Capturing time-varying brain dynamics

Lehnertz, Klaus; Geier, Christian; Rings, Thorsten; Stahn, Kirsten

doi:10.1051/epjnbp/2017001

Cited by 35 publications

(33 citation statements)

References 267 publications

(290 reference statements)

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“…. , S, where S denotes the number of recording sites) in a sliding-window fashion 46 (w denotes the window number, and each window had a length of 20.48 s). For estimating the lag-1 autocorrelation ρ, we chose the smallest time-delay (here τ = 30 ms), for which the autocorrelation function R(τ ) is not dominated by spurious correlations induced by the applied low-pass filter.…”

Section: Methodsmentioning

confidence: 99%

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: Methodsmentioning

confidence: 99%

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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“…Regarding network analysis of iEEG a large amount of studies have been undertaken in the past decade (see, e.g., Lehnertz, Geier, Rings, & Stahn, 2017;Parvizi & Kastner, 2018 for reviews). Directed graphs have been analyzed, for instance, by Wilke et al (2011), van Mierlo et al (2013, and Zubler et al (2015).…”

Section: Patients With Insufficient Seizure Control After Surgerymentioning

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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“…Smith et al, 2017). This fractal nature may be transferred to functional networks (Lehnertz, Geier, Rings, & Stahn, 2017), Figure 6. Automatic classification of two states using the PC1 time series matches visually-classified sleep stages.…”

Section: Discussionmentioning

confidence: 99%

Temporal dynamics of functional networks in long-term infant scalp EEG

Smith

Alipourjeddi

Garner

et al. 2020

Preprint

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Human functional connectivity networks are modulated on time scales ranging from milliseconds to days. Rapid changes in connectivity over short time scales are a feature of healthy cognitive function, and variability over long time scales can impact the likelihood of seizure occurrence. However, relatively little is known about modulation of healthy functional networks over long time scales. To address this, we analyzed functional connectivity networks calculated from long-term EEG recordings from 19 healthy infants. Networks were subject-specific, as inter-subject correlations between weighted adjacency matrices were low. However, within individual subjects, both sleep and wake networks were stable over time, with stronger functional connectivity during sleep than wakefulness. This enabled automatic separation of wakefulness and sleep states via principle components analysis of the functional network time series, with median classification accuracy of 91%. Lastly, we found that network strength, degree, clustering coefficient, and path length significantly varied with time of day, when measured in both wakefulness and sleep. Together, these results suggest that modulation of healthy functional networks occurs over long timescales and is robust and repeatable. Accounting for such temporal periodicities may improve the physiological interpretation and use of functional connectivity analysis to investigate brain function in health and disease.

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Capturing time-varying brain dynamics

Cited by 35 publications

References 267 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

Temporal dynamics of functional networks in long-term infant scalp EEG

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