Elevated ictal brain network ictogenicity enables prediction of optimal seizure control.

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Elevated ictal brain network ictogenicity enables prediction of optimal seizure control. / Lopes, MA; Richardson, MP; Abela, E; Rummel, C; Schindler, K; Goodfellow, M; Terry, JR.

In: Frontiers in neurology, Vol. 9, 98, 01.03.2018.

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Lopes, MA ; Richardson, MP ; Abela, E ; Rummel, C ; Schindler, K ; Goodfellow, M ; Terry, JR. / Elevated ictal brain network ictogenicity enables prediction of optimal seizure control. In: Frontiers in neurology. 2018 ; Vol. 9.

Bibtex

@article{fd62b0ed5cee4429b7afd42bbbeb6bd6,
title = "Elevated ictal brain network ictogenicity enables prediction of optimal seizure control.",
abstract = "Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.",
keywords = "epilepsy surgery, ictogenic network, intracranial EEG, network dynamics, neural mass model",
author = "MA Lopes and MP Richardson and E Abela and C Rummel and K Schindler and M Goodfellow and JR Terry",
year = "2018",
month = mar
day = "1",
doi = "10.3389/fneur.2018.00098",
language = "English",
volume = "9",
journal = "Frontiers in neurology",
issn = "1664-2295",
publisher = "Frontiers",

}

RIS

TY - JOUR

T1 - Elevated ictal brain network ictogenicity enables prediction of optimal seizure control.

AU - Lopes, MA

AU - Richardson, MP

AU - Abela, E

AU - Rummel, C

AU - Schindler, K

AU - Goodfellow, M

AU - Terry, JR

PY - 2018/3/1

Y1 - 2018/3/1

N2 - Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.

AB - Recent studies have shown that mathematical models can be used to analyze brain networks by quantifying how likely they are to generate seizures. In particular, we have introduced the quantity termed brain network ictogenicity (BNI), which was demonstrated to have the capability of differentiating between functional connectivity (FC) of healthy individuals and those with epilepsy. Furthermore, BNI has also been used to quantify and predict the outcome of epilepsy surgery based on FC extracted from pre-operative ictal intracranial electroencephalography (iEEG). This modeling framework is based on the assumption that the inferred FC provides an appropriate representation of an ictogenic network, i.e., a brain network responsible for the generation of seizures. However, FC networks have been shown to change their topology depending on the state of the brain. For example, topologies during seizure are different to those pre- and post-seizure. We therefore sought to understand how these changes affect BNI. We studied peri-ictal iEEG recordings from a cohort of 16 epilepsy patients who underwent surgery and found that, on average, ictal FC yield higher BNI relative to pre- and post-ictal FC. However, elevated ictal BNI was not observed in every individual, rather it was typically observed in those who had good post-operative seizure control. We therefore hypothesize that elevated ictal BNI is indicative of an ictogenic network being appropriately represented in the FC. We evidence this by demonstrating superior model predictions for post-operative seizure control in patients with elevated ictal BNI.

KW - epilepsy surgery

KW - ictogenic network

KW - intracranial EEG

KW - network dynamics

KW - neural mass model

UR - http://europepmc.org/abstract/med/29545769

U2 - 10.3389/fneur.2018.00098

DO - 10.3389/fneur.2018.00098

M3 - Article

C2 - 29545769

VL - 9

JO - Frontiers in neurology

JF - Frontiers in neurology

SN - 1664-2295

M1 - 98

ER -