Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks

Rings, Thorsten; Wrede, Randi von; Lehnertz, Klaus

doi:10.1038/s41598-019-47092-w

Cited by 31 publications

(35 citation statements)

References 68 publications

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“…Also, we found no clear-cut evidence for critical slowing down to be confined to the brain region from which epileptic seizures appear to originate. The lack of a spatial sensitivity and specificity of indicators for critical slowing down not only supports the current notion of a network mechanism to underlie the transition to the pre-seizure state 41,43,45,[59][60][61] but also indicates that the mechanism behind the critical transition assumed here (bifurcation-induced tipping) may be too simplistic for the human epileptic brain. There are other mechanisms behind tipping phenomena, such as noise-induced and rate-dependent tipping 62 .…”

Section: Resultssupporting

confidence: 64%

No evidence for critical slowing down prior to human epileptic seizures

Wilkat

Rings

Lehnertz

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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There is a ongoing debate whether generic early warning signals for critical transitions exist that can be applied across diverse systems. The human epileptic brain is often considered as a prototypical system, given the devastating and, at times, even life-threatening nature of the extreme event epileptic seizure. More than three decades of international effort has successfully identified predictors of imminent seizures. However, the suitability of typically applied early warning indicators for critical slowing down, namely variance and lag-1 autocorrelation, for indexing seizure susceptibility is still controversially discussed. Here, we investigated long-term, multichannel recordings of brain dynamics from 28 subjects with epilepsy. Using a surrogate-based evaluation procedure of sensitivity and specificity of time-resolved estimates of early warning indicators, we found no evidence for critical slowing down prior to 105 epileptic seizures.

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Section: Resultssupporting

confidence: 64%

No evidence for critical slowing down prior to human epileptic seizures

Wilkat

Rings

Lehnertz

2019

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…This motivated efforts to move toward getting a multivariate view by capturing and analyzing dependences between signals and their changes. One can then interpret epochs of recordings as time‐varying networks where EEG channels constitute nodes and relations are defined by some bivariate measure (Rings, von Wrede, & Lehnertz, ). Subsequently, graph theory can be applied to identify critical nodes and subnetworks.…”

Section: Introductionmentioning

confidence: 99%

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

Müller

Caporro

Gast

et al. 2019

Human Brain Mapping

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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. K E Y W O R D S epilepsy, epilepsy surgery, linear, network structure, nonlinear, quantitative EEG Hum Brain Mapp. 2020;41:467-483. wileyonlinelibrary.com/journal/hbm 467 SUPPORTING INFORMATION Additional supporting information may be found online in the Supporting Information section at the end of this article. How to cite this article: Müller M, Caporro M, Gast H, et al. Linear and nonlinear interrelations show fundamentally distinct network structure in preictal intracranial EEG of epilepsy patients. Hum Brain Mapp. 2020;41:467-483.

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“…This approach was used to analyze changes to graph centralities to shed more light on the role of constituents of evolving epileptic networks that recurrently transit into and out of seizures 48 . Furthermore, fundamental questions such as which nodes are connected by a predictive edge and which network modifications constitute a pre-seizure state were explored 48 . However, with ICON we mainly explore the seizure predictive capability of the second eigenvalue which is a measure of network algebraic connectivity.…”

Section: Discussionmentioning

confidence: 99%

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures

Bomela

Wang

Chou

et al. 2020

Sci Rep

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Recent studies in brain science and neurological medicine paid a particular attention to develop machine learning-based techniques for the detection and prediction of epileptic seizures with electroencephalogram (eeG). As a noninvasive monitoring method to record brain electrical activities, eeG has been widely used for capturing the underlying dynamics of disruptive neuronal responses across the brain in real-time to provide clinical guidance in support of epileptic seizure treatments in practice. In this study, we introduce a novel dynamic learning method that first infers a time-varying network constituted by multivariate eeG signals, which represents the overall dynamics of the brain network, and subsequently quantifies its topological property using graph theory. We demonstrate the efficacy of our learning method to detect relatively strong synchronization (characterized by the algebraic connectivity metric) caused by abnormal neuronal firing during a seizure onset. The computational results for a realistic scalp EEG database show a detection rate of 93.6% and a false positive rate of 0.16 per hour (FP/h); furthermore, our method observes potential pre-seizure phenomena in some cases. The human brain is undoubtedly one of the most complex dynamic systems known to mankind, which consists of billions of interconnected neurons 1. The connectivity patterns across different brain regions could convey evidences for advancing and supporting the understanding of cognitive and behavioral functions 2-5. In the past decades, many research studies have been devoted to discovering the process and mechanisms that govern the communication between neurons; in particular, how and why neuronal networks are formed for various neurological disorders. Epilepsy is one of the most common central nervous system disorders, following Alzheimer's disease and stroke. According to recent statistics 6 , nearly 4% of people across different ages are diagnosed with epilepsy and suffer from epileptic seizure occurrence and recurrence during their lifetimes. Epileptic seizures develop with a sudden abnormal surge of electrical activities of pathological, synchronous neuronal firing in all brain regions or some parts. Seizures can be controlled with medications and/or invasive surgeries in 70% of diagnosed patients 7. However, prompt detection or even prediction of seizure onsets remains a critical and challenging task to date because of its high variation patterns of occurrence and manifestation from one individual to another. To this end, a lot of efforts have been given to developing subject-specific, data-driven methodologies aiming at ultimately improving individualized seizure onset detection and prediction 8. Electroencephalography (EEG) recordings in high temporal resolution, regardless of arguable evidence of poor spatial resolution and low signal-to-noise ratio (SNR), are useful amongst neuroimaging techniques allowing clinicians to collect and monitor global neuronal activity information in the brain of epileptic patients. Epilepti...

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Precursors of seizures due to specific spatial-temporal modifications of evolving large-scale epileptic brain networks

Cited by 31 publications

References 68 publications

No evidence for critical slowing down prior to human epileptic seizures

No evidence for critical slowing down prior to human epileptic seizures

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

Real-time Inference and Detection of Disruptive EEG Networks for Epileptic Seizures

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