate measures., Significance: Many measures reported capable of seizure prediction in earlier studies are found to be insignificant in performance, which underlines the need for statistical validation in this field.

Evidence for a response-control-related kind of declarative memory during deep propofol anesthesia has recently been reported. Connectivity within the mediotemporal lobe (MTL), and in particular rhinal-hippocampal synchronization within the gamma band, has been shown to be crucial for declarative memory formation. Thus, we analyzed EEG recordings obtained from the scalp, as well as directly from within the hippocampus and from the anterior parahippocampal gyrus, which is covered by rhinal cortex, in patients with unilateral temporal lobe epilepsy during propofol anesthesia, which preceded electrode explantation. For the gamma band a power decrease starting with induction of anesthesia was observed at scalp position Cz, but a power increase was detected at MTL locations. In contrast to prior results for sleep recordings, rhinal-hippocampal coherence did not decrease within the gamma band at deeper levels of anesthesia. These findings may represent an indirect electrophysiological correlate of partially intact declarative memory formation during deep propofol sedation. Furthermore, we investigated how well the plasma propofol level, as well as different stages of anesthesia including the burst suppression phase, could be monitored by different spectral as well as by nonlinear EEG measures. We observed that conventional spectral power measures, most prominently those recorded from mediotemporal locations, are most closely correlated with the plasma propofol level, whereas different stages of anesthesia can be distinguished best by nonconventional spectral as well as nonlinear measures.

In a growing number of publications it is claimed that epileptic seizures can be predicted by analyzing the electroencephalogram (EEG) with different characterizing measures. However, many of these studies suffer from a severe lack of statistical validation. Only rarely are results passed to a statistical test and verified against some null hypothesis H0 in order to quantify their significance. In this paper we propose a method to statistically validate the performance of measures used to predict epileptic seizures. From measure profiles rendered by applying a moving-window technique to the electroencephalogram we first generate an ensemble of surrogates by a constrained randomization using simulated annealing. Subsequently the seizure prediction algorithm is applied to the original measure profile and to the surrogates. If detectable changes before seizure onset exist, highest performance values should be obtained for the original measure profiles and the null hypothesis. "The measure is not suited for seizure prediction" can be rejected. We demonstrate our method by applying two measures of synchronization to a quasicontinuous EEG recording and by evaluating their predictive performance using a straightforward seizure prediction statistics. We would like to stress that the proposed method is rather universal and can be applied to many other prediction and detection problems.

The deficiency of declarative memory compared with waking state is an often overlooked characteristic of sleep. Here, we investigated whether rhinal-hippocampal coherence, an electrophysiological correlate of declarative memory formation, is significantly altered during sleep as compared with waking state. For this purpose, we analysed recordings of intracranial EEG activity during sleep obtained directly from within the medial temporal lobe in patients with unilateral temporal lobe epilepsy. We found a general reduction of rhinal-hippocampal EEG coherence during sleep compared with waking state, which was most pronounced within the upper gamma bands (average decrease up to 56%). The observed coherence changes clearly differ from findings reported for surface EEG data and thus appear to be specific for the medial temporal lobe. The decrease of rhinal-hippocampal EEG coherence from waking state towards sleep may yield an electrophysiological explanation for the sleep-related deficiency of declarative memory.

Transient phase synchronization of EEG oscillations was reported to be essential for conscious brain activity. However, it has been argued that EEG synchrony is not the only necessary factor, but at least must be accompanied by a sufficient complexity of brain dynamics. Here, we propose that, besides complexity, EEG chaoticity may be another necessary condition for conscious brain functioning.

A number of recent studies indicate that nonlinear electroencephalogram (EEG) analyses allow to define a state predictive of an impending epileptic seizure. In this paper, we combine a method for detecting nonlinear determinism with a novel test for stationarity to characterize EEG recordings from both the seizure-free interval and the preseizure phase. We discuss differences between these periods, particularly an increased occurrence of stationary, nonlinear segments prior to seizures. These differences seem most prominent for recording sites within the seizure-generating area and for EEG segments less than one minute's length.

Purpose: To examine interictal EEG abnormalities in patients with psychogenic nonepileptic seizures (PNESs)., Methods: (a) Retrospective study of EEG reports of 187 consecutive patients with PNES seen at the Department of Epileptology, Bonn, Germany; (b) Blinded, multirater comparison of EEGs of all PNES patients with no other clinically recognizable cause of EEG disturbance (n = 50) and healthy controls (n = 50)., Results: Of 187 consecutive patients with PNESs, 57 patients had PNESs and epilepsy (PNES+E), and 130 patients, PNESs alone. The diagnosis of additional epilepsy was based on ictal (video-) EEG or on the critical assessment of all clinical data by an experienced epileptologist. Retrospective review of all available EEG reports showed that 92.9% of patients in the PNES+E and 53.8% in the PNES-only group had one or more abnormal EEGs (median number of EEGs per patient, three; range, one to 42). In the PNES-only group, EEG changes were nonspecific in 42.3% of patients. Only 50 of 130 patients with PNESs alone had no other clinically recognizable cause of EEG disturbance and entered the controlled study. In this study, 18% of patients and 10% of controls had abnormal EEGs. The frequency of epileptiform EEG changes was similar to that in previous population studies in both groups (2.0%)., Conclusions: PNESs often occur in patients with organic brain disease. Even in patients with PNESs alone and no clinically recognizable cause of EEG disturbance, nonspecific abnormalities are found 1.8 times as often as in healthy controls. Interictal EEG changes are common in patients with PNESs and, in isolation, should not be interpreted as evidence of epilepsy.

This study investigated how different spectral and nonlinear EEG measures covaried with alpha power during auditory alpha biofeedback training, performed by 13 healthy subjects. We found a significant positive correlation of alpha power with the largest Lyapunov-exponent, pointing to an increased dynamical instability of the EEG accompanying alpha enhancement. Alpha power amplification, moreover, was significantly correlated with a decrease of spectral entropy within the alpha range. This outcome reflects a sharpening of the alpha peak during biofeedback training. The fact that the sharpening effect clearly preceded the increase of alpha amplitude could be exploited in future biofeedback settings.

The EEG during deep sleep exhibits a distinct cortically generated slow oscillation of around and below 1 Hz which can be distinguished from other delta (0.5-3.5 Hz) activity. Intracranial studies showed that this slow oscillation triggers and groups cortical network firing. In the present study, we examined whether the phases of the slow oscillation during sleep stage 4 are correlated with the magnitude of sigma (12-16 Hz) and gamma (> 20 Hz) scalp activity. For this purpose, 10-min segments of uninterrupted stage 4 sleep EEG from 9 subjects were analyzed by applying wavelet techniques. We found that scalp recorded sigma, but not gamma, activity is modulated by the phases of the slow oscillation during deep sleep. Enhancement of sigma activity was observed to be triggered by the peak of the surface positive slow wave component, whereas reduction of sigma activity started around the peak of the negative component.

The attentional blink (AB) is an impairment of attention, which occurs when subjects have to report a target stimulus (T2) following a previous target (T1) with a short delay (up to 600 ms). Theories explaining the AB assume that processing of T2 is more vulnerable to decay or substitution, as long as attention is allocated to T1. Existing models of the AB, however, do not account for the fact that T2 detection accuracy reaches the minimum when T2 is presented after about 300 ms and not immediately following T1. Therefore, a new model is suggested, which is based on chronometrical considerations together with recent neurophysiological findings concerning the relation between the P3 event-related potential and the AB, the interaction between P3 and gamma oscillations, and the significance of the early evoked gamma band response. We hypothesize that suppression of the early gamma response to T2, accompanying the P3 related to T1, causes the AB., (Copyright 2002 Elsevier Science (USA).)

ObjectiveParoxysmal epileptiform abnormalities on electroencephalography (EEG) are the hallmark of epilepsies, but it is uncertain to what extent epilepsy and background EEG oscillations share neurobiological underpinnings. Here, we aimed to assess the genetic correlation between epilepsy and background EEG oscillations.MethodsConfounding factors, including the heterogeneous etiology of epilepsies and medication effects, hamper studies on background brain activity in people with epilepsy. To overcome this limitation, we compared genetic data from a genome-wide association study (GWAS) on epilepsy (n = 12 803 people with epilepsy and 24 218 controls) with that from a GWAS on background EEG (n = 8425 subjects without epilepsy), in which background EEG oscillation power was quantified in four different frequency bands: alpha, beta, delta, and theta. We replicated our findings in an independent epilepsy replication dataset (n = 4851 people with epilepsy and 20 428 controls). To assess the genetic overlap between these phenotypes, we performed genetic correlation analyses using linkage disequilibrium score regression, polygenic risk scores, and Mendelian randomization analyses.ResultsOur analyses show strong genetic correlations of genetic generalized epilepsy (GGE) with background EEG oscillations, primarily in the beta frequency band. Furthermore, we show that subjects with higher beta and theta polygenic risk scores have a significantly higher risk of having generalized epilepsy. Mendelian randomization analyses suggest a causal effect of GGE genetic liability on beta oscillations.SignificanceOur results point to shared biological mechanisms underlying background EEG oscillations and the susceptibility for GGE, opening avenues to investigate the clinical utility of background EEG oscillations in the diagnostic workup of epilepsy.

Objective Cardiorespiratory function alterations are commonly observed with epileptic seizures and may lead to syncope and sudden unexpected death in epilepsy ( SUDEP). Although most previous research has focused on controlling heart rate ( HR) and respiration, little is known about seizure-related regulation of systemic blood pressure ( BP). Herein, we have investigated whether the periictal modulation of systemic BP and HR depends on seizure characteristics. Methods Systemic arterial BP, HR, and peripheral capillary oxygen saturation ( SPO2) were continuously and noninvasively monitored using the ccNexfin device in those epilepsy patients undergoing video-electroencephalography ( EEG) telemetry. Data are given as mean ± standard deviation ( SD). Results Forty-five seizures in 37 patients were included. In focal seizures ( FS, n = 35), the mean arterial BP ( MAP) increased by 33 ± 35% and the HR by 53 ± 44%, whereas the SPO2 remained unaltered. The MAP and HR increases were significantly greater in FS with alterations in consciousness than in those without. For those FS that evolved to bilateral convulsive seizures ( BCS, n = 10), all of the ictal recordings were compromised by artifacts. However, 2 min after seizure cessation, the MAP was enhanced by only 16 ± 14% and returned to a baseline slightly below preictal levels after 5 min, whereas the HR was increased by 77 ± 33% and remained elevated throughout the postictal phase. Significance Periictal regulation of systemic BP and HR displays distinct patterns depending on the type of seizure with focal onset. These changes were unrelated to alterations in SPO2. The potential clinical implications of these findings are discussed in the article. [ABSTRACT FROM AUTHOR]

The theta-gamma neural coding theory suggests that multiple items are represented in working memory (WM) by a superposition of gamma cycles on theta oscillations. To enable a stable, non-interfering representation of multiple items, such a theta-gamma neural code may be reflected by phase-phase coupling, i.e., a precise locking of gamma subcycles to specific theta phases. Recent data have indicated that the hippocampus critically contributes to multi-item working memory. Therefore, we investigated phase-phase coupling patterns in the hippocampus based on intracranial EEG recordings in presurgical epilepsy patients performing a variant of the serial Sternberg WM task. In accordance with predictions of the theta-gamma coding theory, we observed increased phase-phase coupling between theta and beta/gamma activity during working memory maintenance compared to inter-trial intervals. These phase-phase coupling patterns were apparent during maintenance of two and four items, but not during maintenance of a single item, where prominent lower coupling ratios occurred. Furthermore, we observed that load-dependent changes of coupling factors correlated with individual WM capacities. Our data demonstrate that multi-item WM is associated with changes in hippocampal phase-phase coupling between theta and beta/gamma activity. [ABSTRACT FROM PUBLISHER]

Auditory stimulation with monaural or binaural auditory beats (i.e. sine waves with nearby frequencies presented either to both ears or to each ear separately) represents a non-invasive approach to influence electrical brain activity. It is still unclear exactly which brain sites are affected by beat stimulation. In particular, an impact of beat stimulation on mediotemporal brain areas could possibly provide new options for memory enhancement or seizure control. Therefore, we examined how electroencephalography ( EEG) power and phase synchronization are modulated by auditory stimulation with beat frequencies corresponding to dominant EEG rhythms based on intracranial recordings in presurgical epilepsy patients. Monaural and binaural beat stimuli with beat frequencies of 5, 10, 40 and 80 Hz and non-superposed control signals were administered with low amplitudes (60 dB SPL) and for short durations (5 s). EEG power was intracranially recorded from mediotemporal, temporo-basal and temporo-lateral and surface sites. Evoked and total EEG power and phase synchronization during beat vs. control stimulation were compared by the use of Bonferroni-corrected non-parametric label-permutation tests. We found that power and phase synchronization were significantly modulated by beat stimulation not only at temporo-basal, temporo-lateral and surface sites, but also at mediotemporal sites. Generally, more significant decreases than increases were observed. The most prominent power increases were seen after stimulation with monaural 40-Hz beats. The most pronounced power and synchronization decreases resulted from stimulation with monaural 5-Hz and binaural 80-Hz beats. Our results suggest that beat stimulation offers a non-invasive approach for the modulation of intracranial EEG characteristics. [ABSTRACT FROM AUTHOR]