Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization

Mierlo, Pieter van; Papadopoulou, Margarita; Carrette, Evelien; Boon, Paul; Vandenberghe, Stefaan; Vonck, Kristl; Marinazzo, Daniele

doi:10.1016/j.pneurobio.2014.06.004

Cited by 243 publications

(207 citation statements)

References 136 publications

Supporting

Mentioning

202

Contrasting

Unclassified

Order By: Relevance

“…The degree has a straightforward neurobiological interpretation: nodes with a high degree interact with many other nodes in the network. The degree can be directional and characterized as in‐ and out‐degree; that is, the number of incoming connections, or outgoing connections, respectively (van Mierlo, Papadopoulou, et al, 2013). …”

Section: Introductionmentioning

confidence: 99%

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Blooijs

Leijten

Rijen

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

We investigated effective networks constructed from single pulse electrical stimulation (SPES) in epilepsy patients who underwent intracranial electrocorticography. Using graph analysis, we compared network characteristics of tissue within and outside the epileptogenic area. In 21 patients with subdural electrode grids (1 cm interelectrode distance), we constructed a binary, directional network derived from SPES early responses (<100 ms). We calculated in‐degree, out‐degree, betweenness centrality, the percentage of bidirectional, receiving and activating connections, and the percentage of connections toward the (non‐)epileptogenic tissue for each node in the network. We analyzed whether these network measures were significantly different in seizure onset zone (SOZ)‐electrodes compared to non‐SOZ electrodes, in resected area (RA)‐electrodes compared to non‐RA electrodes, and in seizure free compared to not seizure‐free patients. Electrodes in the SOZ/RA showed significantly higher values for in‐degree and out‐degree, both at group level, and at patient level, and more so in seizure‐free patients. These differences were not observed for betweenness centrality. There were also more bidirectional and fewer receiving connections in the SOZ/RA in seizure‐free patients. It appears that the SOZ/RA is densely connected with itself, with only little input arriving from non‐SOZ/non‐RA electrodes. These results suggest that meso‐scale effective network measures are different in epileptogenic compared to normal brain tissue. Local connections within the SOZ/RA are increased and the SOZ/RA is relatively isolated from the surrounding cortex. This offers the prospect of enhanced prediction of epilepsy‐prone brain areas using SPES.

show abstract

Section: Introductionmentioning

confidence: 99%

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Blooijs

Leijten

Rijen

et al. 2018

Human Brain Mapping

View full text Add to dashboard Cite

show abstract

“…EEG has been successfully used in epilepsy to observe the hypersynchronous activity that occurs during seizures (van Mierlo et al 2014). Because this hypersynchronous activity often involves large areas of the brain, it has been a major challenge to observe brainwave patterns at the level of the neurons and their individual connections.…”

Section: Heterogeneity In Epileptiform Activity: Examining Seizures Amentioning

confidence: 99%

“…However, EEG has notable limitations in terms of spatiotemporal sensitivity because of the fact that it is essentially an average measurement of the activity of large populations of cells; it is not able to discern the firing patterns of individual neurons, or even activity at the level of microcircuits, which defines the most fundamental connections between neurons . Therefore, although we can detect functional abnormalities in the network and pinpoint the general location of these abnormalities, the mechanisms that govern seizure activity are largely unknown (van Mierlo et al 2014).…”

mentioning

confidence: 99%

Microcircuits in Epilepsy: Heterogeneity and Hub Cells in Network Synchronization

Bui

Kim

Maroso

et al. 2015

Cold Spring Harb Perspect Med

View full text Add to dashboard Cite

Epilepsy is a complex disorder involving neurological alterations that lead to the pathological development of spontaneous, recurrent seizures. For decades, seizures were thought to be largely repetitive, and had been examined at the macrocircuit level using electrophysiological recordings. However, research mapping the dynamics of large neuronal populations has revealed that seizures are not simply recurrent bursts of hypersynchrony. Instead, it is becoming clear that seizures involve a complex interplay of different neurons and circuits. Herein, we will review studies examining microcircuit changes that may underlie network hyperexcitability, discussing observations from network theory, computational modeling, and optogenetics. We will delve into the idea of hub cells as pathological centers for seizure activity, and will explore optogenetics as a novel avenue to target and treat pathological circuits. Finally, we will conclude with a discussion on future directions in the field.

show abstract

“…Robust seizure detection however, can't be sustained by depending on FD estimation algorithms due to latency inherently present in seizure detection and false positive rates associated with FD computation. Multivariate analysis of EEG effectiveness in capturing the changes in spatial domain was studied and discussed by Mierlo et al [6]. These changes in spatial domains are indicative of a seizure onset and can be utilized for early detection.…”

Section: Introductionmentioning

confidence: 99%

EEG seizure detection by integrating slantlet transform with sparse coding

Ibrahim

Zhuang

Erdöl

et al. 2017

2017 25th European Signal Processing Conference (EUSIPCO)

View full text Add to dashboard Cite

EEG signals, recording both normal and abnormal activities of neurons in the brain, are widely used in epilepsy detection. In this paper, an EEG signal classification method based on Slantlet Transform and sparse coding is proposed to greatly reduce number of false alarms and improve speed of detection. With Slantlet Transform, salient information of EEG signals is mapped into a sparse space. In order to ensure good detection rates from the EEG signals, a Sparse Representation Classifier is adopted. Experimental results demonstrate that the in terms of detection rate, the proposed method, which integrates Slantlet Transform with sparse coding, outperforms the current state-of-the-art EEG seizure detection methods.

show abstract

Functional brain connectivity from EEG in epilepsy: Seizure prediction and epileptogenic focus localization

Cited by 243 publications

References 136 publications

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Evoked directional network characteristics of epileptogenic tissue derived from single pulse electrical stimulation

Microcircuits in Epilepsy: Heterogeneity and Hub Cells in Network Synchronization

EEG seizure detection by integrating slantlet transform with sparse coding

Contact Info

Product

Resources

About