State-of-the-Art of Seizure Prediction

Lehnertz, Klaus; Mormann, Florian; Osterhage, Hannes; Müller, Andy; Prusseit, Jens; Chernihovskyi, Anton; Staniek, Matthäus; Krug, Dieter; Bialonski, Stephan; Elger, Christian E.

doi:10.1097/wnp.0b013e3180336f16

Cited by 79 publications

(39 citation statements)

References 69 publications

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“…Presently the data in the literature regarding the preictal state originate almost exclusively from surface and intracranial EEG recordings (Martinerie et al, 1998;Le Van Quyen et al, 2001;Litt et al, 2001;Litt and Echauz, 2002;Navarro et al, 2002;Chaovalitwongse et al, 2005;Mormann et al, 2006Mormann et al, , 2007Haas et al, 2007;Lehnertz et al, 2007;Derchansky et al, 2008). EEG recordings sum the electrical activity from many thousands of neurons and millions of synapses.…”

Section: Discussionmentioning

confidence: 99%

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Network Dynamics during Development of Pharmacologically Induced Epileptic Seizures in RatsIn Vivo

Cymerblit‐Sabba¹,

Schiller²

2010

J. Neurosci.

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In epilepsy, the cortical network fluctuates between the asymptomatic interictal state and the symptomatic ictal state of seizures. Despite their importance, the network dynamics responsible for the transition between the interictal and ictal states are largely unknown. Here we used multielectrode single-unit recordings from the hippocampus to investigate the network dynamics during the development of seizures evoked by various chemoconvulsants in vivo. In these experiments, we detected a typical network dynamics signature that preceded seizure initiation. The preictal state preceding pilocarpine-, kainate-, and picrotoxin-induced seizures was characterized by biphasic network dynamics composed of an early desynchronization phase in which the tendency of neurons to fire correlated action potentials decreased, followed by a late resynchronization phase in which the activity and synchronization of the network gradually increased. This biphasic network dynamics preceded the initiation both of the initial seizure and of recurrent spontaneous seizures that followed. During seizures, firing of individual neurons and interneuronal synchronization further increased. These findings advance our understanding of the network dynamics leading to seizure initiation and may in future help in the development of novel seizure prediction algorithms.

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Section: Discussionmentioning

confidence: 99%

“…CC, Cross-correlation; Sz, seizure. Lehnertz et al, 2007;Salanova and Worth, 2007;Sackellares, 2008;Sun et al, 2008) . In this study, we report a novel "network dynamics signature" that characterized the preictal state preceding acute chemoconvulsant-induced seizures.…”

Section: Discussionmentioning

confidence: 99%

Network Dynamics during Development of Pharmacologically Induced Epileptic Seizures in RatsIn Vivo

Cymerblit‐Sabba¹,

Schiller²

2010

J. Neurosci.

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“…Another important category of computer models are not addressed in this report and can be directed towards the prediction of seizure onset and are well reviewed [51]. The advantage of stochastic modelling is that we can use observations to create noise models that implement a deterministic model ensuring we are capturing all the brain activity variability and not only the part of the signal that we are able to understand theoretically.…”

Section: A4 Summarymentioning

confidence: 99%

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

Dunn¹,

Abbott²,

Masterton³

et al. 2011

ANZIAMJ

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The Brain Research Institute (bri) uses various types of indirect measurements, including eeg and fmri, to understand and assess brain activity and function. As well as the recovery of generic information about brain function, research also focuses on the utilisation of such data and understanding to study the initiation, dynamics, spread and suppression of epileptic seizures. To assist with the future focussing of this aspect of their research, the bri asked the misg 2010 participants to examine how the available eeg and fmri data and current knowledge about epilepsy should be analysed and interpreted to yield an enhanced understanding about brain activity occurring before, at commencement of, during, and after a seizure. Though the deliberations of the study group were wide ranging in terms of the related matters considered and discussed, considerable progress was

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“…The duration of such behavior may vary from a few seconds to several minutes [1]. For approximately two thirds of the patients, seizures can be suppressed by anticonvulsive drugs [2]. Epilepsy surgery may treat 7 % to 8 % of patients [1].…”

Section: Introductionmentioning

confidence: 99%

A new bifurcation parameter in a modified Huber-Braun model

2017

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Original scientific paper Thermally sensitive neurons represent a bursting-spiking activity that is indicated by rapid repetitive spiking trains of action potentials pursued by dormant periods. Synchronization of such behavior in a network of coupled spiking neurons such as the epileptogenic zone in the brain may cause some neurological disorders such as epileptic seizures. This paper introduces a new approach for predicting the seizure onset in a model of an epileptic neuron. The parameters which are used for simulations have been selected in such a way that they would be potentially applicable in the non-invasive brain stimulation approaches such as repetitive transcranial magnetic stimulation (rTMS). In this regard, a modified Huber-Braun model of a thermally sensitive neuron exposed to external rTMS-induced voltages is presented. Applying the chaos theory, the bifurcation diagram of a modified Huber-Braun model with a new bifurcation parameter is used to estimate the time at which the bifurcation takes place whereby allowing a more accurate prediction of the seizure onset based on the modified model. Keywords: bifurcation theory; Huber-Braun model; seizure Istraživanje novog parametra bifurkacije u modificiranom Huber-Braun modeluIzvorni znanstveni članak Toplinsko osjetljivi neuroni predstavljaju pulsirajuće-prskajuću aktivnost predskazanu brzim ponavljajućim iznenadnim razvojem događanja praćenim periodima spavanja. Sinhronizacija takvog ponašanja u mreži spojenih prskajućih neurona kao što je epileptogena zona u mozgu može dovesti do neuroloških poremećaja poput epileptičkih napada. U radu se predlaže novi pristup predviđanju napada modelom epileptičkog neurona. Parametri upotrebljeni u simulaciji izabrani su tako da se mogu potencijalno primijeniti u pristupima neinvazivnim stimulacijama mozga kao što je ponavljajuća transkranijalna magnetska stimulacija (rTMS). U tom pogledu, predstavlja se modificirani Huber-Braun model termalno osjetljivog neurona izloženog rTMS-induciranoj voltaži. Primjenjujući teoriju kaosa dijagram bifurkacije modificiranog Huber-Braun modela s novim parametrom bifurkacije primijenjen je za procjenu vremena u kojem dolazi do bifurkacije čime se omogućuje točnije predviđanje početka napada na temelju modificiranog modela.

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State-of-the-Art of Seizure Prediction

Cited by 79 publications

References 69 publications

Network Dynamics during Development of Pharmacologically Induced Epileptic Seizures in RatsIn Vivo

Network Dynamics during Development of Pharmacologically Induced Epileptic Seizures in RatsIn Vivo

Understanding the abnormal brain activity in epilepsy as a potential predictor of the onset of an epileptic seizure

A new bifurcation parameter in a modified Huber-Braun model

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