Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients.

Resection of the seizure generating tissue can be highly beneficial in patients with drug-resistant epilepsy. However, only about half of all patients undergoing surgery get permanently and completely seizure free. Investigating the dependences between intracranial EEG signals adds a multivariate perspective largely unavailable to visual EEG analysis, which is the current clinical practice. We examined linear and nonlinear interrelations between intracranial EEG signals regarding their spatial distribution and network characteristics. The analyzed signals were recorded immediately before clinical seizure onset in epilepsy patients who received a standardized electrode implantation targeting the mesiotemporal structures. The linear interrelation networks were predominantly locally connected and highly reproducible between patients. In contrast, the nonlinear networks had a clearly centralized structure, which was specific for the individual pathology. The nonlinear interrelations were overrepresented in the focal hemisphere and in patients with no or only rare seizures after surgery specifically in the resected tissue. Connections to the outside were predominantly nonlinear. In all patients without worthwhile improvement after resective treatment, tissue producing strong nonlinear interrelations was left untouched by surgery. Our findings indicate that linear and nonlinear interrelations play fundamentally different roles in preictal intracranial EEG. Moreover, they suggest nonlinear signal interrelations to be a marker of epileptogenic tissue and not a characteristic of the mesiotemporal structures. Our results corroborate the network-based nature of epilepsy and suggest the application of network analysis to support the planning of resective epilepsy surgery.

Assessing Epileptogenicity Using Phase-Locked High Frequency Oscillations: A Systematic Comparison of Methods.

High frequency oscillations (HFOs) are traditional biomarkers to identify the epileptogenic tissue during presurgical evaluation in pharmacoresistant epileptic patients. Recently, the resection of brain tissue exhibiting coupling between the amplitude of HFOs and the phase of low frequencies demonstrated a more favorable surgical outcome. Here we compare the predictive value of ictal HFOs and four methods for quantifying the ictal phase-amplitude coupling, namely mean vector length, phase-locked high gamma, phase locking value, and modulation index (MI). We analyzed 32 seizures from 16 patients to identify the channels that exhibit HFOs and phase-locked HFOs during seizures. We compared the resection ratio, defined as the percentage of channels exhibiting coupling located in the resected tissue, with the postsurgical outcome. We found that the MI is the only method to show a significant difference between the resection ratios of patients with good and poor outcomes. We further show that the whole seizure, not only the onset, is critical to assess epileptogenicity using the phase-locked HFOs. We postulate that the superiority of MI stems from its capacity to assess coupling of discrete HFO events and its independence from the HFO power. These results confirm that quantitative analysis of HFOs can boost presurgical evaluation and indicate the paramount importance of algorithm selection for clinical applications.

A randomized controlled trial comparing guided internet-based multi-component treatment and internet-based guided sleep restriction treatment to care as usual in insomnia.

BACKGROUND: Internet-based cognitive behavioral treatment (iCBT-I) for insomnia comprising different sleep-related cognitive and behavioral interventional components has shown some promise. However, it is not known which components are necessary for a good treatment outcome. METHOD: People suffering from insomnia (N = 104) without any other comorbid psychiatric disorders were randomized (2:2:1) to two guided internet-based self-help interventions for insomnia [multi-component cognitive behavioral self-help intervention (MCT); sleep restriction intervention for insomnia (SRT)], and care as usual [CAU]. In all three conditions, additional care or treatment was allowed. The primary outcome was insomnia severity measured with the insomnia severity index (ISI) at eight weeks. Furthermore, the two active conditions were compared regarding sleep efficacy from daily diary data over the eight weeks, and other measures from the daily protocols. Secondary outcomes included sleep quality, depressive symptoms, dysfunctional beliefs, and quality of life at post-treatment (eight weeks) and follow-up (six months after randomization). RESULTS: Both conditions were more effective than CAU at post-treatment, with medium to large between-group effect sizes on the primary outcome (ISI; MCT: Cohen's d = -1.15; SRT: d = -0.68) and small to medium between-group effect sizes for secondary outcomes. Treatment gains were maintained at six-month follow-up. Active conditions did not differ from each other on all measures from pre to post, except for dysfunctional beliefs about sleep, and sleep protocol data throughout the intervention. Participants in MCT were significantly more satisfied with the intervention than participants in SRT. CONCLUSIONS: Results of the present study indicate that CAU + MCT and CAU + SRT are both effective compared to CAU. There were no statistical differences regarding efficacy between the two active conditions, but participants in MCT reported to be more satisfied with the intervention.

Characteristic Fluctuations Around Stable Attractor Dynamics Extracted from Highly Nonstationary Electroencephalographic Recordings.

Since the discovery of electrical activity of the brain, electroencephalographic (EEG) recordings constitute one of the most popular techniques of brain research. However, EEG signals are highly nonstationary and one should expect that averages of the cross-correlation coefficient, which may take positive and negative values with equal probability, (almost) vanish when estimated over long data segments. Instead, we found that the average zero-lag cross-correlation matrix estimated with a running window over the whole night of sleep EEGs, or of resting state during eyes-open and eyes-closed conditions of healthy subjects shows a characteristic correlation pattern containing pronounced nonzero values. A similar correlation structure has already been encountered in scalp EEG signals containing focal onset seizures. Therefore, we conclude that this structure is independent of the physiological state. Because of its pronounced similarity across subjects, we believe that it depicts a generic feature of the brain dynamics. Namely, we interpret this pattern as a manifestation of a dynamical ground state of the brain activity, necessary to preserve an efficient operational mode, or, expressed in terms of dynamical system theory, we interpret it as a "shadow" of the evolution on (or close to) an attractor in phase space. Nonstationary dynamical aspects of higher cerebral processes should manifest in deviations from this stable pattern. We confirm this hypothesis through a correlation analysis of EEG recordings of 10 healthy subjects during night sleep, 20 recordings of 9 epilepsy patients, and 42 recordings of 21 healthy subjects in resting state during eyes-open and eyes-closed conditions. In particular, we show that the estimation of deviations from the stationary correlation structures provides a more significant differentiation of physiological states and more homogeneous results across subjects.

Stroke causes a transient imbalance of interhemispheric information flow in EEG during non-REM sleep.

OBJECTIVE: Large-scale connectivity, especially interhemispheric connections, plays a crucial role for recovery after stroke. Here we used methods from information theory to characterize interhemispheric information flow in wake- and sleep-EEG after cerebral ischemia. METHODS: 34 patients with unilateral ischemic stroke were included. Symbolic Transfer Entropy (STE) was applied between bipolar EEG signals on the left and the right cerebral hemisphere during polysomnographic recordings in the acute phase and 3 months after stroke. RESULTS: In the acute phase, we found a sleep stage-dependent preferred interhemispheric asymmetry: during non-REM sleep the information flow was predominantly directed from the contralesional toward the ipsilesional hemisphere. This effect was greatly reduced in a follow-up recording 3 months after stroke onset. CONCLUSION: Our findings are consistent with functional imaging studies showing a transient hyperactivity of contralesional areas after stroke. We conclude that STE is a robust method for detecting post-stroke connectivity reorganizations, and that sleep stages have to be taken into account when assessing functional connectivity. SIGNIFICANCE: EEG is more widely available than functional MRI. Future studies will have to confirm whether EEG derived STE can be useful in a clinical setting during rehabilitation after stroke.

EEG synchronization measures are early outcome predictors in comatose patients after cardiac arrest.

OBJECTIVE: Outcome prognostication in comatose patients after cardiac arrest (CA) remains a major challenge. Here we investigated the prognostic value of combinations of linear and non-linear bivariate EEG synchronization measures. METHODS: 94 comatose patients with EEG within 24h after CA were included. Clinical outcome was assessed at 3months using the Cerebral Performance Categories (CPC). EEG synchronization between the left and right parasagittal, and between the frontal and parietal brain regions was assessed with 4 different quantitative measures (delta power asymmetry, cross-correlation, mutual information, and transfer entropy). 2/3 of patients were used to assess the predictive power of all possible combinations of these eight features (4 measures×2 directions) using cross-validation. The predictive power of the best combination was tested on the remaining 1/3 of patients. RESULTS: The best combination for prognostication consisted of 4 of the 8 features, and contained linear and non-linear measures. Predictive power for poor outcome (CPC 3-5), measured with the area under the ROC curve, was 0.84 during cross-validation, and 0.81 on the test set. At specificity of 1.0 the sensitivity was 0.54, and the accuracy 0.81. CONCLUSION: Combinations of EEG synchronization measures can contribute to early prognostication after CA. In particular, combining linear and non-linear measures is important for good predictive power. SIGNIFICANCE: Quantitative methods might increase the prognostic yield of currently used multi-modal approaches.

Burst firing of single neurons in the human medial temporal lobe changes before epileptic seizures.

OBJECTIVE: To better understand the mechanisms that lead to the sudden and unexpected occurrence of seizures, with the neuronal correlate being abnormally synchronous discharges that disrupt neuronal function. METHODS: To address this problem, we recorded single neuron activity in epilepsy patients during the transition to seizures to uncover specific changes of neuronal firing patterns. We focused particularly on neurons repeatedly firing discrete groups of high-frequency action potentials (so called bursters) that have been associated with ictogenesis. We analyzed a total of 459 single neurons and used the mean autocorrelation time as a quantitative measure of burstiness. To unravel the intricate roles of excitation and inhibition, we also examined differential contributions from putative principal cells and interneurons. RESULTS: During interictal recordings, burstiness was significantly higher in the seizure onset hemisphere, an effect found only for principal cells, but not for interneurons, and which disappeared before seizures. CONCLUSION: These findings deviate from conventional views of ictogenesis that propose slowly-increasing aggregates of bursting neurons which give rise to seizures once they reach a critical mass. SIGNIFICANCE: Instead our results are in line with recent hypotheses that bursting may represent a protective mechanism by preventing direct transmission of postsynaptic high-frequency oscillations.

Ictal time-irreversible intracranial EEG signals as markers of the epileptogenic zone.

OBJECTIVE: To show that time-irreversible EEG signals recorded with intracranial electrodes during seizures can serve as markers of the epileptogenic zone. METHODS: We use the recently developed method of mapping time series into directed horizontal graphs (dHVG). Each node of the dHVG represents a time point in the original intracranial EEG (iEEG) signal. Statistically significant differences between the distributions of the nodes' number of input and output connections are used to detect time-irreversible iEEG signals. RESULTS: In 31 of 32 seizure recordings we found time-irreversible iEEG signals. The maximally time-irreversible signals always occurred during seizures, with highest probability in the middle of the first seizure half. These signals spanned a large range of frequencies and amplitudes but were all characterized by saw-tooth like shaped components. Brain regions removed from patients who became post-surgically seizure-free generated significantly larger time-irreversibilities than regions removed from patients who still had seizures after surgery. CONCLUSIONS: Our results corroborate that ictal time-irreversible iEEG signals can indeed serve as markers of the epileptogenic zone and can be efficiently detected and quantified in a time-resolved manner by dHVG based methods. SIGNIFICANCE: Ictal time-irreversible EEG signals can help to improve pre-surgical evaluation in patients suffering from pharmaco-resistant epilepsies.

Prognostic and diagnostic value of EEG signal coupling measures in coma.

OBJECTIVE: Our aim was to assess the diagnostic and predictive value of several quantitative EEG (qEEG) analysis methods in comatose patients. METHODS: In 79 patients, coupling between EEG signals on the left-right (inter-hemispheric) axis and on the anterior-posterior (intra-hemispheric) axis was measured with four synchronization measures: relative delta power asymmetry, cross-correlation, symbolic mutual information and transfer entropy directionality. Results were compared with etiology of coma and clinical outcome. Using cross-validation, the predictive value of measure combinations was assessed with a Bayes classifier with mixture of Gaussians. RESULTS: Five of eight measures showed a statistically significant difference between patients grouped according to outcome; one measure revealed differences in patients grouped according to the etiology. Interestingly, a high level of synchrony between the left and right hemisphere was associated with mortality on intensive care unit, whereas higher synchrony between anterior and posterior brain regions was associated with survival. The combination with the best predictive value reached an area-under the curve of 0.875 (for patients with post anoxic encephalopathy: 0.946). CONCLUSIONS: EEG synchronization measures can contribute to clinical assessment, and provide new approaches for understanding the pathophysiology of coma. SIGNIFICANCE: Prognostication in coma remains a challenging task. qEEG could improve current multi-modal approaches.

Neutralization of colony-stimulating factor 1 receptor prevents sickness behavior syndrome by reprogramming inflammatory monocytes to produce IL-10.

Sickness behavior syndrome (SBS) as characterized by fatigue and depression impairs quality of life in patients with inflammatory diseases caused by infections and autoimmunity. Systemic engagement of CD40 in mice leads to an inflammatory syndrome with acute hepatitis, lymphadenopathy and development of SBS as evidenced by induction of sleep and weight loss. In the study presented here we show that the elimination of resident tissue macrophages in mice by antibody-mediated neutralization of colony-stimulating factor-1 receptor (CSF1R) did not prevent CD40 induced hepatitis, but conferred resistance to the development of SBS. The protective effect of CSF1R mAb on weight loss and behavior changes induced by CD40 activation coincided with the transformation of pro-inflammatory monocytes to IL-10 producing myeloid cells. In IL-10 knockout mice CSF1R neutralization failed to exert protection from the occurrence of SBS. This study establishes the unexpected key role of CSF1R in the polarization of inflammatory monocytes and thereby SBS in inflammatory liver diseases.

Detecting functional hubs of ictogenic networks.

Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.

Seizure termination.

A better understanding of the mechanisms by which most focal epileptic seizures stop spontaneously within a few minutes would be of highest importance, because they could potentially help to improve existing and develop novel therapeutic measures for seizure control. Studies devoted to unraveling mechanisms of seizure termination often take one of the two following approaches. The first approach focuses on metabolic mechanisms such as ionic concentrations, acidity, or neuromodulator release, studying how they are dependent on, and in turn affect changes of neuronal activity. The second approach uses quantitative tools to derive functional networks from electrophysiological recordings and analyzes these networks with mathematical methods, without focusing on actual details of cell biology. In this chapter, we summarize key results obtained by both of these approaches and attempt to show that they are complementary and equally necessary in our aim to gain a better understanding of seizure termination.

Epileptic seizures as condensed sleep: an analysis of network dynamics from electroencephalogram signals.

Both deepening sleep and evolving epileptic seizures are associated with increasing slow-wave activity. Larger-scale functional networks derived from electroencephalogram indicate that in both transitions dramatic changes of communication between brain areas occur. During seizures these changes seem to be 'condensed', because they evolve more rapidly than during deepening sleep. Here we set out to assess quantitatively functional network dynamics derived from electroencephalogram signals during seizures and normal sleep. Functional networks were derived from electroencephalogram signals from wakefulness, light and deep sleep of 12 volunteers, and from pre-seizure, seizure and post-seizure time periods of 10 patients suffering from focal onset pharmaco-resistant epilepsy. Nodes of the functional network represented electrical signals recorded by single electrodes and were linked if there was non-random cross-correlation between the two corresponding electroencephalogram signals. Network dynamics were then characterized by the evolution of global efficiency, which measures ease of information transmission. Global efficiency was compared with relative delta power. Global efficiency significantly decreased both between light and deep sleep, and between pre-seizure, seizure and post-seizure time periods. The decrease of global efficiency was due to a loss of functional links. While global efficiency decreased significantly, relative delta power increased except between the time periods wakefulness and light sleep, and pre-seizure and seizure. Our results demonstrate that both epileptic seizures and deepening sleep are characterized by dramatic fragmentation of larger-scale functional networks, and further support the similarities between sleep and seizures.

A systems-level approach to human epileptic seizures.

Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).

Inhibition of meningitis-associated neutrophil apoptosis by TNF-α depends on functional PI3-kinase in monocytes.

In bacterial meningitis, neutrophils cope with bacterial infection but also lead to tissue damage. The balance of beneficial and harmful effects may depend on the lifespan of the neutrophils in the CNS. Here, we show that CSF of patients with meningococcal meningitis contains a neutrophil apoptosis-inhibiting capacity that correlates with TNF-α content. In vitro experiments show that Neisseria meningitidis as well as LPS derived from these bacteria regulated neutrophil apoptosis mainly by stimulating TNF-α production in monocytes. Whereas LPS-induced PI3K-dependent survival signals in monocytes are critical for neutrophil survival, PI3K signaling in granulocytes did not contribute to the increased lifespan of neutrophils. We conclude that LPS-driven PI3K signaling in monocytes regulates neutrophil apoptosis and thereby, may be crucial in the initiation of secondary brain damage in bacterial meningitis.

CD40 activation induces NREM sleep and modulates genes associated with sleep homeostasis.

The T-cell derived cytokine CD40 ligand is overexpressed in patients with autoimmune diseases. Through activation of its receptor, CD40 ligand leads to a tumor necrosis factor (TNF) receptor 1 (TNFR1) dependent impairment of locomotor activity in mice. Here we report that this effect is explained through a promotion of sleep, which was specific to non-rapid eye movement (NREM) sleep while REM sleep was suppressed. The increase in NREM sleep was accompanied by a decrease in EEG delta power during NREM sleep and by a decrease in the expression of transcripts in the cerebral cortex known to be associated with homeostatic sleep drive, such as Homer1a, Early growth response 2, Neuronal pentraxin 2, and Fos-like antigen 2. The effect of CD40 activation was mimicked by peripheral TNF injection and prevented by the TNF blocker etanercept. Our study indicates that sleep-wake dysregulation in autoimmune diseases may result from CD40 induced TNF:TNFR1 mediated alterations of molecular pathways, which regulate sleep-wake behavior.

Transforming growth factor-beta inhibits the expression of clock genes.

Disturbances of sleep-wake rhythms are an important problem in Alzheimer's disease (AD). Circadian rhythms are regulated by clock genes. Transforming growth factor-beta (TGF-ß) is overexpressed in neurons in AD and is the only cytokine that is increased in cerebrospinal fluid (CSF). Our data show that TGF-ß2 inhibits the expression of the clock genes Period (Per)1, Per2, and Rev-erbα, and of the clock-controlled genes D-site albumin promoter binding protein (Dbp) and thyrotroph embryonic factor (Tef). However, our results showed that TGF-ß2 did not alter the expression of brain and muscle Arnt-like protein-1 (Bmal1). The concentrations of TGF-ß2 in the CSF of 2 of 16 AD patients and of 1 of 7 patients with mild cognitive impairment were in the dose range required to suppress the expression of clock genes. TGF-ß2-induced dysregulation of clock genes may alter neuronal pathways, which may be causally related to abnormal sleep-wake rhythms in AD patients.

On seeing the trees and the forest: single-signal and multisignal analysis of periictal intracranial EEG.

PURPOSE: Epileptic seizures are associated with a dysregulation of electrical brain activity on many different spatial scales. To better understand the dynamics of epileptic seizures, that is, how the seizures initiate, propagate, and terminate, it is important to consider changes of electrical brain activity on different spatial scales. Herein we set out to analyze periictal electrical brain activity on comparatively small and large spatial scales by assessing changes in single intracranial electroencephalography (EEG) signals and of averaged interdependences of pairs of EEG signals. METHODS: Single and multiple EEG signals are analyzed by combining methods from symbolic dynamics and information theory. This computationally efficient approach is chosen because at its core it consists of analyzing the occurrence of patterns and bears analogy to classical visual EEG reading. Symbolization is achieved by first mapping the EEG signals into bit strings, that is, long sequences of zeros and ones, depending solely on whether their amplitudes increase or decrease. Bit strings reflect relational aspects between consecutive values of the original EEG signals, but not the values themselves. For each bit string the relative frequencies of the different constituent short bit patterns are then determined and used to compute two information theoretical measures: (1) redundancy (R) of single bit strings characterizes electrical brain activity on a comparatively small spatial scale represented by a single EEG signal and (2) averaged pair-wise mutual information with all other bit strings (M), which allows tracking of larger-scale EEG dynamics. KEY FINDINGS: We analyzed 20 periictal intracranial EEG recordings from five patients with pharmacoresistant temporal lobe epilepsy. At seizure onset, R first strongly increased and then decreased toward seizure termination, whereas M gradually increased throughout the seizure. Bit strings with maximal R were always derived from EEG signals recorded from the visually identified seizure-onset zone. When compared to the bit strings derived from other EEG signals, their M was relatively smaller. These findings are consistent with a strong but transient occurrence of information-poor, that is, redundant electrical brain activity on a smaller spatial scale, which is particularly pronounced in the seizure-onset zone. On a larger spatial scale, a progressively more collective state emerges, as revealed by increasing amounts of mutual information. SIGNIFICANCE: Information theoretical analysis of bit patterns derived from EEG signals helps to characterize periictal brain activity on different spatial scales in a quantitative and efficient way and may provide clinically relevant results.

Forbidden ordinal patterns of periictal intracranial EEG indicate deterministic dynamics in human epileptic seizures.

PURPOSE: Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy. METHODS: We assessed the number of forbidden ordinal patterns of 110 periictal multichannel intracranial EEG studies of 16 patients. Ordinal patterns are derived from the rank order of short sequences of consecutive EEG values. Ordinal patterns are well suited for analyzing real-world time series, for they have low sensitivity for many forms of noise and are applicable to nonstationary data. Although Gaussian random dynamics generate all possible ordinal patterns for a given sequence length, deterministic dynamics typically manifest with less random and more regular signals that miss a certain number of all the possible ordinal patterns. These missing ordinal patterns are referred to as "forbidden ordinal patterns." In this study, the number of forbidden ordinal patterns n(fp) of an EEG signal was interpreted as an indication of determinism, when it was larger than the number of forbidden patterns occurring in amplitude adjusted Fourier transform surrogates. We computed n(fp) for each EEG signal in a time-resolved way by using a moving-window approach. Then we specifically investigated n(mean)(fp) denoting the average number of forbidden patterns across all EEG signals, and n(max)(fp), which represents the number of forbidden patterns occurring in the EEG signal with the largest n(fp) during the seizure-onset period. KEY FINDINGS: The average number of forbidden patterns of all EEG signals, n(mean)(fp), typically first increased and then decreased during the seizures. However, these changes were not statistically significant relative to the preseizure time period. In contrast, n(max)(fp)typically increased significantly during the first third of the seizure period and then gradually decreased toward and beyond seizure termination. In those patients who became seizure free following surgery, a larger percentage of the EEG signals containing the maximal number of forbidden patterns during the seizure-onset period tended to be recorded from within the visually identified seizure-onset zones. SIGNIFICANCE: Our findings demonstrate a spatiotemporally limited shift of neuronal dynamics toward a more deterministic dynamic regimen, specifically pronounced during the seizure-onset period. Assessing the number of forbidden ordinal patterns of intracranial EEG provides quantitative and observer-independent information. We propose that it is at least partially complementary to classical visual EEG reading and may be diagnostically helpful to better delineate ictogenic parts of the brain.

EEG correlation and power during maintenance of wakefulness test after sleep-deprivation.

OBJECTIVES: To investigate whether there are any objective EEG characteristics that change significantly between specific time periods during maintenance of wakefulness test (MWT) and whether such changes are associated with the ability to appropriately communicate sleepiness. METHODS: After a night of total sleep deprivation, 12 healthy young subjects underwent a MWT whilst being instructed to communicate the experience of subjective sleepiness by pressing a button. EEG analysis consisted of average relative power and correlation between EEG signals. RESULTS: A comparison of the 30 s before microsleep (MS) with 30 s before subjects communicated experience of sleepiness (PB) showed increased ß correlation as well as increased power in the ß band (13-20 Hz) whereas power in the θ (4.5-7.5 Hz) and α (8-12.5 Hz) band was significantly decreased. When subjects later failed to communicate the experience of subjective sleepiness before (micro-)sleep occurred, average relative power and EEG correlation were significantly higher during 30 s following lights off in the δ (1-4 Hz) band and power in the α and ß bands was decreased. CONCLUSIONS: EEG spectral power and correlation change significantly in specific frequency bands between different time periods of MWT. Failure to communicate sleepiness is associated with certain precursors of EEG power and correlation. SIGNIFICANCE: This study demonstrates that there are specific EEG characteristics associated with impending failure to communicate sleepiness.