High-frequency neuronal network modulations encoded in scalp EEG precede the onset of focal seizures

A Spectral Clustering Approach for the Classification of Waveform Anomalies in High-Dimensional Brain Signals

Transient electrophysiological anomalies in the human brain have been associated with neurological disorders such as epilepsy, may signal impending adverse events (e.g, seizurse), or may reflect the effects of a stressor, such as insufficient sleep. These, typically brief, high-frequency and heterogeneous signal anomalies remain poorly understood, particularly at long time scales, and their morphology and variability have not been systematically characterized. In continuous neural recordings, their inherent sparsity, short duration and low amplitude makes their detection and classification difficult. In turn, this limits their evaluation as potential biomarkers of abnormal neurodynamic processes (e.g., ictogenesis) and predictors of impending adverse events. A novel algorithm is presented that leverages the inherent sparsity of high-frequency abnormalities in neural signals recorded at the scalp and uses spectral clustering to classify them in very high-dimensional signals spanning several days. It is shown that estimated clusters vary dynamically with time and their distribution changes substantially both as a function of time and space.

Seizure prediction - ready for a new era

Epilepsy is a common disorder characterized by recurrent seizures. An overwhelming majority of people with epilepsy regard the unpredictability of seizures as a major issue. More than 30 years of international effort have been devoted to the prediction of seizures, aiming to remove the burden of unpredictability and to couple novel, time-specific treatment to seizure prediction technology. A highly influential review published in 2007 concluded that insufficient evidence indicated that seizures could be predicted. Since then, several advances have been made, including successful prospective seizure prediction using intracranial EEG in a small number of people in a trial of a real-time seizure prediction device. In this Review, we examine advances in the field, including EEG databases, seizure prediction competitions, the prospective trial mentioned and advances in our understanding of the mechanisms of seizures. We argue that these advances, together with statistical evaluations, set the stage for a resurgence in efforts towards the development of seizure prediction methodologies. We propose new avenues of investigation involving a synergy between mechanisms, models, data, devices and algorithms and refine the existing guidelines for the development of seizure prediction technology to instigate development of a solution that removes the burden of the unpredictability of seizures.

High-Frequency Oscillations in the Scalp Electroencephalogram: Mission Impossible without Computational Intelligence

High-frequency oscillations (HFOs) in the electroencephalogram (EEG) are thought to be a promising marker for epileptogenicity. A number of automated detection algorithms have been developed for reliable analysis of invasively recorded HFOs. However, invasive recordings are not widely applicable since they bear risks and costs, and the harm of the surgical intervention of implantation needs to be weighted against the informational benefits of the invasive examination. In contrast, scalp EEG is widely available at low costs and does not bear any risks. However, the detection of HFOs on the scalp represents a challenge that was taken on so far mostly via visual detection. Visual detection of HFOs is, in turn, highly time-consuming and subjective. In this review, we discuss that automated detection algorithms for detection of HFOs on the scalp are highly warranted because the available algorithms were all developed for invasively recorded EEG and do not perform satisfactorily in scalp EEG because of the low signal-to-noise ratio and numerous artefacts as well as physiological activity that obscures the tiny phenomena in the high-frequency range.

The role of high-frequency oscillations in epilepsy surgery planning

BACKGROUND

Epilepsy is a serious brain disorder characterized by recurrent unprovoked seizures. Approximately two-thirds of seizures can be controlled with antiepileptic medications (Kwan 2000). For some of the others, surgery can completely eliminate or significantly reduce the occurrence of disabling seizures. Localization of epileptogenic areas for resective surgery is far from perfect, and new tools are being investigated to more accurately localize the epileptogenic zone (the zone of the brain where the seizures begin) and improve the likelihood of freedom from postsurgical seizures. Recordings of pathological high-frequency oscillations (HFOs) may be one such tool.

OBJECTIVES

To assess the ability of HFOs to improve the outcomes of epilepsy surgery by helping to identify more accurately the epileptogenic areas of the brain.

SEARCH METHODS

For the latest update, we searched the Cochrane Epilepsy Group Specialized Register (25 July 2016), the Cochrane Central Register of Controlled Trials (CENTRAL) via the Cochrane Register of Studies Online (CRSO, 25 July 2016), MEDLINE (Ovid, 1946 to 25 July 2016), CINAHL Plus (EBSCOhost, 25 July 2016), Web of Science (Thomson Reuters, 25 July 2016), ClinicalTrials.gov (25 July 2016), and the World Health Organization International Clinical Trials Registry Platform ICTRP (25 July 2016).

SELECTION CRITERIA

We included studies that provided information on the outcomes of epilepsy surgery for at least six months and which used high-frequency oscillations in making decisions about epilepsy surgery.

DATA COLLECTION AND ANALYSIS

The primary outcome of the review was the Engel Class Outcome System (class I = no disabling seizures, II = rare disabling seizures, III = worthwhile improvement, IV = no worthwhile improvement). Secondary outcomes were responder rate, International League Against Epilepsy (ILAE) epilepsy surgery outcome, frequency of adverse events from any source and quality of life outcomes. We intended to analyse outcomes via an aggregated data fixed-effect model meta-analysis.

MAIN RESULTS

Two studies representing 11 participants met the inclusion criteria. Both studies were small non-randomised trials, with no control group and no blinding. The quality of evidence for all outcomes was very low. The combination of these two studies resulted in 11 participants who prospectively used ictal HFOs for epilepsy surgery decision making. Results of the postsurgical seizure freedom Engel class I to IV outcome were determined over a period of 12 to 38 months (average 23.4 months) and indicated that six participants had an Engel class I outcome (seizure freedom), two had class II (rare disabling seizures), three had class III (worthwhile improvement). No adverse effects were reported. Neither study compared surgical results guided by HFOs versus surgical results guided without HFOs.

AUTHORS' CONCLUSIONS

No reliable conclusions can be drawn regarding the efficacy of using HFOs in epilepsy surgery decision making at present.

Early Psychosocial Neglect Adversely Impacts Developmental Trajectories of Brain Oscillations and Their Interactions

Rhythmicity is a fundamental property of neural activity at multiple spatiotemporal scales, and associated oscillations represent a critical mechanism for communication and transmission of information across brain regions. During development, these oscillations evolve dynamically as a function of neural maturation and may be modulated by early experiences, positive and/or negative. This study investigated the impact of psychosocial deprivation associated with institutional rearing in early life and the effects of subsequent foster care intervention on developmental trajectories of neural oscillations and their cross-frequency correlations. Longitudinally acquired nontask EEGs from three cohorts of children from the Bucharest Early Intervention Project were analyzed. These included abandoned children initially reared in institutions and subsequently randomized to be placed in foster care or receive care as usual (prolonged institutional rearing) and a group of never-institutionalized children. Oscillation trajectories were estimated from 42 to 96 months, that is, 1-3 years after all children in the intervention arm of the study had been placed in foster care. Significant differences between groups were estimated for the amplitude trajectories of cognitive-related gamma, beta, alpha, and theta oscillations. Similar differences were identified as a function of time spent in institutions, suggesting that increased time spent in psychosocial neglect may have profound and widespread effects on brain activity. Significant group differences in cross-frequency coupling were estimated longitudinally between gamma and lower frequencies as well as alpha and lower frequencies. Lower cross-gamma coupling was estimated at 96 months in the group of children that remained in institutions at that age compared to the other two groups, suggesting potentially impaired communication between local and long-distance brain networks in these children. In contrast, higher cross-alpha coupling was estimated in this group compared to the other two groups at 96 months, suggesting impaired suppression of alpha-theta and alpha-delta activity, which has been associated with neuropsychiatric disorders. Age at foster care placement had a significant positive modulatory effect on alpha and beta trajectories and their mutual coupling, although by 96 months these trajectories remained distinct from those of never-institutionalized children. Overall, these findings suggest that early psychosocial neglect may profoundly impact neural maturation, particularly the evolution of neural oscillations and their interactions across a broad frequency range. These differences may result in widespread deficits across multiple cognitive domains.

On the proper selection of preictal period for seizure prediction

Supervised machine learning-based seizure prediction methods consider preictal period as an important prerequisite parameter during training. However, the exact length of the preictal state is unclear and varies from seizure to seizure. We propose a novel statistical approach for proper selection of the preictal period, which can also be considered either as a measure of predictability of a seizure or as the prediction capability of an understudy feature. The optimal preictal periods (OPPs) obtained from the training samples can be used for building a more accurate classifier model. The proposed method uses amplitude distribution histograms of features extracted from electroencephalogram (EEG) recordings. To evaluate this method, we extract spectral power features in different frequency bands from monopolar and space-differential EEG signals of 18 patients suffering from pharmacoresistant epilepsy. Furthermore, comparisons among monopolar channels with space-differential channels, as well as intracranial EEG (iEEG) and surface EEG (sEEG) signals, indicate that while monopolar signals perform better in iEEG recordings, no significant difference is noticeable in sEEG recordings.

The role of high-frequency oscillations in epilepsy surgery planning

BACKGROUND

Epilepsy is a serious brain disorder characterized by recurrent unprovoked seizures. Approximately two-thirds of seizures can be controlled with antiepileptic medications (Kwan 2000). For some of the others, surgery can completely eliminate or significantly reduce the occurrence of disabling seizures. Localization of epileptogenic areas for resective surgery is far from perfect, and new tools are being investigated to more accurately localize the epileptogenic zone (the zone of the brain where the seizures begin) and improve the likelihood of freedom from postsurgical seizures. Recordings of pathological high-frequency oscillations (HFOs) may be one such tool.

OBJECTIVES

To assess the ability of HFOs to improve the outcomes of epilepsy surgery by helping to identify more accurately the epileptogenic areas of the brain.

SEARCH METHODS

We searched the Cochrane Epilepsy Group Specialized Register (15 April 2013), the Cochrane Central Register of Controlled Trials (CENTRAL) in The Cochrane Library (2013, Issue 3), MEDLINE (Ovid) (1946 to 15 April 2013), CINAHL (EBSCOhost) (15 April 2013), Web of Knowledge (Thomson Reuters) (15 April 2013), www.clinicaltrials.gov (15 April 2013), and the World Health Organization International Clinical Trials Registry Platform (15 April 2013).

SELECTION CRITERIA

We included studies that provided information on the outcomes of epilepsy surgery at at least six months and which used high-frequency oscillations in making decisions about epilepsy surgery.

DATA COLLECTION AND ANALYSIS

The primary outcome of the review was the Engel Class Outcome System. Secondary outcomes were responder rate, International League Against Epilepsy (ILAE) epilepsy surgery outcome, frequency of adverse events from any source and quality of life outcomes. We intended to analyse outcomes via an aggregated data fixed-effect model meta-analysis.

MAIN RESULTS

Two studies met the inclusion criteria. Both studies were small non-randomised trials, with no control group and no blinding. The quality of evidence for all outcomes was very low. The combination of these two studies resulted in 11 participants who prospectively used ictal HFOs for epilepsy surgery decision making. Results of the postsurgical seizure freedom Engel class I to IV outcome were determined over a period of 12 to 38 months (average 23.4 months) and indicated that six participants had an Engel class I outcome (seizure freedom), two had class II (rare disabling seizures), three had class III (worthwhile improvement). No adverse effects were reported. Neither study compared surgical results guided by HFOs versus surgical results guided without HFOs.

AUTHORS' CONCLUSIONS

No reliable conclusions can be drawn regarding the efficacy of using HFOs in epilepsy surgery decision making at present.

Information theoretic measures of network coordination in high-frequency scalp EEG reveal dynamic patterns associated with seizure termination

How a seizure terminates is still under-studied and, despite its clinical importance, remains an obscure phase of seizure evolution. Recent studies of seizure-related scalp EEGs at frequencies >100 Hz suggest that neural activity, in the form of oscillations and/or neuronal network interactions, may play an important role in preictal/ictal seizure evolution (Andrade-Valenca et al., 2011; Stamoulis et al., 2012). However, the role of high-frequency activity in seizure termination, is unknown, if it exists at all. Using information theoretic measures of network coordination, this study investigated ictal and immediate postictal neurodynamic interactions encoded in scalp EEGs from a relatively small sample of 8 patients with focal epilepsy and multiple seizures originating in temporal and/or frontal brain regions, at frequencies ≤ 100 Hz and >100 Hz, respectively. Despite some heterogeneity in the dynamics of these interactions, consistent patterns were also estimated. Specifically, in several seizures, linear or non-linear increase in high-frequency neuronal coordination during ictal intervals, coincided with a corresponding decrease in coordination at frequencies <100 Hz, suggesting a potential interference role of high-frequency activity, to disrupt abnormal ictal synchrony at lower frequencies. These changes in network synchrony started at least 20-30s prior to seizure offset, depending on the seizure duration. Opposite patterns were estimated at frequencies ≤ 100 Hz in several seizures. These results raise the possibility that high-frequency interference may occur in the form of progressive network coordination during the ictal interval, which continues during the postictal interval. This may be one of several possible mechanisms that facilitate seizure termination. In fact, inhibition of pairwise interactions between EEGs by other signals in their spatial neighborhood, quantified by negative interaction information, was estimated at frequencies ≤ 100 Hz, at least in some seizures.

Space-time adaptive processing for improved estimation of preictal seizure activity

Detection of precursory, seizure-related activity in electroencephalograms (EEG) is a clinically important and difficult problem in the field of epilepsy. Seizure detection methods often aim to identify specific features and correlations between preictal EEG signals that differentiate them from interictal/nonictal signals. Typically, these methods use information from nonictal EEGs to establish detection thresholds, and do not otherwise incorporate their characteristics into the detection. A space-time adaptive approach is proposed to improve detection of seizure-related preictal activity in scalp EEG, using multiple patient-specific baseline signals to optimize the estimate of the baseline covariance matrix. A simplified model of the preictal EEG is assumed, which describes this signal as a linear superposition of seizure-related activity and baseline activity (treated as an interference signal). It is shown that when an improved estimate of the baseline covariance is included in the preictal detector, the true positive rate increases significantly and also the false positive rate decreases significantly.

Signal subspace integration for improved seizure localization

A subspace signal processing approach is proposed for improved scalp EEG-based localization of broad-focus epileptic seizures, and estimation of the directions of source arrivals (DOA). Ictal scalp EEGs from adult and pediatric patients with broad-focus seizures were first decomposed into dominant signal modes, and signal and noise subspaces at each modal frequency, to improve the signal-to-noise ratio while preserving the original data correlation structure. Transformed (focused) modal signals were then resynthesized into wideband signals from which the number of sources and DOA were estimated. These were compared to denoised signals via principal components analysis (PCA). Coherent subspace processing performed better than PCA, significantly improved the localization of ictal EEGs and the estimation of distinct sources and corresponding DOAs.

Modeling noninvasive neurostimulation in epilepsy as stochastic interference in brain networks

Noninvasive brain stimulation is one of very few potential therapies for medically refractory epilepsy. However, its efficacy remains suboptimal and its therapeutic value has not been consistently assessed. This is in part due to the nonoptimized spatio-temporal application of stimulation protocols for seizure prevention or arrest, and incomplete knowledge of the neurodynamics of seizure evolution. Through simulations, this study investigated electroencephalography (EEG)-guided, stochastic interference with aberrantly coordinated neuronal networks, to prevent seizure onset or interrupt a propagating partial seizure, and prevent it from spreading to large areas of the brain. Brain stimulation was modeled as additive white or band-limited noise, and simulations using real EEGs and data generated from a network of integrate-and-fire neuronal ensembles were used to quantify spatio-temporal noise effects. It was shown that additive stochastic signals (noise) may destructively interfere with network dynamics and decrease or abolish synchronization associated with progressively coupled networks. Furthermore, stimulation parameters, particularly amplitude and spatio-temporal application, may be optimized based on patient-specific neurodynamics estimated directly from noninvasive EEGs.